Search results for: ray patterns

Authors: Ruth Leitch, Joanne Hughes

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This paper presents the findings of a three-year government-funded study (ILiAD) that aimed to understand the reasons for differential educational achievement within and between socially and economically deprived areas in Northern Ireland. Previous international studies have concluded that there is a positive correlation between deprivation and underachievement. Our preliminary secondary data analysis suggested that the factors involved in educational achievement within multiple deprived areas may be more complex than this, with some areas of high multiple deprivation having high levels of student attainment, whereas other less deprived areas demonstrated much lower levels of student attainment, as measured by outcomes on high stakes national tests. The study proposed that no single explanation or disparate set of explanations could easily account for the linkage between levels of deprivation and patterns of educational achievement. Using a social capital perspective that centralizes the connections within and between individuals and social networks in a community as a valuable resource for educational achievement, the ILiAD study involved a multi-level case study analysis of seven community sites in Northern Ireland, selected on the basis of religious composition (housing areas are largely segregated by religious affiliation), measures of multiple deprivation and differentials in educational achievement. The case study approach involved three (interconnecting) levels of qualitative data collection and analysis - what we have termed Micro (or community/grassroots level) understandings, Meso (or school level) explanations and Macro (or policy/structural) factors. The analysis combines a statistical mapping of factors with qualitative, in-depth data interpretation which, together, allow for deeper understandings of the dynamics and contributory factors within and between the case study sites. Thematic analysis of the qualitative data reveals both cross-cutting factors (e.g. demographic shifts and loss of community, place of the school in the community, parental capacity) and analytic case studies of explanatory factors associated with each of the community sites also permit a comparative element. Issues arising from the qualitative analysis are classified either as drivers or inhibitors of educational achievement within and between communities. Key issues that are emerging as inhibitors/drivers to attainment include: the legacy of the community conflict in Northern Ireland, not least in terms of inter-generational stress, related with substance abuse and mental health issues; differing discourses on notions of ‘community’ and ‘achievement’ within/between community sites; inter-agency and intra-agency levels of collaboration and joined-up working; relationship between the home/school/community triad and; school leadership and school ethos. At this stage, the balance of these factors can be conceptualized in terms of bonding social capital (or lack of it) within families, within schools, within each community, within agencies and also bridging social capital between the home/school/community, between different communities and between key statutory and voluntary organisations. The presentation will outline the study rationale, its methodology, present some cross-cutting findings and use an illustrative case study of the findings from a community site to underscore the importance of attending to community differences when trying to engage in research to understand and improve educational attainment for all.

Keywords: educational achievement, multiple deprivation, community case studies, social capital

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Authors: Saeed Abbas, Rahilla Tabassum, Haider Abbas, Babar Khan, Shahid Hussain, Muhammad Zafar Khan, Fazal Karim, Yawar Abbas, Rizwan Karim

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Human–carnivore conflict is considered to be a major conservation and rural livelihood concern because many carnivore species have been heavily victimized due to elevated conflict levels with communities. Like other snow leopard range countries, this situation prevails in Pakistan, where WWF is currently working under Asia High Mountain Project (AHMP) in Gilgit-Baltistan of Pakistan. To mitigate such conflicts requires a firm understanding of grazing and predation pattern including human-carnivore interaction. For this purpose we conducted a survey in Hoper valley (one of the AHMP project sites in Pakistan), during August, 2013 through a questionnaire based survey and unstructured interviews covering 647 households, permanently residing in the project area out of the total 900 households. The valley, spread over 409 km2 between 36°7'46" N and 74°49'2"E, at 2900m asl in Karakoram ranges is considered to be one of an important habitat of snow leopard and associated prey species such as Himalayan ibex. The valley is home of 8100 Brusho people (ancient tribe of Northern Pakistan) dependent on agro-pastoral livelihoods including farming and livestock rearing. The total number of livestock reported were (N=15,481) out of which 8346 (53.91%) were sheep, 3546 (22.91%) goats, 2193 (14.16%) cows, 903 (5.83%) yaks, 508 (3.28%) bulls, 28 (0.18%) donkeys, 27 (0.17%) zo/zomo (cross breed of yak and cow), and 4 (0.03%) horses. 83 percent respondent (n=542 households) confirmed loss of their livestock during the last one year July, 2012 to June, 2013 which account for 2246 (14.51%) animals. The major reason of livestock loss include predation by large carnivores such as snow leopards and wolf (1710, 76.14%) followed by diseases (536, 23.86%). Of the total predation cases snow leopard is suspected to kill 1478 animals (86.43%). Among livestock sheep were found to be the major prey of snow leopard (810, 55%) followed by goats (484, 32.7%) cows (151, 10.21%), yaks (15, 1.015%), zo/zomo (7, 0.5%) and donkey (1, 0.07%). The reason for the mass depredation of sheep and goats is that they tend to browse on twigs of bushes and graze on soft grass near cliffs. They are also considered to be very active as compared to other species in moving quickly and covering more grazing area. This makes them more vulnerable to snow leopard attack. The majority (1283, 75%) of livestock killed by predators occurred during the warm season (May-September) in alpine and sub-alpine pastures and remaining (427, 25%) occurred in the winter season near settlements in valley. It was evident from the recent study that Snow leopard kills outside the pen were (1351, 79.76%) as compared to inside pen (359, 20.24%). Assessing the economic loss of livestock predation we found that the total loss of livestock predation in the study area is equal to PKR 11,230,000 (USD 105,797), which is about PRK 17, 357 (USD 163.51) per household per year. Economic loss incurred by the locals due to predation is quite significant where the average cash income per household per year is PKR 85,000 (USD 800.75).

Keywords: carnivores, conflict, predation, livelihood, conservation, rural, snow leopard, livestock

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Authors: Mazaher Yarmohamadi-Vasel, Ali Reza Modarresi-Alama, Sahar Shabzendedara

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Purpose/Objectives: The first purpose was synthesize Poly(N-(sulfophenyl)aniline) nanoflowers (PSANFLs) and Poly(N-(sulfophenyl)aniline) nanofibers/titanium dioxide nanoparticles ((PSANFs/TiO2NPs) by a solid-state mechano-chemical reaction and template-free method and use them in hybrid solar cell. Also, our second aim was to increase the solubility and the processability of conjugated nanomaterials in water through polar functionalized materials. poly[N-(4-sulfophenyl)aniline] is easily soluble in water because of the presence of polar groups of sulfonic acid in the polymer chain. Materials/Methods: Iron (III) chloride hexahydrate (FeCl3∙6H2O) were bought from Merck Millipore Company. Titanium oxide nanoparticles (TiO2, <20 nm, anatase) and Sodium diphenylamine-4-sulfonate (99%) were bought from Sigma-Aldrich Company. Titanium dioxide nanoparticles paste (PST-20T) was prepared from Sharifsolar Co. Conductive glasses coated with indium tin oxide (ITO) were bought from Xinyan Technology Co (China). For the first time we used the solid-state mechano-chemical reaction and template-free method to synthesize Poly(N-(sulfophenyl)aniline) nanoflowers. Moreover, for the first time we used the same technique to synthesize nanocomposite of Poly(N-(sulfophenyl)aniline) nanofibers and titanium dioxide nanoparticles (PSANFs/TiO2NPs) also for the first time this nanocomposite was synthesized. Examining the results of electrochemical calculations energy gap obtained by CV curves and UV–vis spectra demonstrate that PSANFs/TiO2NPs nanocomposite is a p-n type material that can be used in photovoltaic cells. Doctor blade method was used to creat films for three kinds of hybrid solar cells in terms of different patterns like ITO│TiO2NPs│Semiconductor sample│Al. In the following, hybrid photovoltaic cells in bilayer and bulk heterojunction structures were fabricated as ITO│TiO2NPs│PSANFLs│Al and ITO│TiO2NPs│PSANFs /TiO2NPs│Al, respectively. Fourier-transform infrared spectra, field emission scanning electron microscopy (FE-SEM), ultraviolet-visible spectra, cyclic voltammetry (CV) and electrical conductivity were the analysis that used to characterize the synthesized samples. Results and Conclusions: FE-SEM images clearly demonstrate that the morphology of the synthesized samples are nanostructured (nanoflowers and nanofibers). Electrochemical calculations of band gap from CV curves demonstrated that the forbidden band gap of the PSANFLs and PSANFs/TiO2NPs nanocomposite are 2.95 and 2.23 eV, respectively. I–V characteristics of hybrid solar cells and their power conversion efficiency (PCE) under 100 mWcm−2 irradiation (AM 1.5 global conditions) were measured that The PCE of the samples were 0.30 and 0.62%, respectively. At the end, all the results of solar cell analysis were discussed. To sum up, PSANFLs and PSANFLs/TiO2NPs were successfully synthesized by an affordable and straightforward mechanochemical reaction in solid-state under the green condition. The solubility and processability of the synthesized compounds have been improved compared to the previous work. We successfully fabricated hybrid photovoltaic cells of synthesized semiconductor nanostructured polymers and TiO2NPs as different architectures. We believe that the synthesized compounds can open inventive pathways for the development of other Poly(N-(sulfophenyl)aniline based hybrid materials (nanocomposites) proper for preparing new generation solar cells.

Keywords: mechanochemical synthesis, PSANFLs, PSANFs/TiO2NPs, solar cell

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Authors: I. Stathopoulos, V. Syrgiamiotis, E. Karavasilis, A. Ploussi, I. Nikas, C. Hatzigiorgi, K. Platoni, E. P. Efstathopoulos

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Introduction: Brain and central nervous system (CNS) tumors form the second most common group of cancer in children, accounting for 30% of all childhood cancers. MRI is the key imaging technique used for the visualization and management of pediatric brain tumors. Initial characterization of tumors from MRI scans is usually performed via a radiologist’s visual assessment. However, different brain tumor types do not always demonstrate clear differences in visual appearance. Using only conventional MRI to provide a definite diagnosis could potentially lead to inaccurate results, and so histopathological examination of biopsy samples is currently considered to be the gold standard for obtaining definite diagnoses. Machine learning is defined as the study of computational algorithms that can use, complex or not, mathematical relationships and patterns from empirical and scientific data to make reliable decisions. Concerning the above, machine learning techniques could provide effective and accurate ways to automate and speed up the analysis and diagnosis for medical images. Machine learning applications in radiology are or could potentially be useful in practice for medical image segmentation and registration, computer-aided detection and diagnosis systems for CT, MR or radiography images and functional MR (fMRI) images for brain activity analysis and neurological disease diagnosis. Purpose: The objective of this study is to provide an automated tool, which may assist in the imaging evaluation and classification of brain neoplasms in pediatric patients by determining the glioma type, grade and differentiating between different brain tissue types. Moreover, a future purpose is to present an alternative way of quick and accurate diagnosis in order to save time and resources in the daily medical workflow. Materials and Methods: A cohort, of 80 pediatric patients with a diagnosis of posterior fossa tumor, was used: 20 ependymomas, 20 astrocytomas, 20 medulloblastomas and 20 healthy children. The MR sequences used, for every single patient, were the following: axial T1-weighted (T1), axial T2-weighted (T2), FluidAttenuated Inversion Recovery (FLAIR), axial diffusion weighted images (DWI), axial contrast-enhanced T1-weighted (T1ce). From every sequence only a principal slice was used that manually traced by two expert radiologists. Image acquisition was carried out on a GE HDxt 1.5-T scanner. The images were preprocessed following a number of steps including noise reduction, bias-field correction, thresholding, coregistration of all sequences (T1, T2, T1ce, FLAIR, DWI), skull stripping, and histogram matching. A large number of features for investigation were chosen, which included age, tumor shape characteristics, image intensity characteristics and texture features. After selecting the features for achieving the highest accuracy using the least number of variables, four machine learning classification algorithms were used: k-Nearest Neighbour, Support-Vector Machines, C4.5 Decision Tree and Convolutional Neural Network. The machine learning schemes and the image analysis are implemented in the WEKA platform and MatLab platform respectively. Results-Conclusions: The results and the accuracy of images classification for each type of glioma by the four different algorithms are still on process.

Keywords: image classification, machine learning algorithms, pediatric MRI, pediatric oncology

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Authors: A. Shahriari, M. Khalatbari Jafari, M. Faridi

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Abstract The Muradlu volcanic area is located in Azerbaijan province, NW Iran. The studied area exposed in a vast region includes lesser Caucasus, Southeastern Turkey, and northwestern Iran, comprising Cenozoic volcanic and plutonic massifs. The geology of this extended region was under the influence of the Alpine-Himalayan orogeny. Cenozoic magmatic activities in this vast region evolved through the northward subduction of the Neotethyan subducted slab and subsequence collision of the Arabian and Eurasian plates. Based on stratigraphy and paleontology data, most of the volcanic activities in the Muradlu area occurred in the Eocene period. The Studied volcanic rocks overly late Cretaceous limestone with disconformity. The volcanic sequence includes thick epiclastic and hyaloclastite breccia at the base, laterally changed to pillow lava and continued by hyaloclastite and lave flows at the top of the series. The lava flows display different textures from megaporphyric-phyric to fluidal and microlithic textures. The studied samples comprise picrobasalt basalt, tephrite basanite, trachybasalt, basaltic trachyandesite, phonotephrite, tephrophonolite, trachyandesite, and trachyte in compositions. Some xenoliths with lherzolitic composition are found in picrobasalt. These xenoliths are made of olivine, cpx (diopside), and opx (enstatite), probably the remain of mantle origin. Some feldspathoid minerals such as sodalite presence in the phonotephrite confirm an alkaline trend. Two types of augite phenocrysts are found in picrobasalt, basalt and trachybasalt. The first types are shapeless, with disharmony zoning and sponge texture with reaction edges probably resulted from sodic magma, which is affected by a potassic magma. The second shows a glomerocryst shape. In discriminative diagrams, the volcanic rocks show alkaline-shoshonitic trends. They contain (0.5-7.7) k2O values and plot in the shoshonitic field. Most of the samples display transitional to potassic alkaline trends, and some samples reveal sodic alkaline trends. The transitional trend probably results from the mixing of the sodic alkaline and potassic magmas. The Rare Earth Elements (REE) patterns and spider diagrams indicate enrichment of Large-Ione Lithophile Element (LILE) and depletion of High Field Strength Elements (HFSE) relative to Heavy Rare Earth Elements (HREE). Enrichment of K, Rb, Sr, Ba, Zr, Th, and U and the enrichment of Light Rare Earth Elements (LREE) relative to Heavy Rare Earth Elements (HREE) indicate the effect of subduction-related fluids over the mantle source, which has been reported in the arc and continental collision zones. The studied samples show low Nb/La ratios. Our studied samples plot in the lithosphere and lithosphere-asthenosphere fields in the Nb/La versus La/Yb ratios diagram. These geochemical characters allow us to conclude that a lithospheric mantle source previously metasomatized by subduction components was the origin of the Muradlu volcanic rocks.

Keywords: alkaline, asthenosphere, lherzolite, lithosphere, Muradlu, potassic, shoshonitic, sodic, volcanism

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Authors: Zachary Blanks, Solomon Sonya

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Poaching presents a serious threat to endangered animal species, environment conservations, and human life. Additionally, some poaching activity has even been linked to supplying funds to support terrorist networks elsewhere around the world. Consequently, agencies dedicated to protecting wildlife habitats have a near intractable task of adequately patrolling an entire area (spanning several thousand kilometers) given limited resources, funds, and personnel at their disposal. Thus, agencies need predictive tools that are both high-performing and easily implementable by the user to help in learning how the significant features (e.g. animal population densities, topography, behavior patterns of the criminals within the area, etc) interact with each other in hopes of abating poaching. This research develops a classification model using machine learning algorithms to aid in forecasting future attacks that is both easy to train and performs well when compared to other models. In this research, we demonstrate how data imputation methods (specifically predictive mean matching, gradient boosting, and random forest multiple imputation) can be applied to analyze data and create significant predictions across a varied data set. Specifically, we apply these methods to improve the accuracy of adopted prediction models (Logistic Regression, Support Vector Machine, etc). Finally, we assess the performance of the model and the accuracy of our data imputation methods by learning on a real-world data set constituting four years of imputed data and testing on one year of non-imputed data. This paper provides three main contributions. First, we extend work done by the Teamcore and CREATE (Center for Risk and Economic Analysis of Terrorism Events) research group at the University of Southern California (USC) working in conjunction with the Department of Homeland Security to apply game theory and machine learning algorithms to develop more efficient ways of reducing poaching. This research introduces ensemble methods (Random Forests and Stochastic Gradient Boosting) and applies it to real-world poaching data gathered from the Ugandan rain forest park rangers. Next, we consider the effect of data imputation on both the performance of various algorithms and the general accuracy of the method itself when applied to a dependent variable where a large number of observations are missing. Third, we provide an alternate approach to predict the probability of observing poaching both by season and by month. The results from this research are very promising. We conclude that by using Stochastic Gradient Boosting to predict observations for non-commercial poaching by season, we are able to produce statistically equivalent results while being orders of magnitude faster in computation time and complexity. Additionally, when predicting potential poaching incidents by individual month vice entire seasons, boosting techniques produce a mean area under the curve increase of approximately 3% relative to previous prediction schedules by entire seasons.

Keywords: ensemble methods, imputation, machine learning, random forests, statistical analysis, stochastic gradient boosting, wildlife protection

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Authors: Rosimeire Araújo Silva, Philip Martin Fearnside

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Extreme droughts and floods in the Amazon have serious consequences for natural ecosystems and the human population in the region. The frequency of these events has increased in recent years, and projections of climate change predict greater frequency and intensity of these events. Understanding the links between these extreme events and different patterns of sea surface temperature in the Atlantic and Pacific Oceans is essential, both to improve the modeling of climate change and its consequences and to support efforts of adaptation in the region. The relationship between sea temperatures and events in the Amazon is much more complex than is usually assumed in climatic models. Warming and cooling of different parts of the oceans, as well as the interaction between simultaneous temperature changes in different parts of each ocean and between the two oceans, have specific consequences for the Amazon, with effects on precipitation that vary in different parts of the region. Simplistic generalities, such as the association between El Niño events and droughts in the Amazon, do not capture this complexity. We investigated the variability of Sea Surface Temperature (SST) in the Tropical Pacific Ocean during the period 1950-2022, using Empirical Orthogonal Functions (FOE), spectral analysis coherence and wavelet phase. The two were identified as the main modes of variability, which explain about 53,9% and 13,3%, respectively, of the total variance of the data. The spectral and coherence analysis and wavelets phase showed that the first selected mode represents the warming in the central part of the Pacific Ocean (the “Central El Niño”), while the second mode represents warming in the eastern part of the Pacific (the “Eastern El Niño The effects of the 1982-1983 and 1976-1977 El Niño events in the Amazon, although both events were characterized by an increase in sea surface temperatures in the Equatorial Pacific, the impact on rainfall in the Amazon was distinct. In the rainy season, from December to March, the sub-basins of the Japurá, Jutaí, Jatapu, Tapajós, Trombetas and Xingu rivers were the regions that showed the greatest reductions in rainfall associated with El Niño Central (1982-1983), while the sub-basins of the Javari, Purus, Negro and Madeira rivers had the most pronounced reductions in the year of Eastern El Niño (1976-1977). In the transition to the dry season, in April, the greatest reductions were associated with the Eastern El Niño year for the majority of the study region, with the exception only of the sub-basins of the Madeira, Trombetas and Xingu rivers, which had their associated reductions to Central El Niño. In the dry season from July to September, the sub-basins of the Japurá Jutaí Jatapu Javari Trombetas and Madeira rivers were the rivers that showed the greatest reductions in rainfall associated with El Niño Central, while the sub-basins of the Tapajós Purus Negro and Xingu rivers had the most pronounced reductions. In the Eastern El Niño year this season. In this way, it is possible to conclude that the Central (Eastern) El Niño controlled the reductions in soil moisture in the dry (rainy) season for all sub-basins shown in this study. Extreme drought events associated with these meteorological phenomena can lead to a significant increase in the occurrence of forest fires. These fires have a devastating impact on Amazonian vegetation, resulting in the irreparable loss of biodiversity and the release of large amounts of carbon stored in the forest, contributing to the increase in the greenhouse effect and global climate change.

Keywords: sea surface temperature, variability, climate, Amazon

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Authors: Esmael Makarian, Ayub Elyasi, Fatemeh Saberi, Olusegun Stanley Tomomewo

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Rock mechanical investigation is one of the most crucial activities in underground operations, especially in surveys related to hydrocarbon exploration and production, geothermal reservoirs, energy storage, mining, and geotechnics. There is a wide range of traditional methods for driving, collecting, and analyzing rock mechanics data. However, these approaches may not be suitable or work perfectly in some situations, such as fractured zones. Cutting-edge technologies have been provided to solve and optimize the mentioned issues. Internet of Things (IoT), Edge, and Cloud Computing technologies (ECt & CCt, respectively) are among the most widely used and new artificial intelligence methods employed for geomechanical studies. IoT devices act as sensors and cameras for real-time monitoring and mechanical-geological data collection of rocks, such as temperature, movement, pressure, or stress levels. Structural integrity, especially for cap rocks within hydrocarbon systems, and rock mass behavior assessment, to further activities such as enhanced oil recovery (EOR) and underground gas storage (UGS), or to improve safety risk management (SRM) and potential hazards identification (P.H.I), are other benefits from IoT technologies. EC techniques can process, aggregate, and analyze data immediately collected by IoT on a real-time scale, providing detailed insights into the behavior of rocks in various situations (e.g., stress, temperature, and pressure), establishing patterns quickly, and detecting trends. Therefore, this state-of-the-art and useful technology can adopt autonomous systems in rock mechanical surveys, such as drilling and production (in hydrocarbon wells) or excavation (in mining and geotechnics industries). Besides, ECt allows all rock-related operations to be controlled remotely and enables operators to apply changes or make adjustments. It must be mentioned that this feature is very important in environmental goals. More often than not, rock mechanical studies consist of different data, such as laboratory tests, field operations, and indirect information like seismic or well-logging data. CCt provides a useful platform for storing and managing a great deal of volume and different information, which can be very useful in fractured zones. Additionally, CCt supplies powerful tools for predicting, modeling, and simulating rock mechanical information, especially in fractured zones within vast areas. Also, it is a suitable source for sharing extensive information on rock mechanics, such as the direction and size of fractures in a large oil field or mine. The comprehensive review findings demonstrate that digital transformation through integrated IoT, Edge, and Cloud solutions is revolutionizing traditional rock mechanical investigation. These advanced technologies have empowered real-time monitoring, predictive analysis, and data-driven decision-making, culminating in noteworthy enhancements in safety, efficiency, and sustainability. Therefore, by employing IoT, CCt, and ECt, underground operations have experienced a significant boost, allowing for timely and informed actions using real-time data insights. The successful implementation of IoT, CCt, and ECt has led to optimized and safer operations, optimized processes, and environmentally conscious approaches in underground geological endeavors.

Keywords: rock mechanical studies, internet of things, edge computing, cloud computing, underground surveys, geological operations

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Authors: A. Coetzee, C. F. Kelly, E. Even-Zahav

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Dominant approaches to livestock production are harmful to the environment, human health and animal welfare, yet global meat consumption is rising. Sustainable alternative production approaches are therefore urgently required, and ‘free range’ is the main alternative for chicken meat offered in South Africa (and globally). Although the South African Poultry Association provides non-binding guidelines, there is a lack of formal definition and regulation of free range chicken production, meaning it is unclear what this alternative entails and if it is consistently practised (a trend observed globally). The objective of this exploratory qualitative case study is therefore to investigate who and what determines free range chicken. The case study, conducted from a social constructivist worldview, uses semi-structured interviews, photographs and document analysis to collect data. Interviews are conducted with those involved with bringing free range chicken to the market - farmers, chefs, retailers, and regulators. Data is analysed using thematic analysis to establish dominant patterns in the data. The five major themes identified (based on prevalence in data and on achieving the research objective) are: 1) free range means a bird reared with good animal welfare in mind, 2) free range means quality meat, 3) free range means a profitable business, 4) free range is determined by decision makers or by access to markets, and 5) free range is coupled with concerns about the lack of regulation. Unpacking the findings in the context of the literature reveals who and what determines free range. The research uncovers wide-ranging interpretations of ‘free range’, driven by the absence of formal regulation for free range chicken practices and the lack of independent private certification. This means that the term ‘free range’ is socially constructed, thus varied and complex. The case study also shows that whether chicken meat is free range is generally determined by those who have access to markets. Large retailers claim adherence to the internationally recognised Five Freedoms, also include in the South African Poultry Association Code of Good Practice, which others in the sector say are too broad to be meaningful. Producers describe animal welfare concerns as the main driver for how they practice/view free range production, yet these interpretations vary. An additional driver is a focus on human health, which participants achieve mainly through the use of antibiotic-free feed, resulting in what participants regard as higher quality meat. The participants are also strongly driven by business imperatives, with most stating that free range chicken should carry a higher price than conventionally-reared chicken due to increased production costs. Recommendations from this study focus on, inter alia, a need to understand consumers’ perspectives on free range chicken, given that those in the sector claim they are responding to consumer demand, and conducting environmental research such as life cycle assessment studies to establish the true (environmental) sustainability of free range production. At present, it seems the sector mostly responds to social sustainability: human health and animal welfare.

Keywords: chicken meat production, free range, socially constructed, sustainability

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Authors: Muhammad Haris, Syed Kamran Ali, Mubeen Islam, Tariq Mehmood, Faisal Shah

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Jacobabad Khairpur High, part of a Sukkur rift zone, is the separating boundary between Central and Southern Indus Basin, formed as a result of Post-Jurassic uplift after the deposition of Middle Jurassic Chiltan Formation. Habib Rahi Formation of Middle to Late Eocene outcrops in the vicinity of Jacobabad Khairpur High, a section at Rohri near Sukkur is measured in detail for lithofacies, microfacies, diagenetic analysis and sequence stratigraphy. Habib Rahi Formation is richly fossiliferous and consists of mostly limestone with subordinate clays and marl. The total thickness of the formation in this section is 28.8m. The bottom of the formation is not exposed, while the upper contact with the Sirki Shale of the Middle Eocene age is unconformable in some places. A section is measured using Jacob’s Staff method, and traverses were made perpendicular to the strike. Four different lithofacies were identified based on outcrop geology which includes coarse-grained limestone facies (HR-1 to HR-5), massive bedded limestone facies (HR-6 HR-7), and micritic limestone facies (HR-8 to HR-13) and algal dolomitic limestone facie (HR-14). Total 14 rock samples were collected from outcrop for detailed petrographic studies, and thin sections of respective samples were prepared and analyzed under the microscope. On the basis of Dunham’s (1962) classification systems after studying textures, grain size, and fossil content and using Folk’s (1959) classification system after reviewing Allochems type, four microfacies were identified. These microfacies include HR-MF 1: Benthonic Foraminiferal Wackstone/Biomicrite Microfacies, HR-MF 2: Foramineral Nummulites Wackstone-Packstone/Biomicrite Microfacies HR-MF 3: Benthonic Foraminiferal Packstone/Biomicrite Microfacies, HR-MF 4: Bioclasts Carbonate Mudstone/Micrite Microfacies. The abundance of larger benthic Foraminifera’s (LBF), including Assilina sp., A. spiral abrade, A. granulosa, A. dandotica, A. laminosa, Nummulite sp., N. fabiani, N. stratus, N. globulus, Textularia, Bioclasts, and Red algae indicates shallow marine (Tidal Flat) environment of deposition. Based on variations in rock types, grain size, and marina fauna Habib Rahi Formation shows progradational stacking patterns, which indicates coarsening upward cycles. The second order of sea-level rise is identified (spanning from Y-Persian to Bartonian age) that represents the Transgressive System Tract (TST) and a third-order Regressive System Tract (RST) (spanning from Bartonian to Priabonian age). Diagenetic processes include fossils replacement by mud, dolomitization, pressure dissolution associated stylolites features and filling with dark organic matter. The presence of the microfossils includes Nummulite. striatus, N. fabiani, and Assilina. dandotica, signify Bartonian to Priabonian age of Habib Rahi Formation.

Keywords: Jacobabad Khairpur High, Habib Rahi Formation, lithofacies, microfacies, sequence stratigraphy, diagenetic history

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Authors: Karen L. Lindsay, Sonja Entringer, Claudia Buss, Pathik D. Wadhwa

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Maternal nutrition and stress are independently recognized as among the most important factors that influence prenatal biology, with implications for fetal development and poor pregnancy outcomes. While there is substantial evidence from non-pregnancy human and animal studies that a complex, bi-directional relationship exists between nutrition and stress, to the author’s best knowledge, their interaction in the context of pregnancy has been significantly understudied. The aim of this study is to assess the interaction between maternal psychological stress and diet quality across pregnancy and its effects on biomarkers of prenatal insulin resistance and inflammation. This is a prospective longitudinal study of N=235 women carrying a healthy, singleton pregnancy, recruited from prenatal clinics of the University of California, Irvine Medical Center. Participants completed a 4-day ambulatory assessment in early, middle and late pregnancy, which included multiple daily electronic diary entries using Ecological Momentary Assessment (EMA) technology on a dedicated study smartphone. The EMA diaries gathered moment-level data on maternal perceived stress, negative mood, positive mood and quality of social interactions. The numerical scores for these variables were averaged across each study time-point and converted to Z-scores. A single composite variable for 'STRESS' was computed as follows: (Negative mood+Perceived stress)–(Positive mood+Social interaction quality). Dietary intakes were assessed by three 24-hour dietary recalls conducted within two weeks of each 4-day assessment. Daily nutrient and food group intakes were averaged across each study time-point. The Alternative Healthy Eating Index adapted for pregnancy (AHEI-P) was computed for early, middle and late pregnancy as a validated summary measure of diet quality. At the end of each 4-day ambulatory assessment, women provided a fasting blood sample, which was assayed for levels of glucose, insulin, Interleukin (IL)-6 and Tumor Necrosis Factor (TNF)-α. Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) was computed. Pearson’s correlation was used to explore the relationship between maternal STRESS and AHEI-P within and between each study time-point. Linear regression was employed to test the association of the stress-diet interaction (STRESS*AHEI-P) with the biological markers HOMA-IR, IL-6 and TNF-α at each study time-point, adjusting for key covariates (pre-pregnancy body mass index, maternal education level, race/ethnicity). Maternal STRESS and AHEI-P were significantly inversely correlated in early (r=-0.164, p=0.018) and mid-pregnancy (-0.160, p=0.019), and AHEI-P from earlier gestational time-points correlated with later STRESS (early AHEI-P x mid STRESS: r=-0.168, p=0.017; mid AHEI-P x late STRESS: r=-0.142, p=0.041). In regression models, the interaction term was not associated with HOMA-IR or IL-6 at any gestational time-point. The stress-diet interaction term was significantly associated with TNF-α according to the following patterns: early AHEI-P*early STRESS vs early TNF-α (p=0.005); early AHEI-P*early STRESS vs mid TNF-α (p=0.002); early AHEI-P*mid STRESS vs mid TNF-α (p=0.005); mid AHEI-P*mid STRESS vs mid TNF-α (p=0.070); mid AHEI-P*late STRESS vs late TNF-α (p=0.011). Poor diet quality is significantly related to higher psychosocial stress levels in pregnant women across gestation, which may promote inflammation via TNF-α. Future prenatal studies should consider the combined effects of maternal stress and diet when evaluating either one of these factors on pregnancy or infant outcomes.

Keywords: diet quality, inflammation, insulin resistance, nutrition, pregnancy, stress, tumor necrosis factor-alpha

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Authors: Utkarsh Tripathi, Bikalp C. Mandal, Ravi Kumar Umrao, Sirsha Das, M. K. Bhowmic, Joyesh Bagchi, Hemant Kumar

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The Central India Diamond Province (CIDP) is known for the occurrences of primary and secondary sources for diamonds from the Vindhyan platformal sediments, which host several kimberlites, with one operating mine. The known kimberlites are Neo-Proterozoic in age and intrude into the Kaimur Group of rocks. Based on the interpretation of areo-geomagnetic data, three potential zones were demarcated in parts of Chitrakoot and Banda districts, Uttar Pradesh, and Satna district, Madhya Pradesh, India. To validate the aero-geomagnetic interpretation, ground magnetic coupled with a gravity survey was conducted to validate the anomaly and explore the possibility of some pipes concealed beneath the Vindhyan sedimentary cover. Geologically the area exposes the milky white to buff-colored arkosic and arenitic sandstone belonging to the Dhandraul Formation of the Kaimur Group, which are undeformed and unmetamorphosed providing almost transparent media for geophysical exploration. There is neither surface nor any geophysical indication of intersections of linear structures, but the joint patterns depict three principal joints along NNE-SSW, ENE-WSW, and NW-SE directions with vertical to sub-vertical dips. Aeromagnetic data interpretation brings out three promising zones with the bi-polar magnetic anomaly (69-602nT) that represent potential kimberlite intrusive concealed below at an approximate depth of 150-170m. The ground magnetic survey has brought out the above-mentioned anomalies in zone-I, which is congruent with the available aero-geophysical data. The magnetic anomaly map shows a total variation of 741 nT over the area. Two very high magnetic zones (H1 and H2) have been observed with around 500 nT and 400 nT magnitudes, respectively. Anomaly zone H1 is located in the west-central part of the area, south of Madulihai village, while anomaly zone H2 is located 2km apart in the north-eastern direction. The Euler 3D solution map indicates the possible existence of the ultramafic body in both the magnetic highs (H1 and H2). The H2 high shows the shallow depth, and H1 shows a deeper depth solution. In the reduced-to-pole (RTP) method, the bipolar anomaly disappears and indicates the existence of one causative source for both anomalies, which is, in all probabilities, an ultramafic suite of rock. The H1 magnetic high represents the main body, which persists up to depths of ~500m, as depicted through the upward continuation derivative map. Radially Averaged Power Spectrum (RAPS) shows the thickness of loose sediments up to 25m with a cumulative depth of 154m for sandstone overlying the ultramafic body. The average depth range of the shallower body (H2) is 60.5-86 meters, as estimated through the Peters half slope method. Magnetic (TF) anomaly with BA contour also shows high BA value around the high zones of magnetic anomaly (H1 and H2), which suggests that the causative body is with higher density and susceptibility for the surrounding host rock. The ground magnetic survey coupled with the gravity confirms a potential target for further exploration as the findings are co-relatable with the presence of the known diamondiferous kimberlites in this region, which post-date the rocks of the Kaimur Group.

Keywords: Kaimur, kimberlite, Euler 3D solution, magnetic

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Authors: Yusuf Berkay Metinal, Gulden Gumusburun Ayalp

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Architectural education is characterized by its distinctive amalgamation of studio-based pedagogy and theoretical instruction. It offers students a comprehensive learning experience that blends practical skill development with critical inquiry and conceptual exploration. Design studios are central to this educational paradigm, which serve as dynamic hubs of creativity and innovation, providing students with immersive environments for experimentation and collaborative engagement. The physical presence and interactive dynamics inherent in studio-based learning underscore the indispensability of face-to-face instruction and interpersonal interaction in nurturing the next generation of architects. However, architectural education underwent a seismic transformation in response to the global COVID-19 pandemic, precipitating an abrupt transition from traditional, in-person instruction to online education modalities. While this shift introduced newfound flexibility in terms of temporal and spatial constraints, it also brought many challenges to the fore. Chief among these challenges was maintaining effective communication and fostering meaningful collaboration among students in virtual learning environments. Besides these challenges, lack of peer learning emerged as a vital issue of the educational experience, particularly crucial for novice students navigating the intricacies of architectural practice. Nevertheless, the pivot to online education also laid bare a discernible decline in educational efficacy, prompting inquiries regarding the enduring viability of online education in architectural pedagogy. Moreover, as educational institutions grappled with the exigencies of remote instruction, discernible disparities between different institutional contexts emerged. While state universities often contended with fiscal constraints that shaped their operational capacities, private institutions encountered challenges from a lack of institutional fortification and entrenched educational traditions. Acknowledging the multifaceted nature of these challenges, this study endeavored to undertake a comprehensive inquiry into the dynamics of online education within architectural pedagogy by interrogating variables such as class level and type of university; the research aimed to elucidate demographic critical success factors that underpin the effectiveness of online education initiatives. To this end, a meticulously constructed questionnaire was administered to architecture students from diverse academic institutions across Turkey, informed by an exhaustive review of extant literature and scholarly discourse. The resulting dataset, comprising responses from 232 participants, underwent rigorous statistical analysis, including independent samples t-test and one-way ANOVA, to discern patterns and correlations indicative of overarching trends and salient insights. In sum, the findings of this study serve as a scholarly compass for educators, policymakers, and stakeholders navigating the evolving landscapes of architectural education. By elucidating the intricate interplay of demographical factors that shape the efficacy of online education in architectural pedagogy, this research offers a scholarly foundation upon which to anchor informed decisions and strategic interventions to elevate the educational experience for future cohorts of aspiring architects.

Keywords: architectural education, COVID-19, distance education, online education

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Authors: Alexander Vandesompele, Joni Dambre

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The diversity in dendritic arborization, as first illustrated by Santiago Ramon y Cajal, has always suggested a role for dendrites in the functionality of neurons. In the past decades, thanks to new recording techniques and optical stimulation methods, it has become clear that dendrites are not merely passive electrical components. They are observed to integrate inputs in a non-linear fashion and actively participate in computations. Regardless, in simulations of neural networks dendritic structure and functionality are often overlooked. Especially in a machine learning context, when designing artificial neural networks, point neuron models such as the leaky-integrate-and-fire (LIF) model are dominant. These models mimic the integration of inputs at the neuron soma, and ignore the existence of dendrites. In this work, the LIF point neuron model is extended with a simple form of dendritic computation. This gives the LIF neuron increased capacity to discriminate spatiotemporal input sequences, a dendritic functionality as observed in another study. Simulations of the spiking neurons are performed using the Bindsnet framework. In the common LIF model, incoming synapses are independent. Here, we introduce a dependency between incoming synapses such that the post-synaptic impact of a spike is not only determined by the weight of the synapse, but also by the activity of other synapses. This is a form of short term plasticity where synapses are potentiated or depressed by the preceding activity of neighbouring synapses. This is a straightforward way to prevent inputs from simply summing linearly at the soma. To implement this, each pair of synapses on a neuron is assigned a variable,representing the synaptic relation. This variable determines the magnitude ofthe short term plasticity. These variables can be chosen randomly or, more interestingly, can be learned using a form of Hebbian learning. We use Spike-Time-Dependent-Plasticity (STDP), commonly used to learn synaptic strength magnitudes. If all neurons in a layer receive the same input, they tend to learn the same through STDP. Adding inhibitory connections between the neurons creates a winner-take-all (WTA) network. This causes the different neurons to learn different input sequences. To illustrate the impact of the proposed dendritic mechanism, even without learning, we attach five input neurons to two output neurons. One output neuron isa regular LIF neuron, the other output neuron is a LIF neuron with dendritic relationships. Then, the five input neurons are allowed to fire in a particular order. The membrane potentials are reset and subsequently the five input neurons are fired in the reversed order. As the regular LIF neuron linearly integrates its inputs at the soma, the membrane potential response to both sequences is similar in magnitude. In the other output neuron, due to the dendritic mechanism, the membrane potential response is different for both sequences. Hence, the dendritic mechanism improves the neuron’s capacity for discriminating spa-tiotemporal sequences. Dendritic computations improve LIF neurons even if the relationships between synapses are established randomly. Ideally however, a learning rule is used to improve the dendritic relationships based on input data. It is possible to learn synaptic strength with STDP, to make a neuron more sensitive to its input. Similarly, it is possible to learn dendritic relationships with STDP, to make the neuron more sensitive to spatiotemporal input sequences. Feeding structured data to a WTA network with dendritic computation leads to a significantly higher number of discriminated input patterns. Without the dendritic computation, output neurons are less specific and may, for instance, be activated by a sequence in reverse order.

Keywords: dendritic computation, spiking neural networks, point neuron model

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Authors: Michael Sonne Kristensen, Frank Loesche, James Foster, Elif Ozcan, Judy Edworthy

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Auditory alarms play an integral role in intensive care nurses’ daily work. Most medical devices in the intensive care unit (ICU) are designed to produce alarm sounds in order to make nurses aware of immediate or prospective safety risks. The utilisation of sound as a carrier of crucial patient information is highly dependent on nurses’ presence - both physically and mentally. For ICU nurses, especially the ones who work with stationary alarm devices at the patient bed space, it is a challenge to display ‘appropriate’ alarm responses at all times as they have to navigate with great flexibility in a complex work environment. While being primarily responsible for a small number of allocated patients they are often required to engage with other nurses’ patients, relatives, and colleagues at different locations inside and outside the unit. This work explores the social strategies used by a team of nurses to comprehend and react to the information conveyed by the alarms in the ICU. Two main research questions guide the study: To what extent do alarms from a patient bed space reach the relevant responsible nurse by direct auditory exposure? By which means do responsible nurses get informed about their patients’ alarms when not directly exposed to the alarms? A comprehensive video-ethnographic field study was carried out to capture and evaluate alarm-related events in an ICU. The study involved close collaboration with four nurses who wore eye-level cameras and ear-level binaural audio recorders during several work shifts. At all time the entire unit was monitored by multiple video and audio recorders. From a data set of hundreds of hours of recorded material information about the nurses’ location, social interaction, and alarm exposure at any point in time was coded in a multi-channel replay-interface. The data shows that responsible nurses’ direct exposure and awareness of the alarms of their allocated patients vary significantly depending on work load, social relationships, and the location of the patient’s bed space. Distributed listening is deliberately employed by the nursing team as a social strategy to respond adequately to alarms, but the patterns of information flow prompted by alarm-related events are not uniform. Auditory Spatiotemporal Trajectory (AST) is proposed as a methodological label to designate the integration of temporal, spatial and auditory load information. As a mixed-method metrics it provides tangible evidence of how nurses’ individual alarm-related experiences differ from one another and from stationary points in the ICU. Furthermore, it is used to demonstrate how alarm-related information reaches the individual nurse through principles of social and distributed cognition, and how that information relates to the actual alarm event. Thereby it bridges a long-standing gap in the literature on medical alarm utilisation between, on the one hand, initiatives to measure objective data of the medical sound environment without consideration for any human experience, and, on the other hand, initiatives to study subjective experiences of the medical sound environment without detailed evidence of the objective characteristics of the environment.

Keywords: auditory spatiotemporal trajectory, medical alarms, social cognition, video-ethography

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Authors: Nafiah Solikhah Thoha

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One area that has spatial character as Heritage area is Kauman Villages. Kauman village in The City of Surakarta, Central Java, Indonesia was formed in 1757 by Paku Buwono III as the King of Kasunanan kingdom (Mataram Kingdom) for Kasunanan kingdom courtiers and scholars of Madrasa. Spatial character of Kauman village influenced by Islamic planning and socio-cultural rules of Kasunanan Kingdom. As traditional settlements influenced by Islamic planning, the Grand Mosque is a binding part of the whole area. Circulation pattern forming network (labyrinth) with narrow streets that ended at the Grand Mosque. The outdoor space can be used for circulation. Social activity is dominated by step movement from one place to a different place. Stalemate (the fina/cul de sac) generally only passable on foot, bicycles, and motorcycles. While the pass (main and branch) can be traversed by motor, vehicles. Kauman village has an area that can not be used as a public road that penetrates and serves as a liaison between the outside world to the other. Hierarchy of hall in Kauman village shows that the existence of a space is getting into more important. Firstly, woman in Kauman make the handmade batik for themself. In 2005 many people improving batik tradisional into commercial, and developed program named "Batik Tourism village of Kauman". That program affects the spatial transformations. This study aimed to explore the influence of tourism program towards spatial transformations. The factors that studied are the organization of space, circulation patterns, hierarchical space, and orientation through the descriptive-evaluation approach methods. Based on the study, tourism activity engenders transformations on the spatial scale (macro), residential block (mezo), homes (micro). First, the Grand Mosque and madrasa (religious school) as a binding zoning; tangle of roads as forming the structure of the area developed as a liaison with outside Kauman; organization of space in the residential of batik entrepreneurs firstly just a residential, then develop into residential, factory of batik including showroom. Second, the circulation pattern forming network (labyrinth) and ends at the Grand Mosque. Third, the hierarchy in the form of public space (the shari), semi-public, and private (the fina/culdesac) is no longer to provide protection to women, only as hierarchy of circulation path. Fourth, cluster building orientation does not follow the kiblat direction or axis oriented to cosmos, but influence by the new function as the showroom. It was need the direction of the main road. Kauman grow as an appropriate area for the community. During its development, the settlement function changes according to community activities, especially economic activities. The new function areas as tourism area affect spatial pattern of Kauman village. Spatial existence and activity as a local wisdom that has been done for generations have meaning of holistic, encompassing socio-cultural sustainability, economics, and the heritage area. By reviewing the local wisdom and the way of life of that society, we can learn how to apply the culture as education for sustainable of heritage area.

Keywords: impact of tourism, Kauman village, spatial transformation, sustainable of heritage area

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Authors: L. Salomon, P. Sklenar

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The Andes hold the highest plant species diversity in the world. How this occurred is one of the most intriguing questions in studies addressing the origin and patterning of plant diversity worldwide. Recently, the explosive adaptive radiations found in high Andean groups have been pointed as triggers to this spectacular diversity. The Andes is the species-richest area for the biggest genus from the Asteraceae family: Senecio. There, the genus presents an incredible diversity of species, striking growth form variation, and large niche span. Even when some studies tried to disentangle the evolutionary story for some Andean species in Senecio, they obtained partially resolved and low supported phylogenies, as expected for recently radiated groups. The high-throughput sequencing (HTS) approaches have proved to be a powerful tool answering phylogenetic questions in those groups whose evolutionary stories are recent and traditional techniques like Sanger sequencing are not informative enough. Although these tools have been used to understand the evolution of an increasing number of Andean groups, nowadays, their scope has not been applied for Senecio. This project aims to contribute to a better knowledge of the mechanisms shaping the hyper diversity of Senecio in the Andean region, using HTS focusing on Senecio ser. Culcitium (Asteraceae), recently recircumscribed. Firstly, reconstructing a highly resolved and supported phylogeny, and after assessing the role of allopatric differentiation, hybridization, and genome duplication in the diversification of the group. Using the Hyb-Seq approach, combining target enrichment using Asteraceae COS loci baits and genome skimming, more than 100 new accessions were generated. HybPhyloMaker and HybPiper pipelines were used for the phylogenetic analyses, and another pipeline in development (Paralogue Wizard) was used to deal with paralogues. RAxML was used to generate gene trees and Astral for species tree reconstruction. Phyparts were used to explore as first step of gene tree discordance along the clades. Fully resolved with moderated supported trees were obtained, showing Senecio ser. Culcitium as monophyletic. Within the group, some species formed well-supported clades with morphologically related species, while some species would not have exclusive ancestry, in concordance with previous studies using amplified fragment length polymorphism (AFLP) showing geographical differentiation. Discordance between gene trees was detected. Paralogues were detected for many loci, indicating possible genome duplications; ploidy level estimation using flow cytometry will be carried out during the next months in order to identify the role of this process in the diversification of the group. Likewise, TreeSetViz package for Mesquite, hierarchical likelihood ratio congruence test using Concaterpillar, and Procrustean Approach to Cophylogeny (PACo), will be used to evaluate the congruence among different inheritance patterns. In order to evaluate the influence of hybridization and Incomplete Lineage Sorting (ILS) in each resultant clade from the phylogeny, Joly et al.'s 2009 method in a coalescent scenario and Paterson’s D-statistic will be performed. Even when the main discordance sources between gene trees were not explored in detail yet, the data show that at least to some degree, processes such as genome duplication, hybridization, and/or ILS could be involved in the evolution of the group.

Keywords: adaptive radiations, Andes, genome duplication, hybridization, Senecio

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Authors: Chenyi Ma

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Background: The second costliest hurricane in U.S. history, Sandy landed in southern New Jersey on October 29, 2012, and struck the entire state with high winds and torrential rains. The disaster killed more than 100 people, left more than 8.5 million households without power, and damaged or destroyed more than 200,000 homes across the state. Immediately after the disaster, public policy support was provided in nine coastal counties that constituted 98% of the major and severely damaged housing units in NJ overall. The programs include Individuals and Households Assistance Program, Small Business Loan Program, National Flood Insurance Program, and the Federal Emergency Management Administration (FEMA) Public Assistance Grant Program. In the most severely affected counties, additional funding was provided through Community Development Block Grant: Reconstruction, Rehabilitation, Elevation, and Mitigation Program, and Homeowner Resettlement Program. How these policies individually and as a whole impacted housing recovery across communities with different socioeconomic and demographic profiles has not yet been studied, particularly in relation to damage levels. The concept of community social vulnerability has been widely used to explain many aspects of natural disasters. Nevertheless, how communities are vulnerable has been less fully examined. Community resilience has been conceptualized as a protective factor against negative impacts from disasters, however, how community resilience buffers the effects of vulnerability is not yet known. Because housing recovery is a dynamic social and economic process that varies according to context, this study examined the path from community vulnerability and resilience to housing recovery looking at both community characteristics and policy interventions. Sample/Methods: This retrospective longitudinal case study compared a literature-identified set of pre-disaster community characteristics, the effects of multiple public policy programs, and a set of time-variant community resilience indicators to changes in housing stock (operationally defined by percent of building permits to total occupied housing units/households) between 2010 and 2014, two years before and after Hurricane Sandy. The sample consisted of 51 municipalities in the nine counties in which between 4% and 58% of housing units suffered either major or severe damage. Structural equation modeling (SEM) was used to determine the path from vulnerability to the housing recovery, via multiple public programs, separately and as a whole, and via the community resilience indicators. The spatial analytical tool ArcGIS 10.2 was used to show the spatial relations between housing recovery patterns and community vulnerability and resilience. Findings: Holding damage levels constant, communities with higher proportions of Hispanic households had significantly lower levels of housing recovery while communities with households with an adult >age 65 had significantly higher levels of the housing recovery. The contrast was partly due to the different levels of total public support the two types of the community received. Further, while the public policy programs individually mediated the negative associations between African American and female-headed households and housing recovery, communities with larger proportions of African American, female-headed and Hispanic households were “vulnerable” to lower levels of housing recovery because they lacked sufficient public program support. Even so, higher employment rates and incomes buffered vulnerability to lower housing recovery. Because housing is the "wobbly pillar" of the welfare state, the housing needs of these particular groups should be more fully addressed by disaster policy.

Keywords: community social vulnerability, community resilience, hurricane, public policy

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Authors: Nicki Mohammadi, Emma Reford, Natalia Romano Spica, Laura Tabacof, Jenna Tosto-Mancuso, David Putrino, Christopher P. Kellner

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Introductory Statement: The onset of the COVID-19 pandemic demanded an unprecedented need for the rapid development, dispersal, and application of infection testing. However, despite the impressive mobilization of resources, individuals were incredibly limited in their access to tests, particularly during the initial months of the pandemic (March-April 2020) in New York City (NYC). Access to COVID-19 testing is crucial in understanding patients’ illness experiences and integral to the development of COVID-19 standard-of-care protocols, especially in the context of overall access to healthcare resources. Succinct Description of basic methodologies: 18 Patients in a COVID-19 Remote Patient Monitoring Program (Precision Recovery within the Mount Sinai Health System) were interviewed regarding their experience with COVID-19 during the first wave (March-May 2020) of the COVID-19 pandemic in New York City. Patients were asked about their experiences navigating COVID-19 diagnoses, the health care system, and their recovery process. Transcribed interviews were analyzed for thematic codes, using grounded theory to guide the identification of emergent themes and codebook development through an iterative process. Data coding was performed using NVivo12. References for the domain “testing” were then extracted and analyzed for themes and statistical patterns. Clear Indication of Major Findings of the study: 100% of participants (18/18) referenced COVID-19 testing in their interviews, with a total of 79 references across the 18 transcripts (average: 4.4 references/interview; 2.7% interview coverage). 89% of participants (16/18) discussed the difficulty of access to testing, including denial of testing without high severity of symptoms, geographical distance to the testing site, and lack of testing resources at healthcare centers. Participants shared varying perspectives on how the lack of certainty regarding their COVID-19 status affected their course of recovery. One participant shared that because she never tested positive she was shielded from her anxiety and fear, given the death toll in NYC. Another group of participants shared that not having a concrete status to share with family, friends and professionals affected how seriously onlookers took their symptoms. Furthermore, the absence of a positive test barred some individuals from access to treatment programs and employment support. Concluding Statement: Lack of access to COVID-19 testing in the first wave of the pandemic in NYC was a prominent element of patients’ illness experience, particularly during their recovery phase. While for some the lack of concrete results was protective, most emphasized the invalidating effect this had on the perception of illness for both self and others. COVID-19 testing is now widely accessible; however, those who are unable to demonstrate a positive test result but who are still presumed to have had COVID-19 in the first wave must continue to adapt to and live with the effects of this gap in knowledge and care on their recovery. Future efforts are required to ensure that patients do not face barriers to care due to the lack of testing and are reassured regarding their access to healthcare. Affiliations- 1Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY 2Abilities Research Center, Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, NY

Keywords: accessibility, COVID-19, recovery, testing

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Authors: Ikele Chika Bright, Mgbenka Bernard Obialo, Ikele Chioma Faith

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Icthyophthiriasis is a prevalent protozoan (ectoparasite) mostly affecting cultured and aquarium fishes. The majority of the chemotherapeutants lack efficacy for completely eliminating Ich parasite without affecting the environment and they are not safe for human health. The present work is focused on the evaluating different immersion treatments of African catfish (Clarias gariepinus) infected with ichthyophthiriasis and treated with a non-chemical and environmental friendly parasiticides Moringa oleifera. A total number of 800 apparently healthy parasites free (examined) post juvenile catfish were obtained from a reputable farm, disinfected with potassium permanganate in a quarantine tank to remove any possible external parasites. The fish were further challenged with approximately 44,000 infective stages of theronts which were obtained through serial passages by cohabitation. Seven groups (A-G) of post Juvenile were used for the experiment which was carried out into three stages; Dips (60minutes), short term treatment (24-96h) and prolong bath treatment (0-15 days). The concentrations selected were dependent on the outcome of the LC50 of the plant material from which dose-dependent factors were used to select various concentrations of the treatment. In Dips treatment, group D-G were treated with 1,500mg/L, 2500mg/L., 3500mg/L and 4500mg/L, short-term treatment was treated with 150mg/L, 250mg/L, 350mg/L and 450mg/L and prolong bath was treated with 15mg/L, 25mg/L, 35mg/L and 45mg/L of the plant extract whereas group A, B and C were normal control, Ich- infested not treated and Ich- infested treated with standard drug (Acriflavin), respectively. The various types of treatment applied with corresponding concentrations showed almost complete elimination of the adult parasites (trophonts) both in the gills and the body smear, thereby making M. oleifera a potential parasiticides. There were serious pathological alterations in the skin and gills which are usually the main point for Ich parasites invasion but no significant morphological characteristics was noted among the treated groups subjected to different immersion treatment patterns. Epitheliocystis, aneurysm, oedema, hemorrhage, and localization of the adult parasite in the gills were the overall common observations made in the gills whereas degeneration of muscle fibre, dermatitis, hemorrhage, oedema, abscess formation and keratinisation were observed in the skin. However, there are no pathological changes in the control group. Moreover, biochemical parameters such as urea, creatinine, albumin., globulin, total protein, ALT, AST), blood chemistry (sodium, chloride, potassium, bicarbonate), antioxidants (CAT, SOD, GPx, LPO), enzymatic activities (myeloperoxidase, thioreadoxin reductase), Inflammatory response (C-reactive protein), Stress markers (lactate dehydrogenase), heamatological parameters (RBC, PCV, WBC, HB and differential count), lipid profile (total cholesterol, tryglycerides , high density lipoprotein and low density lipoprotein) all showed various significant (P<0.05) and no significant (P>0.05) responses among the Ich-infested fish treated under three immersion treatments. It is suggested that M. oleifera may serve as an alternatives to chemotherapeutants for control of Ichthyophthiriasis in African catfish Clarias gariepinus.

Keywords: Icthyophthirius multifilis, immersion treatment, pathophysiology, African catfish

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Authors: Ankush, Harshit Sosan Lakra, Rachita Kuthial

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Climate change poses significant challenges to vulnerable communities, particularly indigenous populations in ecologically sensitive regions. Jharkhand, located in the heart of India, is home to several indigenous communities, including the Particularly Vulnerable Tribal Groups (PVTGs). The Indigenous architecture of the region functions as a significant reservoir of climate adaptation wisdom. It explores the architectural analysis encompassing the construction materials, construction techniques, design principles, climate responsiveness, cultural relevance, adaptation, integration with the environment and traditional wisdom that has evolved through generations, rooted in cultural and socioeconomic traditions, and has allowed these communities to thrive in a variety of climatic zones, including hot and dry, humid, and hilly terrains to withstand the test of time. Despite their historical resilience to adverse climatic conditions, PVTG tribal communities face new and amplified challenges due to the accelerating pace of climate change. There is a significant research void that exists in assimilating their traditional practices and local wisdom into contemporary climate resilience initiatives. Most of the studies place emphasis on technologically advanced solutions, often ignoring the invaluable Indigenous Local knowledge that can complement and enhance these efforts. This research gap highlights the need to bridge the disconnect between indigenous knowledge and contemporary climate adaptation strategies. The study aims to explore and leverage indigenous knowledge of vernacular architecture as a strategic tool for enhancing climatic resilience among PVTGs of the region. The first objective is to understand the traditional wisdom of vernacular architecture by analyzing and documenting distinct architectural practices and cultural significance of PVTG communities, emphasizing construction techniques, materials and spatial planning. The second objective is to develop culturally sensitive climatic resilience strategies based on findings of vernacular architecture by employing a multidisciplinary research approach that encompasses ethnographic fieldwork climate data assessment considering multiple variables such as temperature variations, precipitation patterns, extreme weather events and climate change reports. This will be a tailor-made solution integrating indigenous knowledge with modern technology and sustainable practices. With the involvement of indigenous communities in the process, the research aims to ensure that the developed strategies are practical, culturally appropriate, and accepted. To foster long-term resilience against the global issue of climate change, we can bridge the gap between present needs and future aspirations with Traditional wisdom, offering sustainable solutions that will empower PVTG communities. Moreover, the study emphasizes the significance of preserving and reviving traditional Architectural wisdom for enhancing climatic resilience. It also highlights the need for cooperative endeavors of communities, stakeholders, policymakers, and researchers to encourage integrating traditional Knowledge into Modern sustainable design methods. Through these efforts, this research will contribute not only to the well-being of PVTG communities but also to the broader global effort to build a more resilient and sustainable future. Also, the Indigenous communities like PVTG in the state of Jharkhand can achieve climatic resilience while respecting and safeguarding the cultural heritage and peculiar characteristics of its native population.

Keywords: vernacular architecture, climate change, resilience, PVTGs, Jharkhand, indigenous people, India

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Authors: Carolyn Hatch, Laurent Simon

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Urban mobility and the transportation industry are undergoing a transformation, shifting from an auto production-consumption model that has dominated since the early 20th century towards new forms of personal and shared multi-modality [1]. This is shaped by key forces such as climate change, which has induced a shift in production and consumption patterns and efforts to decarbonize and improve transport services through, for instance, the integration of vehicle automation, electrification and mobility sharing [2]. Advanced innovation practices and platforms for experimentation and validation of new mobility products and services that are increasingly complex and multi-stakeholder-oriented are shaping this new world of mobility. Transportation hubs – such as airports - are emblematic of these disruptive forces playing out in the mobility industry. Airports are emerging as the core of innovation ecosystems on and around contemporary mobility issues, and increasingly recognized as complex public/private nodes operating in many societal dimensions [3,4]. These include urban development, sustainability transitions, digital experimentation, customer experience, infrastructure development and data exploitation (for instance, airports generate massive and often untapped data flows, with significant potential for use, commercialization and social benefit). Yet airport innovation practices have not been well documented in the innovation literature. This paper addresses this gap by proposing a model of airport innovation that aims to equip airport stakeholders to respond to these new and complex innovation needs in practice. The methodology involves: 1 – a literature review bringing together key research and theory on airport innovation management, open innovation and innovation ecosystems in order to evaluate airport practices through an innovation lens; 2 – an international benchmarking of leading airports and their innovation practices, including such examples as Aéroports de Paris, Schipol in Amsterdam, Changi in Singapore, and others; and 3 – semi-structured interviews with airport managers on key aspects of organizational practice, facilitated through a close partnership with the Airport Council International (ACI), a major stakeholder in this research project. Preliminary results find that the most successful airports are those that have shifted to a multi-stakeholder, platform ecosystem model of innovation. The recent entrance of new actors in airports (Google, Amazon, Accor, Vinci, Airbnb and others) have forced the opening of organizational boundaries to share and exchange knowledge with a broader set of ecosystem players. This has also led to new forms of governance and intermediation by airport actors to connect complex, highly distributed knowledge, along with new kinds of inter-organizational collaboration, co-creation and collective ideation processes. Leading airports in the case study have demonstrated a unique capacity to force traditionally siloed activities to “think together”, “explore together” and “act together”, to share data, contribute expertise and pioneer new governance approaches and collaborative practices. In so doing, they have successfully integrated these many disruptive change pathways and forced their implementation and coordination towards innovative mobility outcomes, with positive societal, environmental and economic impacts. This research has implications for: 1 - innovation theory, 2 - urban and transport policy, and 3 - organizational practice - within the mobility industry and across the economy.

Keywords: airport management, ecosystem, innovation, mobility, platform, transport hubs

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Authors: Shreyas Kumar, Mili Shangari

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Integrating artificial intelligence (AI) in the cyber insurance sector represents a significant advancement, offering the potential to revolutionize risk assessment, fraud detection, and claims processing. However, this integration introduces a range of regulatory and economic challenges that must be addressed to ensure responsible and effective deployment of AI technologies. This paper examines the multifaceted regulatory landscape governing AI in cyber insurance and explores the economic implications of compliance, innovation, and market dynamics. AI's capabilities in processing vast amounts of data and identifying patterns make it an invaluable tool for insurers in managing cyber risks. Yet, the application of AI in this domain is subject to stringent regulatory scrutiny aimed at safeguarding data privacy, ensuring algorithmic transparency, and preventing biases. Regulatory bodies, such as the European Union with its General Data Protection Regulation (GDPR), mandate strict compliance requirements that can significantly impact the deployment of AI systems. These regulations necessitate robust data protection measures, ethical AI practices, and clear accountability frameworks, all of which entail substantial compliance costs for insurers. The economic implications of these regulatory requirements are profound. Insurers must invest heavily in upgrading their IT infrastructure, implementing robust data governance frameworks, and training personnel to handle AI systems ethically and effectively. These investments, while essential for regulatory compliance, can strain financial resources, particularly for smaller insurers, potentially leading to market consolidation. Furthermore, the cost of regulatory compliance can translate into higher premiums for policyholders, affecting the overall affordability and accessibility of cyber insurance. Despite these challenges, the potential economic benefits of AI integration in cyber insurance are significant. AI-enhanced risk assessment models can provide more accurate pricing, reduce the incidence of fraudulent claims, and expedite claims processing, leading to overall cost savings and increased efficiency. These efficiencies can improve the competitiveness of insurers and drive innovation in product offerings. However, balancing these benefits with regulatory compliance is crucial to avoid legal penalties and reputational damage. The paper also explores the potential risks associated with AI integration, such as algorithmic biases that could lead to unfair discrimination in policy underwriting and claims adjudication. Regulatory frameworks need to evolve to address these issues, promoting fairness and transparency in AI applications. Policymakers play a critical role in creating a balanced regulatory environment that fosters innovation while protecting consumer rights and ensuring market stability. In conclusion, the integration of AI in cyber insurance presents both regulatory and economic challenges that require a coordinated approach involving regulators, insurers, and other stakeholders. By navigating these challenges effectively, the industry can harness the transformative potential of AI, driving advancements in risk management and enhancing the resilience of the cyber insurance market. This paper provides insights and recommendations for policymakers and industry leaders to achieve a balanced and sustainable integration of AI technologies in cyber insurance.

Keywords: artificial intelligence (AI), cyber insurance, regulatory compliance, economic impact, risk assessment, fraud detection, cyber liability insurance, risk management, ransomware

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Authors: Athanasios Karasimos, Vasiliki Makri

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This research focuses on the design and development of the first text Corpus based on Board Game Rulebooks (BGRC) with direct application on the morphological analysis of neologisms and tendencies in word-formation processes. Corpus linguistics is a dynamic field that examines language through the lens of vast collections of texts. These corpora consist of diverse written and spoken materials, ranging from literature and newspapers to transcripts of everyday conversations. By morphologically analyzing these extensive datasets, morphologists can gain valuable insights into how language functions and evolves, as these extensive datasets can reflect the byproducts of inflection, derivation, blending, clipping, compounding, and neology. This entails scrutinizing how words are created, modified, and combined to convey meaning in a corpus of challenging, creative, and straightforward texts that include rules, examples, tutorials, and tips. Board games teach players how to strategize, consider alternatives, and think flexibly, which are critical elements in language learning. Their rulebooks reflect not only their weight (complexity) but also the language properties of each genre and subgenre of these games. Board games are a captivating realm where strategy, competition, and creativity converge. Beyond the excitement of gameplay, board games also spark the art of word creation. Word games, like Scrabble, Codenames, Bananagrams, Wordcraft, Alice in the Wordland, Once uUpona Time, challenge players to construct words from a pool of letters, thus encouraging linguistic ingenuity and vocabulary expansion. These games foster a love for language, motivating players to unearth obscure words and devise clever combinations. On the other hand, the designers and creators produce rulebooks, where they include their joy of discovering the hidden potential of language, igniting the imagination, and playing with the beauty of words, making these games a delightful fusion of linguistic exploration and leisurely amusement. In this research, more than 150 rulebooks in English from all types of modern board games, either language-independent or language-dependent, are used to create the BGRC. A representative sample of each genre (family, party, worker placement, deckbuilding, dice, and chance games, strategy, eurogames, thematic, role-playing, among others) was selected based on the score from BoardGameGeek, the size of the texts and the level of complexity (weight) of the game. A morphological model with morphological networks, multi-word expressions, and word-creation mechanics based on the complexity of the textual structure, difficulty, and board game category will be presented. In enabling the identification of patterns, trends, and variations in word formation and other morphological processes, this research aspires to make avail of this creative yet strict text genre so as to (a) give invaluable insight into morphological creativity and innovation that (re)shape the lexicon of the English language and (b) test morphological theories. Overall, it is shown that corpus linguistics empowers us to explore the intricate tapestry of language, and morphology in particular, revealing its richness, flexibility, and adaptability in the ever-evolving landscape of human expression.

Keywords: board game rulebooks, corpus design, morphological innovations, neologisms, word-formation processes

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Authors: Marit D. Murry, Amy K. Marks

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The transition to college is a critical period that affords abundant opportunities for growth in conjunction with novel challenges for emerging adults. During this time, emerging adults are garnering experiences and acquiring hosts of new information that they are required to synthesize and use to inform life-shaping decisions. This stage is characterized by instability and exploration, which necessitates a diverse set of coping skills to successfully navigate and positively adapt to their evolving environment. However, important sociocultural factors result in differences that occur developmentally for minority emerging adults (i.e., emerging adults with an identity that has been or is marginalized). While the transition to college holds vast potential, not all are afforded the same chances, and many individuals enter into this stage at varying degrees of readiness. Understanding the nuance and diversity of student preparedness for college and contextualizing these factors will better equip systems to support incoming students. Emerging adulthood for ethnic, racial minority students presents itself as an opportunity for growth and resiliency in the face of systemic adversity. Ethnic, racial identity (ERI) is defined as an identity that develops as a function of one’s ethnic-racial group membership. Research continues to demonstrate ERI as a resilience factor that promotes positive adjustment in young adulthood. Adaptive coping responses (e.g., engaging in help-seeking behavior, drawing on personal and community resources) have been identified as possible mechanisms through which ERI buffers youth against stressful life events, including discrimination. Additionally, trait mindfulness has been identified as a significant predictor of general psychological health, and mindfulness practice has been shown to be a self-regulatory strategy that promotes healthy stress responses and adaptive coping strategy selection. The current study employed a person-centered approach to explore emerging patterns across ethnic identity development and psychological well-being criterion variables among college freshmen. Data from 283 incoming college freshmen at Northeastern University were analyzed. The Brief COPE Acceptance and Emotional Support scales, the Five Factor Mindfulness Questionnaire, and MIEM Exploration and Affirmation measures were used to inform the cluster profiles. The TwoStep auto-clustering algorithm revealed an optimal three-cluster solution (BIC = 848.49), which classified 92.6% (n = 262) of participants in the sample into one of the three clusters. The clusters were characterized as ‘Mixed Adjustment’, ‘Lowest Adjustment’, and ‘Moderate Adjustment.’ Cluster composition varied significantly by ethnicity X² (2, N = 262) = 7.74 (p = .021) and gender X² (2, N = 259) = 10.40 (p = .034). The ‘Lowest Adjustment’ cluster contained the highest proportion of students of color, 41% (n = 32), and male-identifying students, 44.2% (n = 34). Follow-up analyses showed higher ERI exploration in ‘Moderate Adjustment’ cluster members, also reported higher levels of psychological distress, with significantly elevated depression scores (p = .011), psychological diagnoses of depression (p = .013), anxiety (p = .005) and psychiatric disorders (p = .025). Supporting prior research, students engaging with identity exploration processes often endure more psychological distress. These results indicate that students undergoing identity development may require more socialization and different services beyond normal strategies.

Keywords: adjustment, coping, college, emerging adulthood, ethnic-racial identity, psychological well-being, resilience

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Authors: Paulo Roberto Vasconcellos-Silva

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The "Janeiro Roxo" (Purple January) campaign in Brazil aims to promote awareness of leprosy and its early symptoms. The COVID-19 pandemic has adversely affected institutional campaigns, mostly considering leprosy a neglected disease by the media. Google Trends (GT) is a tool that tracks user searches on Google, providing insights into the popularity of specific search terms. Our prior research has categorized online searches into two types: "Reactive searches," driven by transient campaign-related stimuli, and "Proactive searches," driven by personal interest in early symptoms and self-diagnosis. Using GT we studied: (i) the impact of "Janeiro Roxo" on public interest in leprosy (assessed through reactive searches) and its early symptoms (evaluated through proactive searches) over the past five years; (ii) changes in public interest during and after the COVID-19 pandemic; (iii) patterns in the dynamics of reactive and proactive searches Methods: We used GT's "Relative Search Volume" (RSV) to gauge public interest on a scale from 0 to 100. "HANSENÍASE" (HAN) was a proxy for reactive searches, and "HANSENÍASE SINTOMAS" (leprosy symptoms) (H.SIN) for proactive searches (interest in leprosy or in self-diagnosis). We analyzed 261 weeks of data from 2018 to 2023, using polynomial trend lines to model trends over this period. Analysis of Variance (ANOVA) was used to compare weekly RSV, monthly (MM) and annual means (AM). Results: Over a span of 261 weeks, there was consistently higher Relative Search Volume (RSV) for HAN compared to H.SIN. Both search terms exhibited their highest (MM) in January months during all periods. COVID-19 pandemic: a decline was observed during the pandemic years (2020-2021). There was a 24% decrease in RSV for HAN and a 32.5% decrease for H.SIN. Both HAN and H.SIN regained their pre-pandemic search levels in January 2022-2023. Breakpoints indicated abrupt changes - in the 26th week (February 2019), 55th and 213th weeks (September 2019 and 2022) related to September regional campaigns (interrupted in 2020-2021). Trend lines for HAN exhibited an upward curve between 33rd-45th week (April to June 2019), a pandemic-related downward trend between 120th-136th week (December 2020 to March 2021), and an upward trend between 220th-240th week (November 2022 to March 2023). Conclusion: The "Janeiro Roxo" campaign, along with other media-driven activities, exerts a notable influence on both reactive and proactive searches related to leprosy topics. Reactive searches, driven by campaign stimuli, significantly outnumber proactive searches. Despite the interruption of the campaign due to the pandemic, there was a subsequent resurgence in both types of searches. The recovery observed in reactive and proactive searches post-campaign interruption underscores the effectiveness of such initiatives, particularly at the national level. This suggests that regional campaigns aimed at leprosy awareness can be considered highly successful in stimulating proactive public engagement. The evaluation of internet-based campaign programs proves valuable not only for assessing their impact but also for identifying the needs of vulnerable regions. These programs can play a crucial role in integrating regions and highlighting their needs for assistance services in the context of leprosy awareness.

Keywords: health communication, leprosy, health campaigns, information seeking behavior, Google Trends, reactive searches, proactive searches, leprosy early identification

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Authors: Neusa Fernanda Torres, Buyisile Chibi, Lyn E. Middleton, Vernon P. Solomon, Tivani P. Mashamba-Thompson

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Background: Self-medication with antibiotics (SMA) is a global concern, with a higher incidence in low and middle-income countries (LMICs). Despite intense world-wide efforts to control and promote the rational use of antibiotics, continuing practices of SMA systematically exposes individuals and communities to the risk of antibiotic resistance and other undesirable antibiotic side effects. Moreover, it increases the health systems costs of acquiring more powerful antibiotics to treat the resistant infection. This review thus maps evidence on the factors influencing self-medication with antibiotics in these settings. Methods: The search strategy for this review involved electronic databases including PubMed, Web of Knowledge, Science Direct, EBSCOhost (PubMed, CINAHL with Full Text, Health Source - Consumer Edition, MEDLINE), Google Scholar, BioMed Central and World Health Organization library, using the search terms:’ Self-Medication’, ‘antibiotics’, ‘factors’ and ‘reasons’. Our search included studies published from 2007 to 2017. Thematic analysis was performed to identify the patterns of evidence on SMA in LMICs. The mixed method quality appraisal tool (MMAT) version 2011 was employed to assess the quality of the included primary studies. Results: Fifteen studies met the inclusion criteria. Studies included population from the rural (46,4%), urban (33,6%) and combined (20%) settings, of the following LMICs: Guatemala (2 studies), India (2), Indonesia (2), Kenya (1), Laos (1), Nepal (1), Nigeria (2), Pakistan (2), Sri Lanka (1), and Yemen (1). The total sample size of all 15 included studies was 7676 participants. The findings of the review show a high prevalence of SMA ranging from 8,1% to 93%. Accessibility, affordability, conditions of health facilities (long waiting, quality of services and workers) as long well as poor health-seeking behavior and lack of information are factors that influence SMA in LMICs. Antibiotics such as amoxicillin, metronidazole, amoxicillin/clavulanic, ampicillin, ciprofloxacin, azithromycin, penicillin, and tetracycline, were the most frequently used for SMA. The major sources of antibiotics included pharmacies, drug stores, leftover drugs, family/friends and old prescription. Sore throat, common cold, cough with mucus, headache, toothache, flu-like symptoms, pain relief, fever, running nose, toothache, upper respiratory tract infections, urinary symptoms, urinary tract infection were the common disease symptoms managed with SMA. Conclusion: Although the information on factors influencing SMA in LMICs is unevenly distributed, the available information revealed the existence of research evidence on antibiotic self-medication in some countries of LMICs. SMA practices are influenced by social-cultural determinants of health and frequently associated with poor dispensing and prescribing practices, deficient health-seeking behavior and consequently with inappropriate drug use. Therefore, there is still a need to conduct further studies (qualitative, quantitative and randomized control trial) on factors and reasons for SMA to correctly address the public health problem in LMICs.

Keywords: antibiotics, factors, reasons, self-medication, low and middle-income countries (LMICs)

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Authors: Chandu Rathnayake, Isuri Anuradha

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Early detection and accurate diagnosis of lung diseases play a crucial role in improving patient prognosis. However, conventional diagnostic methods heavily rely on subjective symptom assessments and medical imaging, often causing delays in diagnosis and treatment. To overcome this challenge, we propose a novel lung disease prediction system that integrates patient symptoms and X-ray images to provide a comprehensive and reliable diagnosis.In this project, develop a mobile application specifically designed for detecting lung diseases. Our application leverages both patient symptoms and X-ray images to facilitate diagnosis. By combining these two sources of information, our application delivers a more accurate and comprehensive assessment of the patient's condition, minimizing the risk of misdiagnosis. Our primary aim is to create a user-friendly and accessible tool, particularly important given the current circumstances where many patients face limitations in visiting healthcare facilities. To achieve this, we employ several state-of-the-art algorithms. Firstly, the Decision Tree algorithm is utilized for efficient symptom-based classification. It analyzes patient symptoms and creates a tree-like model to predict the presence of specific lung diseases. Secondly, we employ the Random Forest algorithm, which enhances predictive power by aggregating multiple decision trees. This ensemble technique improves the accuracy and robustness of the diagnosis. Furthermore, we incorporate a deep learning model using Convolutional Neural Network (CNN) with the RestNet50 pre-trained model. CNNs are well-suited for image analysis and feature extraction. By training CNN on a large dataset of X-ray images, it learns to identify patterns and features indicative of lung diseases. The RestNet50 architecture, known for its excellent performance in image recognition tasks, enhances the efficiency and accuracy of our deep learning model. By combining the outputs of the decision tree-based algorithms and the deep learning model, our mobile application generates a comprehensive lung disease prediction. The application provides users with an intuitive interface to input their symptoms and upload X-ray images for analysis. The prediction generated by the system offers valuable insights into the likelihood of various lung diseases, enabling individuals to take appropriate actions and seek timely medical attention. Our proposed mobile application has significant potential to address the rising prevalence of lung diseases, particularly among young individuals with smoking addictions. By providing a quick and user-friendly approach to assessing lung health, our application empowers individuals to monitor their well-being conveniently. This solution also offers immense value in the context of limited access to healthcare facilities, enabling timely detection and intervention. In conclusion, our research presents a comprehensive lung disease prediction system that combines patient symptoms and X-ray images using advanced algorithms. By developing a mobile application, we provide an accessible tool for individuals to assess their lung health conveniently. This solution has the potential to make a significant impact on the early detection and management of lung diseases, benefiting both patients and healthcare providers.

Keywords: CNN, random forest, decision tree, machine learning, deep learning

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Authors: Elham Abbas Aljaaly, Nahlaa Abdulwahab Khalifa

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The active method of disease prevention is seen as the most affordable and sustainable action to deal with risks of non-communicable diseases such as obesity. This eight-week project aimed to pilot the feasibility and acceptability of a school-based programme, which is proposed to prevent and modify overweight status and possible related risk factors among student girls 'at the intermediate level' in Jeddah city. The programme was conducted through comprehensible approach targeting physical environment and school policies (nutritional/exercise/behavioural approach). The programme was designed to cultivate the personal and environmental awareness in schools for girls. This was applied by promoting healthy eating and physical activity through policies, physical education, healthier options for school canteens, and the creation of school health teams. The prevention programme was applied on 68 students (who agreed to participate) from grades 7th, 8th and 9th. A pre and post assessment questionnaire was employed on 66 students. The questionnaires were designed to obtain information on students' knowledge about health, nutrition and physical activity. Survey questions included information about nutrients, food consumption patterns, food intake and lifestyle. Physical education included training sessions for new opportunities for physical activities to be performed during school or after school hours. A running competition 'to enhance students’ performance for physical activities' was also conducted during the school visit. A visit to the school canteen was conducted to check, observe, record and assess all available food/beverage items and meals. The assessment method was a subjective method for the type of food/beverages if high in saturated fat, salt and sugar (HFSS) or non-HFSS. The school canteen administrators were encouraged to provide healthy food/beverage items and a sample healthy canteen was provided for implementation. Two healthy options were introduced to the school canteen. A follow up for students’ preferences for the introduced options and the purchasing power were assessed. Thirty-eight percent of young girls (n=26) were not participating in any form of physical activities inside or outside school. Skipping breakfast was stated by 42% (n=28) of students with no daily consumption (19%, n=13) for fruit/vegetables. Significant changes were noticed in students’ (n=66) overall responses to the pre and post questions (P value=.001). All students had participated in the conducted running competition sessions and reported satisfaction and enjoyment about the sessions. No absence was reported by the research team for attending physical education and activity sessions throughout the delivered programme. The purchasing power of the introduced healthy options of 'Salad and oatmeal' was increased to 18% in 8 weeks at the school canteen, and slightly affected the purchase for other less healthy options. The piloted programme indorsed better health and nutrition knowledge, healthy eating and lifestyle attitude, which could help young girls to obtain sustainable changes. It is expected that the outcomes of the programme will be a cornerstone for the futuristic national study that will assist policy makers and participants to build a knowledgeable health promotion scenario and make sure that school students have access to healthy foods, physical exercise and healthy lifestyle.

Keywords: adolescents, diet, exercise, behaviours, overweight/obesity, prevention-intervention programme, Saudi Arabia, schoolgirls

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Authors: Tabbye Chavous, Felecia Webb, Bridget Richardson, Gloryvee Fonseca-Bolorin, Seanna Leath, Robert Sellers

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There has been recent renewed attention to Black students’ experiences at predominantly White U.S. universities (PWIs), e.g., the #BBUM (“Being Black at the University of Michigan”), “I too am Harvard” social media campaigns, and subsequent student protest activities nationwide. These campaigns illuminate how many minority students encounter challenges to their racial/ethnic identities as they enter PWI contexts. Students routinely report experiences such as being ignored or treated as a token in classes, receiving messages of low academic expectations by faculty and peers, being questioned about their academic qualifications or belonging, being excluded from academic and social activities, and being racially profiled and harassed in the broader campus community due to race. Researchers have linked such racial marginalization and stigma experiences to student motivation and achievement. One potential mechanism is through the impact of college experiences on students’ identities, given the relevance of the college context for students’ personal identity development, including personal beliefs systems around social identities salient in this context. However, little research examines the impact of the college context on Black students’ racial identities. This study examined change in Black college students’ (N=329) racial identity beliefs over the freshman year at three predominantly White U.S. universities. Using cluster analyses, we identified profile groups reflecting different patterns of stability and change in students’ racial centrality (importance of race to overall self-concept), private regard (personal group affect/group pride), and public regard (perceptions of societal views of Blacks) from beginning of year (Time 1) to end of year (Time 2). Multinomial logit regression analyses indicated that the racial identity change clusters were predicted by pre-college background (racial composition of high school and neighborhood), as well as college-based experiences (racial discrimination, interracial friendships, and perceived campus racial climate). In particular, experiencing campus racial discrimination related to high, stable centrality, and decreases in private regard and public regard. Perceiving racial climates norms of institutional support for intergroup interactions on campus related to maintaining low and decreasing in private and public regard. Multivariate Analyses of Variance results showed change cluster effects on achievement motivation outcomes at the end of students’ academic year. Having high, stable centrality and high private regard related to more positive outcomes overall (academic competence, positive academic affect, academic curiosity and persistence). Students decreasing in private regard and public regard were particularly vulnerable to negative motivation outcomes. Findings support scholarship indicating both stability in racial identity beliefs and the importance of critical context transitions in racial identity development and adjustment outcomes among emerging adults. Findings also are consistent with research suggesting promotive effects of a strong, positive racial identity on student motivation, as well as research linking awareness of racial stigma to decreased academic engagement.

Keywords: diversity, motivation, learning, ethnic minority achievement, higher education

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