Search results for: threshold detecting

Authors: Yichao Liu, Peter Fransson, Julian Heidecke, Jonas Wallin, Joacim Rockloev

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Dengue, a mosquito-borne viral disease, poses a significant public health challenge in endemic tropical or subtropical countries, including Sri Lanka. To reveal insights into the complexity of the dynamics of this disease and study the drivers, a comprehensive model capable of both robust forecasting and insightful inference of drivers while capturing the co-circulating of several virus strains is essential. However, existing studies mostly focus on only one aspect at a time and do not integrate and carry insights across the siloed approach. While mechanistic models are developed to capture immunity dynamics, they are often oversimplified and lack integration of all the diverse drivers of disease transmission. On the other hand, purely data-driven methods lack constraints imposed by immuno-epidemiological processes, making them prone to overfitting and inference bias. This research presents a hybrid model that combines machine learning techniques with mechanistic modelling to overcome the limitations of existing approaches. Leveraging eight years of newly reported dengue case data, along with socioeconomic factors, such as human mobility, weekly climate data from 2011 to 2018, genetic data detecting the introduction and presence of new strains, and estimates of seropositivity for different districts in Sri Lanka, we derive a data-driven vector (SEI) to human (SEIR) model across 16 regions in Sri Lanka at the weekly time scale. By conducting ablation studies, the lag effects allowing delays up to 12 weeks of time-varying climate factors were determined. The model demonstrates superior predictive performance over a pure machine learning approach when considering lead times of 5 and 10 weeks on data withheld from model fitting. It further reveals several interesting interpretable findings of drivers while adjusting for the dynamics and influences of immunity and introduction of a new strain. The study uncovers strong influences of socioeconomic variables: population density, mobility, household income and rural vs. urban population. The study reveals substantial sensitivity to the diurnal temperature range and precipitation, while mean temperature and humidity appear less important in the study location. Additionally, the model indicated sensitivity to vegetation index, both max and average. Predictions on testing data reveal high model accuracy. Overall, this study advances the knowledge of dengue transmission in Sri Lanka and demonstrates the importance of incorporating hybrid modelling techniques to use biologically informed model structures with flexible data-driven estimates of model parameters. The findings show the potential to both inference of drivers in situations of complex disease dynamics and robust forecasting models.

Keywords: compartmental model, climate, dengue, machine learning, social-economic

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Authors: Seyed Reza Aghili, Tahereh Shokohi, Shirin Sadat Hashemi Fesharaki, Mohammad Ali Boroumand, Bahar Salmanian

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Introduction: Intravenous catheter-associated fungal infection as nosocomial infection continue to be a deep problem among hospitalized patients, decreasing quality of life and adding healthcare costs. The capacity of catheters in the spread of candidemia in heart failure patients is obvious. The aim of this study was to evaluate the prevalence and genetic identification of Candida species in heart disorder patients. Material and Methods: This study was conducted in Tehran Hospital of Cardiology Center (Tehran, Iran, 2014) during 1.5 years on the patients hospitalized for at least 7 days and who had central or peripheral vein catheter. Culture of catheters, blood and skin of the location of catheter insertion were applied for detecting Candida colonies in 223 patients. Identification of Candida species was made on the basis of a combination of various phenotypic methods and confirmed by sequencing the ITS1-5.8S-ITS2 region amplified from the genomic DNA using PCR and the NCBI BLAST. Results: Of the 223 patients samples tested, we identified totally 15 Candida isolates obtained from 9 (4.04%) catheter cultures, 3 (1.35%) blood cultures and 2 (0.90%) skin cultures of the catheter insertion areas. On the base of ITS region sequencing, out of nine Candida isolates from catheter, 5(55.6%) C. albicans, 2(22.2%) C. glabrata, 1(11.1%) C. membranifiaciens and 1 (11.1%) C. tropicalis were identified. Among three Candida isolates from blood culture, C. tropicalis, C. carpophila and C. membranifiaciens were identified. Non-candida yeast isolated from one blood culture was Cryptococcus albidus. One case of C. glabrata and one case of Candida albicans were isolated from skin culture of the catheter insertion areas in patients with positive catheter culture. In these patients, ITS region of rDNA sequence showed a similarity between Candida isolated from the skin and catheter. However, the blood samples of these patients were negative for fungal growth. We report two cases of catheter-related candidemia caused by C. membranifiaciens and C. tropicalis on the base of genetic similarity of species isolated from blood and catheter which were treated successfully with intravenous fluconazole and catheter removal. In phenotypic identification methods, we could only identify C. albicans and C. tropicalis and other yeast isolates were diagnosed as Candida sp. Discussion: Although more than 200 species of Candida have been identified, only a few cause diseases in humans. There is some evidence that non-albicans infections are increasing. Many risk factors, including prior antibiotic therapy, use of a central venous catheter, surgery, and parenteral nutrition are considered to be associated with candidemia in hospitalized heart failure patients. Identifying the route of infection in candidemia is difficult. Non-albicans candida as the cause of candidemia is increasing dramatically. By using conventional method, many non-albicans isolates remain unidentified. So, using more sensitive and specific molecular genetic sequencing to clarify the aspects of epidemiology of the unknown candida species infections is essential. The positive blood and catheter cultures for candida isolates and high percentage of similarity of their ITS region of rDNA sequence in these two patients confirmed the diagnosis of intravenous catheter-associated candidemia.

Keywords: catheter-associated infections, heart failure patient, molecular genetic sequencing, ITS region of rDNA, Candidemia

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Authors: Ammar Alali, Mahmoud Abughaban

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Cost reduction and drilling optimization is the goal of many drilling operators. Historically, stuck pipe incidents were a major segment of non-productive time (NPT) associated costs. Traditionally, stuck pipe problems are part of the operations and solved post-sticking. However, the real key to savings and success is in predicting the stuck pipe incidents and avoiding the conditions leading to its occurrences. Previous attempts in stuck-pipe predictions have neglected the local geology of the problem. The proposed predictive tool utilizes geophysical data processing techniques and Machine Learning (ML) algorithms to predict drilling activities events in real-time using surface drilling data with minimum computational power. The method combines two types of analysis: (1) real-time prediction, and (2) cause analysis. Real-time prediction aggregates the input data, including historical drilling surface data, geological formation tops, and petrophysical data, from wells within the same field. The input data are then flattened per the geological formation and stacked per stuck-pipe incidents. The algorithm uses two physical methods (stacking and flattening) to filter any noise in the signature and create a robust pre-determined pilot that adheres to the local geology. Once the drilling operation starts, the Wellsite Information Transfer Standard Markup Language (WITSML) live surface data are fed into a matrix and aggregated in a similar frequency as the pre-determined signature. Then, the matrix is correlated with the pre-determined stuck-pipe signature for this field, in real-time. The correlation used is a machine learning Correlation-based Feature Selection (CFS) algorithm, which selects relevant features from the class and identifying redundant features. The correlation output is interpreted as a probability curve of stuck pipe incidents prediction in real-time. Once this probability passes a fixed-threshold defined by the user, the other component, cause analysis, alerts the user of the expected incident based on set pre-determined signatures. A set of recommendations will be provided to reduce the associated risk. The validation process involved feeding of historical drilling data as live-stream, mimicking actual drilling conditions, of an onshore oil field. Pre-determined signatures were created for three problematic geological formations in this field prior. Three wells were processed as case studies, and the stuck-pipe incidents were predicted successfully, with an accuracy of 76%. This accuracy of detection could have resulted in around 50% reduction in NPT, equivalent to 9% cost saving in comparison with offset wells. The prediction of stuck pipe problem requires a method to capture geological, geophysical and drilling data, and recognize the indicators of this issue at a field and geological formation level. This paper illustrates the efficiency and the robustness of the proposed cross-disciplinary approach in its ability to produce such signatures and predicting this NPT event.

Keywords: drilling optimization, hazard prediction, machine learning, stuck pipe

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Authors: Till Gramberg, Erwin Gross, Christoph Birenbaum

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This paper presents the approach of the Easy2IoT research project. Easy2IoT aims to enable companies in the prefabrication sheet metal and sheet metal processing industry to enter the Industrial Internet of Things (IIoT) with a low-threshold and cost-effective approach. It focuses on the development of physical hardware and software to easily capture machine activities from on a sawing machine, benefiting various stakeholders in the SME value chain, including machine operators, tool manufacturers and service providers. The methodological approach of Easy2IoT includes an in-depth requirements analysis and customer interviews with stakeholders along the value chain. Based on these insights, actions, requirements and potential solutions for smart services are derived. The focus is on providing actionable recommendations, competencies and easy integration through no-/low-code applications to facilitate implementation and connectivity within production networks. At the core of the project is a novel, non-invasive measurement and analysis system that can be easily deployed and made IIoT-ready. This system collects machine data without interfering with the machines themselves. It does this by non-invasively measuring the tension on a sawing machine. The collected data is then connected and analyzed using artificial intelligence (AI) to provide smart services through a platform-based application. Three Smart Services are being developed within Easy2IoT to provide immediate benefits to users: Wear part and product material condition monitoring and predictive maintenance for sawing processes. The non-invasive measurement system enables the monitoring of tool wear, such as saw blades, and the quality of consumables and materials. Service providers and machine operators can use this data to optimize maintenance and reduce downtime and material waste. Optimize Overall Equipment Effectiveness (OEE) by monitoring machine activity. The non-invasive system tracks machining times, setup times and downtime to identify opportunities for OEE improvement and reduce unplanned machine downtime. Estimate CO2 emissions for connected machines. CO2 emissions are calculated for the entire life of the machine and for individual production steps based on captured power consumption data. This information supports energy management and product development decisions. The key to Easy2IoT is its modular and easy-to-use design. The non-invasive measurement system is universally applicable and does not require specialized knowledge to install. The platform application allows easy integration of various smart services and provides a self-service portal for activation and management. Innovative business models will also be developed to promote the sustainable use of the collected machine activity data. The project addresses the digitalization gap between large enterprises and SME. Easy2IoT provides SME with a concrete toolkit for IIoT adoption, facilitating the digital transformation of smaller companies, e.g. through retrofitting of existing machines.

Keywords: smart services, IIoT, IIoT-platform, industrie 4.0, big data

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Authors: Diane Ghanem, Arjun Gupta, Rohan Vijayan, Ali Uneri, Babar Shafiq

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Introduction: Ankle sprains and fractures often result in syndesmosis injuries. Unstable syndesmotic injuries result from relative motion between the distal ends of the tibia and fibula, anatomic juncture which should otherwise be rigid, and warrant operative management. Clinical and radiological evaluations of intraoperative syndesmosis stability remain a challenging task as traditional 2D fluoroscopy is limited to a uniplanar translational displacement. The purpose of this pilot cadaveric study is to compare the 2D fluoroscopy and 3D cone beam computed tomography (CBCT) stress-induced syndesmosis displacements. Methods: Three fresh-frozen lower legs underwent 2D fluoroscopy and 3D CIOS CBCT to measure syndesmosis position before dissection. Syndesmotic injury was simulated by resecting the (1) anterior inferior tibiofibular ligament (AITFL), the (2) posterior inferior tibiofibular ligament (PITFL) and the inferior transverse ligament (ITL) simultaneously, followed by the (3) interosseous membrane (IOM). Manual external rotation and Cotton stress test were performed after each of the three resections and 2D and 3D images were acquired. Relevant 2D and 3D parameters included the tibiofibular overlap (TFO), tibiofibular clear space (TCS), relative rotation of the fibula, and anterior-posterior (AP) and medial-lateral (ML) translations of the fibula relative to the tibia. Parameters were measured by two independent observers. Inter-rater reliability was assessed by intraclass correlation coefficient (ICC) to determine measurement precision. Results: Significant mismatches were found in the trends between the 2D and 3D measurements when assessing for TFO, TCS and AP translation across the different resection states. Using 3D CBCT, TFO was inversely proportional to the number of resected ligaments while TCS was directly proportional to the latter across all cadavers and ‘resection + stress’ states. Using 2D fluoroscopy, this trend was not respected under the Cotton stress test. 3D AP translation did not show a reliable trend whereas 2D AP translation of the fibula was positive under the Cotton stress test and negative under the external rotation. 3D relative rotation of the fibula, assessed using the Tang et al. ratio method and Beisemann et al. angular method, suggested slight overall internal rotation with complete resection of the ligaments, with a change < 2mm - threshold which corresponds to the commonly used buffer to account for physiologic laxity as per clinical judgment of the surgeon. Excellent agreement (>0.90) was found between the two independent observers for each of the parameters in both 2D and 3D (overall ICC 0.9968, 95% CI 0.995 - 0.999). Conclusions: The 3D CIOS CBCT appears to reliably depict the trend in TFO and TCS. This might be due to the additional detection of relevant rotational malpositions of the fibula in comparison to the standard 2D fluoroscopy which is limited to a single plane translation. A better understanding of 3D imaging may help surgeons identify the precise measurements planes needed to achieve better syndesmosis repair.

Keywords: 2D fluoroscopy, 3D computed tomography, image processing, syndesmosis injury

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Authors: Bensaid A., Mostephaoui T., Nedjai R.

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A considerable area of Algerian lands are threatened by the phenomenon of wind erosion. For a long time, wind erosion and its associated harmful effects on the natural environment have posed a serious threat, especially in the arid regions of the country. In recent years, as a result of increases in the irrational exploitation of natural resources (fodder) and extensive land clearing, wind erosion has particularly accentuated. The extent of degradation in the arid region of the Algerian Mécheriadepartment generated a new situation characterized by the reduction of vegetation cover, the decrease of land productivity, as well as sand encroachment on urban development zones. In this study, we attempt to investigate the potential of remote sensing and geographic information systems for detecting the spatial dynamics of the ancient dune cords based on the numerical processing of PlanetScope PSB.SB sensors images by September 29, 2021. As a second step, we prospect the use of a multi-scale geographic object-based image analysis (GEOBIA) approach to segment the high spatial resolution images acquired on heterogeneous surfaces that vary according to human influence on the environment. We have used the fractal net evolution approach (FNEA) algorithm to segment images (Baatz&Schäpe, 2000). Multispectral data, a digital terrain model layer, ground truth data, a normalized difference vegetation index (NDVI) layer, and a first-order texture (entropy) layer were used to segment the multispectral images at three segmentation scales, with an emphasis on accurately delineating the boundaries and components of the sand accumulation areas (Dune, dunes fields, nebka, and barkhane). It is important to note that each auxiliary data contributed to improve the segmentation at different scales. The silted areas were classified using a nearest neighbor approach over the Naâma area using imagery. The classification of silted areas was successfully achieved over all study areas with an accuracy greater than 85%, although the results suggest that, overall, a higher degree of landscape heterogeneity may have a negative effect on segmentation and classification. Some areas suffered from the greatest over-segmentation and lowest mapping accuracy (Kappa: 0.79), which was partially attributed to confounding a greater proportion of mixed siltation classes from both sandy areas and bare ground patches. This research has demonstrated a technique based on very high-resolution images for mapping sanded and degraded areas using GEOBIA, which can be applied to the study of other lands in the steppe areas of the northern countries of the African continent.

Keywords: land development, GIS, sand dunes, segmentation, remote sensing

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Authors: Rajasekar Elangopandian, Anand Shanmugam

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Now-a-days for surveillances unmanned aerial vehicle plays a vital role. Not only for surveillances, aerial photography disaster management and the notice of earth behavior UAV1s envisages meticulously. To reduce the maintenance and fuel nuclear powered Vehicles are greater support. The design consideration is much important for the UAV manufacturing industry and Research and development agency. Eventually design is looking like a pentagon shaped fuselage and black rubber coated paint in order to escape from the enemy radar and other targets. The pentagon shape fuselage has large space to keep the mini nuclear reactor inside and the material is carbon – carbon fiber specially designed by the software called cosmol and hyper mesh 14.2. So the weight consideration will produce the positive result for productivity. The walls of the fuselage are coated with lead and protective shield. A double layer of W/Bi sheet is proposed for radiation protection at the energy range of 70 Kev to 90 Kev. The designed W/bi sheet, only 0.14 mm thick and is 36% light. The properties of the fillers were determined from zeta potential and particle size measurements. The Exposes of the radiation can be attenuated by 3 ways such as minimizing exposure time, Maximizing distance from the radiation source and shielding the whole vehicle. The inside reactor will be switched ON when the UAV starts its cruise. The moderators and the control rods can be inserted by automation technique by newly developed software. The heat generated by the reactor will be used to run the turbine which is fixed inside the UAV called mini turbine with natural rubber composite Shaft radiation shield. Cooling system will be in two mode such as liquid and air cooled. Liquid coolant for the heat regeneration is ordinary water, liquid sodium, helium and the walls are made up of regenerative and radiation protective material. The other components like camera and arms bay will be located at the bottom of the UAV high are specially made products in order to escape from the radiation. They are coated with lead Pb and natural rubber composite material. This technique provides the long rang and endurance for eternal flight mission until we need any changeability of parts or product. This UAV has the special advantage of ` land on String` means it`ll land at electric line to charge the automated electronics. Then the fuel is enriched uranium (< 5% U - 235) contains hundreds of fuel pins. This technique provides eternal duty for surveillances and aerial photography. The landing of the vehicle is ease of operation likewise the takeoff is also easier than any other mechanism which present in nowadays. This UAV gives great immense and immaculate technology for surveillance and target detecting and smashing the target.

Keywords: mini turbine, liquid coolant for the heat regeneration, in order to escape from the radiation, eternal flight mission, it`ll land at electric line

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Authors: Anna Mucha, Ewa Wojtyna, Anita Pollak

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The environment in which an individual resides and observes may play a meaningful role in well-being and related constructs. Contact with nature may have a positive influence of natural environments on individuals, impacting mood and psychophysical sensations, such as pain relief. Conversely, urban settings, dominated by concrete elements, might lead to mood decline and heightened stress levels. Similarly, the situation may appear in the case of the perception of virtual environments. However, this is a topic that requires further exploration, especially in the context of relationships with pain. The aforementioned matters served as the basis for formulating and executing the outlined experimental research within the realm of environmental psychology, leveraging new technologies, notably virtual reality (VR), which is progressively gaining prominence in the domain of mental health. The primary objective was to investigate the impact of a simulated virtual environment, mirroring a natural setting abundant in greenery, on the perception of acute pain induced by thermal stimuli (high temperature) – encompassing intensity, unpleasantness, and pain tolerance. Comparative analyses were conducted between the virtual natural environment (intentionally constructed in the likeness of a therapeutic garden), virtual urban environment, and a control group devoid of virtual projections. Secondary objectives aimed to determine the mutual relationships among variables such as positive and negative emotions, preferences regarding virtual environments, sense of presence, and restorative experience in the context of the perception of presented virtual environments and induced thermal pain. The study encompassed 126 physically healthy Polish adults, distributing 42 individuals across each of the three comparative groups. Oculus Rift VR technology and the TSA-II neurosensory analyzer facilitated the experiment. Alongside demographic data, participants' subjective feelings concerning virtual reality and pain were evaluated using the Visual Analogue Scale (VAS), the original Restorative Experience in the Virtual World questionnaire (Doświadczenie Regeneracji w Wirtualnym Świecie), and an adapted Slater-Usoh-Steed (SUS) questionnaire. Results of statistical and psychometric analyses, such as Kruskal-Wallis tests, Wilcoxon tests, and contrast analyses, underscored the positive impact of the virtual natural environment on individual pain perception and mood. The virtual natural environment outperformed the virtual urban environment and the control group without virtual projection, particularly in subjective pain components like intensity and unpleasantness. Variables such as restorative experience, sense of presence and virtual environment preference also proved pivotal in pain perception and pain tolerance threshold alterations, contingent on specific conditions. This implies considerable application potential for virtual natural environments across diverse realms of psychology and related fields, among others as a supportive analgesic approach and a form of relaxation following psychotherapeutic sessions.

Keywords: environmental psychology, nature, acute pain, emotions, vitrual reality, virtual environments

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Authors: Khachen Kongpakwattana, Nathorn Chaiyakunapruk

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Background: Decision-analytic models for Alzheimer’s disease (AD) have been advanced to discrete-event simulation (DES), in which individual-level modelling of disease progression across continuous severity spectra and incorporation of key parameters such as treatment persistence into the model become feasible. This study aimed to apply the DES to perform a cost-effectiveness analysis of treatment for AD in Thailand. Methods: A dataset of Thai patients with AD, representing unique demographic and clinical characteristics, was bootstrapped to generate a baseline cohort of patients. Each patient was cloned and assigned to donepezil, galantamine, rivastigmine, memantine or no treatment. Throughout the simulation period, the model randomly assigned each patient to discrete events including hospital visits, treatment discontinuation and death. Correlated changes in cognitive and behavioral status over time were developed using patient-level data. Treatment effects were obtained from the most recent network meta-analysis. Treatment persistence, mortality and predictive equations for functional status, costs (Thai baht (THB) in 2017) and quality-adjusted life year (QALY) were derived from country-specific real-world data. The time horizon was 10 years, with a discount rate of 3% per annum. Cost-effectiveness was evaluated based on the willingness-to-pay (WTP) threshold of 160,000 THB/QALY gained (4,994 US$/QALY gained) in Thailand. Results: Under a societal perspective, only was the prescription of donepezil to AD patients with all disease-severity levels found to be cost-effective. Compared to untreated patients, although the patients receiving donepezil incurred a discounted additional costs of 2,161 THB, they experienced a discounted gain in QALY of 0.021, resulting in an incremental cost-effectiveness ratio (ICER) of 138,524 THB/QALY (4,062 US$/QALY). Besides, providing early treatment with donepezil to mild AD patients further reduced the ICER to 61,652 THB/QALY (1,808 US$/QALY). However, the dominance of donepezil appeared to wane when delayed treatment was given to a subgroup of moderate and severe AD patients [ICER: 284,388 THB/QALY (8,340 US$/QALY)]. Introduction of a treatment stopping rule when the Mini-Mental State Exam (MMSE) score goes below 10 to a mild AD cohort did not deteriorate the cost-effectiveness of donepezil at the current treatment persistence level. On the other hand, none of the AD medications was cost-effective when being considered under a healthcare perspective. Conclusions: The DES greatly enhances real-world representativeness of decision-analytic models for AD. Under a societal perspective, treatment with donepezil improves patient’s quality of life and is considered cost-effective when used to treat AD patients with all disease-severity levels in Thailand. The optimal treatment benefits are observed when donepezil is prescribed since the early course of AD. With healthcare budget constraints in Thailand, the implementation of donepezil coverage may be most likely possible when being considered starting with mild AD patients, along with the stopping rule introduced.

Keywords: Alzheimer's disease, cost-effectiveness analysis, discrete event simulation, health technology assessment

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Authors: V. Mantey, N. Findlay, I. Maddox

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The quality and quantity of global satellite data have been increasing exponentially in recent years as spaceborne systems become more affordable and the sensors themselves become more sophisticated. This is a valuable resource for many applications, including disaster management and relief. However, while more information can be valuable, the volume of data available is impossible to manually examine. Therefore, the question becomes how to extract as much information as possible from the data with limited manpower. Buildings are a key feature of interest in satellite imagery with applications including telecommunications, population models, and disaster relief. Machine learning tools are fast becoming one of the key resources to solve this problem, and models have been developed to detect buildings in optical satellite imagery. However, by and large, most models focus on affluent regions where buildings are generally larger and constructed further apart. This work is focused on the more difficult problem of detection in populated regions. The primary challenge with detecting small buildings in densely populated regions is both the spatial and spectral resolution of the optical sensor. Densely packed buildings with similar construction materials will be difficult to separate due to a similarity in color and because the physical separation between structures is either non-existent or smaller than the spatial resolution. This study finds that training models until they are overfitting the input sample can perform better in these areas than a more robust, generalized model. An overfitted model takes less time to fine-tune from a generalized pre-trained model and requires fewer input data. The model developed for this study has also been fine-tuned using existing, open-source, building vector datasets. This is particularly valuable in the context of disaster relief, where information is required in a very short time span. Leveraging existing datasets means that little to no manpower or time is required to collect data in the region of interest. The training period itself is also shorter for smaller datasets. Requiring less data means that only a few quality areas are necessary, and so any weaknesses or underpopulated regions in the data can be skipped over in favor of areas with higher quality vectors. In this study, a landcover classification model was developed in conjunction with the building detection tool to provide a secondary source to quality check the detected buildings. This has greatly reduced the false positive rate. The proposed methodologies have been implemented and integrated into a configurable production environment and have been employed for a number of large-scale commercial projects, including continent-wide DEM production, where the extracted building footprints are being used to enhance digital elevation models. Overfitted machine learning models are often considered too specific to have any predictive capacity. However, this study demonstrates that, in cases where input data is scarce, overfitted models can be judiciously applied to solve time-sensitive problems.

Keywords: building detection, disaster relief, mask-RCNN, satellite mapping

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Authors: Maari Sugawara

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This arts-based research is about "self-torture": the interplay of seemingly opposing elements of pain, pleasure, submission, and power. It asserts that "self-torture" can be considered a nontrivial mediation between the aesthetic and the sociopolitical. It explores what the author calls queered self-torture; "self-torture" marked by an ambivalence that allows the oppressed to resist, and their counter-valorization occasionally functions as therapeutic solutions to the problems they highlight and condense. The research goal is to deconstruct normative self-torture and propose queered self-torture as a fertile ground for considering the complexities of desire that allow the oppressed to practice freedom. While “self-torture” manifests in many societies, this research focuses on cultural and national identity in post-WWII Japan using this lens of self-torture, as masochism functions as the very basis for Japanese cultural and national identity to ensure self-preservation. This masochism is defined as an impulse to realize a sense of pride and construct an identity through the acceptance of subordination, shame, and humiliation in the face of an all-powerful Other; the dominant Euro-America. It could be argued that this self-torture is a result of Japanese cultural annihilation and the trauma of the nation's defeat to the US. This is the definition of "self-torturous thresholds," the author’s post-WWII Japan psycho-historical diagnosis; when this threshold is crossed, the oppressed begin to torture themselves; the oppressors no longer need to do anything to maintain their power. The oppressed are already oppressing themselves. The term "oppressed" here refers to Japanese individuals and residents of Japan who are subjected to oppressive “white” heteropatriarchal supremacist structures and values that serve colonialist interests. There are three stages in "self-torturous thresholds": (1) the oppressors no longer need to oppress because the oppressed voluntarily commit to self-torture; (2) the oppressed find pleasure in self-torture; and (3) the oppressed achieve queered self-torture, to achieve alternative futures. Using the conceptualization of "self-torture," this research examines and critiques pleasure, desire, capital, and power in postwar Japan, which enables the discussion of the data-colonizing “Moonshot Research and Development program”. If the oppressed want to divest from the habits of normative self-torture, which shape what is possible in both our present and future, we need methods to feel and know that the alternative results of self-torture are possible. Phase three will be enacted using Sarah Ahmed's queer methodology to reorient national and cultural identity away from heteronormativity. Through theoretical analysis, textual analysis, archival research, ethnographic interviews, and digital art projects, including experimental documentary as a method to capture the realities of the individuals who are practicing self-torture, this research seeks to reveal how self-torture may become not just a vehicle of pleasure but also a mode of critiquing power and achieving freedom. It seeks to encourage the imaginings of queer Japanese futures, where the marginalized survive Japan’s natural and man-made disasters and Japan’s Imperialist past and present rather than submitting to the country’s continued violence.

Keywords: arts-based research, Japanese studies, interdisciplinary arts, queer studies, cultural studies, popular culture, BDSM, sadomasochism, sexuality, VR, AR, digital art, visual arts, speculative fiction

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Authors: Hirseland Andreas, Kerschbaumer Lukas

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In the case of Germany, there are about one million long-term unemployed – a figure that did not vary much during the past years. These long-term unemployed did not benefit from the prospering labor market while most short-term unemployed did. Instead, they are continuously dependent on welfare and sometimes precarious short-term employment, experiencing work poverty. Long-term unemployment thus turns into a main obstacle to become employed again, especially if it is accompanied by other impediments such as low-level education (school/vocational), poor health (especially chronical illness), advanced age (older than fifty), immigrant status, motherhood or engagement in care for other relatives. As can be shown by this current research project, in these cases the chance to regain employment decreases to near nil. Almost two-thirds of all welfare recipients have multiple impediments which hinder a successful transition from welfare back to sustainable and sufficient employment. Prospective employers are unlikely to hire long-term unemployed with additional impediments because they evaluate potential employees on their negative signaling (e.g. low-level education) and the implicit assumption of unproductiveness (e.g. poor health, age). Some findings of the panel survey “Labor market and social security” (PASS) carried out by the Institute of Employment Research (the research institute of the German Federal Labor Agency) spread a ray of hope, showing that unlikely does not necessarily mean impossible. The presentation reports on current research on these very scarce “success stories” of unlikely transitions from long-term unemployment to work and how these cases were able to perform this switch against all odds. The study is based on a mixed-method design. Within the panel survey (~15,000 respondents in ~10,000 households), only 66 cases of such unlikely transitions were observed. These cases have been explored by qualitative inquiry – in depth-interviews and qualitative network techniques. There is strong evidence that sustainable transitions are influenced by certain biographical resources like habits of network use, a set of informal skills and particularly a resilient way of dealing with obstacles, combined with contextual factors rather than by job-placement procedures promoted by Job-Centers according to activation rules or by following formal paths of application. On the employer’s side small and medium-sized enterprises are often found to give job opportunities to a wider variety of applicants, often based on a slow but steadily increasing relationship leading to employment. According to these results it is possible to show and discuss some limitations of (German) activation policies targeting the labor market and their impact on welfare dependency and long-term unemployment. Based on these findings, indications for more supportive small-scale measures in the field of labor-market policies are suggested to help long-term unemployed with multiple impediments to overcome their situation (e.g. organizing small-scale-structures and low-threshold services to encounter possible employers on a more informal basis like “meet and greet”).

Keywords: against-all-odds, mixed-methods, Welfare State, long-term unemployment

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Authors: Patrick Cormier, Cesar Meylan, Matt Jensen, Dana Agar-Newman, Chloe Werle, Ming-Chang Tsai, Marc Klimstra

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Sprint-derived horizontal force-velocity-power (FVP) profiles can be developed with adequate validity and reliability with satellite (GPS/GNSS) systems. However, FVP metrics are sensitive to small nuances in data processing procedures such that minor differences in defining the onset and end of the sprint could result in different FVP metric outcomes. Furthermore, in team-sports, there is a requirement for rapid analysis and feedback of results from multiple athletes, therefore developing standardized and automated methods to improve the speed, efficiency and reliability of this process are warranted. Thus, the purpose of this study was to compare different methods of sprint end detection on the development of FVP profiles from 10Hz GPS/GNSS data through goodness-of-fit and intertrial reliability statistics. Seventeen national team female soccer players participated in the FVP protocol which consisted of 2x40m maximal sprints performed towards the end of a soccer specific warm-up in a training session (1020 hPa, wind = 0, temperature = 30°C) on an open grass field. Each player wore a 10Hz Catapult system unit (Vector S7, Catapult Innovations) inserted in a vest in a pouch between the scapulae. All data were analyzed following common procedures. Variables computed and assessed were the model parameters, estimated maximal sprint speed (MSS) and the acceleration constant τ, in addition to horizontal relative force (F₀), velocity at zero (V₀), and relative mechanical power (Pmax). The onset of the sprints was standardized with an acceleration threshold of 0.1 m/s². The sprint end detection methods were: 1. Time when peak velocity (MSS) was achieved (zero acceleration), 2. Time after peak velocity drops by -0.4 m/s, 3. Time after peak velocity drops by -0.6 m/s, and 4. When the integrated distance from the GPS/GNSS signal achieves 40-m. Goodness-of-fit of each sprint end detection method was determined using the residual sum of squares (RSS) to demonstrate the error of the FVP modeling with the sprint data from the GPS/GNSS system. Inter-trial reliability (from 2 trials) was assessed utilizing intraclass correlation coefficients (ICC). For goodness-of-fit results, the end detection technique that used the time when peak velocity was achieved (zero acceleration) had the lowest RSS values, followed by -0.4 and -0.6 velocity decay, and 40-m end had the highest RSS values. For intertrial reliability, the end of sprint detection techniques that were defined as the time at (method 1) or shortly after (method 2 and 3) when MSS was achieved had very large to near perfect ICC and the time at the 40 m integrated distance (method 4) had large to very large ICCs. Peak velocity was reached at 29.52 ± 4.02-m. Therefore, sport scientists should implement end of sprint detection either when peak velocity is determined or shortly after to improve goodness of fit to achieve reliable between trial FVP profile metrics. Although, more robust processing and modeling procedures should be developed in future research to improve sprint model fitting. This protocol was seamlessly integrated into the usual training which shows promise for sprint monitoring in the field with this technology.

Keywords: automated, biomechanics, team-sports, sprint

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Authors: Iman Farasat, Howard M. Salis

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Engineered genetic circuits reprogram cellular behavior to act as living computers with applications in detecting cancer, creating self-controlling artificial tissues, and dynamically regulating metabolic pathways. Phenemenological models are often used to simulate and design genetic circuit behavior towards a desired behavior. While such models assume that each circuit component’s function is modular and independent, even small changes in a circuit (e.g. a new promoter, a change in transcription factor expression level, or even a new media) can have significant effects on the circuit’s function. Here, we use statistical thermodynamics to account for the several factors that control transcriptional regulation in bacteria, and experimentally demonstrate the model’s accuracy across 825 measurements in several genetic contexts and hosts. We then employ our first principles model to design, experimentally construct, and characterize a family of signal amplifying genetic circuits (genetic OpAmps) that expand the dynamic range of cell sensors. To develop these models, we needed a new approach to measuring the in vivo binding free energies of transcription factors (TFs), a key ingredient of statistical thermodynamic models of gene regulation. We developed a new high-throughput assay to measure RNA polymerase and TF binding free energies, requiring the construction and characterization of only a few constructs and data analysis (Figure 1A). We experimentally verified the assay on 6 TetR-homolog repressors and a CRISPR/dCas9 guide RNA. We found that our binding free energy measurements quantitatively explains why changing TF expression levels alters circuit function. Altogether, by combining these measurements with our biophysical model of translation (the RBS Calculator) as well as other measurements (Figure 1B), our model can account for changes in TF binding sites, TF expression levels, circuit copy number, host genome size, and host growth rate (Figure 1C). Model predictions correctly accounted for how these 8 factors control a promoter’s transcription rate (Figure 1D). Using the model, we developed a design framework for engineering multi-promoter genetic circuits that greatly reduces the number of degrees of freedom (8 factors per promoter) to a single dimensionless unit. We propose the Ptashne (Pt) number to encapsulate the 8 co-dependent factors that control transcriptional regulation into a single number. Therefore, a single number controls a promoter’s output rather than these 8 co-dependent factors, and designing a genetic circuit with N promoters requires specification of only N Pt numbers. We demonstrate how to design genetic circuits in Pt number space by constructing and characterizing 15 2-repressor OpAmp circuits that act as signal amplifiers when within an optimal Pt region. We experimentally show that OpAmp circuits using different TFs and TF expression levels will only amplify the dynamic range of input signals when their corresponding Pt numbers are within the optimal region. Thus, the use of the Pt number greatly simplifies the genetic circuit design, particularly important as circuits employ more TFs to perform increasingly complex functions.

Keywords: transcription factor, synthetic biology, genetic circuit, biophysical model, binding energy measurement

Procedia PDF Downloads 455

Authors: Jara Kaye S. Villanueva, M. Rosario Concepcion O. Ang

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For the past few years, Philippines has depended most of its energy source on oil, coal, and fossil fuel. According to the Department of Energy (DOE), the dominance of coal in the energy mix will continue until the year 2020. The expanding energy needs in the country have led to increasing efforts to promote and develop renewable energy. This research is a part of the government initiative in preparation for renewable energy development and expansion in the country. The Philippine Renewable Energy Resource Mapping from Light Detection and Ranging (LiDAR) Surveys is a three-year government project which aims to assess and quantify the renewable energy potential of the country and to put them into usable maps. This study focuses on the site suitability analysis of the four renewable energy sources – biomass (coconut, corn, rice, and sugarcane), hydro, solar, and wind energy. The site assessment is a key component in determining and assessing the most suitable locations for the construction of renewable energy power plants. This method maximizes the use of both the technical methods in resource assessment, as well as taking into account the environmental, social, and accessibility aspect in identifying potential sites by utilizing and integrating two different methods: the Multi-Criteria Decision Analysis (MCDA) method and Geographic Information System (GIS) tools. For the MCDA, Analytical Hierarchy Processing (AHP) is employed to determine the parameters needed for the suitability analysis. To structure these site suitability parameters, various experts from different fields were consulted – scientists, policy makers, environmentalists, and industrialists. The need to have a well-represented group of people to consult with is relevant to avoid bias in the output parameter of hierarchy levels and weight matrices. AHP pairwise matrix computation is utilized to derive weights per level out of the expert’s gathered feedback. Whereas from the threshold values derived from related literature, international studies, and government laws, the output values were then consulted with energy specialists from the DOE. Geospatial analysis using GIS tools translate this decision support outputs into visual maps. Particularly, this study uses Euclidean distance to compute for the distance values of each parameter, Fuzzy Membership algorithm which normalizes the output from the Euclidean Distance, and the Weighted Overlay tool for the aggregation of the layers. Using the Natural Breaks algorithm, the suitability ratings of each of the map are classified into 5 discrete categories of suitability index: (1) not suitable (2) least suitable, (3) suitable, (4) moderately suitable, and (5) highly suitable. In this method, the classes are grouped based on the best groups similar values wherein each subdivision are set from the rest based on the big difference in boundary values. Results show that in the entire Philippine area of responsibility, biomass has the highest suitability rating with rice as the most suitable at 75.76% suitability percentage, whereas wind has the least suitability percentage with score 10.28%. Solar and Hydro fall in the middle of the two, with suitability values 28.77% and 21.27%.

Keywords: site suitability, biomass energy, hydro energy, solar energy, wind energy, GIS

Procedia PDF Downloads 131

Authors: Fei Jia, Xingjian Bai, Xiaowei Zhang, Wenjie Yan, Ruitong Dai, Xingmin Li, Jozef Kokini

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Pseudomonas aeruginosa is a Gram-negative, aerobic, opportunistic human pathogen that is present in the soil, water, and food. This microbe has been recognized as a representative food-borne spoilage bacterium that can lead to many types of infections. Considering the casualties and property loss caused by P. aeruginosa, the development of a rapid and reliable technique for the detection of P. aeruginosa is crucial. The whole-cell aptasensor, an emerging biosensor using aptamer as a capture probe to bind to the whole cell, for food-borne pathogens detection has attracted much attention due to its convenience and high sensitivity. Here, a low-field magnetic resonance imaging (LF-MRI) aptasensor for the rapid detection of P. aeruginosa was developed. The basic detection principle of the magnetic relaxation switch (MRSw) nanosensor lies on the ‘T₂-shortening’ effect of magnetic nanoparticles in NMR measurements. Briefly speaking, the transverse relaxation time (T₂) of neighboring water protons get shortened when magnetic nanoparticles are clustered due to the cross-linking upon the recognition and binding of biological targets, or simply when the concentration of the magnetic nanoparticles increased. Such shortening is related to both the state change (aggregation or dissociation) and the concentration change of magnetic nanoparticles and can be detected using NMR relaxometry or MRI scanners. In this work, two different sizes of magnetic nanoparticles, which are 10 nm (MN₁₀) and 400 nm (MN₄₀₀) in diameter, were first immobilized with anti- P. aeruginosa aptamer through 1-Ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC)/N-hydroxysuccinimide (NHS) chemistry separately, to capture and enrich the P. aeruginosa cells. When incubating with the target, a ‘sandwich’ (MN₁₀-bacteria-MN₄₀₀) complex are formed driven by the bonding of MN400 with P. aeruginosa through aptamer recognition, as well as the conjugate aggregation of MN₁₀ on the surface of P. aeruginosa. Due to the different magnetic performance of the MN₁₀ and MN₄₀₀ in the magnetic field caused by their different saturation magnetization, the MN₁₀-bacteria-MN₄₀₀ complex, as well as the unreacted MN₄₀₀ in the solution, can be quickly removed by magnetic separation, and as a result, only unreacted MN₁₀ remain in the solution. The remaining MN₁₀, which are superparamagnetic and stable in low field magnetic field, work as a signal readout for T₂ measurement. Under the optimum condition, the LF-MRI platform provides both image analysis and quantitative detection of P. aeruginosa, with the detection limit as low as 100 cfu/mL. The feasibility and specificity of the aptasensor are demonstrated in detecting real food samples and validated by using plate counting methods. Only two steps and less than 2 hours needed for the detection procedure, this robust aptasensor can detect P. aeruginosa with a wide linear range from 3.1 ×10² cfu/mL to 3.1 ×10⁷ cfu/mL, which is superior to conventional plate counting method and other molecular biology testing assay. Moreover, the aptasensor has a potential to detect other bacteria or toxins by changing suitable aptamers. Considering the excellent accuracy, feasibility, and practicality, the whole-cell aptasensor provides a promising platform for a quick, direct and accurate determination of food-borne pathogens at cell-level.

Keywords: magnetic resonance imaging, meat spoilage, P. aeruginosa, transverse relaxation time

Procedia PDF Downloads 133

Authors: Stefan W. Kienzle

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The increasing number of occurrences of extreme weather and climate events have significant impacts on society and are the cause of continued and increasing loss of human and animal lives, loss or damage to property (houses, cars), and associated stresses to the public in coping with a changing climate. A climate index breaks down daily climate time series into meaningful derivatives, such as the annual number of frost days. Climate indices allow for the spatially consistent analysis of a wide range of climate-dependent variables, which enables the quantification and mapping of historical and future climate change across regions. As trends of phenomena such as the length of the growing season change differently in different hydro-climatological regions, mapping needs to be carried out at a high spatial resolution, such as the 10km by 10km Canadian Climate Grid, which has interpolated daily values from 1950 to 2017 for minimum and maximum temperature and precipitation. Climate indices form the basis for the analysis and comparison of means, extremes, trends, the quantification of changes, and their respective confidence levels. A total of 39 temperature indices and 16 precipitation indices were computed for the period 1951 to 2017 for the Province of Alberta. Temperature indices include the annual number of days with temperatures above or below certain threshold temperatures (0, +-10, +-20, +25, +30ºC), frost days, and timing of frost days, freeze-thaw days, growing or degree days, and energy demands for air conditioning and heating. Precipitation indices include daily and accumulated 3- and 5-day extremes, days with precipitation, period of days without precipitation, and snow and potential evapotranspiration. The rank-based nonparametric Mann-Kendall statistical test was used to determine the existence and significant levels of all associated trends. The slope of the trends was determined using the non-parametric Sen’s slope test. The Google mapping interface was developed to create the website albertaclimaterecords.com, from which beach of the 55 climate indices can be queried for any of the 6833 grid cells that make up Alberta. In addition to the climate indices, climate normals were calculated and mapped for four historical 30-year periods and one future period (1951-1980, 1961-1990, 1971-2000, 1981-2017, 2041-2070). While winters have warmed since the 1950s by between 4 - 5°C in the South and 6 - 7°C in the North, summers are showing the weakest warming during the same period, ranging from about 0.5 - 1.5°C. New agricultural opportunities exist in central regions where the number of heat units and growing degree days are increasing, and the number of frost days is decreasing. While the number of days below -20ºC has about halved across Alberta, the growing season has expanded by between two and five weeks since the 1950s. Interestingly, both the number of days with heat waves and cold spells have doubled to four-folded during the same period. This research demonstrates the enormous potential of using climate indices at the best regional spatial resolution possible to enable society to understand historical and future climate changes of their region.

Keywords: climate change, climate indices, habitat risk, regional, mapping, extremes

Procedia PDF Downloads 76

Authors: Xingyu Gao, Qiang Wu, Yuanyuan Liu, Yue Yang

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In the era of the knowledge economy, the relationship between scientific knowledge and patents has garnered significant attention. Understanding the intricate interplay between the foundations of science and technological innovation has emerged as a pivotal challenge for both researchers and policymakers. This study establishes a coupled network of artificial intelligence patents based on co-cited scientific papers. Leveraging centrality metrics from network analysis offers a fresh perspective on understanding the influence of information flow and knowledge sharing within the network on patent impact. The study initially obtained patent numbers for 446,890 granted US AI patents from the United States Patent and Trademark Office’s artificial intelligence patent database for the years 2002-2020. Subsequently, specific information regarding these patents was acquired using the Lens patent retrieval platform. Additionally, a search and deduplication process was performed on scientific non-patent references (SNPRs) using the Web of Science database, resulting in the selection of 184,603 patents that cited 37,467 unique SNPRs. Finally, this study constructs a coupled network comprising 59,379 artificial intelligence patents by utilizing scientific papers co-cited in patent backward citations. In this network, nodes represent patents, and if patents reference the same scientific papers, connections are established between them, serving as edges within the network. Nodes and edges collectively constitute the patent coupling network. Structural characteristics such as node degree centrality, betweenness centrality, and closeness centrality are employed to assess the scientific connections between patents, while citation count is utilized as a quantitative metric for patent influence. Finally, a negative binomial model is employed to test the nonlinear relationship between these network structural features and patent influence. The research findings indicate that network structural features such as node degree centrality, betweenness centrality, and closeness centrality exhibit inverted U-shaped relationships with patent influence. Specifically, as these centrality metrics increase, patent influence initially shows an upward trend, but once these features reach a certain threshold, patent influence starts to decline. This discovery suggests that moderate network centrality is beneficial for enhancing patent influence, while excessively high centrality may have a detrimental effect on patent influence. This finding offers crucial insights for policymakers, emphasizing the importance of encouraging moderate knowledge flow and sharing to promote innovation when formulating technology policies. It suggests that in certain situations, data sharing and integration can contribute to innovation. Consequently, policymakers can take measures to promote data-sharing policies, such as open data initiatives, to facilitate the flow of knowledge and the generation of innovation. Additionally, governments and relevant agencies can achieve broader knowledge dissemination by supporting collaborative research projects, adjusting intellectual property policies to enhance flexibility, or nurturing technology entrepreneurship ecosystems.

Keywords: centrality, patent coupling network, patent influence, social network analysis

Procedia PDF Downloads 37

Authors: Vasundhara Arora, Anupam Jhobta, Suresh Thakur, Sanjiv Sharma

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Aims and Objectives: Intracranial tuberculosis (TB) is one of the most devastating manifestations of TB and a challenging public health issue of considerable importance and magnitude world over. This study elaborates on the imaging spectrum of neurotuberculosis on magnetic resonance imaging (MRI) in 29 clinically suspected cases from a tertiary care hospital. Materials and Methods: The prospective hospital based evaluation of MR imaging features of neuro-tuberculosis in 29 clinically suspected cases was carried out in Department of Radio-diagnosis, Indira Gandhi Medical Hospital from July 2017 to August 2018. MR Images were obtained on a 1.5 T Magnetom Avanto machine and were analyzed to identify any abnormal meningeal enhancement or parenchymal lesions. Microbiological and Biochemical CSF analysis was performed in radio-logically suspected cases and the results were compared with the imaging data. Clinical follow up of the patients started on anti-tuberculous treatment was done to evaluate the response to treatment and clinical outcome. Results: Age range of patients in the study was between 1 year to 73 years. The mean age of presentation was 11.5 years. No significant difference in the distribution of cerebral tuberculosis was noted among the two genders. Imaging findings of neuro-tuberculosis obtained were varied and non specific ranging from lepto-meningeal enhancement, cerebritis to space occupying lesions such as tuberculomas and tubercular abscesses. Complications presenting as hydrocephalus (n= 7) and infarcts (n=9) was noted in few of these patients. 29 patients showed radiological suspicion of CNS tuberculosis with meningitis alone observed in 11 cases, tuberculomas alone were observed in 4 cases, meningitis with parenchymal tuberculomas in 11 cases. Tubercular abscess and cerebritis were observed in one case each. Tuberculous arachnoiditis was noted in one patient. Gene expert positivity was obtained in 11 out of 29 radiologically suspected patients; none of the patients showed culture positivity. Meningeal form of the disease alone showed higher positivity rate of gene Xpert (n=5) followed by combination of meningeal and parenchymal forms of disease (n=4). The parenchymal manifestation of disease alone showed least positivity rates (n= 3) with gene xpert testing. All 29 patients were started on anti tubercular treatment based on radiological suspicion of the disease with clinical improvement observed in 27 treated patients. Conclusions: In our study, higher incidence of neuro- tuberculosis was noted in paediatric population with predominance of the meningeal form of the disease. Gene Xpert positivity obtained was low due to paucibacillary nature of cerebrospinal fluid (CSF) with even lower positivity of CSF samples in parenchymal form of the manifestation. MRI showed high accuracy in detecting CNS lesions in neuro-tuberculosis. Hence, it can be concluded that MRI plays a crucial role in the diagnosis because of its inherent sensitivity and specificity and is an indispensible imaging modality. It caters to the need of early diagnosis owing to poor sensitivity of microbiological tests more so in the parenchymal manifestation of the disease.

Keywords: neurotuberculosis, tubercular abscess, tuberculoma, tuberculous meningitis

Procedia PDF Downloads 148

Authors: T. Ul Rehman, S. Agnello, F. M. Gelardi, M. M. Calvino, G. Lazzara, G. Buscarino, M. Cannas

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Metal-Organic Frameworks (MOFs) have emerged as promising candidates for detecting metal ions owing to their large surface area, customizable porosity, and diverse functionalities. In recent years, there has been a surge in research focused on MOFs with luminescent properties. These frameworks are constructed through coordinated bonding between metal ions and multi-dentate ligands, resulting in inherent fluorescent structures. Their luminescent behavior is influenced by factors like structural composition, surface morphology, pore volume, and interactions with target analytes, particularly metal ions. MOFs exhibit various sensing mechanisms, including photo-induced electron transfer (PET) and charge transfer processes such as ligand-to-metal (LMCT) and metal-to-ligand (MLCT) transitions. Among these, MIL-53(Al) stands out due to its flexibility, stability, and specific affinity towards certain metal ions, making it a promising platform for selective metal ion sensing. This study investigates the structural, thermal, and luminescent properties of MIL-53(Al) metal-organic framework (MOF) upon Fe3+ cation exchange. Two separate sets of samples were prepared to activate the MOF powder at different temperatures. The first set of samples, referred to as MIL-53(Al), activated (120°C), was prepared by activating the raw powder in a glass tube at 120°C for 12 hours and then sealing it. The second set of samples, referred to as MIL-53(Al), activated (300°C), was prepared by activating the MIL-53(Al) powder in a glass tube at 300°C for 70 hours. Additionally, 25 mg of MIL-53(Al) powder was dispersed in 5 mL of Fe3+ solution at various concentrations (0.1-100 mM) for the cation exchange experiment. The suspension was centrifuged for five minutes at 10,000 rpm to extract MIL-53(Al) powder. After three rounds of washing with ultrapure water, MIL-53(Al) powder was heated at 120°C for 12 hours. For PXRD and TGA analyses, a sample of the obtained MIL-53(Al) was used. We also activated the cation-exchanged samples for time-resolved photoluminescence (TRPL) measurements at two distinct temperatures (120 and 300°C) for comparative analysis. Powder X-ray diffraction patterns reveal amorphization in samples with higher Fe3+ concentrations, attributed to alterations in coordination environments and ion exchange dynamics. Thermal decomposition analysis shows reduced weight loss in Fe3+-exchanged MOFs, indicating enhanced stability due to stronger metal-ligand bonds and altered decomposition pathways. Raman spectroscopy demonstrates intensity decrease, shape disruption, and frequency shifts, indicative of structural perturbations induced by cation exchange. Photoluminescence spectra exhibit ligand-based emission (π-π* or n-π*) and ligand-to-metal charge transfer (LMCT), influenced by activation temperature and Fe3+ incorporation. Quenching of luminescence intensity and shorter lifetimes upon Fe3+ exchange result from structural distortions and Fe3+ binding to organic linkers. In a nutshell, this research underscores the complex interplay between composition, structure, and properties in MOFs, offering insights into their potential for diverse applications in catalysis, gas storage, and luminescent devices.

Keywords: Fe³⁺ cation exchange, luminescent metal-organic frameworks (LMOFs), MIL-53(Al), solid-state analysis

Procedia PDF Downloads 37

Authors: Óscar Muñoz Garrigós, José Manuel Bernabeu Aubán

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Time-sensitive services are the base of the cloud services industry. Keeping low service saturation is essential for controlling response time. All auto-scalable services make use of reactive auto-scaling. However, reactive auto-scaling has few in-depth studies. This presentation shows a model for reactive auto-scaling methodologies with a MAPE-k architecture. Queuing theory can compute different properties of static services but lacks some parameters related to the transition between models. Our model uses queuing theory parameters to relate the transition between models. It associates MAPE-k related times, the sampling frequency, the cooldown period, the number of requests that an instance can handle per unit of time, the number of incoming requests at a time instant, and a function that describes the acceleration in the service's ability to handle more requests. This model is later used as a solution to horizontally auto-scale time-sensitive services composed of microservices, reevaluating the model’s parameters periodically to allocate resources. The solution requires limiting the acceleration of the growth in the number of incoming requests to keep a constrained response time. Business benefits determine such limits. The solution can add a dynamic number of instances and remains valid under different system sizes. The study includes performance recommendations to improve results according to the incoming load shape and business benefits. The exposed methodology is tested in a simulation. The simulator contains a load generator and a service composed of two microservices, where the frontend microservice depends on a backend microservice with a 1:1 request relation ratio. A common request takes 2.3 seconds to be computed by the service and is discarded if it takes more than 7 seconds. Both microservices contain a load balancer that assigns requests to the less loaded instance and preemptively discards requests if they are not finished in time to prevent resource saturation. When load decreases, instances with lower load are kept in the backlog where no more requests are assigned. If the load grows and an instance in the backlog is required, it returns to the running state, but if it finishes the computation of all requests and is no longer required, it is permanently deallocated. A few load patterns are required to represent the worst-case scenario for reactive systems: the following scenarios test response times, resource consumption and business costs. The first scenario is a burst-load scenario. All methodologies will discard requests if the rapidness of the burst is high enough. This scenario focuses on the number of discarded requests and the variance of the response time. The second scenario contains sudden load drops followed by bursts to observe how the methodology behaves when releasing resources that are lately required. The third scenario contains diverse growth accelerations in the number of incoming requests to observe how approaches that add a different number of instances can handle the load with less business cost. The exposed methodology is compared against a multiple threshold CPU methodology allocating/deallocating 10 or 20 instances, outperforming the competitor in all studied metrics.

Keywords: reactive auto-scaling, auto-scaling, microservices, cloud computing

Procedia PDF Downloads 74

Authors: Zahirah Dhurmeea, Iker Zudaire, Heidi Pethybridge, Emmanuel Chassot, Maria Cedras, Natacha Nikolic, Jerome Bourjea, Wendy West, Chandani Appadoo, Nathalie Bodin

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Scientific advice on the status of fish stocks relies on indicators that are based on strong assumptions on biological parameters such as condition, maturity and fecundity. Currently, information on the biology of albacore tuna, Thunnus alalunga, in the Indian Ocean is scarce. Consequently, many parameters used in stock assessment models for Indian Ocean albacore originate largely from other studied stocks or species of tuna. Inclusion of incorrect biological data in stock assessment models would lead to inappropriate estimates of stock status used by fisheries manager’s to establish future catch allowances. The reproductive biology of albacore tuna in the western Indian Ocean was examined through analysis of the sex ratio, spawning season, length-at-maturity (L50), spawning frequency, fecundity and fish condition. In addition, the total lipid content (TL) and lipid class composition in the gonads, liver and muscle tissues of female albacore during the reproductive cycle was investigated. A total of 923 female and 867 male albacore were sampled from 2013 to 2015. A bias in sex-ratio was found in favour of females with fork length (LF) <100 cm. Using histological analyses and gonadosomatic index, spawning was found to occur between 10°S and 30°S, mainly to the east of Madagascar from October to January. Large females contributed more to reproduction through their longer spawning period compared to small individuals. The L50 (mean ± standard error) of female albacore was estimated at 85.3 ± 0.7 cm LF at the vitellogenic 3 oocyte stage maturity threshold. Albacore spawn on average every 2.2 days within the spawning region and spawning months from November to January. Batch fecundity varied between 0.26 and 2.09 million eggs and the relative batch fecundity (mean  standard deviation) was estimated at 53.4 ± 23.2 oocytes g-1 of somatic-gutted weight. Depending on the maturity stage, TL in ovaries ranged from 7.5 to 577.8 mg g-1 of wet weight (ww) with different proportions of phospholipids (PL), wax esters (WE), triacylglycerol (TAG) and sterol (ST). The highest TL were observed in immature (mostly TAG and PL) and spawning capable ovaries (mostly PL, WE and TAG). Liver TL varied from 21.1 to 294.8 mg g-1 (ww) and acted as an energy (mainly TAG and PL) storage prior to reproduction when the lowest TL was observed. Muscle TL varied from 2.0 to 71.7 g-1 (ww) in mature females without a clear pattern between maturity stages, although higher values of up to 117.3 g-1 (ww) was found in immature females. TL results suggest that albacore could be viewed predominantly as a capital breeder relying mostly on lipids stored before the onset of reproduction and with little additional energy derived from feeding. This study is the first one to provide new information on the reproductive development and classification of albacore in the western Indian Ocean. The reproductive parameters will reduce uncertainty in current stock assessment models which will eventually promote sustainability of the fishery.

Keywords: condition, size-at-maturity, spawning behaviour, temperate tuna, total lipid content

Procedia PDF Downloads 240

Authors: Vittorio Astarita, Giuseppe Guido, Vincenzo Pasquale Giofre, Alessandro Vitale

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Traffic microsimulation has been used extensively to evaluate consequences of different traffic planning and control policies in terms of travel time delays, queues, pollutant emissions, and every other common measured performance while at the same time traffic safety has not been considered in common traffic microsimulation packages as a measure of performance for different traffic scenarios. Vehicle conflict techniques that were introduced at intersections in the early traffic researches carried out at the General Motor laboratory in the USA and in the Swedish traffic conflict manual have been applied to vehicles trajectories simulated in microscopic traffic simulators. The concept is that microsimulation can be used as a base for calculating the number of conflicts that will define the safety level of a traffic scenario. This allows engineers to identify unsafe road traffic maneuvers and helps in finding the right countermeasures that can improve safety. Unfortunately, most commonly used indicators do not consider conflicts between single vehicles and roadside obstacles and barriers. A great number of vehicle crashes take place with roadside objects or obstacles. Only some recent proposed indicators have been trying to address this issue. This paper introduces a new procedure based on the simulation of potential crash events for the evaluation of safety levels in microsimulation traffic scenarios, which takes into account also potential crashes with roadside objects and barriers. The procedure can be used to define new conflict indicators. The proposed simulation procedure generates with the random perturbation of vehicle trajectories a set of potential crashes which can be evaluated accurately in terms of DeltaV, the energy of the impact, and/or expected number of injuries or casualties. The procedure can also be applied to real trajectories giving birth to new surrogate safety performance indicators, which can be considered as “simulation-based”. The methodology and a specific safety performance indicator are described and applied to a simulated test traffic scenario. Results indicate that the procedure is able to evaluate safety levels both at the intersection level and in the presence of roadside obstacles. The procedure produces results that are expressed in the same unity of measure for both vehicle to vehicle and vehicle to roadside object conflicts. The total energy for a square meter of all generated crash can be used and is shown on the map, for the test network, after the application of a threshold to evidence the most dangerous points. Without any detailed calibration of the microsimulation model and without any calibration of the parameters of the procedure (standard values have been used), it is possible to identify dangerous points. A preliminary sensitivity analysis has shown that results are not dependent on the different energy thresholds and different parameters of the procedure. This paper introduces a specific new procedure and the implementation in the form of a software package that is able to assess road safety, also considering potential conflicts with roadside objects. Some of the principles that are at the base of this specific model are discussed. The procedure can be applied on common microsimulation packages once vehicle trajectories and the positions of roadside barriers and obstacles are known. The procedure has many calibration parameters and research efforts will have to be devoted to make confrontations with real crash data in order to obtain the best parameters that have the potential of giving an accurate evaluation of the risk of any traffic scenario.

Keywords: road safety, traffic, traffic safety, traffic simulation

Procedia PDF Downloads 121

Authors: J. Chamorro-Servent, S. Tassani, M. A. Gonzalez-Ballester, L. J. Roca, J. Romeu, O. Camara

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Electromagnetic and microwave imaging (MWI) have been used in medical imaging in the last years, being the most common applications of breast cancer and stroke detection or monitoring. In those applications, the subject or zone to observe is surrounded by a number of antennas, and the Nyquist criterium can be satisfied. Additionally, the space between the antennas (transmitting and receiving the electromagnetic fields) and the zone to study can be prepared in a homogeneous scenario. However, this may differ in other cases as could be intracardiac catheters, stomach monitoring devices, pelvic organ systems, liver ablation monitoring devices, or uterine fibroids’ ablation systems. In this work, we analyzed different MWI algorithms to find the most suitable method for dealing with an intrabody scenario. Due to the space limitations usually confronted on those applications, the device would have a cylindrical configuration of a maximum of eight transmitters and eight receiver antennas. This together with the positioning of the supposed device inside a body tract impose additional constraints in order to choose a reconstruction method; for instance, it inhabitants the use of well-known algorithms such as filtered backpropagation for diffraction tomography (due to the unusual configuration with probes enclosed by the imaging region). Finally, the difficulty of simulating a realistic non-homogeneous background inside the body (due to the incomplete knowledge of the dielectric properties of other tissues between the antennas’ position and the zone to observe), also prevents the use of Born and Rytov algorithms due to their limitations with a heterogeneous background. Instead, we decided to use a time-reversed algorithm (mostly used in geophysics) due to its characteristics of ignoring heterogeneities in the background medium, and of focusing its generated field onto the scatters. Therefore, a 2D time-reversed finite difference time domain was developed based on the time-reversed approach for microwave breast cancer detection. Simultaneously an in-silico testbed was also developed to compare ground-truth dielectric properties with corresponding microwave imaging reconstruction. Forward and inverse problems were computed varying: the frequency used related to a small zone to observe (7, 7.5 and 8 GHz); a small polyp diameter (5, 7 and 10 mm); two polyp positions with respect to the closest antenna (aligned or disaligned); and the (transmitters-to-receivers) antenna combination used for the reconstruction (1-1, 8-1, 8-8 or 8-3). Results indicate that when using the existent time-reversed method for breast cancer here for the different combinations of transmitters and receivers, we found false positives due to the high degrees of freedom and unusual configuration (and the possible violation of Nyquist criterium). Those false positives founded in 8-1 and 8-8 combinations, highly reduced with the 1-1 and 8-3 combination, being the 8-3 configuration de most suitable (three neighboring receivers at each time). The 8-3 configuration creates a region-of-interest reduced problem, decreasing the ill-posedness of the inverse problem. To conclude, the proposed algorithm solves the main limitations of the described intrabody application, successfully detecting the angular position of targets inside the body tract.

Keywords: FDTD, time-reversed, medical imaging, microwave imaging

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Authors: Massimiliano Biagini, Marco Spinsanti, Gabriella De Angelis, Sara Tomei, Ilaria Ferlenghi, Maria Scarselli, Alessia Biolchi, Alessandro Muzzi, Brunella Brunelli, Silvana Savino, Marzia M. Giuliani, Isabel Delany, Paolo Costantino, Rino Rappuoli, Vega Masignani, Nathalie Norais

Abstract:

The gram-negative bacterium Neisseria meningitidis serogroup B (MenB) is an exclusively human pathogen representing the major cause of meningitides and severe sepsis in infants and children but also in young adults. This pathogen is usually present in the 30% of healthy population that act as a reservoir, spreading it through saliva and respiratory fluids during coughing, sneezing, kissing. Among surface-exposed protein components of this diplococcus, factor H binding protein is a lipoprotein proved to be a protective antigen used as a component of the recently licensed Bexsero vaccine. fHbp is a highly variable meningococcal protein: to reflect its remarkable sequence variability, it has been classified in three variants (or two subfamilies), and with poor cross-protection among the different variants. Furthermore, the level of fHbp expression varies significantly among strains, and this has also been considered an important factor for predicting MenB strain susceptibility to anti-fHbp antisera. Different methods have been used to assess fHbp expression on meningococcal strains, however, all these methods use anti-fHbp antibodies, and for this reason, the results are affected by the different affinity that antibodies can have to different antigenic variants. To overcome the limitations of an antibody-based quantification, we developed a quantitative Mass Spectrometry (MS) approach. Selected Reaction Monitoring (SRM) recently emerged as a powerful MS tool for detecting and quantifying proteins in complex mixtures. SRM is based on the targeted detection of ProteoTypicPeptides (PTPs), which are unique signatures of a protein that can be easily detected and quantified by MS. This approach, proven to be highly sensitive, quantitatively accurate and highly reproducible, was used to quantify the absolute amount of fHbp antigen in total extracts derived from 105 clinical isolates, evenly distributed among the three main variant groups and selected to be representative of the fHbp circulating subvariants around the world. We extended the study at the genetic level investigating the correlation between the differential level of expression and polymorphisms present within the genes and their promoter sequences. The implications of fHbp expression on the susceptibility of the strain to killing by anti-fHbp antisera are also presented. To date this is the first comprehensive fHbp expression profiling in a large panel of Neisseria meningitidis clinical isolates driven by an antibody-independent MS-based methodology, opening the door to new applications in vaccine coverage prediction and reinforcing the molecular understanding of released vaccines.

Keywords: quantitative mass spectrometry, Neisseria meningitidis, vaccines, bexsero, molecular epidemiology

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Authors: Seonaid Hume, Gordon Dobie, Graeme West

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Detecting and localizing radioactive sources is a necessity for safe and secure decommissioning of nuclear facilities. An important aspect for the management of the sort-and-segregation process is establishing the spatial distributions and quantities of the waste radionuclides, their type, corresponding activity, and ultimately classification for disposal. The data received from surveys directly informs decommissioning plans, on-site incident management strategies, the approach needed for a new cell, as well as protecting the workforce and the public. Manual classification of nuclear waste from a nuclear cell is time-consuming, expensive, and requires significant expertise to make the classification judgment call. Also, in-cell decommissioning is still in its relative infancy, and few techniques are well-developed. As with any repetitive and routine tasks, there is the opportunity to improve the task of classifying nuclear waste using autonomous systems. Hence, this paper proposes a new framework for the automatic classification of nuclear waste. This framework consists of five main stages; 3D spatial mapping and object detection, object classification, radiological mapping, source localisation based on gathered evidence and finally, waste classification. The first stage of the framework, 3D visual mapping, involves object detection from point cloud data. A review of related applications in other industries is provided, and recommendations for approaches for waste classification are made. Object detection focusses initially on cylindrical objects since pipework is significant in nuclear cells and indeed any industrial site. The approach can be extended to other commonly occurring primitives such as spheres and cubes. This is in preparation of stage two, characterizing the point cloud data and estimating the dimensions, material, degradation, and mass of the objects detected in order to feature match them to an inventory of possible items found in that nuclear cell. Many items in nuclear cells are one-offs, have limited or poor drawings available, or have been modified since installation, and have complex interiors, which often and inadvertently pose difficulties when accessing certain zones and identifying waste remotely. Hence, this may require expert input to feature match objects. The third stage, radiological mapping, is similar in order to facilitate the characterization of the nuclear cell in terms of radiation fields, including the type of radiation, activity, and location within the nuclear cell. The fourth stage of the framework takes the visual map for stage 1, the object characterization from stage 2, and radiation map from stage 3 and fuses them together, providing a more detailed scene of the nuclear cell by identifying the location of radioactive materials in three dimensions. The last stage involves combining the evidence from the fused data sets to reveal the classification of the waste in Bq/kg, thus enabling better decision making and monitoring for in-cell decommissioning. The presentation of the framework is supported by representative case study data drawn from an application in decommissioning from a UK nuclear facility. This framework utilises recent advancements of the detection and mapping capabilities of complex radiation fields in three dimensions to make the process of classifying nuclear waste faster, more reliable, cost-effective and safer.

Keywords: nuclear decommissioning, radiation detection, object detection, waste classification

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Authors: Lynn Sheridan, Dennis Alonzo, Hoa Nguyen, Andy Gao, Tracy Durksen

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Disruptions to teaching (e.g., COVID-related) have increased work demands for teachers. There is an opportunity for research to explore evidence-informed steps to support teachers. Collective evidence informs data on teachers’ personal attributes (e.g., self-efficacy beliefs) in the workplace are seen to promote success in teaching and support teacher engagement. Teacher engagement plays a role in students’ learning and teachers’ effectiveness. Engaged teachers are better at overcoming work-related stress, burnout and are more likely to take on active roles. Teachers’ commitment is influenced by a host of personal (e.g., teacher well-being) and environmental factors (e.g., job stresses). The job demands-resources model provided a conceptual basis for examining how teachers’ well-being, and is influenced by job demands and job resources. Job demands potentially evoke strain and exceed the employee’s capability to adapt. Job resources entail what the job offers to individual teachers (e.g., organisational support), helping to reduce job demands. The application of the job demands-resources model involves gathering an evidence-base of and connection to personal attributes (job resources). The study explored the association between constructs (resilience, adaptability, commitment, self/collective efficacy) and a teacher’s engagement with the job. The paper sought to elaborate on the model and determine the associations between key constructs of well-being (resilience, adaptability), commitment, and motivation (self and collective-efficacy beliefs) to teachers’ engagement in teaching. Data collection involved online a multi-dimensional instrument using validated items distributed from 2020-2022. The instrument was designed to identify construct relationships. The participant number was 170. Data Analysis: The reliability coefficients, means, standard deviations, skewness, and kurtosis statistics for the six variables were completed. All scales have good reliability coefficients (.72-.96). A confirmatory factor analysis (CFA) and structural equation model (SEM) were performed to provide measurement support and to obtain latent correlations among factors. The final analysis was performed using structural equation modelling. Several fit indices were used to evaluate the model fit, including chi-square statistics and root mean square error of approximation. The CFA and SEM analysis was performed. The correlations of constructs indicated positive correlations exist, with the highest found between teacher engagement and resilience (r=.80) and the lowest between teacher adaptability and collective teacher efficacy (r=.22). Given the associations; we proceeded with CFA. The CFA yielded adequate fit: CFA fit: X (270, 1019) = 1836.79, p < .001, RMSEA = .04, and CFI = .94, TLI = .93 and SRMR = .04. All values were within the threshold values, indicating a good model fit. Results indicate that increasing teacher self-efficacy beliefs will increase a teacher’s level of engagement; that teacher ‘adaptability and resilience are positively associated with self-efficacy beliefs, as are collective teacher efficacy beliefs. Implications for school leaders and school systems: 1. investing in increasing teachers’ sense of efficacy beliefs to manage work demands; 2. leadership approaches can enhance teachers' adaptability and resilience; and 3. a culture of collective efficacy support. Preparing teachers for now and in the future offers an important reminder to policymakers and school leaders on the importance of supporting teachers’ personal attributes when faced with the challenging demands of the job.

Keywords: collective teacher efficacy, teacher self-efficacy, job demands, teacher engagement

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Authors: Vijaya Dixit Aasheesh Dixit

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Shipbuilding industry operates in Engineer Procure Construct (EPC) context. Product mix of a shipyard comprises of various types of ships like bulk carriers, tankers, barges, coast guard vessels, sub-marines etc. Each order is unique based on the type of ship and customized requirements, which are engineered into the product right from design stage. Thus, to execute every new project, a shipyard needs to upgrade its production expertise. As a result, over the long run, holistic learning occurs across different types of projects which contributes to the knowledge base of the shipyard. Simultaneously, in the short term, during execution of a project comprising of multiple sister ships, repetition of similar tasks leads to learning at activity level. This research aims to capture above learnings of a shipyard and incorporate learning curve effect in project scheduling and materials procurement to improve project performance. Extant literature provides support for the existence of such learnings in an organization. In shipbuilding, there are sequences of similar activities which are expected to exhibit learning curve behavior. For example, the nearly identical structural sub-blocks which are successively fabricated, erected, and outfitted with piping and electrical systems. Learning curve representation can model not only a decrease in mean completion time of an activity, but also a decrease in uncertainty of activity duration. Sister ships have similar material requirements. The same supplier base supplies materials for all the sister ships within a project. On one hand, this provides an opportunity to reduce transportation cost by batching the order quantities of multiple ships. On the other hand, it increases the inventory holding cost at shipyard and the risk of obsolescence. Further, due to learning curve effect the production scheduled of each consequent ship gets compressed. Thus, the material requirement schedule of every next ship differs from its previous ship. As more and more ships get constructed, compressed production schedules increase the possibility of batching the orders of sister ships. This work aims at integrating materials management with project scheduling of long duration projects for manufacturing of multiple sister ships. It incorporates the learning curve effect on progressively compressing material requirement schedules and addresses the above trade-off of transportation cost and inventory holding and shortage costs while satisfying budget constraints of various stages of the project. The activity durations and lead time of items are not crisp and are available in the form of probabilistic distribution. A Stochastic Mixed Integer Programming (SMIP) model is formulated which is solved using evolutionary algorithm. Its output provides ordering dates of items and degree of order batching for all types of items. Sensitivity analysis determines the threshold number of sister ships required in a project to leverage the advantage of learning curve effect in materials management decisions. This analysis will help materials managers to gain insights about the scenarios: when and to what degree is it beneficial to treat a multiple ship project as an integrated one by batching the order quantities and when and to what degree to practice distinctive procurement for individual ship.

Keywords: learning curve, materials management, shipbuilding, sister ships

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Authors: Felix Lankester, Alicia Davis, Safari Kinung'hi, Catherine Bunga, Shayo Alkara, Imam Mzimbiri, Jonathan Yoder, Sarah Cleaveland, Guy H. Palmer

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Achieving the London Declaration goal of a 90% reduction in neglected tropical diseases (NTDs) by 2030 requires cost-effective strategies that attain high and comprehensive coverage. The first objective of this trial was to assess the impact on cost and coverage of employing a novel integrative One Health approach linking two NTD control programs: mass drug administration (MDA) for soil-transmitted helminths in humans (STH) and mass dog rabies vaccination (MDRV). The second objective was to compare the coverage achieved by the MDA, a community-wide deworming intervention, with that of the existing national primary school-based deworming program (NSDP), with particular focus on the proportion of primary school-age children reached and their school enrolment status. Our approach was unconventional because, in line with the One Health approach to disease control, it coupled the responsibilities and resources of the Ministries responsible for human and animal health into one program with the shared aim of preventing multiple NTDs. The trial was carried out in hard-to-reach pastoral communities comprising 24 villages of the Ngorongoro District, Tanzania, randomly allocated to either Arm A (MDA and MDRV), Arm B (MDA only) or Arm C (MDRV only). Objective one: The percentage of people in each target village that received treatment through MDA in Arms A and B was 63% and 65%, respectively (χ2 = 1, p = 0.32). The percentage of dogs vaccinated in Arm A and C was 70% and 81%, respectively (χ2 =9, p = 0.003). It took 33% less time for a single person and a dog to attend the integrated delivery than two separate events. Cost per dose (including delivery) was lower under the integrated strategy, with delivery of deworming and rabies vaccination reduced by $0.13 (54%) and $0.85 (19%) per dose, respectively. Despite a slight reduction in the proportion of village dogs vaccinated in the integrated event, both the integrated and non-integrated strategies achieved the target threshold of 70% required to eliminate rabies. Objective two: The percentages of primary school age children enrolled in school that was reached by this trial (73%) and the existing NSDP (80%) were not significantly different (F = 0.9, p = 0.36). However, of the primary school age children treated in this trial, 46% were not enrolled in school. Furthermore, 86% of the people treated would have been outside the reach of the NSDP because they were not primary school age or were primary school age children not enrolled in school. The comparable reach, the substantial reductions in cost per dose delivered and the decrease in participants’ time support this integrated One Health approach to control multiple NTDs. Further, the recorded level of non-enrolment at primary school suggests that, in remote areas, school-based delivery strategies could miss a large fraction of school-age children and that programs that focus delivery solely at the level of the primary school will miss a substantial proportion of both primary school age children as well as other individuals from the community. We have shown that these populations can be effectively reached through extramural programs.

Keywords: canine mediated human rabies, integrated health interventions, mass drug administration, neglected tropical disease, One Health, soil-transmitted helminths

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Authors: Anitha Kandasamy, Thirumurugan Ramaiah

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Interestingly, organic materials exhibit large optical nonlinearity with quick responses and having the flexibility of molecular tailoring using computational modelling and favourable synthetic methodologies. Pyridine based organic compounds and carboxylic acid contained aromatic compounds play a crucial role in crystal engineering of NCS complexes that displays admirable optical nonlinearity with fast response and favourable physicochemical properties such as low dielectric constant, wide optical transparency and large laser damage threshold value requires for optoelectronics device applications. Based on these facts, it was projected to form an acentric molecule of π-conjugated system interaction with appropriately replaced electron donor and acceptor groups for achieving higher SHG activity in which quinoline-2-carboyxlic acid is chosen as an electron acceptor and capable of acting as an acid as well as a base molecule, while 2-amino-4-methylpyridine is used as an electron donor and previously employed in numerous proton transfer complexes for synthesis of NLO materials for optoelectronic applications. 2-amino-4-mehtylpyridinium quinoline-2-carboxylate molecular complex (2AQ) is having π-donor-acceptor groups in which 2-amino-4-methylpyridine donates one of its electron to quinoline -2-carboxylic acid thereby forming a protonated 2-amino-4-methyl pyridinium moiety and mono ionized quinoline-2-carboxylate moiety which are connected via N-H…O intermolecular interactions with non-centrosymmetric crystal packing arrangement at microscopic scale is accountable to the enhancement of macroscopic second order NLO activity. The 2AQ crystal was successfully grown by a slow evaporation solution growth technique and its structure was determined in orthorhombic crystal system with acentric, P212121, space group. Hirshfeld surface analysis reveals that O…H intermolecular interactions primarily contributed with 31.0 % to the structural stabilization of 2AQ. The molecular structure of title compound has been confirmed by 1H and 13C NMR spectral studies. The vibrational modes of functional groups present in 2AQ have been assigned by using FTIR and FT-Raman spectroscopy. The grown 2AQ crystal exhibits high optical transparency with lower cut-off wavelength (275 nm) within the region of 275-1500 nm. The laser study confirmed that 2AQ exhibits high SHG efficiency of 12.6 times greater than that of KDP. TGA-DTA analysis revealed that 2AQ crystal had a thermal stability of 223 °C. The low dielectric constant and low dielectric loss at higher frequencies confirmed good crystalline nature with fewer defects of grown 2AQ crystal. The grown crystal exhibits soft material and positive photoconduction behaviour. Mulliken atomic distribution and FMOs analysis suggested that the strong intermolecular hydrogen bonding which lead to the enhancement of NLO activity. These properties suggest that 2AQ crystal is a suitable material for optoelectronic and laser frequency conversion applications.

Keywords: crystal growth, NLO activity, proton transfer complex, quantum chemical investigation

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