Search results for: forest fire

Authors: Haya Salah, Srinivas Sharan

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Healthcare facilities use appointment systems to schedule their appointments and to manage access to their medical services. With the growing demand for outpatient care, it is now imperative to manage physician's time effectively. However, high variation in consultation duration affects the clinical scheduler's ability to estimate the appointment duration and allocate provider time appropriately. Underestimating consultation times can lead to physician's burnout, misdiagnosis, and patient dissatisfaction. On the other hand, appointment durations that are longer than required lead to doctor idle time and fewer patient visits. Therefore, a good estimation of consultation duration has the potential to improve timely access to care, resource utilization, quality of care, and patient satisfaction. Although the literature on factors influencing consultation length abound, little work has done to predict it using based data-driven approaches. Therefore, this study aims to predict consultation duration using supervised machine learning algorithms (ML), which predicts an outcome variable (e.g., consultation) based on potential features that influence the outcome. In particular, ML algorithms learn from a historical dataset without explicitly being programmed and uncover the relationship between the features and outcome variable. A subset of the data used in this study has been obtained from the electronic medical records (EMR) of four different outpatient clinics located in central Pennsylvania, USA. Also, publicly available information on doctor's characteristics such as gender and experience has been extracted from online sources. This research develops three popular ML algorithms (deep learning, random forest, gradient boosting machine) to predict the treatment time required for a patient and conducts a comparative analysis of these algorithms with respect to predictive performance. The findings of this study indicate that ML algorithms have the potential to predict the provider service time with superior accuracy. While the current approach of experience-based appointment duration estimation adopted by the clinic resulted in a mean absolute percentage error of 25.8%, the Deep learning algorithm developed in this study yielded the best performance with a MAPE of 12.24%, followed by gradient boosting machine (13.26%) and random forests (14.71%). Besides, this research also identified the critical variables affecting consultation duration to be patient type (new vs. established), doctor's experience, zip code, appointment day, and doctor's specialty. Moreover, several practical insights are obtained based on the comparative analysis of the ML algorithms. The machine learning approach presented in this study can serve as a decision support tool and could be integrated into the appointment system for effectively managing patient scheduling.

Keywords: clinical decision support system, machine learning algorithms, patient scheduling, prediction models, provider service time

Procedia PDF Downloads 94

Authors: Michael Josef Schwerer

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Background: Analyzing any aircraft accident is mandatory based on the regulations of the International Civil Aviation Organization and the respective country’s criminal prosecution authorities. Legal medicine investigations are unavoidable when fatalities involve the flight crew or when doubts arise concerning the pilot’s aeromedical health status before the event. As a result of frequently tremendous blunt and sharp force trauma along with the impact of the aircraft to the ground, consecutive blast or fire exposition of the occupants or putrefaction of the dead bodies in cases of delayed recovery, relevant findings can be masked or destroyed and therefor being inaccessible in standard pathology practice comprising just forensic autopsy and histopathology. Such cases are of considerable risk of remaining unsolved without legal consequences for those responsible. Further, no lessons can be drawn from these scenarios to improve flight safety and prevent future mishaps. Aims and Methods: To learn from previously unsolved aircraft accidents, re-evaluations of the investigation files and modern molecular pathology studies were performed. Genetic testing involved predominantly PCR-based analysis of gene regulation, studying DNA promotor methylations, RNA transcription and posttranscriptional regulation. In addition, the presence or absence of infective agents, particularly DNA- and RNA-viruses, was studied. Technical adjustments of molecular genetic procedures when working with archived sample material were necessary. Standards for the proper interpretation of the respective findings had to be settled. Results and Discussion: Additional molecular genetic testing significantly contributes to the quality of forensic pathology assessment in aviation mishaps. Previously undetected cardiotropic viruses potentially explain e.g., a pilot’s sudden incapacitation resulting from cardiac failure or myocardial arrhythmia. In contrast, negative results for infective agents participate in ruling out concerns about an accident pilot’s fitness to fly and the aeromedical examiner’s precedent decision to issue him or her an aeromedical certificate. Care must be taken in the interpretation of genetic testing for pre-existing diseases such as hypertrophic cardiomyopathy or ischemic heart disease. Molecular markers such as mRNAs or miRNAs, which can establish these diagnoses in clinical patients, might be misleading in-flight crew members because of adaptive changes in their tissues resulting from repeated mild hypoxia during flight, for instance. Military pilots especially demonstrate significant physiological adjustments to their somatic burdens in flight, such as cardiocirculatory stress and air combat maneuvers. Their non-pathogenic alterations in gene regulation and expression will likely be misinterpreted for genuine disease by inexperienced investigators. Conclusions: The growing influence of molecular pathology on legal medicine practice has found its way into aircraft accident investigation. As appropriate quality standards for laboratory work and data interpretation are provided, forensic genetic testing supports the medico-legal analysis of aviation mishaps and potentially reduces the number of unsolved events in the future.

Keywords: aviation medicine, aircraft accident investigation, forensic pathology, molecular pathology

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Authors: Alireza Barari, Ahmad Abdi

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Introduction: Plyometric training (PT) is popular among individuals involved in dynamic sports, and is executed with a goal to improve muscular performance. Cytokines are considered as immunoregulatory molecules for regulation of immune function and other body responses. In addition, the pro-inflammatory cytokines, TNF-α andIL-1β, have been reported to be increased during and after exercises. If some of the cytokines which cause responses such as inflammation of cells in skeletal muscles, with manipulating of training program or optimizing nutrition, it can be avoided or limited from those injuries caused by cytokines release. Its shows that mistletoe extracts show immune-modulating effects. Materials and methods: present study was to investigate the effect of six weeks PT with or without mistletoe supplementation (MS)(10 mg/kg) on cytokine responses and performance in male basketball players. This study is semi-experimental. Statistic society of this study was basketball player’s male students of Mahmoud Abad city. Statistic samples are concluded of 32 basketball players with an age range of 14–17 years was selected from randomly. Selection of samples in four groups of 8 individuals Participants were randomly assigned to either an experimental group (E, n=16) that performed plyometric exercises with (n=8) or without (n=8) MS, or a control group that rested (C, n=16) with (n=8) or without (n=8) MS. Plants were collected in June from the Mazandaran forest in north of Iran. Then they dried in exposure to air without any exposition to sunlight, on a clean textile. For better drying the plants were high and down until they lost their water. Each subject consumed 10 mg/kg/day of extract for six weeks of intervention. Pre and post-testing was performed in the afternoon of the same day. Blood samples (10 ml) were collected from the intermediate cubital vein of the subjects. Serum concentration of IL-1β and TNF-α were measured by ELISA method. Data analysis was performed using pretest to posttest changes that assessed by t-test for paired samples. After the last plyometric training program, the second blood samples were in the next day. Group differences at baseline were evaluated using One-way ANOVA (post-hock Tukey) test is used for analysis and comparison of three group’s variables. Results: PT with or without MS improved the one repetition maximum leg and chest press, Sargeant test and power in RAST (P < 0.05). However there were no statistically significant differences between groups in Vo2max measures (P > 0.05). PT resulted in a significant increase in plasma IL-1β concentration from 1.08±0.4 mg/ml in pre-training to 1.68±0.18 mg/ml in post-training (P=0.006). While the MS significantly decreased the training-induced increment of IL-1β (P=0.007). In contrast, neither PT nor MS had any effect on TNF-α levels (P > 0.05). Discussion: The results of this investigation indicate that PT improved muscular performance and increases the IL-1β concentration. Increasing of IL-1β after exercise in damaged skeletal muscle has shown of the role of this cytokine in inflammation processes and damaged skeletal muscle repair. However mistletoe supplementation ameliorates the increment of IL-1β levels, indicating the beneficial effect of mistletoe on immune response following plyometric training.

Keywords: mistletoe supplementation, training, IL-1β, TNF-α

Procedia PDF Downloads 628

Authors: Bassey O. Bassey

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Benchmarking has progressively become entrenched as a requisite activity for process improvement and enhancing service delivery at petroleum jetties and terminals, most especially during tanker discharge operations at the ship – shore interface, as avoidable delays result in extra operating costs, non-productive time, high demurrage payments and ultimate product scarcity. The jetty and terminal in focus had been operational for 3 and 8 years respectively, with proper operational and logistic records maintained to evaluate their progress over time in order to plan and implement modifications and review of procedures for greater technical and economic efficiency. Regular and emergency staff meetings were held on a team, departmental and company-wide basis to progressively address major challenges that were encountered during each operation. The process and outcome of the resultant collectively planned changes carried out within the past two years forms the basis of this paper, which mirrors the initiatives effected to enhance operational and maintenance excellence at the affected facilities. Operational modifications included a second cargo receipt line designated for gasoline, product loss control at jetty and shore ends, enhanced product recovery and quality control, and revival of terminal–jetty backloading operations. Logistic improvements were the incorporation of an internal logistics firm and shipping agency, fast tracking of discharge procedures for tankers, optimization of tank vessel selection process, and third party product receipt and throughput. Maintenance excellence was achieved through construction of two new lay barges and refurbishment of the existing one; revamping of existing booster pump and purchasing of a modern one as reserve capacity; extension of Phase 1 of the jetty to accommodate two vessels and construction of Phase 2 for two more vessels; regular inspection, draining, drying and replacement of cargo hoses; corrosion management program for all process facilities; and an improved, properly planned and documented maintenance culture. Safety, environmental and security compliance were enhanced by installing state-of-the-art fire fighting facilities and equipment, seawater intake line construction as backup for borehole at the terminal, remediation of the shoreline and marine structures, modern spill containment equipment, improved housekeeping and accident prevention practices, and installation of hi-technology security enhancements, among others. The end result has been observed over the past two years to include improved tanker turnaround time, higher turnover on product sales, consistent product availability, greater indigenous human capacity utilisation by way of direct hires and contracts, as well as customer loyalty. The lessons learnt from this exercise would, therefore, serve as a model to be adapted by other operators of similar facilities, contractors, academics and consultants in a bid to deliver greater sustainability and profitability of operations at the ship – shore interface to this strategic industry.

Keywords: benchmarking, optimisation, petroleum jetty, petroleum terminal

Procedia PDF Downloads 337

Authors: Naima Umar

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Mid-1960’s has been landmark in the history of Indian agriculture. It was in 1966-67 when a New Agricultural Strategy was put into practice to tide over chronic shortages of food grains in the country. This strategy adopted was the use High-Yielding Varieties (HYV) of seeds (wheat and rice), which was popularly known as the Green Revolution. This phase of agricultural development has saved us from hunger and starvation and made the peasants more confident than ever before, but it has also created a number of socio-economic and environmental implications such as the reduction in area under forest, salinization, waterlogging, soil erosion, lowering of underground water table, soil, water and air pollution, decline in soil fertility, silting of rivers and emergence of several diseases and health hazards. The state of Uttar Pradesh in the north is bounded by the country of Nepal, the states of Uttrakhand on the northwest, Haryana on the west, Rajasthan on the southwest, Madhya Pradesh on the south and southwest, and Bihar on the east. It is situated between 23052´N and 31028´N latitudes and 7703´ and 84039´E longitudes. It is the fifth largest state of the country in terms of area, and first in terms of population. Forming the part of Ganga plain the state is crossed by a number of rivers which originate from the snowy peaks of Himalayas. The fertile plain of the Ganga has led to a high concentration of population with high density and the dominance of agriculture as an economic activity. Present paper highlights the negative impact of new agricultural technology on health of the people and environment and will attempt to find out factors which are responsible for these implications. Karl Pearson’s Correlation coefficient technique has been applied by selecting 1 dependent variable (i.e. Productivity Index) and some independent variables which may impact crop productivity in the districts of the state. These variables have categorized as: X1 (Cropping Intensity), X2 (Net irrigated area), X3 (Canal Irrigated area), X4 (Tube-well Irrigated area), X5 (Irrigated area by other sources), X6 (Consumption of chemical fertilizers (NPK) Kg. /ha.), X7 (Number of wooden plough), X8 (Number of iron plough), X9 (Number of harrows and cultivators), X10 (Number of thresher machines), X11(Number of sprayers), X12 (Number of sowing instruments), X13 (Number of tractors) and X14 (Consumption of insecticides and pesticides (in Kg. /000 ha.). The entire data during 2001-2005 and 2006- 2010 have been taken and 5 years average value is taken into consideration, based on secondary sources obtained from various government, organizations, master plan report, economic abstracts, district census handbooks and village and town directories etc,. put on a standard computer programmed SPSS and the results obtained have been properly tabulated.

Keywords: agricultural technology, environmental implications, health hazards, socio-economic problems

Procedia PDF Downloads 282

Authors: T. Lakshmi Priya

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With the widening definition of heritage, the challenges of heritage management has become more complex . Today heritage not only includes significant monuments but comprises historic areas / sites, historic cities, cultural landscapes, and living heritage sites. There is a need for a comprehensive understanding of the values associated with these heritage resources, which will enable their protection and management. These diverse cultural resources are managed by multiple agencies having their own way of operating in the heritage sites. An Integrated approach to management of these cultural resources ensures its sustainability for the future generation. This paper outlines the importance of an integrated approach for the management and protection of complex heritage sites in India by examining four case studies. The methodology for this study is based on secondary research and primary surveys conducted during the preparation of the conservation management plansfor the various sites. The primary survey included basic documentation, inventorying, and community surveys. Red Fort located in the city of Delhi is one of the most significant forts built in 1639 by the Mughal Emperor Shahjahan. This fort is a national icon and stands testimony to the various historical events . It is on the ramparts of Red Fort that the national flag was unfurled on 15th August 1947, when India became independent, which continues even today. Management of this complex fort necessitated the need for an integrated approach, where in the needs of the official and non official stakeholders were addressed. The understanding of the inherent values and significance of this site was arrived through a systematic methodology of inventorying and mapping of information. Hampi, located in southern part of India, is a living heritage site inscribed in the World Heritage list in 1986. The site comprises of settlements, built heritage structures, traditional water systems, forest, agricultural fields and the remains of the metropolis of the 16th century Vijayanagar empire. As Hampi is a living heritage site having traditional systems of management and practices, the aim has been to include these practices in the current management so that there is continuity in belief, thought and practice. The existing national, regional and local planning instruments have been examined and the local concerns have been addressed.A comprehensive understanding of the site, achieved through an integrated model, is being translated to an action plan which safeguards the inherent values of the site. This paper also examines the case of the 20th century heritage building of National Archives of India, Delhi and protection of a 12th century Tomb of Sultan Ghari located in south Delhi. A comprehensive understanding of the site, lead to the delineation of the Archaeological Park of Sultan Ghari, in the current Master Plan for Delhi, for the protection of the tomb and the settlement around it. Through this study it is concluded that the approach of Integrated Conservation has enabled decision making that sustains the values of these complex heritage sites in Indian context.

Keywords: conservation, integrated, management, approach

Procedia PDF Downloads 65

Authors: Ronahlee Asuncion

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The nature of their work and employment relationship make domestic workers easy prey to abuse, maltreatment, and exploitation. Considering their plight, this research was conceptualized and examined the: a) level of awareness of Filipino domestic workers on occupational safety and health (OSH); b) their issues/problems/concerns on OSH; c) their intervention strategies at work to address OSH related issues/problems/concerns; d) issues/problems/concerns of government, employers, and non-government organizations with regard to implementation of OSH to Filipino domestic workers; e) the role of government, employers and non-government organizations to help Filipino domestic workers address OSH related issues/problems/concerns; and f) the necessary policy amendments/initiatives/programs to address OSH related issues/problems/concerns of Filipino domestic workers. The study conducted a survey using non-probability sampling, two focus group discussions, two group interviews, and fourteen face-to-face interviews. These were further supplemented with an email correspondence to a key informant based in another country. Books, journals, magazines, and relevant websites further substantiated and enriched data of the research. Findings of the study point to the fact that domestic workers have low level of awareness on OSH because of poor information drive, fragmented implementation of the Domestic Workers Act, inactive campaign at the barangay level, weakened advocacy for domestic workers, absence of law on OSH for domestic workers, and generally low safety culture in the country among others. Filipino domestic workers suffer from insufficient rest, long hours of work, heavy workload, occupational stress, poor accommodation, insufficient hours of sleep, deprivation of day off, accidents and injuries such as cuts, burns, slipping, stumbling, electrical grounding, and fire, verbal, physical and sexual abuses, lack of medical assistance, none provision of personal protective equipment (PPE), absence of knowledge on the proper way of lifting, working at heights, and insufficient food provision. They also suffer from psychological problems because of separation from one’s family, limited mobility in the household where they work, injuries and accidents from using advanced home appliances and taking care of pets, low self-esteem, ergonomic problems, the need to adjust to all household members who have various needs and demands, inability to voice their complaints, drudgery of work, and emotional stress. With regard to illness or health problems, they commonly experience leg pains, back pains, and headaches. In the absence of intervention programs like those offered in the formal employment set up, domestic workers resort to praying, turn to family, relatives and friends for social and emotional support, connect with them through social media like Facebook which also serve as a means of entertainment to them, talk to their employer, and just try to be optimistic about their situation. Promoting OSH for domestic workers is very challenging and complicated because of interrelated factors such as cultural, knowledge, attitudinal, relational, social, resource, economic, political, institutional and legal problems. This complexity necessitates using a holistic and integrated approach as this is not a problem requiring simple solutions. With this recognition comes the full understanding that its success involves the action and cooperation of all duty bearers in attaining decent work for domestic workers.

Keywords: decent work, Filipino domestic workers, occupational safety and health, working conditions

Procedia PDF Downloads 232

Authors: Vivek Mahadev, Manoj Kumar, Neelu Mathur, Brahm Dutt Pandey

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Perimeter guarding and protection of critical installations require prompt intrusion detection and assessment to take effective countermeasures. Currently, visual and electronic surveillance are the primary methods used for perimeter guarding. These methods can be costly and complicated, requiring careful planning according to the location and terrain. Moreover, these methods often struggle to detect stealthy and camouflaged insurgents. The object of the present work is to devise a surveillance technique using seismic sensors that overcomes the limitations of existing systems. The aim is to improve intrusion detection, assessment, and characterization by utilizing seismic sensors. Most of the similar systems have only two types of intrusion detection capability viz., human or vehicle. In our work we could even categorize further to identify types of intrusion activity such as walking, running, group walking, fence jumping, tunnel digging and vehicular movements. A virtual fence of 60 meters at GCNEP, Bahadurgarh, Haryana, India, was created by installing four underground geophones at a distance of 15 meters each. The signals received from these geophones are then processed to find unique seismic signatures called features. Various feature optimization and selection methodologies, such as LightGBM, Boruta, Random Forest, Logistics, Recursive Feature Elimination, Chi-2 and Pearson Ratio were used to identify the best features for training the machine learning models. The trained models were developed using algorithms such as supervised support vector machine (SVM) classifier, kNN, Decision Tree, Logistic Regression, Naïve Bayes, and Artificial Neural Networks. These models were then used to predict the category of events, employing weighted ensemble voting to analyze and combine their results. The models were trained with 1940 training events and results were evaluated with 831 test events. It was observed that using the weighted ensemble voting increased the efficiency of predictions. In this study we successfully developed and deployed the virtual fence using geophones. Since these sensors are passive, do not radiate any energy and are installed underground, it is impossible for intruders to locate and nullify them. Their flexibility, quick and easy installation, low costs, hidden deployment and unattended surveillance make such systems especially suitable for critical installations and remote facilities with difficult terrain. This work demonstrates the potential of utilizing seismic sensors for creating better perimeter guarding and protection systems using multiple machine learning models in weighted ensemble voting. In this study the virtual fence achieved an intruder detection efficiency of over 97%.

Keywords: geophone, seismic perimeter surveillance, machine learning, weighted ensemble method

Procedia PDF Downloads 47

Authors: Sarah M. Schoellhammer, Stephen Gibb

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Bureaucracy, in particular, its core element, a formal and stable hierarchy of authority, is proving less and less appropriate under the conditions of today’s knowledge economy. Centralization and formalization were consistently found to hinder innovation, undermining cross-functional collaboration, personal responsibility, and flexibility. With its focus on systematical planning, controlling and monitoring the development of new or improved solutions for customers, even innovation management as a discipline is to a significant extent based on a mechanistic understanding of organizations. The most important drivers of innovation, human creativity, and initiative, however, can be more hindered than supported by central elements of classic innovation management, such as predefined innovation strategies, rigid stage gate processes, and decisions made in management gate meetings. Heterarchy, as an alternative network form of organization, is essentially characterized by its dynamic influence structures, whereby the biggest influence is allocated by the collective to the persons perceived the most competent in a certain issue. Theoretical arguments that the non-hierarchical concept better supports innovation than bureaucracy have been supported by empirical research. These prior studies either focus on the structure and general functioning of non-hierarchical organizations or on their innovativeness, that means innovation as an outcome. Complementing classic innovation management approaches, this work aims to shed light on how innovations are initiated and realized in heterarchies in order to identify alternative solutions practiced under conditions of the post-bureaucratic organization. Through an initial individual case study, which is part of a multiple-case project, the innovation practices of an innovative and highly heterarchical medium-sized company in the German fire engineering industry are investigated. In a pragmatic mixed methods approach media resonance, company documents, and workspace architecture are analyzed, in addition to qualitative interviews with the CEO and employees of the case company, as well as a quantitative survey aiming to characterize the company along five scaled dimensions of a heterarchy spectrum. The analysis reveals some similarities and striking differences to approaches suggested by classic innovation management. The studied heterarchy has no predefined innovation strategy guiding new product and service development. Instead, strategic direction is provided by the CEO, described as visionary and creative. Procedures for innovation are hardly formalized, with new product ideas being evaluated on the basis of gut feeling and flexible, rather general criteria. Employees still being hesitant to take responsibility and make decisions, hierarchical influence is still prominent. Described as open-minded and collaborative, culture and leadership were found largely congruent with definitions of innovation culture. Overall, innovation efforts at the case company tend to be coordinated more through cultural than through formal organizational mechanisms. To better enable innovation in mainstream organizations, responsible practitioners are recommended not to limit changes to reducing the central elements of the bureaucratic organization, formalization, and centralization. The freedoms this entails need to be sustained through cultural coordination mechanisms, with personal initiative and responsibility by employees as well as common innovation-supportive norms and values. These allow to integrate diverse competencies, opinions, and activities and, thus, to guide innovation efforts.

Keywords: bureaucracy, heterarchy, innovation management, values

Procedia PDF Downloads 166

Authors: M. Amo, A. Alvaro, A. Astudillo, R. Mc Culloch, J. C. del Castillo, M. Gómez, J. M. Martín

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Polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) of course comprise a range of highly toxic compounds that may exist as particulates within the air or accumulate within water supplies, soil, or vegetation. They may be created either ubiquitously or naturally within the environment as a product of forest fires or volcanic eruptions. It is only since the industrial revolution, however, that it has become necessary to closely monitor their generation as a byproduct of manufacturing/combustion processes, in an effort to mitigate widespread contamination events. Of course, the environmental concentrations of these toxins are expected to be extremely low, therefore highly sensitive and accurate methods are required for their determination. Since ionization of non-polar compounds through electrospray and APCI is difficult and inefficient, we evaluate the performance of a novel low-flow Atmospheric Pressure Photoionization (APPI) source for the trace detection of various dioxins and furans using rapid Mass Spectrometry workflows. Air, soil and biota (vegetable matter) samples were collected monthly during one year from various locations within the vicinity of an industrial incinerator in Spain. Analytes were extracted and concentrated using soxhlet extraction in toluene and concentrated by rotavapor and nitrogen flow. Various ionization methods as electrospray (ES) and atmospheric pressure chemical ionization (APCI) were evaluated, however, only the low-flow APPI source was capable of providing the necessary performance, in terms of sensitivity, required for detecting all targeted analytes. In total, 10 analytes including 2,3,7,8-tetrachlorodibenzodioxin (TCDD) were detected and characterized using the APPI-MS method. Both PCDDs and PCFDs were detected most efficiently in negative ionization mode. The most abundant ion always corresponded to the loss of a chlorine and addition of an oxygen, yielding [M-Cl+O]- ions. MRM methods were created in order to provide selectivity for each analyte. No chromatographic separation was employed; however, matrix effects were determined to have a negligible impact on analyte signals. Triple Quadrupole Mass Spectrometry was chosen because of its unique potential for high sensitivity and selectivity. The mass spectrometer used was a Sciex´s Qtrap3200 working in negative Multi Reacting Monitoring Mode (MRM). Typically mass detection limits were determined to be near the 1-pg level. The APPI-MS2 technology applied to the detection of PCDD/Fs allows fast and reliable atmospheric analysis, minimizing considerably operational times and costs, with respect other technologies available. In addition, the limit of detection can be easily improved using a more sensitive mass spectrometer since the background in the analysis channel is very low. The APPI developed by SEADM allows polar and non-polar compounds ionization with high efficiency and repeatability.

Keywords: atmospheric pressure photoionization-mass spectrometry (APPI-MS), dioxin, furan, incinerator

Procedia PDF Downloads 183

Authors: Fatemeh Modirzare

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Smart interior design represents a transformative approach to creating living spaces that integrate technology seamlessly into our daily lives, enhancing comfort, convenience, and sustainability. This paper explores the concept of smart interior design, its principles, benefits, challenges, and future prospects. It also highlights various examples and applications of smart interior design to illustrate its potential in shaping the way we live and interact with our surroundings. In an increasingly digitized world, the boundaries between technology and interior design are blurring. Smart interior design, also known as intelligent or connected interior design, involves the incorporation of advanced technologies and automation systems into residential and commercial spaces. This innovative approach aims to make living environments more efficient, comfortable, and adaptable while promoting sustainability and user well-being. Smart interior design seamlessly integrates technology into the aesthetics and functionality of a space, ensuring that devices and systems do not disrupt the overall design. Sustainable materials, energy-efficient systems, and eco-friendly practices are central to smart interior design, reducing environmental impact. Spaces are designed to be adaptable, allowing for reconfiguration to suit changing needs and preferences. Smart homes and spaces offer greater comfort through features like automated climate control, adjustable lighting, and customizable ambiance. Smart interior design can significantly reduce energy consumption through optimized heating, cooling, and lighting systems. Smart interior design integrates security systems, fire detection, and emergency response mechanisms for enhanced safety. Sustainable materials, energy-efficient appliances, and waste reduction practices contribute to a greener living environment. Implementing smart interior design can be expensive, particularly when retrofitting existing spaces with smart technologies. The increased connectivity raises concerns about data privacy and cybersecurity, requiring robust measures to protect user information. Rapid advancements in technology may lead to obsolescence, necessitating updates and replacements. Users must be familiar with smart systems to fully benefit from them, requiring education and ongoing support. Residential spaces incorporate features like voice-activated assistants, automated lighting, and energy management systems. Intelligent office design enhances productivity and employee well-being through smart lighting, climate control, and meeting room booking systems. Hospitals and healthcare facilities use smart interior design for patient monitoring, wayfinding, and energy conservation. Smart retail design includes interactive displays, personalized shopping experiences, and inventory management systems. The future of smart interior design holds exciting possibilities, including AI-powered design tools that create personalized spaces based on user preferences. Smart interior design will increasingly prioritize factors that improve physical and mental health, such as air quality monitoring and mood-enhancing lighting. Smart interior design is revolutionizing the way we interact with our living and working spaces. By embracing technology, sustainability, and user-centric design principles, smart interior design offers numerous benefits, from increased comfort and convenience to energy efficiency and sustainability. Despite challenges, the future holds tremendous potential for further innovation in this field, promising a more connected, efficient, and harmonious way of living and working.

Keywords: smart interior design, home automation, sustainable living spaces, technological integration, user-centric design

Procedia PDF Downloads 42

Authors: Anshu Anshu

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In the red corridor famed for the Left Wing Extremism, lies small district of Jamui in Bihar, India. The district lies at 24º20´ N latitude and 86º13´ E longitude, covering an area of 3,122.8 km2 The undulating topography, with widespread forests provides pristine environment for invigorating experience of tourists. Natural landscape in form of forests, wildlife, rivers, and cultural landscape dotted with historical and religious places is highly purposive for tourism. The study is primarily related to the identification of potential ecotourism sites, using Geographic Information System. Data preparation, analysis and finally identification of ecotourism sites is done. Secondary data used is Survey of India Topographical Sheets with R.F.1:50,000 covering the area of Jamui district. District Census Handbook, Census of India, 2011; ERDAS Imagine and Arc View is used for digitization and the creation of DEM’s (Digital Elevation Model) of the district, depicting the relief and topography and generate thematic maps. The thematic maps have been refined using the geo-processing tools. Buffer technique has been used for the accessibility analysis. Finally, all the maps, including the Buffer maps were overlaid to find out the areas which have potential for the development of ecotourism sites in the Jamui district. Spatial data - relief, slopes, settlements, transport network and forests of Jamui District were marked and identified, followed by Buffer Analysis that was used to find out the accessibility of features like roads, railway stations to the sites available for the development of ecotourism destinations. Buffer analysis is also carried out to get the spatial proximity of major river banks, lakes, and dam sites to be selected for promoting sustainable ecotourism. Overlay Analysis is conducted using the geo-processing tools. Digital Terrain Model (DEM) generated and relevant themes like roads, forest areas and settlements were draped on the DEM to make an assessment of the topography and other land uses of district to delineate potential zones of ecotourism development. Development of ecotourism in Jamui faces several challenges. The district lies in the portion of Bihar that is part of ‘red corridor’ of India. The hills and dense forests are the prominent hideouts and training ground for the extremists. It is well known that any kind of political instability, war, acts of violence directly influence the travel propensity and hinders all kind of non-essential travels to these areas. The development of ecotourism in the district can bring change and overall growth in this area with communities getting more involved in economically sustainable activities. It is a known fact that poverty and social exclusion are the main force that pushes people, resorting towards violence. All over the world tourism has been used as a tool to eradicate poverty and generate good will among people. Tourism, in sustainable form should be promoted in the district to integrate local communities in the development process and to distribute fruits of development with equity.

Keywords: buffer analysis, digital elevation model, ecotourism, red corridor

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Authors: Evren Aysev Denec

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As an urban settlement dating as early as 8000 years and the capital for Byzantine and Ottoman empires; İstanbul has been a significant global city throughout history. The most drastic changes in the macro form of Istanbul have taken place in the last seven decades; starting from 1950’s with rapid industrialization and population growth; pacing up after the 1980’s with the efforts of integration to the global capitalist system; reaching to a climax in the 2000’s with the adaptation of a neoliberal urban regime. Today, the rate of urbanization together with land speculation and real estate investment has been growing enormously. Every inch of urban land is conceptualized as a commodity to be capitalized. This neoliberal mindset has many controversial implementations, from the privatization of public land to the urban transformation of historic neighbourhoods and consumption of natural resources. The planning decisions concerning the city have been mainly top down initiations; conceptualising historical, cultural and natural heritage as commodities to be capitalised and consumed in favour of creating rent value. One of the most crucial implementations of this neoliberal urban regime is the project of establishing a ‘new city’ around northern Istanbul; together with a number of large-scale infrastructural projects such as the Third Bosporus Bridge; a new highway system, a Third Airport Project and a secondary Bosporus project called the ‘Canal Istanbul’. Urbanizing northern Istanbul is highly controversial as this area consists of major natural resources of the city; being the northern forests, water supplies and wildlife; which are bound to be destroyed to a great extent following the implementations. The construction of the third bridge and the third airport has begun in 2013, despite environmental objections and protests. Over five hundred thousand trees are planned be cut for solely the construction of the bridge and the Northern Marmara Motorway. Yet the real damage will be the urbanization of the forest area; irreversibly corrupting the natural resources and attracting millions of additional population towards Istanbul. Furthermore, these projects lack an integrated planning scope as the plans prepared for Istanbul are constantly subjected to alterations forced by the central government. Urban interventions mentioned above are executed despite the rulings of Istanbul Environmental plan, due to top down planning decisions. Instead of an integrated action plan that prepares for the future of the city, Istanbul is governed by partial plans and projects that are issued by a profit based agenda; supported by legal alterations and laws issued by the central government. This paper aims to discuss the ongoing implementations with regards to northern Istanbul; claiming that they are not merely infrastructural interventions but parts of a greater neoliberal urbanization strategy. In the course of the study, firstly a brief account on the northern forests of Istanbul will be presented. Then, the projects will be discussed in detail, addressing how the current planning schemes deal with the natural heritage of the city. Lastly, concluding remarks on how the implementations could affect the future of Istanbul will be presented.

Keywords: Istanbul, urban design, urban planning, natural resources

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Authors: Ronald Orth, Johanna Haunschild, Sara Tsog

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The bioeconomy is an inter- and cross-disciplinary field covering a large number and wide scope of existing and emerging technologies. It has a great potential to contribute to the transformation process of industry landscape and ultimately drive the economy towards sustainability. However, bioeconomy per se is not necessarily sustainable and technology should be seen as an enabler rather than panacea to all our ecological, social and economic issues. Therefore, to draw and maximize benefits from bioeconomy in terms of sustainability, we propose that innovative activities should encompass not only novel technologies and bio-based new materials but also multifocal innovations. For multifocal innovation endeavors, innovation management plays a substantial role, as any innovation emerges in a complex iterative process where communication and knowledge exchange among relevant stake holders has a pivotal role. The knowledge generation and innovation are although at the core of transition towards a more sustainable bio-based economy, to date, there is a significant lack of concepts and models that approach bioeconomy from the innovation management approach. The aim of this paper is therefore two-fold. First, it inspects the role of transformative approach in the adaptation of bioeconomy that contributes to the environmental, ecological, social and economic sustainability. Second, it elaborates the importance of technology and innovation management as a tool for smooth, prompt and effective transition of firms to the bioeconomy. We conduct a qualitative literature study on the sustainability challenges that bioeconomy entails thus far using Science Citation Index and based on grey literature, as major economies e.g. EU, USA, China and Brazil have pledged to adopt bioeconomy and have released extensive publications on the topic. We will draw an example on the forest based business sector that is transforming towards the new green economy more rapidly as expected, although this sector has a long-established conventional business culture with consolidated and fully fledged industry. Based on our analysis we found that a successful transition to sustainable bioeconomy is conditioned on heterogenous and contested factors in terms of stakeholders , activities and modes of innovation. In addition, multifocal innovations occur when actors from interdisciplinary fields engage in intensive and continuous interaction where the focus of innovation is allocated to a field of mutually evolving socio-technical practices that correspond to the aims of the novel paradigm of transformative innovation policy. By adopting an integrated and systems approach as well as tapping into various innovation networks and joining global innovation clusters, firms have better chance of creating an entire new chain of value added products and services. This requires professionals that have certain capabilities and skills such as: foresight for future markets, ability to deal with complex issues, ability to guide responsible R&D, ability of strategic decision making, manage in-depth innovation systems analysis including value chain analysis. Policy makers, on the other hand, need to acknowledge the essential role of firms in the transformative innovation policy paradigm.

Keywords: bioeconomy, innovation and technology management, multifocal innovation, sustainability, transformative innovation policy

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Authors: C. Sancho Salvatierra, J. M. Martinez Nieto, R. García Gonzalez-Gordon, M. I. Martinez Bellido

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Introduction: Nature could act as an asset for human health protecting against possible diseases and promoting the state of both physical and mental health. Goals: This paper aims to determine which natural elements present evidence that show positive influence on human health, on which particular aspects and how. It also aims to determine the best biomarkers to measure such influence. Method: A systematic literature review was carried out. First, a general free text search was performed in databases, such as Scopus, PubMed or PsychInfo. Secondly, a specific search was performed combining keywords in order of increasing complexity. Also the Snowballing technique was used and it was consulted in the CSIC’s (The Spanish National Research Council). Databases: Of the 130 articles obtained and reviewed, 80 referred to natural elements that influenced health. These 80 articles were classified and tabulated according to the nature elements found, the health aspects studied, the health measurement parameters used and the measurement techniques used. In this classification the results of the studies were codified according to whether they were positive, negative or neutral both for the elements of nature and for the aspects of health studied. Finally, the results of the 80 selected studies were summarized and categorized according to the elements of nature that showed the greatest positive influence on health and the biomarkers that had shown greater reliability to measure said influence. Results: Of the 80 articles studied, 24 (30.0%) were reviews and 56 (70.0%) were original research articles. Among the 24 reviews, 18 (75%) found positive results of natural elements on health, and 6 (25%) both positive and negative effects. Of the 56 original articles, 47 (83.9%) showed positive results, 3 (5.4%) both positive and negative, 4 (7.1%) negative effects, and 2 (3.6%) found no effects. The results reflect positive effects of different elements of nature on the following pathologies: diabetes, high blood pressure, stress, attention deficit hyperactivity disorder, psychotic, anxiety and affective disorders. They also show positive effects on the following areas: immune system, social interaction, recovery after illness, mood, decreased aggressiveness, concentrated attention, cognitive performance, restful sleep, vitality and sense of well-being. Among the elements of nature studied, those that show the greatest positive influence on health are forest immersion, natural views, daylight, outdoor physical activity, active transport, vegetation biodiversity, natural sounds and the green residences. As for the biomarkers used that show greater reliability to measure the effects of natural elements are the levels of cortisol (both in blood and saliva), vitamin D levels, serotonin and melatonin, blood pressure, heart rate, muscle tension and skin conductance. Conclusions: Nature is an asset for health, well-being and quality of life. Awareness programs, education and health promotion are needed based on the elements that nature brings us, which in turn generate proactive attitudes in the population towards the protection and conservation of nature. The studies related to this subject in Spain are very scarce. Aknowledgements. This study has been promoted and partially financed by the Environmental Foundation Jaime González-Gordon.

Keywords: health, green areas, nature, well-being

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Authors: Dan Kanmegne

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Africa will become the most populated continent by the end of the century, with around 4 billion inhabitants. Food security and climate changes will become continental issues since agricultural practices depend on climate but also contribute to global emissions and land degradation. Agroforestry has been identified as a cost-efficient and reliable strategy to address these two issues. It is defined as the integrated management of trees and crops/animals in the same land unit. Agroforestry provides benefits in terms of goods (fruits, medicine, wood, etc.) and services (windbreaks, fertility, etc.), and is acknowledged to have a great potential for carbon sequestration; therefore it can be integrated into reduction mechanisms of carbon emissions. Particularly in sub-Saharan Africa, the constraint stands in the lack of information about both areas under agroforestry and the characterization (composition, structure, and management) of each agroforestry system at the country level. This study describes and quantifies “what is where?”, earliest to the quantification of carbon stock in different systems. Remote sensing (RS) is the most efficient approach to map such a dynamic technology as agroforestry since it gives relatively adequate and consistent information over a large area at nearly no cost. RS data fulfill the good practice guidelines of the Intergovernmental Panel On Climate Change (IPCC) that is to be used in carbon estimation. Satellite data are getting more and more accessible, and the archives are growing exponentially. To retrieve useful information to support decision-making out of this large amount of data, satellite data needs to be organized so to ensure fast processing, quick accessibility, and ease of use. A new solution is a data cube, which can be understood as a multi-dimensional stack (space, time, data type) of spatially aligned pixels and used for efficient access and analysis. A data cube for Burkina Faso has been set up from the cooperation project between the international service provider WASCAL and Germany, which provides an accessible exploitation architecture of multi-temporal satellite data. The aim of this study is to map and characterize agroforestry systems using the Burkina Faso earth observation data cube. The approach in its initial stage is based on an unsupervised image classification of a normalized difference vegetation index (NDVI) time series from 2010 to 2018, to stratify the country based on the vegetation. Fifteen strata were identified, and four samples per location were randomly assigned to define the sampling units. For safety reasons, the northern part will not be part of the fieldwork. A total of 52 locations will be visited by the end of the dry season in February-March 2020. The field campaigns will consist of identifying and describing different agroforestry systems and qualitative interviews. A multi-temporal supervised image classification will be done with a random forest algorithm, and the field data will be used for both training the algorithm and accuracy assessment. The expected outputs are (i) map(s) of agroforestry dynamics, (ii) characteristics of different systems (main species, management, area, etc.); (iii) assessment report of Burkina Faso data cube.

Keywords: agroforestry systems, Burkina Faso, earth observation data cube, multi-temporal image classification

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Authors: Asif Ali, Daud Salim Faruquie

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

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

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Authors: Dibyendu Rakshit, Santosh K. Sarkar

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

Keywords: Gangasagar festival, phytoplankton, Sundarban megadelta, tintinnid

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Authors: Simrat Kaur, Fatema Diwan, Brad Reddersen

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

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

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Authors: Bitewulign Mekonnen

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

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

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

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

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

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

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

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

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Authors: Xingyu Gao, Qiang Wu

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

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

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

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

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

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Authors: Hossein Aghababaei, Sergio Vitale, Giampaolo Ferraioli

Abstract:

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

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

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Authors: Kimberly Samaha

Abstract:

In order to attract new types of investors into the emerging Bio-Economy, a new set of metrics and measurement system is needed to better quantify the environmental, social and economic impacts of circular zero-waste design. The Bio-Hub Ecosystem model was developed to address a critical area of concern within the global energy market regarding the use of biomass as a feedstock for power plants. Lack of an economically-viable business model for bioenergy facilities has resulted in the continuation of idled and decommissioned plants. In particular, the forestry-based plants which have been an invaluable outlet for woody biomass surplus, forest health improvement, timber production enhancement, and especially reduction of wildfire risk. This study looked at repurposing existing biomass-energy plants into Circular Zero-Waste Bio-Hub Ecosystems. A Bio-Hub model that first targets a ‘whole-tree’ approach and then looks at the circular economics of co-hosting diverse industries (wood processing, aquaculture, agriculture) in the vicinity of the Biomass Power Plants facilities. It proposes not only models for integration of forestry, aquaculture, and agriculture in cradle-to-cradle linkages of what have typically been linear systems, but the proposal also allows for the early measurement of the circularity and impact of resource use and investment risk mitigation, for these systems. Typically, life cycle analyses measure environmental impacts of different industrial production stages and are not integrated with indicators of material use circularity. This concept paper proposes the further development of a new set of metrics that would illustrate not only the typical life-cycle analysis (LCA), which shows the reduction in greenhouse gas (GHG) emissions, but also the zero-waste circularity measures of mass balance of the full value chain of the raw material and energy content/caloric value. These new measures quantify key impacts in making hyper-efficient use of natural resources and eliminating waste to landfills. The project utilized traditional LCA using the GREET model where the standalone biomass energy plant case was contrasted with the integration of a jet-fuel biorefinery. The methodology was then expanded to include combinations of co-hosts that optimize the life cycle of woody biomass from tree to energy, CO₂, heat and wood ash both from an energy/caloric value and for mass balance to include reuse of waste streams which are typically landfilled. The major findings of both a formal LCA study resulted in the masterplan for the first Bio-Hub to be built in West Enfield, Maine. Bioenergy facilities are currently at a critical juncture where they have an opportunity to be repurposed into efficient, profitable and socially responsible investments, or be idled and scrapped. If proven as a model, the expedited roll-out of these innovative scenarios can set a new standard for circular zero-waste projects that advance the critical transition from the current ‘take-make-dispose’ paradigm inherent in the energy, forestry and food industries to a more sustainable bio-economy paradigm where waste streams become valuable inputs, supporting local and rural communities in simple, sustainable ways.

Keywords: bio-economy, biomass energy, financing, metrics

Procedia PDF Downloads 135

Authors: Caroline Spencer, Suzanne Cross, Dudley McArdle, Frank Archer

Abstract:

Objectives: The evolution of the Victorian Compendium of Community-Based Resilience Building Case Studies and its capacity to help communities implement activities that encourage adaptation to disaster risk reduction and promote community resilience in rural and urban locations provide this paper's objectives. Background: Between 2012 and 2019, community groups presented at the Monash University Disaster Resilience Initiative (MUDRI) 'Advancing Community Resilience Annual Forums', provided opportunities for communities to impart local resilience activities, how to solve challenges and share unforeseen learning and be considered for inclusion in the Compendium. A key tenet of the Compendium encourages compiling and sharing of grass-roots resilience building activities to help communities before, during, and after unexpected emergencies. The online Compendium provides free access for anyone wanting to help communities build expertise, reduce program duplication, and save valuable community resources. Identifying case study features across the emergency phases and analyzing critical success factors helps communities understand what worked and what did not work to achieve success and avoid known barriers. International exemplars inform the Compendium, which represents an Australian first and enhances Victorian community resilience initiatives. Emergency Management Victoria provided seed funding for the Compendium. MUDRI matched this support and continues to fund the project. A joint Steering Committee with broad-based user input and Human ethics approval guides its continued growth. Methods: A thematic analysis of the Compendium identified case study features, including critical success factors. Results: The Compendium comprises 38 case studies, representing all eight Victorian regions. Case studies addressed emergency phases, before (29), during (7), and after (17) events. Case studies addressed all hazards (23), bushfires (11), heat (2), fire safety (1), and house fires (1). Twenty case studies used a framework. Thirty received funding, of which nine received less than $20,000 and five received more than $100,000. Twenty-nine addressed a whole of community perspective. Case studies revealed unique and valuable learning in diverse settings. Critical success factors included strong governance; board support, leadership, and trust; partnerships; commitment, adaptability, and stamina; community-led initiatives. Other success factors included a paid facilitator and local government support; external funding, and celebrating success. Anecdotally, we are aware that community groups reference Compendium and that its value adds to community resilience planning. Discussion: The Compendium offers an innovative contribution to resilience research and practice. It augments the seven resilience characteristics to strengthen and encourage communities as outlined in the Statewide Community Resilience Framework for Emergency Management; brings together people from across sectors to deliver distinct, yet connected actions to strengthen resilience as a part of the Rockefeller funded Resilient Melbourne Strategy, and supports communities and economies to be resilient when a shock occurs as identified in the recently published Australian National Disaster Risk Reduction Framework. Each case study offers learning about connecting with community and how to increase their resilience to disaster risks and to keep their community safe from unexpected emergencies. Conclusion: The Compendium enables diverse communities to adopt or adapt proven resilience activities, thereby preserving valuable community resources and offers the opportunity to extend to a national or international Compendium.

Keywords: case study, community, compendium, disaster risk reduction, resilience

Procedia PDF Downloads 97

Authors: Hossein Aghababaei, Sergio Vitale, Giampaolo ferraioli

Abstract:

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

Keywords: SAR image, deep learning, convolutional neural network, deep neural network, SAR polarimetry

Procedia PDF Downloads 55

Authors: Suikinai Nobre Santos, Ranko Gacesa, Paul F. Long, Itamar Soares de Melo

Abstract:

Extremophilic microorganism are adapted to biotopes combining several stress factors (temperature, pressure, radiation, salinity and pH), which indicate the richness valuable resource for the exploitation of novel biotechnological processes and constitute unique models for investigations their biomolecules (1, 2). The above information encourages us investigate bioprospecting synthesized compounds by a noval actinomycete, designated thermotolerant Streptomyces caatingaensis CMAA 1322, isolated from sample soil tropical dry forest (Caatinga) in the Brazilian semiarid region (3-17°S and 35-45°W). This set of constrating physical and climatic factores provide the unique conditions and a diversity of well adapted species, interesting site for biotechnological purposes. Preliminary studies have shown the great potential in the production of cytotoxic, pesticidal and antimicrobial molecules (3). Thus, to extend knowledge of the genes clusters responsible for producing biosynthetic pathways of natural products in strain CMAA1322, whole-genome shotgun (WGS) DNA sequencing was performed using paired-end long sequencing with PacBio RS (Pacific Biosciences). Genomic DNA was extracted from a pure culture grown overnight on LB medium using the PureLink genomic DNA kit (Life Technologies). An approximately 3- to 20-kb-insert PacBio library was constructed and sequenced on an 8 single-molecule real-time (SMRT) cell, yielding 116,269 reads (average length, 7,446 bp), which were allocated into 18 contigs, with 142.11x coverage and N50 value of 20.548 bp (BioProject number PRJNA288757). The assembled data were analyzed by Rapid Annotations using Subsystems Technology (RAST) (4) the genome size was found to be 7.055.077 bp, comprising 6167 open reading frames (ORFs) and 413 subsystems. The G+C content was estimated to be 72 mol%. The closest-neighbors tool, available in RAST through functional comparison of the genome, revealed that strain CMAA1322 is more closely related to Streptomyces hygroscopicus ATCC 53653 (similarity score value, 537), S. violaceusniger Tu 4113 (score value, 483), S. avermitilis MA-4680 (score value, 475), S. albus J1074 (score value, 447). The Streptomyces sp. CMAA1322 genome contains 98 tRNA genes and 135 genes copies related to stress response, mainly osmotic stress (14), heat shock (16), oxidative stress (49). Functional annotation by antiSMASH version 3.0 (5) identified 41 clusters for secondary metabolites (including two clusters for lanthipeptides, ten clusters for nonribosomal peptide synthetases [NRPS], three clusters for siderophores, fourteen for polyketide synthetase [PKS], six clusters encoding a terpene, two clusters encoding a bacteriocin, and one cluster encoding a phenazine). Our work provide in comparative analyse of genome and extract produced (data no published) by lineage CMAA1322, revealing the potential of microorganisms accessed from extreme environments as Caatinga” to produce a wide range of biotechnological relevant compounds.

Keywords: caatinga, streptomyces, environmental stresses, biosynthetic pathways

Procedia PDF Downloads 216

Authors: Vasundhara H. Krishnani, Ajay Saini, Dibesh Karmacharya, Salit Kark

Abstract:

Illegal Wildlife Trade is one of the most pressing global conservation challenges. Unregulated wildlife trade can threaten biodiversity, contribute to habitat loss, limit sustainable development efforts, and expedite species declines and extinctions. In low-income and middle-income countries, such as Nepal and other countries in Asia and Africa, many of the people engaged in the early stages of illegal wildlife trade, which includes the hunting and transportation of wildlife, belong to Indigenous tribes and local communities.These countries primarily rely on punitive measures to prevent and suppress Illegal Wildlife Trade. For example, in Nepal, people involved in wildlife crimes can often be sentenced to incarceration and a hefty fine and serve up to 15 years in prison. Despite these harsh punitive measures, illegal wildlife trade remains a significant conservation challenge in many countries. The aim of this study was to examine factors affecting the participation of Indigenous communities in Illegal Wildlife Trade while recording the experiences of members of the Indigenous Chepang community, some of whom were imprisoned for their alleged involvement in rhino poaching. Chepangs, belonging to traditionally a hunter-gatherer community, are often considered an isolated and marginalized Indigenous community, some of whom live around the Chitwan National Park in Nepal. Established in 1973, Chitwan National Park is situated in the Chitwan Valley of Nepal and was one of the first regions that was declared as a protected area in Nepal, aiming to protect the one-horned rhinoceros as a flagship species. Conducted over a period of three years, this study used semi-structured interviews and focus group discussions to collect data from Illegal Wildlife Trade offenders, family members of offenders, community Elders, NGO personnel, community forest representatives, Chepang community representatives, and Government school teachers from the region surrounding Chitwan National Park. The study also examined the social, cultural, health, and financial impacts that the imprisonment of offenders had on the families of the community members, especially women and children. The results suggest that involvement of the members of the Chepang community living around Chitwan National Park in the poaching of the one-horned rhinoceros (Rhinoceros unicornis) can be attributed to a range of factors, some of which include: lack of livelihood opportunities, lack of awareness regarding wildlife rules and regulations and poverty.This work emphasises the need for raising awareness and building programs to enhance alternative livelihood training and empower indigenous and marginalised communities that provide sustainable alternatives. Furthermore, the issue needs to be addressed as a community solution which includes all community members. We suggest this multi-pronged approach can benefit wildlife conservation by reducing illegal poaching and wildlife trade, as well as community conservation in regions with similar challenges. By actively involving and empowering local communities, the communities become key stakeholders in the conservation process. This involvement contributes to protecting wildlife and natural ecosystems while simultaneously providing sustainable livelihood options for local communities.

Keywords: alternative livelihoods, chepang community, illegal wildlife trade, low-and middle-income countries, nepal, one-horned rhinoceros

Procedia PDF Downloads 72