Search results for: forest height mapping

Authors: Asif Ali, Daud Salim Faruquie

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With the advancement of knowledge about the utility and impact of sustainability, its feasibility has been explored into different walks of life. Scientists, however; have established their knowledge in four areas viz environmental, economic, social and cultural, popularly termed as four pillars of sustainability. Aspects of environmental and economic sustainability have been rigorously researched and practiced and huge volume of strong evidence of effectiveness has been founded for these two sub-areas. For the social and cultural aspects of sustainability, dependable evidence of effectiveness is still to be instituted as the researchers and practitioners are developing and experimenting methods across the globe. Therefore, the present research aimed to identify globally used practices of social and cultural sustainability and through evidence synthesis assess their outcomes to determine the effectiveness of those practices. A PICO format steered the methodology which included all populations, popular sustainability practices including walkability/cycle tracks, social/recreational spaces, privacy, health & human services and barrier free built environment, comparators included ‘Before’ and ‘After’, ‘With’ and ‘Without’, ‘More’ and ‘Less’ and outcomes included Social well-being, cultural co-existence, quality of life, ethics and morality, social capital, sense of place, education, health, recreation and leisure, and holistic development. Search of literature included major electronic databases, search websites, organizational resources, directory of open access journals and subscribed journals. Grey literature, however, was not included. Inclusion criteria filtered studies on the basis of research designs such as total randomization, quasi-randomization, cluster randomization, observational or single studies and certain types of analysis. Studies with combined outcomes were considered but studies focusing only on environmental and/or economic outcomes were rejected. Data extraction, critical appraisal and evidence synthesis was carried out using customized tabulation, reference manager and CASP tool. Partial meta-analysis was carried out and calculation of pooled effects and forest plotting were done. As many as 13 studies finally included for final synthesis explained the impact of targeted practices on health, behavioural and social dimensions. Objectivity in the measurement of health outcomes facilitated quantitative synthesis of studies which highlighted the impact of sustainability methods on physical activity, Body Mass Index, perinatal outcomes and child health. Studies synthesized qualitatively (and also quantitatively) showed outcomes such as routines, family relations, citizenship, trust in relationships, social inclusion, neighbourhood social capital, wellbeing, habitability and family’s social processes. The synthesized evidence indicates slight effectiveness and efficacy of social and cultural sustainability on the targeted outcomes. Further synthesis revealed that such results of this study are due weak research designs and disintegrated implementations. If architects and other practitioners deliver their interventions in collaboration with research bodies and policy makers, a stronger evidence-base in this area could be generated.

Keywords: built environment, cultural sustainability, social sustainability, sustainable architecture

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Authors: Mustafa M. Donma, Orkide Donma

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Spexin, expressed in central nervous system, has attracted much interest in feeding behavior, obesity, diabetes, energy metabolism and cardiovascular functions. Fetuin A is known as negative acute phase reactant synthesized in the liver. So far, it has become a major concern of many studies in numerous clinical states. The relationship between the concentrations of spexin as well as fetuin A and the risk for cardiovascular diseases (CVDs) were also investigated. Eosinophils, suggested to be associated with the development of CVDs, are introduced as early indicators of cardiometabolic complications. Patients with elevated platelet count, associated with hypercoagulable state in the body, are also more liable to CVDs. In this study, the aim is to examine the profiles of spexin and fetuin A concomitant with the course of variations detected in eosinophil as well as platelet counts in morbid obese children. Thirty-four children with normal-body mass index (N-BMI) and fifty-one morbid obese (MO) children participated in the study. Written-informed consent forms were obtained prior to the study. Institutional ethics committee approved the study protocol. Age- and sex-adjusted BMI percentile tables prepared by World Health Organization were used to classify healthy and obese children. Mean age ± SEM of the children were 9.3 ± 0.6 years and 10.7 ± 0.5 years in N-BMI and MO groups, respectively. Anthropometric measurements of the children were taken. Body mass index values were calculated from weight and height values. Blood samples were obtained after an overnight fasting. Routine hematologic and biochemical tests were performed. Within this context, fasting blood glucose (FBG), insulin (INS), triglycerides (TRG), high density lipoprotein-cholesterol (HDL-C) concentrations were measured. Homeostatic model assessment for insulin resistance (HOMA-IR) values were calculated. Spexin and fetuin A levels were determined by enzyme-linked immunosorbent assay. Data were evaluated from the statistical point of view. Statistically significant differences were found between groups in terms of BMI, fat mass index, INS, HOMA-IR and HDL-C. In MO group, all parameters increased as HDL-C decreased. Elevated concentrations in MO group were detected in eosinophils (p<0.05) and platelets (p>0.05). Fetuin A levels decreased in MO group (p>0.05). However, decrease was statistically significant in spexin levels for this group (p<0.05). In conclusion, these results have suggested that increases in eosinophils and platelets exhibit behavior as cardiovascular risk factors. Decreased fetuin A behaved as a risk factor suitable to increased risk for cardiovascular problems associated with the severity of obesity. Along with increased eosinophils, increased platelets and decreased fetuin A, decreased spexin was the parameter, which reflects best its possible participation in the early development of CVD risk in MO children.

Keywords: cardiovascular diseases , eosinophils , fetuin A , pediatric morbid obesity , platelets , spexin

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Authors: Beatriz Rebocho, Elisabete Valente, Carla Palma, Andreia Guilherme, Filipa Bessa, Paula Sobral

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The global accumulation of plastic in the oceans is a growing problem. Plastic is transported from its source to the oceans via rivers, which are considered the main route for plastic particles from land-based sources to the ocean. These plastics undergo physical and chemical degradation resulting in microplastics. The i-Plastic project aims to understand and predict the dispersion, accumulation and impacts of microplastics (5 mm to 1 µm) and nano plastics (below 1 µm) in marine environments from the tropical and temperate land-ocean interface to the open ocean under distinct flow and climate regimes. Seasonal monitoring of the fluxes of microplastics was carried out in (three) coastal areas in Brazil, Portugal and Spain. The present work shows the first results of in-situ seasonal monitoring and mapping of microplastics in ocean waters between Ovar and Vieira de Leiria (Portugal), in which 43 surface water samples and 43 water column samples were collected in contrasting seasons (spring and autumn). The spring and autumn surface water samples were collected with a 300 µm and 150 µm pore neuston net, respectively. In both campaigns, water column samples were collected using a conical mesh with a 150 µm pore. The experimental procedure comprises the following steps: i) sieving by a metal sieve; ii) digestion with potassium hydroxide to remove the organic matter original from the sample matrix. After a filtration step, the content is retained on a membrane and observed under a stereomicroscope, and physical and chemical characterization (type, color, size, and polymer composition) of the microparticles is performed. Results showed that 84% and 88% of the surface water and water column samples were contaminated with microplastics, respectively. Surface water samples collected during the spring campaign averaged 0.35 MP.m-3, while surface water samples collected during autumn recorded 0.39 MP.m-3. Water column samples from the spring campaign had an average of 1.46 MP.m-3, while those from the autumn recorded 2.54 MP.m-3. In the spring, all microplastics found were fibers, predominantly black and blue. In autumn, the dominant particles found in the surface waters were fibers, while in the water column, fragments were dominant. In spring, the average size of surface water particles was 888 μm, while in the water column was 1063 μm. In autumn, the average size of surface and water column microplastics was 1333 μm and 1393 μm, respectively. The main polymers identified by Attenuated Total Reflectance (ATR) and micro-ATR Fourier Transform Infrared (FTIR) spectroscopy from all samples were low-density polyethylene (LDPE), polypropylene (PP), polyethylene terephthalate (PET), and polyvinyl chloride (PVC). The significant difference between the microplastic concentration in the water column between the two campaigns could be due to the remixing of the water masses that occurred that week due to the occurrence of a storm. This work presents preliminary results since the i-Plastic project is still in progress. These results will contribute to the understanding of the spatial and temporal dispersion and accumulation of microplastics in this marine environment.

Keywords: microplastics, Portugal, Atlantic Ocean, water column, surface water

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Authors: Lei Li, Ming M. Chai, Xiao X. Lu, Jia W. Wang

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The mixing process of two liquid layers in a cylindrical container includes the upper liquid with higher density rushing into the lower liquid with lighter density, the lower liquid rising into the upper liquid, meanwhile the two liquid layers having interactions with each other, forming vortices, spreading or dispersing in others, entraining or mixing with others. It is a complex process constituted of flow instability, turbulent mixing and other multiscale physical phenomena and having a fast evolution velocity. In order to explore the mechanism of the process and make further investigations, some experiments about the interfacial instability and mixing behavior between two liquid layers bounded in different volumes are carried out, applying the planar laser induced fluorescence (PLIF) and the high speed camera (HSC) techniques. According to the results, the evolution of interfacial instability between immiscible liquid develops faster than theoretical rate given by the Rayleigh-Taylor Instability (RTI) theory. It is reasonable to conjecture that some mechanisms except the RTI play key roles in the mixture process of two liquid layers. From the results, it is shown that the invading velocity of the upper liquid into the lower liquid does not depend on the upper liquid's volume (height). Comparing to the cases that the upper and lower containers are of identical diameter, in the case that the lower liquid volume increases to larger geometric space, the upper liquid spreads and expands into the lower liquid more quickly during the evolution of interfacial instability, indicating that the container wall has important influence on the mixing process. In the experiments of miscible liquid layers’ mixing, the diffusion time and pattern of the liquid interfacial mixing also does not depend on the upper liquid's volumes, and when the lower liquid volume increases to larger geometric space, the action of the bounded wall on the liquid falling and rising flow will decrease, and the liquid interfacial mixing effects will also attenuate. Therefore, it is also concluded that the volume weight of upper heavier liquid is not the reason of the fast interfacial instability evolution between the two liquid layers and the bounded wall action is limited to the unstable and mixing flow. The numerical simulations of the immiscible liquid layers’ interfacial instability flow using the VOF method show the typical flow pattern agree with the experiments. However the calculated instability development is much slower than the experimental measurement. The numerical simulation of the miscible liquids’ mixing, which applying Fick’s diffusion law to the components’ transport equation, shows a much faster mixing rate than the experiments on the liquids’ interface at the initial stage. It can be presumed that the interfacial tension plays an important role in the interfacial instability between the two liquid layers bounded in finite volume.

Keywords: interfacial instability and mixing, two liquid layers, Planar Laser Induced Fluorescence (PLIF), High Speed Camera (HSC), interfacial energy and tension, Cahn-Hilliard Navier-Stokes (CHNS) equations

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Authors: Dibyendu Rakshit, Santosh K. Sarkar

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The study illustrates a comprehensive account of large-scale changes plankton community structure in relevance to water quality characteristics due to anthropogenic stresses, mainly concerned for Annual Gangasagar Festival (AGF) at the southern tip of Sagar Island of Indian Sundarban wetland for 3-year duration (2012-2014; n=36). This prograding, vulnerable and tide-dominated megadelta has been formed in the estuarine phase of the Hooghly Estuary infested by largest continuous tract of luxurious mangrove forest, enriched with high native flora and fauna. The sampling strategy was designed to characterize the changes in plankton community and water quality considering three diverse phases, namely during festival period (January) and its pre - (December) as well as post (February) events. Surface water samples were collected for estimation of different environmental variables as well as for phytoplankton and microzooplankton biodiversity measurement. The preservation and identification techniques of both biotic and abiotic parameters were carried out by standard chemical and biological methods. The intensive human activities lead to sharp ecological changes in the context of poor water quality index (WQI) due to high turbidity (14.02±2.34 NTU) coupled with low chlorophyll a (1.02±0.21 mg m-3) and dissolved oxygen (3.94±1.1 mg l-1), comparing to pre- and post-festival periods. Sharp reduction in abundance (4140 to 2997 cells l-1) and diversity (H′=2.72 to 1.33) of phytoplankton and microzooplankton tintinnids (450 to 328 ind l-1; H′=4.31 to 2.21) was very much pronounced. The small size tintinnid (average lorica length=29.4 µm; average LOD=10.5 µm) composed of Tintinnopsis minuta, T. lobiancoi, T. nucula, T. gracilis are predominant and reached some of the greatest abundances during the festival period. Results of ANOVA revealed a significant variation in different festival periods with phytoplankton (F= 1.77; p=0.006) and tintinnid abundance (F= 2.41; P=0.022). RELATE analyses revealed a significant correlation between the variations of planktonic communities with the environmental data (R= 0.107; p= 0.005). Three distinct groups were delineated from principal component analysis, in which a set of hydrological parameters acted as the causative factor(s) for maintaining diversity and distribution of the planktonic organisms. The pronounced adverse impact of anthropogenic stresses on plankton community could lead to environmental deterioration, disrupting the productivity of benthic and pelagic ecosystems as well as fishery potentialities which directly related to livelihood services. The festival can be considered as multiple drivers of changes in relevance to beach erosion, shoreline changes, pollution from discarded plastic and electronic wastes and destruction of natural habitats resulting loss of biodiversity. In addition, deterioration in water quality was also evident from immersion of idols, causing detrimental effects on aquatic biota. The authors strongly recommend for adopting integrated scientific and administrative strategies for resilience, sustainability and conservation of this megadelta.

Keywords: Gangasagar festival, phytoplankton, Sundarban megadelta, tintinnid

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Authors: Christine P. Gibson, Ramona Alex, Michael Farney, Johana Vallejo-Elias, Mitra Esfandiarei

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Aortic aneurysm is the leading cause of death in Marfan syndrome (MFS), a connective tissue disorder caused by mutations in fibrillin-1 gene (FBN1). MFS aneurysm is characterized by weakening of the aortic wall due to elastin fibers fragmentation and disorganization. The above-average height and distinct physical features make young adults with MFS desirable candidates for competitive sports; but little is known about the exercise limit at which they will be at risk for aortic rupture. On the other hand, aerobic cardiovascular exercise has been shown to have protective effects on the heart and aorta. We have previously reported that mild aerobic exercise can delay the formation of aortic aneurysm in a mouse model of MFS. In this study, we aimed to investigate the effects of various levels of exercise intensity on the progression of aortic aneurysm in the mouse model. Starting at 4 weeks of age, we subjected control and MFS mice to different levels of exercise intensity (8m/min, 10m/min, 15m/min, and 20m/min, corresponding to 55%, 65%, 75%, and 85% of VO2 max, respectively) on a treadmill for 30 minutes per day, five days a week for the duration of the study. At 24 weeks of age, aortic tissue were isolated and subjected to structural and functional studies using histology and wire myography in order to evaluate the effects of different exercise routines on elastin fragmentation and organization and aortic wall elasticity/stiffness. Our data shows that exercise training at the intensity levels between 55%-75% significantly reduces elastin fragmentation and disorganization, with less recovery observed in 85% MFS group. The reversibility of elasticity was also significantly restored in MFS mice subjected to 55%-75% intensity; however, the recovery was less pronounced in MFS mice subjected to 85% intensity. Furthermore, our data shows that smooth muscle cells (SMCs) contractilion in response to vasoconstrictor agent phenylephrine (100nM) is significantly reduced in MFS aorta (54.84 ± 1.63 mN/mm2) as compared to control (95.85 ± 3.04 mN/mm2). At 55% of intensity, exercise did not rescue SMCs contraction (63.45 ± 1.70 mN/mm2), while at higher intensity levels, SMCs contraction in response to phenylephrine was restored to levels similar to control aorta [65% (81.88 ± 4.57 mN/mm2), 75% (86.22 ± 3.84 mN/mm2), and 85% (83.91 ± 5.42 mN/mm2)]. This study provides the first time evidence that high intensity exercise (e.g. 85%) may not provide the most beneficial effects on aortic function (vasoconstriction) and structure (elastin fragmentation, aortic wall elasticity) during the progression of aortic aneurysm in MFS mice. On the other hand, based on our observations, medium intensity exercise (e.g. 65%) seems to provide the utmost protective effects on aortic structure and function in MFS mice. These findings provide new insights into the potential capacity, in which MFS patients could participate in various aerobic exercise routines, especially in young adults affected by cardiovascular complications particularly aortic aneurysm. This work was funded by Midwestern University Research Fund.

Keywords: aerobic exercise, aortic aneurysm, aortic wall elasticity, elastin fragmentation, Marfan syndrome

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Authors: C. Villamizar, M. Serrato

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In the pursuit of athletic performance, the role of physical training which is determined by a number of charges or taxes on physiological stress and musculoskeletal systems of the human body generated by the intensity and duration is fundamental. Given the physical demands of these activities both training and competitive must take into account the optimal relationship with a straining process recovery post favoring the process of overcompensation which aims to facilitate the return and rising energy potential and protein synthesis also of different tissues. Allowing muscle function returns to baseline or pre-exercise states. If this recovery process is not performed or is not allowed in a proper way, will result in an increased state of fatigue. Active recovery, is one of the strategies implemented in the sport for a return to pre-exercise physiological states. However, there are some adverse assumptions regarding the negative effects, as is the possibility of increasing the degradation of muscle glycogen and thus delaying the synthesis thereof. For them, it is necessary to investigate what would be the effects generated application made at different times after the effort. The aim of this study was to determine the effects of active recovery post effort made at three different times: immediately, at 12 and 24 hours on biochemical markers creatine kinase in youth soccer player’s categories. A randomized controlled trial with allocation to three groups was performed: A. active recovery immediately after the effort; B. active recovery performed at 12 hours after the effort; C. active recovery made at 24 hours after the effort. This study included 27 subjects belonging to a Colombian soccer team of the second division. Vital signs, weight, height, BMI, the percentage of muscle mass, fat mass percentage, personal medical history, and family were valued. The velocity, explosive force and Creatin Kinase (CK) in blood were tested before and after interventions. SAFT 90 protocol (Soccer Field specific Aerobic Test) was applied to participants for generating fatigue. CK samples were taken one hour before the application of the fatigue test, one hour after the fatigue protocol and 48 of the initial CK sample. Mean age was 18.5 ± 1.1 years old. Improvements in jumping and speed recovery the 3 groups (p < 0.05), but no statistically significant differences between groups was observed after recuperation. In all participants, there was a significant increment of CK when applied SAFT 90 in all the groups (median 103.1-111.1). The CK measurement after 48 hours reflects a recovery in all groups, however the group C, a decline below baseline levels of -55.5 (-96.3 /-20.4) which is a significant find. Other research has shown that CK does not return quickly to their baseline, but our study shows that active recovery favors the clearance of CK and also to perform recovery 24 hours after the effort generates higher clearance of this biomarker.

Keywords: active recuperation, creatine phosphokinase, post training, young soccer players

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Authors: Simrat Kaur, Fatema Diwan, Brad Reddersen

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The unsustainable and injudicious use of natural renewable resources beyond the self-replenishment limits of our planet has proved catastrophic. Most of the Earth’s resources, including land, water, minerals, and biodiversity, have been overexploited. Owing to this, there is a steep rise in the global events of natural calamities of contrasting nature, such as torrential rains, storms, heat waves, rising sea levels, and megadroughts. These are all interconnected through common elements, namely oceanic currents and land’s the green cover. The deforestation fueled by the ‘economic elites’ or the global players have already cleared massive forests and ecological biomes in every region of the globe, including the Amazon. These were the natural carbon sinks prevailing and performing CO2 sequestration for millions of years. The forest biomes have been turned into mono cultivation farms to produce feedstock crops such as soybean, maize, and sugarcane; which are one of the biggest green house gas emitters. Such unsustainable agriculture practices only provide feedstock for livestock and food processing industries with huge carbon and water footprints. These are two main factors that have ‘cause and effect’ relationships in the context of climate change. In contrast to organic and sustainable farming, the mono-cultivation practices to produce food, fuel, and feedstock using chemicals devoid of the soil of its fertility, abstract surface, and ground waters beyond the limits of replenishment, emit green house gases, and destroy biodiversity. There are numerous cases across the planet where due to overuse; the levels of surface water reservoir such as the Lake Mead in Southwestern USA and ground water such as in Punjab, India, have deeply shrunk. Unlike the rain fed food production system on which the poor communities of the world relies; the blue water (surface and ground water) dependent mono-cropping for industrial and processed food create water deficit which put the burden on the domestic users. Excessive abstraction of both surface and ground waters for high water demanding feedstock (soybean, maize, sugarcane), cereal crops (wheat, rice), and cash crops (cotton) have a dual and synergistic impact on the global green house gas emissions and prevalence of megadroughts. Both these factors have elevated global temperatures, which caused cascading events such as soil water deficits, flash fires, and unprecedented burning of the woods, creating megafires in multiple continents, namely USA, South America, Europe, and Australia. Therefore, it is imperative to reduce the green and blue water footprints of agriculture and industrial sectors through recycling of black and gray waters. This paper explores various opportunities for successful implementation of wastewater management for drought preparedness in high risk communities.

Keywords: wastewater, drought, biodiversity, water footprint, nutrient recovery, algae

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Authors: Gerd Wilsch, Tobias Guenther, Tobias Voelker

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In view of the ageing of vital infrastructure facilities, a reliable condition assessment of concrete structures is becoming of increasing interest for asset owners to plan timely and appropriate maintenance and repair interventions. For concrete structures, reinforcement corrosion induced by penetrating chlorides is the dominant deterioration mechanism affecting the serviceability and, eventually, structural performance. The determination of the quantitative chloride ingress is required not only to provide valuable information on the present condition of a structure, but the data obtained can also be used for the prediction of its future development and associated risks. At present, wet chemical analysis of ground concrete samples by a laboratory is the most common test procedure for the determination of the chloride content. As the chloride content is expressed by the mass of the binder, the analysis should involve determination of both the amount of binder and the amount of chloride contained in a concrete sample. This procedure is laborious, time-consuming, and costly. The chloride profile obtained is based on depth intervals of 10 mm. LIBS is an economically viable alternative providing chloride contents at depth intervals of 1 mm or less. It provides two-dimensional maps of quantitative element distributions and can locate spots of higher concentrations like in a crack. The results are correlated directly to the mass of the binder, and it can be applied on-site to deliver instantaneous results for the evaluation of the structure. Examples for the application of the method in the laboratory for the investigation of diffusion and migration of chlorides, sulfates, and alkalis are presented. An example for the visualization of the Li transport in concrete is also shown. These examples show the potential of the method for a fast, reliable, and automated two-dimensional investigation of transport processes. Due to the better spatial resolution, more accurate input parameters for model calculations are determined. By the simultaneous detection of elements such as carbon, chlorine, sodium, and potassium, the mutual influence of the different processes can be determined in only one measurement. Furthermore, the application of a mobile LIBS system in a parking garage is demonstrated. It uses a diode-pumped low energy laser (3 mJ, 1.5 ns, 100 Hz) and a compact NIR spectrometer. A portable scanner allows a two-dimensional quantitative element mapping. Results show the quantitative chloride analysis on wall and floor surfaces. To determine the 2-D distribution of harmful elements (Cl, C), concrete cores were drilled, split, and analyzed directly on-site. Results obtained were compared and verified with laboratory measurements. The results presented show that the LIBS method is a valuable addition to the standard procedures - the wet chemical analysis of ground concrete samples. Currently, work is underway to develop a technical code of practice for the application of the method for the determination of chloride concentration in concrete.

Keywords: chemical analysis, concrete, LIBS, spectroscopy

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Authors: R. Tamborrino, F. Rinaudo

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Digital technologies offer possibilities to effectively convert historical archives into instruments of knowledge able to provide a guide for the interpretation of historical phenomena. Digital conversion and management of those documents allow the possibility to add other sources in a unique and coherent model that permits the intersection of different data able to open new interpretations and understandings. Urban history uses, among other sources, the inventories that register human activities in a specific space (e.g. cadastres, censuses, etc.). The geographic localisation of that information inside cartographic supports allows for the comprehension and visualisation of specific relationships between different historical realities registering both the urban space and the peoples living there. These links that merge the different nature of data and documentation through a new organisation of the information can suggest a new interpretation of other related events. In all these kinds of analysis, the use of GIS platforms today represents the most appropriate answer. The design of the related databases is the key to realise the ad-hoc instrument to facilitate the analysis and the intersection of data of different origins. Moreover, GIS has become the digital platform where it is possible to add other kinds of data visualisation. This research deals with the industrial development of Turin at the beginning of the 20th century. A census of factories realized just prior to WWI provides the opportunity to test the potentialities of GIS platforms for the analysis of urban landscape modifications during the first industrial development of the town. The inventory includes data about location, activities, and people. GIS is shaped in a creative way linking different sources and digital systems aiming to create a new type of platform conceived as an interface integrating different kinds of data visualisation. The data processing allows linking this information to an urban space, and also visualising the growth of the city at that time. The sources, related to the urban landscape development in that period, are of a different nature. The emerging necessity to build, enlarge, modify and join different buildings to boost the industrial activities, according to their fast development, is recorded by different official permissions delivered by the municipality and now stored in the Historical Archive of the Municipality of Turin. Those documents, which are reports and drawings, contain numerous data on the buildings themselves, including the block where the plot is located, the district, and the people involved such as the owner, the investor, and the engineer or architect designing the industrial building. All these collected data offer the possibility to firstly re-build the process of change of the urban landscape by using GIS and 3D modelling technologies thanks to the access to the drawings (2D plans, sections and elevations) that show the previous and the planned situation. Furthermore, they access information for different queries of the linked dataset that could be useful for different research and targets such as economics, biographical, architectural, or demographical. By superimposing a layer of the present city, the past meets to the present-industrial heritage, and people meet urban history.

Keywords: digital urban history, census, digitalisation, GIS, modelling, digital humanities

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Authors: Fatemeh Faramarzi, Hosein Mahjoob

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Building dams on rivers for utilization of water resources causes problems in hydrodynamic equilibrium and results in leaving all or part of the sediments carried by water in dam reservoir. This phenomenon has also significant impacts on water and sediment flow regime and in the long term can cause morphological changes in the environment surrounding the river, reducing the useful life of the reservoir which threatens sustainable development through inefficient management of water resources. In the past, empirical methods were used to predict the sedimentation amount in dam reservoirs and to estimate its efficient lifetime. But recently the mathematical and computational models are widely used in sedimentation studies in dam reservoirs as a suitable tool. These models usually solve the equations using finite element method. This study compares the results from tow software packages, GSTARS4 & HEC-6, in the prediction of the sedimentation amount in Dez dam, southern Iran. The model provides a one-dimensional, steady-state simulation of sediment deposition and erosion by solving the equations of momentum, flow and sediment continuity and sediment transport. GSTARS4 (Generalized Sediment Transport Model for Alluvial River Simulation) which is based on a one-dimensional mathematical model that simulates bed changes in both longitudinal and transverse directions by using flow tubes in a quasi-two-dimensional scheme to calibrate a period of 47 years and forecast the next 47 years of sedimentation in Dez Dam, Southern Iran. This dam is among the highest dams all over the world (with its 203 m height), and irrigates more than 125000 square hectares of downstream lands and plays a major role in flood control in the region. The input data including geometry, hydraulic and sedimentary data, starts from 1955 to 2003 on a daily basis. To predict future river discharge, in this research, the time series data were assumed to be repeated after 47 years. Finally, the obtained result was very satisfactory in the delta region so that the output from GSTARS4 was almost identical to the hydrographic profile in 2003. In the Dez dam due to the long (65 km) and a large tank, the vertical currents are dominant causing the calculations by the above-mentioned method to be inaccurate. To solve this problem, we used the empirical reduction method to calculate the sedimentation in the downstream area which led to very good answers. Thus, we demonstrated that by combining these two methods a very suitable model for sedimentation in Dez dam for the study period can be obtained. The present study demonstrated successfully that the outputs of both methods are the same.

Keywords: Dez Dam, prediction, sedimentation, water resources, computational models, finite element method, GSTARS4, HEC-6

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Authors: Bitewulign Mekonnen

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Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

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Authors: Kathryn Ewing

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Safe and accessible public spaces are vital elements of our South African cities. Public spaces hold the potential to act as important, vibrant places for learning, exchange, and practice. Public walkways, however, are some of the most neglected and extremely dangerous public spaces experienced in the local neighborhood of Gugulethu in Cape Town. Walkways feel insignificant, being recognized as informal and undetermined or retain complex fragments of formal erven. They are generally out of sight connecting minor streets and informal settlements. Community residents refer to the walkways as unsafe and dirty spaces. Local authorities allocate minimal to no municipal budgets nor maintenance plans resulting in a lack of basic services, particularly lighting and green infrastructure. ‘The Walkway Project’ presents a series of urban stories collected from co-design workshops, emotional mapping exercises, and fieldwork, including urban walks and urban talks. The narrative interprets the socio-spatial practice and complexity of informal public space in Gugulethu, Cape Town. The Walkway Project research, interrelated to the Master of Urban Design teaching and design-research studio, has a strong focus on participatory and engaged learning and action research methodology within a deliberate pedagogy. A consolidated urban design implementation plan exposes the impact and challenges of waste and water, opening the debate on relevant local solutions for resilience and safety in Cape Town. A small and neglected passage connecting two streets, commonly referred to as iThemba Walkway, is presented as a case study to show-case strategic urban design intervention strategies for urban upgrading. The iThemba walkway is a community-driven project that demonstrates active and responsible co-design and participatory development opportunities. In March 2021, when visited on an urban walk, the public space was covered by rubble and solid waste. By April 2021, the community cleaned the walkway and created an accessible passage for the school children to pass. Numerous co-design workshops have taken place over the past year. The walkway has emerged as a public space upgrading project facilitated, motivated, and implemented by multiple local partners and residents. Social maps from urban walks and talks illustrate the transformation of iThemba Walkway into an inclusive, safe, resilient, and sustainable urban space, linked to Sustainable Development Goal number 11, sustainable cities and communities. The outcomes of the upgrading project facilitate a deeper understanding of co-design methods, urban upgrading processes, and monitoring of public space and informal urbanism.

Keywords: informal, public space, resilience, safety, upgrade, walkways

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Authors: Brittany Richardson, Ying Wang

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For physical therapy, exercise prescription, athlete training, and regular fitness training, it is crucial to perform health assessments or fitness assessments periodically. An accurate assessment is propitious for tracking recovery progress, preventing potential injury and making long-range training plans. Assessments include necessary measurements, height, weight, blood pressure, heart rate, body fat, etc. and advanced evaluation, muscle group strength, stability-mobility, and movement evaluation, etc. In the current standard assessment procedures, the accuracy of assessments, especially advanced evaluations, largely depends on the experience of physicians, coaches, and personal trainers. And it is challenging to track clients’ progress in the current assessment. Unlike the tradition assessment, in this paper, we present a deep learning based face recognition algorithm for accurate, comprehensive and trackable assessment. Based on the result from our assessment, physicians, coaches, and personal trainers are able to adjust the training targets and methods. The system categorizes the difficulty levels of the current activity for the client or user, furthermore make more comprehensive assessments based on tracking muscle group over time using a designed landmark detection method. The system also includes the function of grading and correcting the form of the clients during exercise. Experienced coaches and personal trainer can tell the clients' limit based on their facial expression and muscle group movements, even during the first several sessions. Similar to this, using a convolution neural network, the system is trained with people’s facial expression to differentiate challenge levels for clients. It uses landmark detection for subtle changes in muscle groups movements. It measures the proximal mobility of the hips and thoracic spine, the proximal stability of the scapulothoracic region and distal mobility of the glenohumeral joint, as well as distal mobility, and its effect on the kinetic chain. This system integrates data from other fitness assistant devices, including but not limited to Apple Watch, Fitbit, etc. for a improved training and testing performance. The system itself doesn’t require history data for an individual client, but the history data of a client can be used to create a more effective exercise plan. In order to validate the performance of the proposed work, an experimental design is presented. The results show that the proposed work contributes towards improving the quality of exercise plan, execution, progress tracking, and performance.

Keywords: exercise prescription, facial recognition, landmark detection, fitness assessments

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Authors: R. R. Galiev, I. I. Lykov, A. E. Shitikov, I. A. Bilenko

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Dual-comb interferometry is based on the mixing of two optical frequency combs with slightly different lines spacing which results in the mapping of the optical spectrum into the radio-frequency domain for future digitizing and numerical processing. The dual-comb approach enables diverse applications, including metrology, fast high-precision spectroscopy, and distance range. Ordinary frequency-modulated continuous-wave (FMCW) laser-based Light Identification Detection and Ranging systems (LIDARs) suffer from two main disadvantages: slow and unreliable mechanical, spatial scan and a rather wide linewidth of conventional lasers, which limits speed measurement resolution. Dual-comb distance measurements with Allan deviations down to 12 nanometers at averaging times of 13 microseconds, along with ultrafast ranging at acquisition rates of 100 megahertz, allowing for an in-flight sampling of gun projectiles moving at 150 meters per second, was previously demonstrated. Nevertheless, pump lasers with EDFA amplifiers made the device bulky and expensive. An alternative approach is a direct coupling of the laser to a reference microring cavity. Backscattering can tune the laser to the eigenfrequency of the cavity via the so-called self-injection locked (SIL) effect. Moreover, the nonlinearity of the cavity allows a solitonic frequency comb generation in the very same cavity. In this work, we developed a fully integrated, power-efficient, electrically driven dual-micro comb source based on the semiconductor lasers SIL to high-quality integrated Si3N4 microresonators. We managed to obtain robust 1400-1700 nm combs generation with a 150 GHz or 1 THz lines spacing and measure less than a 1 kHz Lorentzian withs of stable, MHz spaced beat notes in a GHz band using two separated chips, each pumped by its own, self-injection locked laser. A deep investigation of the SIL dynamic allows us to find out the turn-key operation regime even for affordable Fabry-Perot multifrequency lasers used as a pump. It is important that such lasers are usually more powerful than DFB ones, which were also tested in our experiments. In order to test the advantages of the proposed techniques, we experimentally measured a minimum detectable speed of a reflective object. It has been shown that the narrow line of the laser locked to the microresonator provides markedly better velocity accuracy, showing velocity resolution down to 16 nm/s, while the no-SIL diode laser only allowed 160 nm/s with good accuracy. The results obtained are in agreement with the estimations and open up ways to develop LIDARs based on compact and cheap lasers. Our implementation uses affordable components, including semiconductor laser diodes and commercially available silicon nitride photonic circuits with microresonators.

Keywords: dual-comb spectroscopy, LIDAR, optical microresonator, self-injection locking

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Authors: Mohammed Hmimou, Khalid Amediaz, Imane Sebari, Nabil Bounajma

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Annual crop production is one of the most important macroeconomic indicators for the majority of countries around the world. This information is valuable, especially for exporting countries which need a yield estimation before harvest in order to correctly plan the supply chain. When it comes to estimating agricultural yield, especially for arboriculture, conventional methods are mostly applied. In the case of the citrus industry, the sale before harvest is largely practiced, which requires an estimation of the production when the fruit is on the tree. However, conventional method based on the sampling surveys of some trees within the field is always used to perform yield estimation, and the success of this process mainly depends on the expertise of the ‘estimator agent’. The present study aims to propose a methodology based on the combination of unmanned aerial vehicle (UAV) images and terrestrial laser scanner (TLS) point cloud to estimate citrus production. During data acquisition, a fixed wing and rotatory drones, as well as a terrestrial laser scanner, were tested. After that, a pre-processing step was performed in order to generate point cloud and digital surface model. At the processing stage, a machine vision workflow was implemented to extract points corresponding to fruits from the whole tree point cloud, cluster them into fruits, and model them geometrically in a 3D space. By linking the resulting geometric properties to the fruit weight, the yield can be estimated, and the statistical distribution of fruits size can be generated. This later property, which is information required by importing countries of citrus, cannot be estimated before harvest using the conventional method. Since terrestrial laser scanner is static, data gathering using this technology can be performed over only some trees. So, integration of drone data was thought in order to estimate the yield over a whole orchard. To achieve that, features derived from drone digital surface model were linked to yield estimation by laser scanner of some trees to build a regression model that predicts the yield of a tree given its features. Several missions were carried out to collect drone and laser scanner data within citrus orchards of different varieties by testing several data acquisition parameters (fly height, images overlap, fly mission plan). The accuracy of the obtained results by the proposed methodology in comparison to the yield estimation results by the conventional method varies from 65% to 94% depending mainly on the phenological stage of the studied citrus variety during the data acquisition mission. The proposed approach demonstrates its strong potential for early estimation of citrus production and the possibility of its extension to other fruit trees.

Keywords: citrus, digital surface model, point cloud, terrestrial laser scanner, UAV, yield estimation, 3D modeling

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Authors: Panagiota Katsikouli, William Hamilton Byrne, Thomas Gammeltoft-Hansen, Tijs Slaats

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An individual who demonstrates a well-founded fear of persecution or faces real risk of being subjected to torture is eligible for asylum. In Danish law, the exact legal thresholds reflect those established by international conventions, notably the 1951 Refugee Convention and the 1950 European Convention for Human Rights. These international treaties, however, remain largely silent when it comes to how states should assess asylum claims. As a result, national authorities are typically left to determine an individual’s legal eligibility on a narrow basis consisting of an oral testimony, which may itself be hampered by several factors, including imprecise language interpretation, insecurity or lacking trust towards the authorities among applicants. The leaky ground, on which authorities must assess their subjective perceptions of asylum applicants' credibility, questions whether, in all cases, adjudicators make the correct decision. Moreover, the subjective element in these assessments raises questions on whether individual asylum cases could be afflicted by implicit biases or stereotyping amongst adjudicators. In fact, recent studies have uncovered significant correlations between decision outcomes and the experience and gender of the assigned judge, as well as correlations between asylum outcomes and entirely external events such as weather and political elections. In this study, we analyze a publicly available dataset containing approximately 8,000 summaries of asylum cases, initially rejected, and re-tried by the Refugee Appeals Board (RAB) in Denmark. First, we look for variations in the recognition rates, with regards to a number of applicants’ features: their country of origin/nationality, their identified gender, their identified religion, their ethnicity, whether torture was mentioned in their case and if so, whether it was supported or not, and the year the applicant entered Denmark. In order to extract those features from the text summaries, as well as the final decision of the RAB, we applied natural language processing and regular expressions, adjusting for the Danish language. We observed interesting variations in recognition rates related to the applicants’ country of origin, ethnicity, year of entry and the support or not of torture claims, whenever those were made in the case. The appearance (or not) of significant variations in the recognition rates, does not necessarily imply (or not) bias in the decision-making progress. None of the considered features, with the exception maybe of the torture claims, should be decisive factors for an asylum seeker’s fate. We therefore investigate whether the decision can be predicted on the basis of these features, and consequently, whether biases are likely to exist in the decisionmaking progress. We employed a number of machine learning classifiers, and found that when using the applicant’s country of origin, religion, ethnicity and year of entry with a random forest classifier, or a decision tree, the prediction accuracy is as high as 82% and 85% respectively. tentially predictive properties with regards to the outcome of an asylum case. Our analysis and findings call for further investigation on the predictability of the outcome, on a larger dataset of 17,000 cases, which is undergoing.

Keywords: asylum adjudications, automated decision-making, machine learning, text mining

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Authors: Rakesh Tiwary, D. M. Diwakar, Sandhya Mahapatro

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Agro-forestry offers huge opportunities for diversification of agriculture in middle Gangetic Basin of India, particularly in the state of Bihar as the region is identified with traditional & stagnant agriculture, low productivity, high population pressure, rural poverty and lack of agro- industrial development. The region is endowed with favourable agro-climatic, soil & drainage conditions; interestingly, there has been an age old tradition of agro-forestry in the state. However, due to demographic pressures, declining land holdings and other socio- economic factors, agro forestry practices have declined in recent decades. The government of Bihar has initiated a special program for expansion of agro-forestry based on modern practices with an aim to raise income level of farmers, make available raw material for wood based industries and increase green cover in the state. The Agro-forestry Schemes – Poplar & Other Species are the key components of the program being implemented by Department of Environment & Forest, Govt. of Bihar. The paper is based on fieldwork based evaluation study on experiences of implementation of the agro-forestry schemes. Understanding adoption patterns, identification of key motives for practising agro-forestry, experiences of farmers well analysing the barriers in expansion constituted the major themes of the research study. This paper is based on primary as well as secondary data. The primary data consists of beneficiary household survey, Focus Group Discussions among beneficiary communities, dialogue and multi stakeholder meetings and field visit to the sites. The secondary data information was collected and analysed from official records, policy documents and reports. Primary data was collected from about 500 beneficiary households of Muzaffarpur & Saharsa- two populous, large and agriculture dominated districts of middle Gangetic basin of North Bihar. Survey also covers 100 households of non-beneficiaries. Probability Proportionate to Size method was used to determine the number of samples to be covered in different blocks of two districts. Qualitative tools were also implemented to have better insights about key research questions. Present paper discusses socio-economic background of farmers practising agro-forestry; the adoption patterns of agro- forestry (choice of plants, methods of plantation and others); and motivation behind adoption of agro-forestry and the comparative benefits of agro-forestry (vis-a-vis traditional agriculture). Experience of beneficiary farmers with agro-forestry based on government programs & promotional campaigns (in terms of awareness, ease of access, knowhow and others) have been covered in the paper. Different aspects of survival of plants have been closely examined. Non beneficiaries but potential adopters were also interviewed to understand barriers of adoption of agro- forestry. Paper provides policy recommendations and interventions required for effective expansion of the agro- forestry and realisation of its future prospects for agricultural diversification in the region.

Keywords: agro-forestry adoption patterns, farmers’ motivations & experiences, Indian middle Gangetic plains, strategies for expansion

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Authors: Sukhvinder Kaur, Ashok Agarwal

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Background: For the first time in 2009, India was able to generate estimates of HIV incidence (the number of new HIV infections per year). Analysis of epidemic projections helped in revealing that the number of new annual HIV infections in India had declined by more than 50% during the last decade (GOI Ministry of Health and Family Welfare, 2010). Then, National AIDS Control Organisation (NACO) planned to scale up its efforts in generating projections through epidemiological analysis and modelling by taking recent available sources of evidence such as HIV Sentinel Surveillance (HSS), India Census data and other critical data sets. Recently, NACO generated current round of HIV estimates-2012 through globally recommended tool “Spectrum Software” and came out with the estimates for adult HIV prevalence, annual new infections, number of people living with HIV, AIDS-related deaths and treatment needs. State level prevalence and incidence projections produced were used to project consequences of the epidemic in spectrum. In presence of HIV estimates generated at state level in India by NACO, USIAD funded PIPPSE project under the leadership of NACO undertook the estimations and projections to district level using same Spectrum software. In 2011, adult HIV prevalence in one of the high prevalent States, Maharashtra was 0.42% ahead of the national average of 0.27%. Considering the heterogeneity of HIV epidemic between districts, two districts of Maharashtra – Thane and Mumbai were selected to estimate and project the number of People-Living-with-HIV/AIDS (PLHIV), HIV-prevalence among adults and annual new HIV infections till 2017. Methodology: Inputs in spectrum included demographic data from Census of India since 1980 and sample registration system, programmatic data on ‘Alive and on ART (adult and children)’,‘Mother-Baby pairs under PPTCT’ and ‘High Risk Group (HRG)-size mapping estimates’, surveillance data from various rounds of HSS, National Family Health Survey–III, Integrated Biological and Behavioural Assessment and Behavioural Sentinel Surveillance. Major Findings: Assuming current programmatic interventions in these districts, an estimated decrease of 12% points in Thane and 31% points in Mumbai among new infections in HRGs and migrants is observed from 2011 by 2017. Conclusions: Project also validated decrease in HIV new infection among one of the high risk groups-FSWs using program cohort data since 2012 to 2016. Though there is a decrease in HIV prevalence and new infections in Thane and Mumbai, further decrease is possible if appropriate programme response, strategies and interventions are envisaged for specific target groups based on this evidence. Moreover, evidence need to be validated by other estimation/modelling techniques; and evidence can be generated for other districts of the state, where HIV prevalence is high and reliable data sources are available, to understand the epidemic within the local context.

Keywords: HIV sentinel surveillance, high risk groups, projections, new infections

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Authors: Ioannis Panagiotopoulos, Aristidis Anastasakis, Konstantinos Toutouzas, Ioannis Iakovou, Charalampos Vlachopoulos, Vasilis Voudris, Georgios Tziomalos, Konstantinos Tsioufis, Efstathios Kastritis, Alexandros Briassoulis, Kimon Stamatelopoulos, Alexios Antonopoulos, Paraskevi Exadaktylou, Evanthia Giannoula, Anastasia Katinioti, Maria Kalantzi, Evangelos Leontiadis, Eftychia Smparouni, Ioannis Malakos, Nikolaos Aravanis, Argyrios Doumas, Maria Koutelou

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Introduction: Cardiac amyloidosis from Transthyretin (ATTR-CA) is an infiltrative disease characterized by the deposition of pathological transthyretin complexes in the myocardium. This study describes the characteristics of patients diagnosed with ATTR-CA from 2019 until present at the Nuclear Medicine Department of Onassis Cardiac Surgery Center and AHEPA Hospital. These centers have extensive experience in amyloidosis and modern technological equipment for its diagnosis. Materials and Methods: Records of consecutive patients (N=73) diagnosed with any type of amyloidosis were collected, analyzed, and prospectively followed. The diagnosis of amyloidosis was made using specific myocardial scintigraphy with Tc-99m DPD. Demographic characteristics, including age, gender, marital status, height, and weight, were collected in a database. Clinical characteristics, such as amyloidosis type (ATTR and AL), serum biomarkers (BNP, troponin), electrocardiographic findings, ultrasound findings, NYHA class, aortic valve replacement, device implants, and medication history, were also collected. Some of the most significant results are presented. Results: A total of 73 cases (86% male) were diagnosed with amyloidosis over four years. The mean age at diagnosis was 82 years, and the main symptom was dyspnea. Most patients suffered from ATTR-CA (65 vs. 8 with AL). Out of all the ATTR-CA patients, 61 were diagnosed with wild-type and 2 with two rare mutations. Twenty-eight patients had systemic amyloidosis with extracardiac involvement, and 32 patients had a history of bilateral carpal tunnel syndrome. Four patients had already developed polyneuropathy, and the diagnosis was confirmed by DPD scintigraphy, which is known for its high sensitivity. Among patients with isolated cardiac involvement, only 6 had left ventricular ejection fraction below 40%. The majority of ATTR patients underwent tafamidis treatment immediately after diagnosis. Conclusion: In conclusion, the experiences shared by the two centers and the continuous exchange of information provide valuable insights into the diagnosis and management of cardiac amyloidosis. Clinical suspicion of amyloidosis and early diagnostic approach are crucial, given the availability of non-invasive techniques. Cardiac scintigraphy with DPD can confirm the presence of the disease without the need for a biopsy. The ultimate goal still remains continuous education and awareness of clinical cardiologists so that this systemic and treatable disease can be diagnosed and certified promptly and treatment can begin as soon as possible.

Keywords: amyloidosis, diagnosis, myocardial scintigraphy, Tc-99m DPD, transthyretin

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Authors: Xingyu Gao, Qiang Wu

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Patents play a crucial role in protecting innovation and intellectual property. Early prediction of the impact of artificial intelligence (AI) patents helps researchers and companies allocate resources and make better decisions. Understanding the key factors that influence patent impact can assist researchers in gaining a better understanding of the evolution of AI technology and innovation trends. Therefore, identifying highly impactful patents early and providing support for them holds immeasurable value in accelerating technological progress, reducing research and development costs, and mitigating market positioning risks. Despite the extensive research on AI patents, accurately predicting their early impact remains a challenge. Traditional methods often consider only single factors or simple combinations, failing to comprehensively and accurately reflect the actual impact of patents. This paper utilized the artificial intelligence patent database from the United States Patent and Trademark Office and the Len.org patent retrieval platform to obtain specific information on 35,708 AI patents. Using six machine learning models, namely Multiple Linear Regression, Random Forest Regression, XGBoost Regression, LightGBM Regression, Support Vector Machine Regression, and K-Nearest Neighbors Regression, and using early indicators of patents as features, the paper comprehensively predicted the impact of patents from three aspects: technical, social, and economic. These aspects include the technical leadership of patents, the number of citations they receive, and their shared value. The SHAP (Shapley Additive exPlanations) metric was used to explain the predictions of the best model, quantifying the contribution of each feature to the model's predictions. The experimental results on the AI patent dataset indicate that, for all three target variables, LightGBM regression shows the best predictive performance. Specifically, patent novelty has the greatest impact on predicting the technical impact of patents and has a positive effect. Additionally, the number of owners, the number of backward citations, and the number of independent claims are all crucial and have a positive influence on predicting technical impact. In predicting the social impact of patents, the number of applicants is considered the most critical input variable, but it has a negative impact on social impact. At the same time, the number of independent claims, the number of owners, and the number of backward citations are also important predictive factors, and they have a positive effect on social impact. For predicting the economic impact of patents, the number of independent claims is considered the most important factor and has a positive impact on economic impact. The number of owners, the number of sibling countries or regions, and the size of the extended patent family also have a positive influence on economic impact. The study primarily relies on data from the United States Patent and Trademark Office for artificial intelligence patents. Future research could consider more comprehensive data sources, including artificial intelligence patent data, from a global perspective. While the study takes into account various factors, there may still be other important features not considered. In the future, factors such as patent implementation and market applications may be considered as they could have an impact on the influence of patents.

Keywords: patent influence, interpretable machine learning, predictive models, SHAP

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Authors: Ivelise M. Strozberg, Lucas O. Vale, Maria V. V. Morais

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Expansive soils are characterized by their significant volumetric variations, tending to suffer an increase of this volume when added water in their voids and a decrease of volume when this water is removed. The parameters of resistance (especially the angle of friction, cohesion and specific weight) of expansive or non-expansive soils of the same field present differences, as found in laboratory tests. What is expected is that, through this research, demonstrate that this variation directly affects the results of the calculation of factors of safety for slope stability. The expansibility due to specific clay minerals such as montmorillonites and vermiculites is the most common form of expansion of soils or rocks, causing expansion pressures. These pressures can become an aggravating problem in regions across the globe that, when not previously studied, may present high risks to the enterprise, such as cracks, fissures, movements in structures, breaking of retaining walls, drilling of wells, among others. The study provides results based on analyzes carried out in the Slide 2018 software belonging to the Rocsience group, where the software is a two-dimensional equilibrium slope stability program that calculates the factor of safety or probability of failure of certain surfaces composed of soils or rocks (or both, depending on the situation), - through the methods of: Bishop simplified, Fellenius and Janbu corrected. This research compares the factors of safety of a homogeneous earthfill dam geometry, analysed for operation and end-of-construction situations, having a height of approximately 35 meters, with a slope of 1.5: 1 in the slope downstream and 2: 1 on the upstream slope. As the water level is 32.73m high and the water table is drawn automatically by the Slide program using the finite element method for the operating situation, considering two hypotheses for the use of materials - the first with soils with characteristics of expansion and the second with soils without expansibility. For this purpose, soil samples were collected from the region of São Bento do Una - Pernambuco, Brazil and taken to the soil mechanics laboratory to characterize and determine the percentage of expansibility. There were found 2 types of soils in that area: 1 site of expansive soils (8%) and another with non- expansive ones. Based on the results found, the analysis of the values of factors of safety indicated, both upstream and downstream slopes, the highest values were obtained in the case where there is no presence of materials with expansibility resulting, for one of the situations, values of 1.353 (Fellenius), 1,295 (Janbu corrected) and 1,409 (Bishop simplified). There is a considerable drop in safety factors in cases where soils are potentially expansive, resulting in values for the same situation of 0.859 (Fellenius), 0.809 (Janbu corrected) and 0.842 (Bishop simplified), in the case of higher expansibility (8 %). This shows that the expansibility is a determinant factor in the fall of resistance of soil, determined by the factors of cohesion and angle of friction.

Keywords: dam. slope. software. swelling soil

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Authors: Soha Saad, Jean Bigeon, Florence Ossart, Etienne Sourdille

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Developing better solutions for train rescheduling problems has been drawing the attention of researchers for decades. Most researches in this field deal with minor incidents that affect a large number of trains due to cascading effects. They focus on timetables, rolling stock and crew duties, but do not take into account infrastructure limits. The present work addresses electric infrastructure incidents that limit the power available for train traction, and hence the transportation capacity of the railway system. Rescheduling is needed in order to optimally share the available power among the different trains. We propose a rescheduling process based on dynamic multiphysics railway simulations that include the mechanical and electrical properties of all the system components and calculate physical quantities such as the train speed profiles, voltage along the catenary lines, temperatures, etc. The optimization problem to solve has a large number of continuous and discrete variables, several output constraints due to physical limitations of the system, and a high computation cost. Our approach includes a phase of sensitivity analysis in order to analyze the behavior of the system and help the decision making process and/or more precise optimization. This approach is a quantitative method based on simulation statistics of the dynamic railway system, considering a predefined range of variation of the input parameters. Three important settings are defined. Factor prioritization detects the input variables that contribute the most to the outputs variation. Then, factor fixing allows calibrating the input variables which do not influence the outputs. Lastly, factor mapping is used to study which ranges of input values lead to model realizations that correspond to feasible solutions according to defined criteria or objectives. Generalized Sobol indexes are used for factor prioritization and factor fixing. The approach is tested in the case of a simple railway system, with a nominal traffic running on a single track line. The considered incident is the loss of a feeding power substation, which limits the power available and the train speed. Rescheduling is needed and the variables to be adjusted are the trains departure times, train speed reduction at a given position and the number of trains (cancellation of some trains if needed). The results show that the spacing between train departure times is the most critical variable, contributing to more than 50% of the variation of the model outputs. In addition, we identify the reduced range of variation of this variable which guarantees that the output constraints are respected. Optimal solutions are extracted, according to different potential objectives: minimizing the traveling time, the train delays, the traction energy, etc. Pareto front is also built.

Keywords: optimization, rescheduling, railway system, sensitivity analysis, train timetable

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Authors: Claudio Di Benedetto, Irene Bengo

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The objective of the paper is to study how large European companies develop social business (SB) at the base of the economic pyramid (BoP). BoP markets are defined as the four billions people living with an annual income below $3,260 in local purchasing power. Despite they are heterogeneous in terms of geographic range they present some common characteristics: the presence of significant unmet (social) needs, high level of informal economy and the so-called ‘poverty penalty’. As a result, most people living at BoP are excluded from the value created by the global market economy. But it is worth noting, that BoP population with an aggregate purchasing power of around $5 trillion a year, represent a huge opportunity for companies that want to enhance their long-term profitability perspective. We suggest that in this context, the development of SB is, for companies, an innovative and promising way to satisfy unmet social needs and to experience new forms of value creation. Indeed, SB can be considered a strategic model to develop CSR programs that fully integrate the social dimension into the business to create economic and social value simultaneously. Despite in literature many studies have been conducted on social business, only few have explicitly analyzed such phenomenon from a company perspective and their role in the development of such initiatives remains understudied with fragmented results. To fill this gap the paper analyzes the key characteristics of the social business initiatives developed by European companies at BoP. The study was performed analyzing 1475 European companies participating in the United Nation Global Compact, the world’s leading corporate social responsibility program. Through the analysis of the corporate websites the study identifies companies that actually do SB at BoP. For SB initiatives identified, information were collected according to a framework adapted from the SB model developed by preliminary results show that more than one hundred European companies have already implemented social businesses at BoP accounting for the 6,5% of the total. This percentage increases to 15% if the focus is on companies with more than 10.440 employees. In terms of geographic distribution 80% of companies doing SB at BoP are located in western and southern Europe. The companies more active in promoting SB belong to financial sector (20%), energy sector (17%) and food and beverage sector (12%). In terms of social needs addressed almost 30% of the companies develop SB to provide access to energy and WASH, 25% of companies develop SB to reduce local unemployment or to promote local entrepreneurship and 21% of companies develop SB to promote financial inclusion of poor. In developing SB companies implement different social business configurations ranging from forms of outsourcing to internal development models. The study identifies seven main configurations through which company develops social business and each configuration present distinguishing characteristics respect to the involvement of the company in the management, the resources provided and the benefits achieved. By performing different analysis on data collected the paper provides detailed insights on how European companies develop SB at BoP.

Keywords: base of the economic pyramid, corporate social responsibility, social business, social enterprise

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Authors: C. Fernandez-Plaza, I. Abad, E. Diaz, I. Diaz

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Births occurring before the 37th week of gestation are considered preterm births. A threat of preterm is defined as the beginning of regular uterine contractions, dilation and cervical effacement between 23 and 36 gestation weeks. To author's best knowledge, the factors that determine the beginning of the birth are not completely defined yet. In particular, the incidence of sleep habits on preterm births is weekly studied. The aim of this study is to develop a model to predict the factors affecting premature delivery on pregnancy, based on the above potential risk factors, including those derived from sleep habits and light exposure at night (introduced as 12 variables obtained by a telephone survey using two questionnaires previously used by other authors). Thus, three groups of variables were included in the study (maternal, fetal and sleep habits). The study was approved by Research Ethics Committee of the Principado of Asturias (Spain). An observational, retrospective and descriptive study was performed with 481 births between January 1, 2015 and May 10, 2016 in the University Central Hospital of Asturias (Spain). A statistical analysis using SPSS was carried out to compare qualitative and quantitative variables between preterm and term delivery. Chi-square test qualitative variable and t-test for quantitative variables were applied. Statistically significant differences (p < 0.05) between preterm vs. term births were found for primiparity, multi-parity, kind of conception, place of residence or premature rupture of membranes and interruption during nights. In addition to the statistical analysis, machine learning methods to look for a prediction model were tested. In particular, tree based models were applied as the trade-off between performance and interpretability is especially suitable for this study. C5.0, recursive partitioning, random forest and tree bag models were analysed using caret R-package. Cross validation with 10-folds and parameter tuning to optimize the methods were applied. In addition, different noise reduction methods were applied to the initial data using NoiseFiltersR package. The best performance was obtained by C5.0 method with Accuracy 0.91, Sensitivity 0.93, Specificity 0.89 and Precision 0.91. Some well known preterm birth factors were identified: Cervix Dilation, maternal BMI, Premature rupture of membranes or nuchal translucency analysis in the first trimester. The model also identifies other new factors related to sleep habits such as light through window, bedtime on working days, usage of electronic devices before sleeping from Mondays to Fridays or change of sleeping habits reflected in the number of hours, in the depth of sleep or in the lighting of the room. IF dilation < = 2.95 AND usage of electronic devices before sleeping from Mondays to Friday = YES and change of sleeping habits = YES, then preterm is one of the predicting rules obtained by C5.0. In this work a model for predicting preterm births is developed. It is based on machine learning together with noise reduction techniques. The method maximizing the performance is the one selected. This model shows the influence of variables related to sleep habits in preterm prediction.

Keywords: machine learning, noise reduction, preterm birth, sleep habit

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Authors: Saheed A. Adekola, Mabel A. Charles-Davies, Ridwan A. Adekola

Abstract:

Background: Testosterone is a known androgenic and anabolic steroid, primarily secreted in the testes. It plays an important role in the development of testes and prostate and has a range of biological actions. There is evidence that exposure to mixed chemicals in the workplace leads to the generation of free radicals and inadequate antioxidants leading to oxidative stress, which may serve as an early indicator of a pathophysiologic state. Based on findings, testosterone shows direct antioxidant effects by increasing the activities of antioxidant enzymes like glutathione peroxidase, thus indirectly contributing to antioxidant capacity. Objective: To evaluate the antioxidant role of testosterone as well as the relationship between testosterone and oxidative stress biomarkers in males exposed to mixed chemicals in the automobile repair community. Methods: The study included 43 participants aged 22- 60years exposed to mixed chemicals (EMC) from the automobile repair community. Forty (40) apparently healthy, unexposed, age-matched controls were recruited after informed consent. Demographic, sexual and anthropometric characteristics were obtained from pre-test structured questionnaires using standard methods. Blood samples (10mls) were collected from each subject into plain bottles and sera obtained were used for biochemical analyses. Serum levels of testosterone and luteinizing hormone (LH) were determined by enzyme immunoassay method, EIA (Immunometrics UK.LTD). Levels of total antioxidant capacity (TAC), total plasma peroxide (TPP), Malondialdehyde (MDA), hydrogen peroxide (H2O2), glutathione peroxide (GPX), superoxide dismutase (SOD), glutathione-S-transferase (GST), and reduced glutathione (GSH) were determined using spectrophotometric methods respectively. Results obtained were analyzed using the Student’s t-test and Chi-square test for quantitative variables and qualitative variables respectively. Multiple regression was used to find associations and relationships between the variables. Results: Significant higher concentrations of TPP, MDA, OSI, H2O2 and GST were observed in EMC compared with controls (p < 0.001). Within EMC, significantly higher levels of testosterone, LH and TAC were observed in eugonadic when compared with hypogonadic participants (p < 0.001). Diastolic blood pressure, waist circumference, waist height ratio and waist hip ratio were significantly higher in participants EMC compared with the controls. Sexual history and dietary intake showed that the controls had normal erection during sex and took more vegetables in their diet which may therefore be beneficial. Conclusion: The significantly increased levels of total antioxidant capacity in males exposed to mixed chemicals despite their exposure may probably reflect the contributory antioxidant role testosterone that prevents oxidative stress.

Keywords: mixed chemicals, oxidative stress, antioxidant, hypogonadism testosterone

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Authors: Muhammad Karim Ahmadzai

Abstract:

Floods are natural phenomena and are an integral part of the water cycle. The majority of them are the result of climatic conditions, but are also affected by the geology and geomorphology of the area, topography and hydrology, the water permeability of the soil and the vegetation cover, as well as by all kinds of human activities and structures. However, from the moment that human lives are at risk and significant economic impact is recorded, this natural phenomenon becomes a natural disaster. Flood management is now a key issue at regional and local levels around the world, affecting human lives and activities. The majority of floods are unlikely to be fully predicted, but it is feasible to reduce their risks through appropriate management plans and constructions. The aim of this Case Study is to identify, and map areas of flood risk in the Khinch District of Panjshir Province, Afghanistan specifically in the area of Peshghore, causing numerous damages. The main purpose of this study is to evaluate the contribution of remote sensing technology and Geographic Information Systems (GIS) in assessing the susceptibility of this region to flood events. Panjsher is facing Seasonal floods and human interventions on streams caused floods. The beds of which have been trampled to build houses and hotels or have been converted into roads, are causing flooding after every heavy rainfall. The streams crossing settlements and areas with high touristic development have been intensively modified by humans, as the pressure for real estate development land is growing. In particular, several areas in Khinch are facing a high risk of extensive flood occurrence. This study concentrates on the construction of a flood susceptibility map, of the study area, by combining vulnerability elements, using the Analytical Hierarchy Process/ AHP. The Analytic Hierarchy Process, normally called AHP, is a powerful yet simple method for making decisions. It is commonly used for project prioritization and selection. AHP lets you capture your strategic goals as a set of weighted criteria that you then use to score projects. This method is used to provide weights for each criterion which Contributes to the Flood Event. After processing of a digital elevation model (DEM), important secondary data were extracted, such as the slope map, the flow direction and the flow accumulation. Together with additional thematic information (Landuse and Landcover, topographic wetness index, precipitation, Normalized Difference Vegetation Index, Elevation, River Density, Distance from River, Distance to Road, Slope), these led to the final Flood Risk Map. Finally, according to this map, the Priority Protection Areas and Villages and the structural and nonstructural measures were demonstrated to Minimize the Impacts of Floods on residential and Agricultural areas.

Keywords: flood hazard, flood risk map, flood mitigation measures, AHP analysis

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Authors: Somnath Bhattacharya, Kamal Sharma, Vaibhav Sonkar

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Composite materials emerged in the middle of the 20th century as a promising class of engineering materials providing new prospects for modern technology. Recently, a new class of composite materials known as functionally graded materials (FGMs) has drawn considerable attention of the scientific community. In general, FGMs are defined as composite materials in which the composition or microstructure or both are locally varied so that a certain variation of the local material properties is achieved. This gradual change in composition and microstructure of material is suitable to get gradient of properties and performances. FGMs are synthesized in such a way that they possess continuous spatial variations in volume fractions of their constituents to yield a predetermined composition. These variations lead to the formation of a non-homogeneous macrostructure with continuously varying mechanical and / or thermal properties in one or more than one direction. Lightweight functionally graded composites with high strength to weight and stiffness to weight ratios have been used successfully in aircraft industry and other engineering applications like in electronics industry and in thermal barrier coatings. In the present work, elastic-plastic crack growth problems (using Ramberg-Osgood Model) in an FGM plate under cyclic load has been explored by extended finite element method. Both edge and centre crack problems have been solved by taking additionally holes, inclusions and minor cracks under plane stress conditions. Both soft and hard inclusions have been implemented in the problems. The validity of linear elastic fracture mechanics theory is limited to the brittle materials. A rectangular plate of functionally graded material of length 100 mm and height 200 mm with 100% copper-nickel alloy on left side and 100% ceramic (alumina) on right side is considered in the problem. Exponential gradation in property is imparted in x-direction. A uniform traction of 100 MPa is applied to the top edge of the rectangular domain along y direction. In some problems, domain contains major crack along with minor cracks or / and holes or / and inclusions. Major crack is located the centre of the left edge or the centre of the domain. The discontinuities, such as minor cracks, holes, and inclusions are added either singly or in combination with each other. On the basis of this study, it is found that effect of minor crack in the domain’s failure crack length is minimum whereas soft inclusions have moderate effect and the effect of holes have maximum effect. It is observed that the crack growth is more before the failure in each case when hard inclusions are present in place of soft inclusions.

Keywords: elastic-plastic, fatigue crack, functionally graded materials, extended finite element method (XFEM)

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Authors: Drago Diaz Aleman, Jose Luis Saorin Perez, Cecile Meier, Itahisa Perez Conesa, Jorge De La Torre Cantero

Abstract:

Traditional manufacturing techniques used in artistic contexts compete with highly productive and efficient industrial procedures. The craft techniques and associated business models tend to disappear under the pressure of the appearance of mass-produced products that compete in all niche markets, including those traditionally reserved for the work of art. The surplus value derived from the prestige of the author, the exclusivity of the product or the mastery of the artist, do not seem to be sufficient reasons to preserve this productive model. In the last years, the adoption of open source digital manufacturing technologies in small art workshops can favor their permanence by assuming great advantages such as easy accessibility, low cost, and free modification, adapting to specific needs of each workshop. It is possible to use pieces modeled by computer and made with FDM (Fused Deposition Modeling) 3D printers that use PLA (polylactic acid) in the procedures of artistic casting. Models printed by PLA are limited to approximate minimum sizes of 3 cm, and optimal layer height resolution is 0.1 mm. Due to these limitations, it is not the most suitable technology for artistic casting processes of smaller pieces. An alternative to solve size limitation, are printers from the type (SLS) "selective sintering by laser". And other possibility is a laser hardens, by layers, metal powder and called DMLS (Direct Metal Laser Sintering). However, due to its high cost, it is a technology that is difficult to introduce in small artistic foundries. The low-cost DLP (Digital Light Processing) type printers can offer high resolutions for a reasonable cost (around 0.02 mm on the Z axis and 0.04 mm on the X and Y axes), and can print models with castable resins that allow the subsequent direct artistic casting in precious metals or their adaptation to processes such as electroforming. In this work, the design of a DLP 3D printer is detailed, using backlit LCD screens with ultraviolet light. Its development is totally "open source" and is proposed as a kit made up of electronic components, based on Arduino and easy to access mechanical components in the market. The CAD files of its components can be manufactured in low-cost FDM 3D printers. The result is less than 500 Euros, high resolution and open-design with free access that allows not only its manufacture but also its improvement. In future works, we intend to carry out different comparative analyzes, which allow us to accurately estimate the print quality, as well as the real cost of the artistic works made with it.

Keywords: traditional artistic techniques, DLP 3D printer, artistic casting, electroforming

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Authors: Hossein Aghababaei, Sergio Vitale, Giampaolo Ferraioli

Abstract:

In the context of Synthetic Aperture Radar (SAR) data, polarization is an important source of information for Earth's surface monitoring. SAR Systems are often considered to transmit only one polarization. This constraint leads to either single or dual polarimetric SAR imaging modalities. Single polarimetric systems operate with a fixed single polarization of both transmitted and received electromagnetic (EM) waves, resulting in a single acquisition channel. Dual polarimetric systems, on the other hand, transmit in one fixed polarization and receive in two orthogonal polarizations, resulting in two acquisition channels. Dual polarimetric systems are obviously more informative than single polarimetric systems and are increasingly being used for a variety of remote sensing applications. In dual polarimetric systems, the choice of polarizations for the transmitter and the receiver is open. The choice of circular transmit polarization and coherent dual linear receive polarizations forms a special dual polarimetric system called hybrid polarimetry, which brings the properties of rotational invariance to geometrical orientations of features in the scene and optimizes the design of the radar in terms of reliability, mass, and power constraints. The complete characterization of target scattering, however, requires fully polarimetric data, which can be acquired with systems that transmit two orthogonal polarizations. This adds further complexity to data acquisition and shortens the coverage area or swath of fully polarimetric images compared to the swath of dual or hybrid polarimetric images. The search for solutions to augment dual polarimetric data to full polarimetric data will therefore take advantage of full characterization and exploitation of the backscattered field over a wider coverage with less system complexity. Several methods for reconstructing fully polarimetric images using hybrid polarimetric data can be found in the literature. Although the improvements achieved by the newly investigated and experimented reconstruction techniques are undeniable, the existing methods are, however, mostly based upon model assumptions (especially the assumption of reflectance symmetry), which may limit their reliability and applicability to vegetation and forest scenarios. To overcome the problems of these techniques, this paper proposes a new framework for reconstructing fully polarimetric information from hybrid polarimetric data. The framework uses Deep Learning solutions to augment hybrid polarimetric data without relying on model assumptions. A convolutional neural network (CNN) with a specific architecture and loss function is defined for this augmentation problem by focusing on different scattering properties of the polarimetric data. In particular, the method controls the CNN training process with respect to several characteristic features of polarimetric images defined by the combination of different terms in the cost or loss function. The proposed method is experimentally validated with real data sets and compared with a well-known and standard approach from the literature. From the experiments, the reconstruction performance of the proposed framework is superior to conventional reconstruction methods. The pseudo fully polarimetric data reconstructed by the proposed method also agree well with the actual fully polarimetric images acquired by radar systems, confirming the reliability and efficiency of the proposed method.

Keywords: SAR image, polarimetric SAR image, convolutional neural network, deep learnig, deep neural network

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