Search results for: Lee metric

Authors: Adel A. Ghoneim

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In most existing buildings in hot climate, cooling loads lead to high primary energy consumption and consequently high CO2 emissions. These can be substantially decreased with integrated renewable energy systems. Kuwait is characterized by its dry hot long summer and short warm winter. Kuwait receives annual total radiation more than 5280 MJ/m2 with approximately 3347 h of sunshine. Solar energy systems consist of PV modules and parabolic trough collectors are considered to satisfy electricity consumption, domestic water heating, and cooling loads of an existing building. This paper presents the results of an extensive program of energy conservation and energy generation using integrated photovoltaic (PV) modules and parabolic trough collectors (PTC). The program conducted on an existing institutional building intending to convert it into a Net-Zero Energy Building (NZEB) or near net Zero Energy Building (nNZEB). The program consists of two phases; the first phase is concerned with energy auditing and energy conservation measures at minimum cost and the second phase considers the installation of photovoltaic modules and parabolic trough collectors. The 2-storey building under consideration is the Applied Sciences Department at the College of Technological Studies, Kuwait. Single effect lithium bromide water absorption chillers are implemented to provide air conditioning load to the building. A numerical model is developed to evaluate the performance of parabolic trough collectors in Kuwait climate. Transient simulation program (TRNSYS) is adapted to simulate the performance of different solar system components. In addition, a numerical model is developed to assess the environmental impacts of building integrated renewable energy systems. Results indicate that efficient energy conservation can play an important role in converting the existing buildings into NZEBs as it saves a significant portion of annual energy consumption of the building. The first phase results in an energy conservation of about 28% of the building consumption. In the second phase, the integrated PV completely covers the lighting and equipment loads of the building. On the other hand, parabolic trough collectors of optimum area of 765 m2 can satisfy a significant portion of the cooling load, i.e about73% of the total building cooling load. The annual avoided CO2 emission is evaluated at the optimum conditions to assess the environmental impacts of renewable energy systems. The total annual avoided CO2 emission is about 680 metric ton/year which confirms the environmental impacts of these systems in Kuwait.

Keywords: building integrated renewable systems, Net-Zero energy building, solar fraction, avoided CO2 emission

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Authors: Mekdes Getu Chekol, Biniam Tedros Kahsay, Rahwa Berihu Haile

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The main objective of this study is to investigate the marketing strategy practice in the Case of St. George Brewery Factory in Addis Ababa. One of the core activities in a Business Company to stay in business is having a well-developed marketing strategy. It assessed how the marketing strategies were practiced in the company to achieve its goals aligned with segmentation, target market, positioning, and the marketing mix elements to satisfy customer requirements. Using primary and secondary data, the study is conducted by using both qualitative and quantitative approaches. The primary data was collected through open and closed-ended questionnaires. Considering the size of the population is small, the selection of the respondents was carried out by using a census. The finding shows that the company used all the 4 Ps of the marketing mix elements in its marketing strategies and provided quality products at affordable prices by promoting its products by using high and effective advertising mechanisms. The product availability and accessibility are admirable with the practices of both direct and indirect distribution channels. On the other hand, the company has identified its target customers, and the company’s market segmentation practice is geographical location. Communication effectiveness between the marketing department and other departments is very good. The adjusted R2 model explains 61.6% of the marketing strategy practice variance by product, price, promotion, and place. The remaining 38.4% of variation in the dependent variable was explained by other factors not included in this study. The result reveals that all four independent variables, product, price, promotion, and place, have a positive beta sign, proving that predictor variables have a positive effect on that of the predicting dependent variable marketing strategy practice. Even though the marketing strategies of the company are effectively practiced, there are some problems that the company faces while implementing them. These are infrastructure problems, economic problems, intensive competition in the market, shortage of raw materials, seasonality of consumption, socio-cultural problems, and the time and cost of awareness creation for the customers. Finally, the authors suggest that the company better develop a long-range view and try to implement a more structured approach to attain information about potential customers, competitor’s actions, and market intelligence within the industry. In addition, we recommend conducting the study by increasing the sample size and including different marketing factors.

Keywords: marketing strategy, market segmentation, target marketing, market positioning, marketing mix

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Authors: Xinhai Li, Huidong Tian, Yumin Guo

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Bird migration is fantastic natural phenomenon. In recent years, the use of GPS transmitters has generated a vast amount of data, and the Movebank platform has made these data publicly accessible. For researchers, what they need are data analysis tools. Although there are approximately 90 R packages dedicated to animal movement analysis, the capacity for comprehensive processing of bird migration data remains limited. Hence, we introduce a novel package called migrationR. This package enables the calculation of movement speed, direction, changes in direction, flight duration, daily and annual movement distances. Furthermore, it can pinpoint the starting and ending dates of migration, estimate nest site locations and stopovers, and visualize movement trajectories at various time scales. migrationR distinguishes individuals through NMDS (non-metric multidimensional scaling) coordinates based on movement variables such as speed, flight duration, path tortuosity, and migration timing. A distinctive aspect of the package is the development of a hetero-occurrences species distribution model that takes into account the daily rhythm of individual birds across different landcover types. Habitat use for foraging and roosting differs significantly for many waterbirds. For example, White-naped Cranes at Poyang Lake in China typically forage in croplands and roost in shallow water areas. Both of these occurrence types are of equal importance. Optimal habitats consist of a combination of crop lands and shallow waters, whereas suboptimal habitats lack both, which necessitates birds to fly extensively. With migrationR, we conduct species distribution modeling for foraging and roosting separately and utilize the moving distance between crop lands and shallow water areas as an index of overall habitat suitability. This approach offers a more nuanced understanding of the habitat requirements for migratory birds and enhances our ability to analyze and interpret their movement patterns effectively. The functions of migrationR are demonstrated using our own tracking data of 78 White-naped Crane individuals from 2014 to 2023, comprising over one million valid locations in total. migrationR can be installed from a GitHub repository by executing the following command: remotes::install_github("Xinhai-Li/migrationR").

Keywords: bird migration, hetero-occurrences species distribution model, migrationR, R package, satellite telemetry

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Authors: Charles Mazhazhate

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This study interrogates and analyses the intricate kin- and- kith network patterns of corruption and mismanagement of resources prevalent in public sector supply chains bedeviling the developing economies of Sub-Saharan Africa with particular reference to Zimbabwe. This is forcing governments to resort to harsh fiscal policies that see their citizens paying high taxes against a backdrop of incomes below the poverty datum line, and this negatively affects their quality of life. The corporate world is also affected by the various tax-regime instituted. Mismanagement of resources and corrupt practices are rampant in state-owned enterprises to the extent that institutional policies, procedures, and practices are often flouted for the benefit of a clique of individuals. This interwoven in kith and kin blood human relations in organizations where appointments to critical positions are based on ascribed status. People no longer place value in their systems to make them work thereby violating corporate governance principles. Greediness and ‘unholy friendship connections’ are instrumental in fueling the employment of people who know each other from their discrete backgrounds. Such employments or socio-metric unions are meant to protect those at the top by giving them intelligent information through spying on what other subordinates are doing inside and outside the organization. This practice has led to the underperforming of organizations as those employees with connections and their upper echelons favorites connive to abuse resources for their own benefit. Even if culprits are known, no draconian measures are employed as a deterrence measure. Public value along public sector supply chains is lost. The study used a descriptive case study research design on fifty organizations in Zimbabwe mainly state-owned enterprises. Both qualitative and quantitative instrumentations were used. Both Snowball and random sampling techniques were used. The study found out that in all the fifty SOEs, there were employees in key positions related to top management, with tentacles feeding into the law enforcement agents, judiciary, security systems, and the executive. Such employees in public seem not to know each other with but would be involved in dirty scams and then share the proceeds with top people behind the scenes. The study also established that the same employees do not have the necessary competencies, qualifications, abilities, and capabilities to be in those positions. This culture is now strong that it is difficult to bust. The study recommends recruitment of all employees through an independent employment bureau to ensure strategic fit.

Keywords: corruption, state owned enterprises, strategic fit, public sector supply chains, efficiency

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Authors: Lubna Naz, Munir Ahmad, G. M. Arif

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This study analyzes static and dynamic welfare impacts of food price changes for various landholding groups in Pakistan. The study uses three classifications of land ownership, landless, small landowners and large landowners, for analysis. The study uses Panel Survey, Pakistan Rural Household Survey (PRHS) of Pakistan Institute of Development Economics Islamabad, of rural households from two largest provinces (Sindh and Punjab) of Pakistan. The study uses all three waves (2001, 2004 and 2010) of PRHS. This research work makes three important contributions in literature. First, this study uses Quadratic Almost Ideal Demand System (QUAIDS) to estimate demand functions for eight food groups-cereals, meat, milk and milk products, vegetables, cooking oil, pulses and other food. The study estimates food demand functions with Nonlinear Seemingly Unrelated (NLSUR), and employs Lagrange Multiplier and test on the coefficient of squared expenditure term to determine inclusion of squared expenditure term. Test results support the inclusion of squared expenditure term in the food demand model for each of landholding groups (landless, small landowners and large landowners). This study tests for endogeneity and uses control function for its correction. The problem of observed zero expenditure is dealt with a two-step procedure. Second, it creates low price and high price periods, based on literature review. It uses elasticity coefficients from QUAIDS to analyze static and dynamic welfare effects (first and second order Tylor approximation of expenditure function is used) of food price changes across periods. The study estimates compensation variation (CV), money metric loss from food price changes, for landless, small and large landowners. Third, this study compares the findings on welfare implications of food price changes based on QUAIDS with the earlier research in Pakistan, which used other specification of the demand system. The findings indicate that dynamic welfare impacts of food price changes are lower as compared to static welfare impacts for all landholding groups. The static and dynamic welfare impacts of food price changes are highest for landless. The study suggests that government should extend social security nets to landless poor and categorically to vulnerable landless (without livestock) to redress the short-term impact of food price increase. In addition, the government should stabilize food prices and particularly cereal prices in the long- run.

Keywords: QUAIDS, Lagrange multiplier, NLSUR, and Tylor approximation

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Authors: Misagh Ghobadi, Rich Crane, Karen Hudson-Edwards, Clemens Vinzenz Ullmann

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Lithium is recognized as the critical energy metal of the 21st century, comparable in importance to coal in the 19th century and oil in the 20th century, often termed 'white gold'. Current global demand for lithium, estimated at 0.95-0.98 million metric tons (Mt) of lithium carbonate equivalent (LCE) annually in 2024, is projected to rise to 1.87 Mt by 2027 and 3.06 Mt by 2030. Despite anticipated short-term stability in supply and demand, meeting the forecasted 2030 demand will require the lithium industry to develop an additional capacity of 1.42 Mt of LCE annually, exceeding current planned and ongoing efforts. Brine resources constitute nearly 65% of global lithium reserves, underscoring the importance of exploring lithium recovery from underutilized sources, especially geothermal brines. However, conventional lithium extraction from brine deposits faces challenges due to its time-intensive process, low efficiency (30-50% lithium recovery), unsuitability for low lithium concentrations (<300 mg/l), and notable environmental impacts. Addressing these challenges, direct lithium extraction (DLE) methods have emerged as promising technologies capable of economically extracting lithium even from low-concentration brines (>50 mg/l) with high recovery rates (75-98%). However, most studies (70%) have predominantly focused on synthetic brines instead of native (natural/real), with limited application of these approaches in real-world case studies or industrial settings. This study aims to bridge this gap by investigating a geothermal brine sample collected from a real case study site in the UK. A Mn-based lithium-ion sieve (LIS) adsorbent was synthesized and employed to selectively extract lithium from the sample brine. Adsorbents with a Li:Mn molar ratio of 1:1 demonstrated superior lithium selectivity and adsorption capacity. Furthermore, the pristine Mn-based adsorbent was modified through transition metals doping, resulting in enhanced lithium selectivity and adsorption capacity. The modified adsorbent exhibited a higher separation factor for lithium over major co-existing cations such as Ca, Mg, Na, and K, with separation factors exceeding 200. The adsorption behaviour was well-described by the Langmuir model, indicating monolayer adsorption, and the kinetics followed a pseudo-second-order mechanism, suggesting chemisorption at the solid surface. Thermodynamically, negative ΔG° values and positive ΔH° and ΔS° values were observed, indicating the spontaneity and endothermic nature of the adsorption process.

Keywords: adsorption, critical minerals, DLE, geothermal brines, geochemistry, lithium, lithium-ion sieves

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Authors: Angeliki Gkiouzeli, Christine Rotonda, Elise Eby, Claire Touchet, Marie-Jo Brennstuhl, Cyril Tarquinio

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Resilience is a psychological concept of possible relevance to the development and maintenance of chronic pain (CP). It refers to the ability of individuals to maintain reasonably healthy levels of physical and psychological functioning when exposed to an isolated and potentially highly disruptive event. Extensive research in recent years has supported the importance of this concept in the CP literature. Increased levels of resilience were associated with lower levels of perceived pain intensity and better mental health outcomes in adults with persistent pain. The ongoing project seeks to include the concept of pain-specific resilience in the French literature in order to provide more appropriate measures for assessing and understanding the complexities of CP in the near future. To the best of our knowledge, there is currently no validated version of the pain-specific resilience measure, the Pain Resilience scale (PRS), for French-speaking populations. Therefore, the present work aims to address this gap, firstly by performing a linguistic and cultural translation of the scale into French and secondly by studying the internal validity and reliability of the PRS for French CP populations. The forward-translation-back translation methodology was used to achieve as perfect a cultural and linguistic translation as possible according to the recommendations of the COSMIN (Consensus-based Standards for the selection of health Measurement Instruments) group, and an online survey is currently conducted among a representative sample of the French population suffering from CP. To date, the survey has involved one hundred respondents, with a total target of around three hundred participants at its completion. We further seek to study the metric properties of the French version of the PRS, ''L’Echelle de Résilience à la Douleur spécifique pour les Douleurs Chroniques'' (ERD-DC), in French patients suffering from CP, assessing the level of pain resilience in the context of CP. Finally, we will explore the relationship between the level of pain resilience in the context of CP and other variables of interest commonly assessed in pain research and treatment (i.e., general resilience, self-efficacy, pain catastrophising, and quality of life). This study will provide an overview of the methodology used to address our research objectives. We will also present for the first time the main findings and further discuss the validity of the scale in the field of CP research and pain management. We hope that this tool will provide a better understanding of how CP-specific resilience processes can influence the development and maintenance of this disease. This could ultimately result in better treatment strategies specifically tailored to individual needs, thus leading to reduced healthcare costs and improved patient well-being.

Keywords: chronic pain, pain measure, pain resilience, questionnaire adaptation

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Authors: Mahmood Iqbal, Khalid Nawab, Tachibana Satoshi

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Farmer Field School (FFS) is constantly aims to assist farmers to determine and learn about field ecology and integrated crop management. The study was conducted to examine the change in productivity of tomato crop in the study area; to determine increase in per acre yield of the crop, and find out reduction in per acre input cost. A study of tomato crop was conducted in ten villages namely Jabban, Bijligar Colony, Palonow, Heroshah, Zara Maira, Deghar Ghar, Sidra Jour, Anar Thangi, Miangano Korona and Wartair of district Malakand. From each village 15 respondents were selected randomly on the basis of identical allocation making sample size of 150 respondents. The research was based on primary as well as secondary data. Primary data was collected from farmers while secondary data were taken from Agriculture Extension Department Dargai, District Malakand. Interview schedule was planned and each farmer was interviewed personally. The study was based on comparison of cost, yield and income of tomato before and after FFS. Paired t-test and Statistical Package for Social Sciences (SPSS) was used for analysis; outcome of the study show that integrated pest management project has brought a positive change in the attitude of farmers of the project area through FFS approach. In district Malakand 66.0% of the respondents were between the age group of 31-50 years, 11.3% of respondents had primary level of education, 12.7% of middle level, 28.7% metric level, 3.3% of intermediate level and 2.0% of graduate level of education while 42.0% of respondents were illiterate and have no education. Average land holding size of farmers was 6.47 acres, cost of seed, crop protection from insect pest and crop protection from diseases was reduced by Rs. 210.67, Rs. 2584.43 and Rs. 3044.16 respectively, the cost of fertilizers and cost of farm yard manure was increased by Rs.1548.87 and Rs. 1151.40 respectively while tomato yield was increased by 1585.03 kg/acre from 7663.87 to 9248.90 kg/acre. The role of FFS initiate by integrated pest management project through department of agriculture extension for the development of agriculture was worth mentioning. It has brought enhancement in crop yield of tomato and their income through FFS approach. On the basis of results of the research studies, integrated pest management project should spread their developmental activities for maximum participation of the complete rural masses through participatory FFS approach.

Keywords: agriculture, Farmers field schools, extension education, tomato

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Authors: Oubelkacem Yacine, El Bast Hassan, El Bakkali Abdelmajid, Lamhasni Taibi, Ettakni Mahmoud, Ait Lyazidi Saadia, Haddad Mustapha, Ben-Ncer Abdelouahed, El Ferrane Mohammed, Boufarra Abdelkrim

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The present work relates to an on-site and completely non-invasive investigation of one of the most famous west Mediterranean Torah Scroll housed at the National Library of the Kingdom of Morocco. The scroll is 26 m long and consists of 143 parchment sheets of 59 cm x 19 cm, exhibiting only black writings; it is of unknown age. The artifact has been restored by the curator staff of the library. The investigation exploring separately the parchment support and the writing black ink aims at: i) the examination of the parchment conservation/degradation state, ii) the identification of the black ink and iii) the identification of the parchment handcrafting materials. For this purpose, the analyses have been based on combining all of elemental XRF and structural Raman, ATR-FT Infrared Red and Fiber Optical Reflectance spectroscopies, in addition to chroma-metric and pH measurements. pH measurements showing values around 6.5 are in concordance with the absence of any visual corrosion related to the parchment acidity. However, on the basis of the relative intensities and frequency shift of amid I (AI) and amid II (AII) vibrational bands of the collagen, ATR-FTIR spectra revealed diffuse hydrolysis and gelatinization of the parchment writing support; diffuse and non-homogeny degradation by gelatinization has been also confirmed by the IG gelatinization index deduced from the NIR bands on the FOR spectra. This IG index, defined as the ratio I (6860 cm-1) / I (6685 cm-1), ranges in the interval 0.98 – 1 and highlights collagen degradation at the molecular level. Sequentially Shifted Excitation Raman measurements (SSERS) crossed to X-ray fluorescence (XRF) ones on the black writings revealed that the black ink used is an iron-copper gall one, while FOR spectra are typical of pure metal gall inks. These later reflectance measurements exclude, thus, any intentional addition of carbon black to the ink recipe. Moreover, no lead white had been used while pre-drawing the writing lines. On another side, ATR-FTIR measurements highlighted the presence of oxalates as ink degradation products. Considering the parchment handcrafting, the combination of XRF and ATR-FTIR measurements led to the assumption that this writing support had been prepared according to ancient Middle East practices; the parchment infrared fingerprint seems identical to that of the Dead Sea scroll. The present multi-technical analyses are the first ones performed on an ancient Judaic written parchment of Morocco; it is under furthering. The investigation will be extended to other parchments belonging to the Jewish Cultural Heritage Museum of Morocco in Casablanca.

Keywords: torah scroll, parchment, black ink, non-invasive analyses, XRF/ATR-FTIR/RAMAN/FORS

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Authors: Vinay A. Sharma, Shiva Prasad H. C.

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The initial stages of any project are often observed to be in a mixed set of conditions. Setting up the project is a tough task, but taking the initial decisions is rather not complex, as some of the critical factors are yet to be introduced into the scenario. These simple initial decisions potentially shape the timeline and subsequent events that might later be plotted on it. Proceeding towards the solution for a problem is the primary objective in the initial stages. The optimization in the solutions can come later, and hence, the resources deployed towards attaining the solution are higher than what they would have been in the optimized versions. A ‘logic’ that counters the problem is essentially the core of the desired solution. Thus, if the problem is solved, the deployment of resources has led to the required logic being attained. As the project proceeds along, the individuals working on the project face fresh challenges as a team and are better accustomed to their surroundings. The developed, optimized solutions are then considered for implementation, as the individuals are now experienced, and know better of the consequences and causes of possible failure, and thus integrate the adequate tolerances wherever required. Furthermore, as the team graduates in terms of strength, acquires prodigious knowledge, and begins its efficient transfer, the individuals in charge of the project along with the managers focus more on the optimized solutions rather than the traditional ones to minimize the required resources. Hence, as time progresses, the authorities prioritize attainment of the required logic, at a lower amount of dedicated resources. For empirical analysis of the stated theory, leaders and key figures in organizations are surveyed for their ideas on appropriate logic required for tackling a problem. Key-pointers spotted in successfully implemented solutions are noted from the analysis of the responses and a metric for measuring logic is developed. A graph is plotted with the quantifiable logic on the Y-axis, and the dedicated resources for the solutions to various problems on the X-axis. The dedicated resources are plotted over time, and hence the X-axis is also a measure of time. In the initial stages of the project, the graph is rather linear, as the required logic will be attained, but the consumed resources are also high. With time, the authorities begin focusing on optimized solutions, since the logic attained through them is higher, but the resources deployed are comparatively lower. Hence, the difference between consecutive plotted ‘resources’ reduces and as a result, the slope of the graph gradually increases. On an overview, the graph takes a parabolic shape (beginning on the origin), as with each resource investment, ideally, the difference keeps on decreasing, and the logic attained through the solution keeps increasing. Even if the resource investment is higher, the managers and authorities, ideally make sure that the investment is being made on a proportionally high logic for a larger problem, that is, ideally the slope of the graph increases with the plotting of each point.

Keywords: decision-making, leadership, logic, strategic management

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Authors: Peter Carter

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Comprehensive climate change indicators and trends inform the state of the climate (system) with respect to present and future climate change scenarios and the urgency of mitigation and adaptation. With data records now going back for many decades, indicator trends can complement model projections. They are provided as datasets by several climate monitoring centers, reviewed by state of the climate reports, and documented by the IPCC assessments. Up-to-date indicators are provided here. Rates of change are instructive, as are extremes. The indicators include greenhouse gas (GHG) emissions (natural and synthetic), cumulative CO2 emissions, atmospheric GHG concentrations (including CO2 equivalent), stratospheric ozone, surface ozone, radiative forcing, global average temperature increase, land temperature increase, zonal temperature increases, carbon sinks, soil moisture, sea surface temperature, ocean heat content, ocean acidification, ocean oxygen, glacier mass, Arctic temperature, Arctic sea ice (extent and volume), northern hemisphere snow cover, permafrost indices, Arctic GHG emissions, ice sheet mass, sea level rise, and stratospheric and surface ozone. Global warming is not the most reliable single metric for the climate state. Radiative forcing, atmospheric CO2 equivalent, and ocean heat content are more reliable. Global warming does not provide future commitment, whereas atmospheric CO2 equivalent does. Cumulative carbon is used for estimating carbon budgets. The forcing of aerosols is briefly addressed. Indicator interactions are included. In particular, indicators can provide insight into several crucial global warming amplifying feedback loops, which are explained. All indicators are increasing (adversely), most as fast as ever and some faster. One particularly pressing indicator is rapidly increasing global atmospheric methane. In this respect, methane emissions and sources are covered in more detail. In their application, indicators used in assessing safe planetary boundaries are included. Indicators are considered with respect to recent published papers on possible catastrophic climate change and climate system tipping thresholds. They are climate-change-policy relevant. In particular, relevant policies include the 2015 Paris Agreement on “holding the increase in the global average temperature to well below 2°C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5°C above pre-industrial levels” and the 1992 UN Framework Convention on Climate change, which has “stabilization of greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system.”

Keywords: climate change, climate change indicators, climate change trends, climate system change interactions

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Authors: Sophie-Bo Heinkel, Andrea Rechenburg, Thomas Kistemann

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The current status of wellbeing and life satisfaction of subsistence farmers in a wetland in Uganda and the contributing role of place attachment has been assessed. The aim of this study is to shed light on environmental factors supporting wellbeing in a wetland setting. Furthermore, it has been assessed, how the emotional bonding to the wetland as ‘place’ influences the peoples’ wellbeing and life satisfaction. The results shed light on the human-environment-relationship. A survey was carried out in three communities in urban and rural areas in a wetland basin in Uganda. A sample (n=235) provided information about the attachment to the wetland, the participants’ relation to the place of their residence and their emotional wellbeing. The Wellbeing Index (WHO-5) was assessed as well as the Perceived Stress Scale (PSS-10) and Rosenberg’s Self-Esteem scale (RSE). Furthermore, the Satisfaction With Life Scale (SWLS) was applied as well as the Place Attachment Inventory (PAI), which consists of the two intertwined dimensions of place identity and place dependence. Beside this, binary indicators as ‘feeling save’ and ‘feeling comfortable’ and ‘enjoying to live at the place of residence’ have been assessed. A bivariate correlation analysis revealed a high interconnectivity between all metric scales. Especially, the subscale ‘place identity’ showed significances with all other scales. A cluster analysis revealed three groups, which differed in the perception of place-related indicators and their attachment to the wetland as well as the status of wellbeing. First, a cluster whose majority is dissatisfied with their lives, but mainly had a good status of emotional well-being. This group does not feel attached to the wetland and lives in a town. Comparably less persons of this group feel safe and comfortable at their place of residence. In the second cluster, persons feel highly attached to the wetland and identify with it. This group was characterized by the high number of persons preferring their current place of residence and do not consider moving. All persons feel well and satisfied with their lives. The third group of persons is mainly living in rural areas and feels highly attached to the wetland. They are satisfied with their lives, but only a small minority is in a good emotional state of wellbeing. The emotional attachment to a place influences life satisfaction and, indirectly, the emotional wellbeing. In the present study it could be shown that subsistence farmers are attached to the wetland, as it is the source of their livelihood. While those living in areas with a good infrastructure are less dependent on the wetland and, therefore, less attached to. This feeling also was mirrored in the perception of a place as being safe and comfortable. The identification with a place is crucial for the feeling of being at “home”. Subsistence farmers feel attached to the ecosystem, but they also might be exposed to environmental and social stressors influencing their short-term emotional wellbeing. The provision of place identity is an ecosystem service provided by wetlands, which supports the status of wellbeing in human beings.

Keywords: mental health, positive environments, quality of life, wellbeing

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Authors: Maria Debora Braga, Luigi Riso, Maria Grazia Zoia

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Risk-based approaches to asset allocation are portfolio construction methods that do not rely on the input of expected returns for the asset classes in the investment universe and only use risk information. They include the Minimum Variance Strategy (MV strategy), the traditional (volatility-based) Risk Parity Strategy (SRP strategy), the Most Diversified Portfolio Strategy (MDP strategy) and, for many, the Equally Weighted Strategy (EW strategy). All the mentioned approaches were based on portfolio volatility as a reference risk measure but in 2023, the Kurtosis-based Risk Parity strategy (KRP strategy) and the Minimum Kurtosis strategy (MK strategy) were introduced. Understandably, they used the fourth root of the portfolio-fourth moment as a proxy for portfolio kurtosis to work with a homogeneous function of degree one. This paper contributes mainly theoretically and methodologically to the framework of risk-based asset allocation approaches with two steps forward. First, a new and more flexible objective function considering a linear combination (with positive coefficients that sum to one) of portfolio volatility and portfolio kurtosis is used to alternatively serve a risk minimization goal or a homogeneous risk distribution goal. Hence, the new basic idea consists in extending the achievement of typical risk-based approaches’ goals to a combined risk measure. To give the rationale behind operating with such a risk measure, it is worth remembering that volatility and kurtosis are expressions of uncertainty, to be read as dispersion of returns around the mean and that both preserve adherence to a symmetric framework and consideration for the entire returns distribution as well, but also that they differ from each other in that the former captures the “normal” / “ordinary” dispersion of returns, while the latter is able to catch the huge dispersion. Therefore, the combined risk metric that uses two individual metrics focused on the same phenomena but differently sensitive to its intensity allows the asset manager to express, in the context of an objective function by varying the “relevance coefficient” associated with the individual metrics, alternatively, a wide set of plausible investment goals for the portfolio construction process while serving investors differently concerned with tail risk and traditional risk. Since this is the first study that also implements risk-based approaches using a combined risk measure, it becomes of fundamental importance to investigate the portfolio effects triggered by this innovation. The paper also offers a second contribution. Until the recent advent of the MK strategy and the KRP strategy, efforts to highlight interesting properties of risk-based approaches were inevitably directed towards the traditional MV strategy and SRP strategy. Previous literature established an increasing order in terms of portfolio volatility, starting from the MV strategy, through the SRP strategy, arriving at the EQ strategy and provided the mathematical proof for the “equalization effect” concerning marginal risks when the MV strategy is considered, and concerning risk contributions when the SRP strategy is considered. Regarding the validity of similar conclusions when referring to the MK strategy and KRP strategy, the development of a theoretical demonstration is still pending. This paper fills this gap.

Keywords: risk parity, portfolio kurtosis, risk diversification, asset allocation

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Authors: Shraddha Chaudhary, Raksha Agarwal, Niladri Chatterjee

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This work demonstrates a web crawler-based generalized end-to-end open domain Question Answering (QA) system. An efficient QA system requires a significant amount of domain knowledge to answer any question with the aim to find an exact and correct answer in the form of a number, a noun, a short phrase, or a brief piece of text for the user's questions. Analysis of the question, searching the relevant document, and choosing an answer are three important steps in a QA system. This work uses a web scraper (Beautiful Soup) to extract K-documents from the web. The value of K can be calibrated on the basis of a trade-off between time and accuracy. This is followed by a passage ranking process using the MS-Marco dataset trained on 500K queries to extract the most relevant text passage, to shorten the lengthy documents. Further, a QA system is used to extract the answers from the shortened documents based on the query and return the top 3 answers. For evaluation of such systems, accuracy is judged by the exact match between predicted answers and gold answers. But automatic evaluation methods fail due to the linguistic ambiguities inherent in the questions. Moreover, reference answers are often not exhaustive or are out of date. Hence correct answers predicted by the system are often judged incorrect according to the automated metrics. One such scenario arises from the original Google Natural Question (GNQ) dataset which was collected and made available in the year 2016. Use of any such dataset proves to be inefficient with respect to any questions that have time-varying answers. For illustration, if the query is where will be the next Olympics? Gold Answer for the above query as given in the GNQ dataset is “Tokyo”. Since the dataset was collected in the year 2016, and the next Olympics after 2016 were in 2020 that was in Tokyo which is absolutely correct. But if the same question is asked in 2022 then the answer is “Paris, 2024”. Consequently, any evaluation based on the GNQ dataset will be incorrect. Such erroneous predictions are usually given to human evaluators for further validation which is quite expensive and time-consuming. To address this erroneous evaluation, the present work proposes an automated approach for evaluating time-dependent question-answer pairs. In particular, it proposes a metric using the current timestamp along with top-n predicted answers from a given QA system. To test the proposed approach GNQ dataset has been used and the system achieved an accuracy of 78% for a test dataset comprising 100 QA pairs. This test data was automatically extracted using an analysis-based approach from 10K QA pairs of the GNQ dataset. The results obtained are encouraging. The proposed technique appears to have the possibility of developing into a useful scheme for gathering precise, reliable, and specific information in a real-time and efficient manner. Our subsequent experiments will be guided towards establishing the efficacy of the above system for a larger set of time-dependent QA pairs.

Keywords: web-based information retrieval, open domain question answering system, time-varying QA, QA evaluation

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Authors: Maria C. Almario, Pam Remer, Jeff Resse, Kathy Moran, Linda Theander Adam

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The identification of victims of human trafficking and consequential service provision is characterized by a significant disconnection between the estimated prevalence of this issue and the number of cases identified. This poses as tremendous problem for human rights advocates as it prevents data collection, information sharing, allocation of resources and opportunities for international dialogues. The current paper introduces the Inclusive Human Trafficking Checklist (IHTC) as a measurement methodology with theoretical underpinnings derived from dialectic theory. The presence of human trafficking in a person’s life is conceptualized as a dynamic and dialectic interaction between vulnerability and exploitation. The current papers explores the operationalization of exploitation and vulnerability, evaluates the metric qualities of the instrument, evaluates whether there are differences in assessment based on the participant’s profession, level of knowledge, and training, and assesses if users of the instrument perceive it as useful. A total of 201 participants were asked to rate three vignettes predetermined by experts to qualify as a either human trafficking case or not. The participants were placed in three conditions: business as usual, utilization of the IHTC with and without training. The results revealed a statistically significant level of agreement between the expert’s diagnostic and the application of the IHTC with an improvement of 40% on identification when compared with the business as usual condition While there was an improvement in identification in the group with training, the difference was found to have a small effect size. Participants who utilized the IHTC showed an increased ability to identify elements of identity-based vulnerabilities as well as elements of fraud, which according to the results, are distinctive variables in cases of human trafficking. In terms of the perceived utility, the results revealed higher mean scores for the groups utilizing the IHTC when compared to the business as usual condition. These findings suggest that the IHTC improves appropriate identification of cases and that it is perceived as a useful instrument. The application of the IHTC as a multidisciplinary instrumentation that can be utilized in legal and human services settings is discussed as a pivotal piece of helping victims restore their sense of dignity, and advocate for legal, physical and psychological reparations. It is noteworthy that this study was conducted with a sample in the United States and later re-tested in Colombia. The implications of the instrument for treatment conceptualization and intervention in human trafficking cases are discussed as opportunities for enhancement of victim well-being, restoration engagement and activism. With the idea that what is personal is also political, we believe that the careful observation and data collection in specific cases can inform new areas of human rights activism.

Keywords: exploitation, human trafficking, measurement, vulnerability, screening

Procedia PDF Downloads 330

Authors: Khethiwe Mtshali, Zamantungwa T. H. Khumalo, Stanford Kwenda, Ismail Arshad, Oriel M. M. Thekisoe

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Ecologically, the gut, mammary glands and bloodstream consist of distinct microbial communities of commensals, mutualists and pathogens, forming a complex ecosystem of niches. The by-products derived from these body sites i.e. faeces, milk and blood, respectively, have many uses in rural communities where they aid in the facilitation of day-to-day household activities and occasional rituals. Thus, although livestock rearing plays a vital role in the sustenance of the livelihoods of rural communities, it may serve as a potent reservoir of different pathogenic organisms that could have devastating health and economic implications. This study aimed to simultaneously explore the microbial profiles of corresponding faecal, milk and blood samples from lactating cows using 16S rRNA metagenomic sequencing. Bacterial communities were inferred through the Divisive Amplicon Denoising Algorithm 2 (DADA2) pipeline coupled with SILVA database v138. All downstream analyses were performed in R v3.6.1. Alpha-diversity metrics showed significant differences between faeces and blood, faeces and milk, but did not vary significantly between blood and milk (Kruskal-Wallis, P < 0.05). Beta-diversity metrics on Principal Coordinate Analysis (PCoA) and Non-Metric Dimensional Scaling (NMDS) clustered samples by type, suggesting that microbial communities of the studied niches are significantly different (PERMANOVA, P < 0.05). A number of taxa were significantly differentially abundant (DA) between groups based on the Wald test implemented in the DESeq2 package (Padj < 0.01). The majority of the DA taxa were significantly enriched in faeces than in milk and blood, except for the genus Anaplasma, which was significantly enriched in blood and was, in turn, the most abundant taxon overall. A total of 30 phyla, 74 classes, 156 orders, 243 families and 408 genera were obtained from the overall analysis. The most abundant phyla obtained between the three body sites were Firmicutes, Bacteroidota, and Proteobacteria. A total of 58 genus-level taxa were simultaneously detected between the sample groups, while bacterial signatures of at least 8 of these occurred concurrently in corresponding faeces, milk and blood samples from the same group of animals constituting a pool. The important taxa identified in this study could be categorized into four potentially pathogenic clusters: i) arthropod-borne; ii) food-borne and zoonotic; iii) mastitogenic and; iv) metritic and abortigenic. This study provides insight into the microbial composition of bovine faeces, milk, and blood and its extent of overlapping. It further highlights the potential risk of disease occurrence and transmission between the animals and the inhabitants of the sampled rural community, pertaining to their unsanitary practices associated with the use of cattle by-products.

Keywords: microbial profiling, 16S rRNA, NGS, feces, milk, blood, lactating cows, small-scale farmers

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Authors: Nayana T. Nivangune, Vivek V. Ranade, Ashutosh A. Kelkar

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Organic carbamates are versatile compounds widely employed as pesticides, fungicides, herbicides, dyes, pharmaceuticals, cosmetics and in the synthesis of polyurethanes. Carbamates can be easily transformed into isocyanates by thermal cracking. Isocyantes are used as precursors for manufacturing agrochemicals, adhesives and polyurethane elastomers. Manufacture of polyurethane foams is a major application of aromatic ioscyanates and in 2007 the global consumption of polyurethane was about 12 million metric tons/year and the average annual growth rate was about 5%. Presently Isocyanates/carbamates are manufactured by phosgene based process. However, because of high toxicity of phoegene and formation of waste products in large quantity; there is a need to develop alternative and safer process for the synthesis of isocyanates/carbamates. Recently many alternative processes have been investigated and carbamate synthesis by methoxycarbonylation of aromatic amines using dimethyl carbonate (DMC) as a green reagent has emerged as promising alternative route. In this reaction methanol is formed as a by-product, which can be converted to DMC either by oxidative carbonylation of methanol or by reacting with urea. Thus, the route based on DMC has a potential to provide atom efficient and safer route for the synthesis of carbamates from DMC and amines. Lot of work is being carried out on the development of catalysts for this reaction and homogeneous zinc salts were found to be good catalysts for the reaction. However, catalyst/product separation is challenging with these catalysts. There are few reports on the use of supported Zn catalysts; however, deactivation of the catalyst is the major problem with these catalysts. We wish to report here methoxycarbonylation of aniline to methylphenylcarbamate (MPC) using amino acid complexes of Zn as highly active and selective catalysts. The catalysts were characterized by XRD, IR, solid state NMR and XPS analysis. Methoxycarbonylation of aniline was carried out at 170 °C using 2.5 wt% of the catalyst to achieve >98% conversion of aniline with 97-99% selectivity to MPC as the product. Formation of N-methylated products in small quantity (1-2%) was also observed. Optimization of the reaction conditions was carried out using zinc-proline complex as the catalyst. Selectivity was strongly dependent on the temperature and aniline:DMC ratio used. At lower aniline:DMC ratio and at higher temperature, selectivity to MPC decreased (85-89% respectively) with the formation of N-methylaniline (NMA), N-methyl methylphenylcarbamate (MMPC) and N,N-dimethyl aniline (NNDMA) as by-products. Best results (98% aniline conversion with 99% selectivity to MPC in 4 h) were observed at 170oC and aniline:DMC ratio of 1:20. Catalyst stability was verified by carrying out recycle experiment. Methoxycarbonylation preceded smoothly with various amine derivatives indicating versatility of the catalyst. The catalyst is inexpensive and can be easily prepared from zinc salt and naturally occurring amino acids. The results are important and provide environmentally benign route for MPC synthesis with high activity and selectivity.

Keywords: aniline, heterogeneous catalyst, methoxycarbonylation, methylphenyl carbamate

Procedia PDF Downloads 274

Authors: Te Kīpa Kēpa B. Morgan, Oliver W. Mcmillan, Dylan N. Taute, Tumanako N. Fa'aui

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Epistemologies are ways of knowing. Indigenous Peoples are aware that they do not perceive and experience the world in the same way as others. So it is important when empowering Indigenous epistemologies, such as that of the New Zealand Māori, to also be able to represent a scientific understanding within the same analysis. A geothermal development assessment tool has been developed by adapting the Mauri Model Decision Making Framework. Mauri is a metric that is capable of representing the change in the life-supporting capacity of things and collections of things. The Mauri Model is a method of grouping mauri indicators as dimension averages in order to allow holistic assessment and also to conduct sensitivity analyses for the effect of worldview bias. R-shiny is the coding platform used for this Vision Mātauranga research which has created an expert decision support tool (DST) that combines a stakeholder assessment of worldview bias with an impact assessment of mauri-based indicators to determine the sustainability of proposed geothermal development. The initial intention was to develop guidelines for quantifying mātauranga Māori impacts related to geothermal resources. To do this, three typical scenarios were considered: a resource owner wishing to assess the potential for new geothermal development; another party wishing to assess the environmental and cultural impacts of the proposed development; an assessment that focuses on the holistic sustainability of the resource, including its surface features. Indicator sets and measurement thresholds were developed that are considered necessary considerations for each assessment context and these have been grouped to represent four mauri dimensions that mirror the four well-being criteria used for resource management in Aotearoa, New Zealand. Two case studies have been conducted to test the DST suitability for quantifying mātauranga Māori and other biophysical factors related to a geothermal system. This involved estimating mauri0meter values for physical features such as temperature, flow rate, frequency, colour, and developing indicators to also quantify qualitative observations about the geothermal system made by Māori. A retrospective analysis has then been conducted to verify different understandings of the geothermal system. The case studies found that the expert DST is useful for geothermal development assessment, especially where hapū (indigenous sub-tribal grouping) are conflicted regarding the benefits and disadvantages of their’ and others’ geothermal developments. These results have been supplemented with evaluations for the cumulative impacts of geothermal developments experienced by different parties using integration techniques applied to the time history curve of the expert DST worldview bias weighted plotted against the mauri0meter score. Cumulative impacts represent the change in resilience or potential of geothermal systems, which directly assists with the holistic interpretation of change from an Indigenous Peoples’ perspective.

Keywords: decision support tool, holistic geothermal assessment, indigenous knowledge, mauri model decision-making framework

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Authors: Shankaran Sitarama

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New Product Development (NPD) is invariably a team effort and involves effective teamwork. NPD team has members from different disciplines coming together and working through the different phases all the way from conceptual design phase till the production and product roll out. Creativity and Innovation are some of the key factors of successful NPD. Team members going through the different phases of NPD interact and work closely yet challenge each other during the design phases to brainstorm on ideas and later converge to work together. These two traits require the teams to have a divergent and a convergent thinking simultaneously. There needs to be a good balance. The team dynamics invariably result in conflicts among team members. While some amount of conflict (ideational conflict) is desirable in NPD teams to be creative as a group, relational conflicts (or discords among members) could be detrimental to teamwork. Team communication truly reflect these tensions and team dynamics. In this research, team communication (emails) between the members of the NPD teams is considered for analysis. The email communication is processed through a semantic analysis algorithm (LSA) to analyze the content of communication and a semantic similarity analysis to arrive at a social network graph that depicts the communication amongst team members based on the content of communication. The amount of communication (content and not frequency of communication) defines the interaction strength between the members. Social network adjacency matrix is thus obtained for the team. Standard social network analysis techniques based on the Adjacency Matrix (AM) and Dichotomized Adjacency Matrix (DAM) based on network density yield network graphs and network metrics like centrality. The social network graphs are then rendered for visual representation using a Metric Multi-Dimensional Scaling (MMDS) algorithm for node placements and arcs connecting the nodes (representing team members) are drawn. The distance of the nodes in the placement represents the tie-strength between the members. Stronger tie-strengths render nodes closer. Overall visual representation of the social network graph provides a clear picture of the team’s interactions. This research reveals four distinct patterns of team interaction that are clearly identifiable in the visual representation of the social network graph and have a clearly defined computational scheme. The four computational patterns of team interaction defined are Central Member Pattern (CMP), Subgroup and Aloof member Pattern (SAP), Isolate Member Pattern (IMP), and Pendant Member Pattern (PMP). Each of these patterns has a team dynamics implication in terms of the conflict level in the team. For instance, Isolate member pattern, clearly points to a near break-down in communication with the member and hence a possible high conflict level, whereas the subgroup or aloof member pattern points to a non-uniform information flow in the team and some moderate level of conflict. These pattern classifications of teams are then compared and correlated to the real level of conflict in the teams as indicated by the team members through an elaborate self-evaluation, team reflection, feedback form and results show a good correlation.

Keywords: team dynamics, team communication, team interactions, social network analysis, sna, new product development, latent semantic analysis, LSA, NPD teams

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Authors: Eric Pla Erra, Mariana Jimenez Martinez

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While aggregated loads at a community level tend to be easier to predict, individual household load forecasting present more challenges with higher volatility and uncertainty. Furthermore, the drastic changes that our behavior patterns have suffered due to the COVID-19 pandemic have modified our daily electrical consumption curves and, therefore, further complicated the forecasting methods used to predict short-term electric load. Load forecasting is vital for the smooth and optimized planning and operation of our electric grids, but it also plays a crucial role for individual domestic consumers that rely on a HEMS (Home Energy Management Systems) to optimize their energy usage through self-generation, storage, or smart appliances management. An accurate forecasting leads to higher energy savings and overall energy efficiency of the household when paired with a proper HEMS. In order to study how COVID-19 has affected the accuracy of forecasting methods, an evaluation of the performance of a state-of-the-art LGBM (Light Gradient Boosting Model) will be conducted during the transition between pre-pandemic and lockdowns periods, considering day-ahead electric load forecasting. LGBM improves the capabilities of standard Decision Tree models in both speed and reduction of memory consumption, but it still offers a high accuracy. Even though LGBM has complex non-linear modelling capabilities, it has proven to be a competitive method under challenging forecasting scenarios such as short series, heterogeneous series, or data patterns with minimal prior knowledge. An adaptation of the LGBM model – called “resilient LGBM” – will be also tested, incorporating a concept drift detection technique for time series analysis, with the purpose to evaluate its capabilities to improve the model’s accuracy during extreme events such as COVID-19 lockdowns. The results for the LGBM and resilient LGBM will be compared using standard RMSE (Root Mean Squared Error) as the main performance metric. The models’ performance will be evaluated over a set of real households’ hourly electricity consumption data measured before and during the COVID-19 pandemic. All households are located in the city of Barcelona, Spain, and present different consumption profiles. This study is carried out under the ComMit-20 project, financed by AGAUR (Agència de Gestiód’AjutsUniversitaris), which aims to determine the short and long-term impacts of the COVID-19 pandemic on building energy consumption, incrementing the resilience of electrical systems through the use of tools such as HEMS and artificial intelligence.

Keywords: concept drift, forecasting, home energy management system (HEMS), light gradient boosting model (LGBM)

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Authors: Hyunjoo Park, Taehyun Kim, Taehyun Kim

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Human life, activity, and culture depend on the wider environment. Cities offer economic opportunities for goods and services, but cannot exist in environments without food, energy, and water supply. Technological innovation in energy supply and transport speeds up the expansion of urban areas and the physical separation from agricultural land. As a result, division of urban agricultural areas causes more energy demand for food and goods transport between the regions. As the energy resources are leaking all over the world, the impact on the environment crossing the boundaries of cities is also growing. While advances in energy and other technologies can reduce the environmental impact of consumption, there is still a gap between energy supply and demand by current technology, even in technically advanced countries. Therefore, reducing energy demand is more realistic than relying solely on the development of technology for sustainable development. The purpose of this study is to introduce the application of carbon footprint assessment in fields of urban planning and geography. In urban studies, carbon footprint has been assessed at different geographical scales, such as nation, city, region, household, and individual. Carbon footprint assessment for a nation and a city is available by using national or city level statistics of energy consumption categories. By means of carbon footprint calculation, it is possible to compare the ecological capacity and deficit among nations and cities. Carbon footprint also offers great insight on the geographical distribution of carbon intensity at a regional level in the agricultural field. The study shows the background of carbon footprint applications in urban planning and geography by case studies such as figuring out sustainable land-use measures in urban planning and geography. For micro level, footprint quiz or survey can be adapted to measure household and individual carbon footprint. For example, first case study collected carbon footprint data from the survey measuring home energy use and travel behavior of 2,064 households in eight cities in Gyeonggi-do, Korea. Second case study analyzed the effects of the net and gross population densities on carbon footprint of residents at an intra-urban scale in the capital city of Seoul, Korea. In this study, the individual carbon footprint of residents was calculated by converting the carbon intensities of home and travel fossil fuel use of respondents to the unit of metric ton of carbon dioxide (tCO₂) by multiplying the conversion factors equivalent to the carbon intensities of each energy source, such as electricity, natural gas, and gasoline. Carbon footprint is an important concept not only for reducing climate change but also for sustainable development. As seen in case studies carbon footprint may be measured and applied in various spatial units, including but not limited to countries and regions. These examples may provide new perspectives on carbon footprint application in planning and geography. In addition, additional concerns for consumption of food, goods, and services can be included in carbon footprint calculation in the area of urban planning and geography.

Keywords: carbon footprint, case study, geography, urban planning

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Authors: Muhammad Ahnaf Zahin, Yaw Adu-Gyamfi

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Bus transit is a crucial component of transportation networks, especially in urban areas. Any intelligent transportation system must have accurate real-time information on bus travel times since it minimizes waiting times for passengers at different stations along a route, improves service reliability, and significantly optimizes travel patterns. Bus agencies must enhance the quality of their information service to serve their passengers better and draw in more travelers since people waiting at bus stops are frequently anxious about when the bus will arrive at their starting point and when it will reach their destination. For solving this issue, different models have been developed for predicting bus travel times recently, but most of them are focused on smaller road networks due to their relatively subpar performance in high-density urban areas on a vast network. This paper develops a deep learning-based architecture using a single-step multi-station forecasting approach to predict average bus travel times for numerous routes, stops, and trips on a large-scale network using heterogeneous bus transit data collected from the GTFS database. Over one week, data was gathered from multiple bus routes in Saint Louis, Missouri. In this study, Gated Recurrent Unit (GRU) neural network was followed to predict the mean vehicle travel times for different hours of the day for multiple stations along multiple routes. Historical time steps and prediction horizon were set up to 5 and 1, respectively, which means that five hours of historical average travel time data were used to predict average travel time for the following hour. The spatial and temporal information and the historical average travel times were captured from the dataset for model input parameters. As adjacency matrices for the spatial input parameters, the station distances and sequence numbers were used, and the time of day (hour) was considered for the temporal inputs. Other inputs, including volatility information such as standard deviation and variance of journey durations, were also included in the model to make it more robust. The model's performance was evaluated based on a metric called mean absolute percentage error (MAPE). The observed prediction errors for various routes, trips, and stations remained consistent throughout the day. The results showed that the developed model could predict travel times more accurately during peak traffic hours, having a MAPE of around 14%, and performed less accurately during the latter part of the day. In the context of a complicated transportation network in high-density urban areas, the model showed its applicability for real-time travel time prediction of public transportation and ensured the high quality of the predictions generated by the model.

Keywords: gated recurrent unit, mean absolute percentage error, single-step forecasting, travel time prediction.

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Authors: Santos Maza, Enzo Aldoradin, Carlos Pariona, Eliud Arpi, Maria Rosales

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The objective of this study was to investigate the effect of flaying then bleeding anchovy (Engraulis ringens) immersed within a sodium citrate solution. Anchovy is a pelagic fish that readily deteriorates due to its high content of polyunsaturated fatty acids. As such, within the Peruvian food industry, the shelf life of frozen anchovy is explicitly 6 months, this short duration imparts a barrier to use for direct consumption human. Thus, almost all capture of anchovy by the fishing industry is eventually used in the production of fishmeal. We offer this an alternative to its typical production process in order to increase shelf life. In the present study, 100 kg of anchovies were captured and immediately mixed with ice on ship, maintaining a high quality sensory metric (e.g., with color blue in back) while still arriving for processing less than 2 h after capture. Anchovies with fat content of 3% were immediately flayed (i.e., reducing subcutaneous fat), beheaded, gutted and bled (i.e., removing hemoglobin) by immersion in water (Control) or in a solution of 2.5% sodium citrate (treatment), then subsequently frozen at -30 °C for 8 h in 2 kg batches. Subsequent glazing and storage at -25 °C for 14 months completed the experiments parameters. The peroxide value (PV), acidity (A), fatty acid profile (FAP), thiobarbituric acid reactive substances (TBARS), heme iron (HI), pH and sensory attributes of the samples were evaluated monthly. The results of the PV, TBARS, A, pH and sensory analyses displayed significant differences (p<0.05) between treatment and control sample; where the sodium citrate treated samples showed increased preservation features. Specifically, at the beginning of the study, flayed, beheaded, gutted and bled anchovies displayed low content of fat (1.5%) with moderate amount of PV, A and TBARS, and were not rejected by sensory analysis. HI values and FAP displayed varying behavior, however, results of HI did not reveal a decreasing trend. This result is indicative of the fact that levels of iron were maintained as HI and did not convert into no heme iron, which is known to be the primary catalyst of lipid oxidation in fish. According to the FAP results, the major quantity of fatty acid was of polyunsaturated fatty acid (PFA) followed by saturated fatty acid (SFA) and then monounsaturated fatty acid (MFA). According to sensory analysis, the shelf life of flayed, beheaded and gutted anchovy (control and treatment) was 14 months. This shelf life was reached at laboratory level because high quality anchovies were used and immediately flayed, beheaded, gutted, bled and frozen. Therefore, it is possible to maintain the shelf life of anchovies for a long time. Overall, this method displayed a large increase in shelf life relative to that commonly seen for anchovies in this industry. However, these results should be extrapolated at industrial scales to propose better processing conditions and improve the quality of anchovy for direct human consumption.

Keywords: citrate sodium solution, heme iron, polyunsaturated fatty acids, shelf life of frozen anchovy

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Authors: Miranda E. Karban

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A distinctive pattern of occipital squama convexity, known as the occipital bun or chignon, has traditionally been considered a derived Neandertal trait. However, some early modern and extant Homo sapiens share similar occipital bone morphology, showing pronounced internal and external occipital squama curvature and paralambdoidal flattening. It has been posited that these morphological patterns are homologous in the two groups, but this claim remains disputed. Many developmental hypotheses have been proposed, including assertions that the chignon represents a developmental response to a long and narrow cranial vault, a narrow or flexed basicranium, or a prognathic face. These claims, however, remain to be metrically quantified in a large subadult sample, and little is known about the feature’s developmental, functional, or evolutionary significance. This study assesses patterns of chignon development and covariation in a comparative sample of extant human growth study cephalograms. Cephalograms from a total of 549 European-derived North American subjects (286 male, 263 female) were scored on a 5-stage ranking system of chignon prominence. Occipital squama shape was found to exist along a continuum, with 34 subjects (6.19%) possessing defined chignons, and 54 subjects (9.84%) possessing very little occipital squama convexity. From this larger sample, those subjects represented by a complete radiographic series were selected for metric analysis. Measurements were collected from lateral and posteroanterior (PA) cephalograms of 26 subjects (16 male, 10 female), each represented at 3 longitudinal age groups. Age group 1 (range: 3.0-6.0 years) includes subjects during a period of rapid brain growth. Age group 2 (range: 8.0-9.5 years) includes subjects during a stage in which brain growth has largely ceased, but cranial and facial development continues. Age group 3 (range: 15.9-20.4 years) includes subjects at their adult stage. A total of 16 landmarks and 153 sliding semi-landmarks were digitized at each age point, and geometric morphometric analyses, including relative warps analysis and two-block partial least squares analysis, were conducted to study covariation patterns between midsagittal occipital bone shape and other aspects of craniofacial morphology. A convex occipital squama was found to covary significantly with a low, elongated neurocranial vault, and this pattern was found to exist from the youngest age group. Other tested patterns of covariation, including cranial and basicranial breadth, basicranial angle, midcoronal cranial vault shape, and facial prognathism, were not found to be significant at any age group. These results suggest that the chignon, at least in this sample, should not be considered an independent feature, but rather the result of developmental interactions relating to neurocranial elongation. While more work must be done to quantify chignon morphology in fossil subadults, this study finds no evidence to disprove the developmental homology of the feature in modern humans and Neandertals.

Keywords: chignon, craniofacial covariation, human cranial development, longitudinal growth study, occipital bun

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Authors: Dhanuj M. Gandikota

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Sensor-derived three-dimensional (3D) point clouds of trees are invaluable in remote sensing analysis for the accurate measurement of key structural metrics, bio-inventory values, spatial planning/visualization, and ecological modeling. Machine learning (ML) holds the potential in addressing the restrictive tradeoffs in cost, spatial coverage, resolution, and information gain that exist in current point cloud sensing methods. Terrestrial laser scanning (TLS) remains the highest fidelity source of both canopy and below-canopy structural features, but usage is limited in both coverage and cost, requiring manual deployment to map out large, forested areas. While aerial laser scanning (ALS) remains a reliable avenue of LIDAR active remote sensing, ALS is also cost-restrictive in deployment methods. Space-borne photogrammetry from high-resolution satellite constellations is an avenue of passive remote sensing with promising viability in research for the accurate construction of vegetation 3-D point clouds. It provides both the lowest comparative cost and the largest spatial coverage across remote sensing methods. However, both space-borne photogrammetry and ALS demonstrate technical limitations in the capture of valuable below-canopy point cloud data. Looking to minimize these tradeoffs, we explored a class of powerful ML algorithms called Deep Learning (DL) that show promise in recent research on 3-D point cloud reconstruction and interpolation. Our research details the efficacy of applying these DL techniques to reconstruct accurate below-canopy point clouds from space-borne and aerial remote sensing through learned patterns of tree species fractal symmetry properties and the supplementation of locally sourced bio-inventory metrics. From our dataset, consisting of tree point clouds obtained from TLS, we deconstructed the point clouds of each tree into those that would be obtained through ALS and satellite photogrammetry of varying resolutions. We fed this ALS/satellite point cloud dataset, along with the simulated local bio-inventory metrics, into the DL point cloud reconstruction architectures to generate the full 3-D tree point clouds (the truth values are denoted by the full TLS tree point clouds containing the below-canopy information). Point cloud reconstruction accuracy was validated both through the measurement of error from the original TLS point clouds as well as the error of extraction of key structural metrics, such as crown base height, diameter above root crown, and leaf/wood volume. The results of this research additionally demonstrate the supplemental performance gain of using minimum locally sourced bio-inventory metric information as an input in ML systems to reach specified accuracy thresholds of tree point cloud reconstruction. This research provides insight into methods for the rapid, cost-effective, and accurate construction of below-canopy tree 3-D point clouds, as well as the supported potential of ML and DL to learn complex, unmodeled patterns of fractal tree growth symmetry.

Keywords: deep learning, machine learning, satellite, photogrammetry, aerial laser scanning, terrestrial laser scanning, point cloud, fractal symmetry

Procedia PDF Downloads 102

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

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

Keywords: automated, biomechanics, team-sports, sprint

Procedia PDF Downloads 119

Authors: Francesco Pandolfi, Georgios Baltzopoulos, Iunio Iervolino

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The impact of extreme winds on industrial assets and the built environment is gaining increasing attention from stakeholders, including the corporate insurance industry. This has led to a progressively more in-depth study of building vulnerability and fragility to wind. Wind vulnerability models are used in probabilistic risk assessment to relate a loss metric to an intensity measure of the natural event, usually a gust or a mean wind speed. In fact, vulnerability models can be integrated with the wind hazard, which consists of associating a probability to each intensity level in a time interval (e.g., by means of return periods) to provide an assessment of future losses due to extreme wind. This has also given impulse to the world- and regional-scale wind hazard studies.Another approach often adopted for the probabilistic description of building vulnerability to the wind is the use of fragility functions, which provide the conditional probability that selected building components will exceed certain damage states, given wind intensity. In fact, in wind engineering literature, it is more common to find structural system- or component-level fragility functions rather than wind vulnerability models for an entire building. Loss assessment based on component fragilities requires some logical combination rules that define the building’s damage state given the damage state of each component and the availability of a consequence model that provides the losses associated with each damage state. When risk calculations are based on numerical simulation of a structure’s behavior during extreme wind scenarios, the interaction of component fragilities is intertwined with the computational procedure. However, simulation-based approaches are usually computationally demanding and case-specific. In this context, the present work introduces the ExtReMe wind risk assESsment prototype Software, ERMESS, which is being developed at the University of Naples Federico II. ERMESS is a wind risk assessment tool for insurance applications to industrial facilities, collecting a wide assortment of available wind vulnerability models and fragility functions to facilitate their incorporation into risk calculations based on in-built or user-defined wind hazard data. This software implements an alternative method for building-specific risk assessment based on existing component-level fragility functions and on a number of simplifying assumptions for their interactions. The applicability of this alternative procedure is explored by means of an illustrative proof-of-concept example, which considers four main building components, namely: the roof covering, roof structure, envelope wall and envelope openings. The application shows that, despite the simplifying assumptions, the procedure can yield risk evaluations that are comparable to those obtained via more rigorous building-level simulation-based methods, at least in the considered example. The advantage of this approach is shown to lie in the fact that a database of building component fragility curves can be put to use for the development of new wind vulnerability models to cover building typologies not yet adequately covered by existing works and whose rigorous development is usually beyond the budget of portfolio-related industrial applications.

Keywords: component wind fragility, probabilistic risk assessment, vulnerability model, wind-induced losses

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

Abstract:

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

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

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Authors: Xingyu Gao, Qiang Wu

Abstract:

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: Erica Cochran Hameen, Bobuchi Ken-Opurum, Mounica Guturu

Abstract:

The United Stated (U.S.) populous spends the majority of their time indoors in spaces where building codes and voluntary sustainability standards provide clear Indoor Environmental Quality (IEQ) metrics. The existing sustainable building standards and codes are aimed towards improving IEQ, health of occupants, and reducing the negative impacts of buildings on the environment. While they address the post-occupancy stage of buildings, there are fewer standards on the pre-occupancy stage thereby placing a large labor population in environments much less regulated. Construction personnel are often exposed to a variety of uncomfortable and unhealthy elements while on construction sites, primarily thermal, visual, acoustic, and air quality related. Construction site power generators, equipment, and machinery generate on average 9 decibels (dBA) above the U.S. OSHA regulations, creating uncomfortable noise levels. Research has shown that frequent exposure to high noise levels leads to chronic physiological issues and increases noise induced stress, yet beyond OSHA no other metric focuses directly on the impacts of noise on contractors’ well-being. Research has also associated natural light with higher productivity and attention span, and lower cases of fatigue in construction workers. However, daylight is not always available as construction workers often perform tasks in cramped spaces, dark areas, or at nighttime. In these instances, the use of artificial light is necessary, yet lighting standards for use during lengthy tasks and arduous activities is not specified. Additionally, ambient air, contaminants, and material off-gassing expelled at construction sites are one of the causes of serious health effects in construction workers. Coupled with extreme hot and cold temperatures for different climate zones, health and productivity can be seriously compromised. This research evaluates the impact of existing green building metrics on construction and risk management, by analyzing two codes and nine standards including LEED, WELL, and BREAM. These metrics were chosen based on the relevance to the U.S. construction industry. This research determined that less than 20% of the sustainability context within the standards and codes (texts) are related to the pre-occupancy building sector. The research also investigated the impact of construction personnel’s health and well-being on construction management through two surveys of project managers and on-site contractors’ perception of their work environment on productivity. To fully understand the risks of limited Environmental and Health Quality metrics for contractors (EHQ) this research evaluated the connection between EHQ factors such as inefficient lighting, on construction workers and investigated the correlation between various site coping strategies for comfort and productivity. Outcomes from this research are three-pronged. The first includes fostering a discussion about the existing conditions of EQH elements, i.e. thermal, lighting, ergonomic, acoustic, and air quality on the construction labor force. The second identifies gaps in sustainability standards and codes during the pre-occupancy stage of building construction from ground-breaking to substantial completion. The third identifies opportunities for improvements and mitigation strategies to improve EQH such as increased monitoring of effects on productivity and health of contractors and increased inclusion of the pre-occupancy stage in green building standards.

Keywords: construction contractors, health and well-being, environmental quality, risk management

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