Search results for: naturally regenerated acacia forest

Authors: Alireza Barari, Ahmad Abdi

Abstract:

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 651

Authors: Utkarsh A. Mishra, Ankit Bansal

Abstract:

At high temperature, radiative heat transfer is the dominant mode of heat transfer. It is governed by various phenomena such as photon emission, absorption, and scattering. The solution of the governing integrodifferential equation of radiative transfer is a complex process, more when the effect of participating medium and wavelength properties are taken into consideration. Although a generic formulation of such radiative transport problem can be modeled for a wide variety of problems with non-gray, non-diffusive surfaces, there is always a trade-off between simplicity and accuracy of the problem. Recently, solutions of complicated mathematical problems with statistical methods based on randomization of naturally occurring phenomena have gained significant importance. Photon bundles with discrete energy can be replicated with random numbers describing the emission, absorption, and scattering processes. Photon Monte Carlo (PMC) is a simple, yet powerful technique, to solve radiative transfer problems in complicated geometries with arbitrary participating medium. The method, on the one hand, increases the accuracy of estimation, and on the other hand, increases the computational cost. The participating media -generally a gas, such as CO₂, CO, and H₂O- present complex emission and absorption spectra. To model the emission/absorption accurately with random numbers requires a weighted sampling as different sections of the spectrum carries different importance. Importance sampling (IS) was implemented to sample random photon of arbitrary wavelength, and the sampled data provided unbiased training of MC estimators for better results. A better replacement to uniform random numbers is using deterministic, quasi-random sequences. Halton, Sobol, and Faure Low-Discrepancy Sequences are used in this study. They possess better space-filling performance than the uniform random number generator and gives rise to a low variance, stable Quasi-Monte Carlo (QMC) estimators with faster convergence. An optimal supervised learning scheme was further considered to reduce the computation costs of the PMC simulation. A one-dimensional plane-parallel slab problem with participating media was formulated. The history of some randomly sampled photon bundles is recorded to train an Artificial Neural Network (ANN), back-propagation model. The flux was calculated using the standard quasi PMC and was considered to be the training target. Results obtained with the proposed model for the one-dimensional problem are compared with the exact analytical and PMC model with the Line by Line (LBL) spectral model. The approximate variance obtained was around 3.14%. Results were analyzed with respect to time and the total flux in both cases. A significant reduction in variance as well a faster rate of convergence was observed in the case of the QMC method over the standard PMC method. However, the results obtained with the ANN method resulted in greater variance (around 25-28%) as compared to the other cases. There is a great scope of machine learning models to help in further reduction of computation cost once trained successfully. Multiple ways of selecting the input data as well as various architectures will be tried such that the concerned environment can be fully addressed to the ANN model. Better results can be achieved in this unexplored domain.

Keywords: radiative heat transfer, Monte Carlo Method, pseudo-random numbers, low discrepancy sequences, artificial neural networks

Procedia PDF Downloads 223

Authors: Naima Umar

Abstract:

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 307

Authors: T. Lakshmi Priya

Abstract:

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 88

Authors: William Michael Short

Abstract:

Semantic Web’ technologies such as the Unified Medical Language System Metathesaurus, SNOMED-CT, and MeSH have been touted as transformational for the way users access online medical and health information, enabling both the automated analysis of natural-language data and the integration of heterogeneous healthrelated resources distributed across the Internet through the use of standardized terminologies that capture concepts and relationships between concepts that are expressed differently across datasets. However, the approaches that have so far characterized ‘semantic bioinformatics’ have not yet fulfilled the promise of the Semantic Web for medical and health information retrieval applications. This paper argues within the perspective of cognitive linguistics and cognitive anthropology that four features of human meaning-making must be taken into account before the potential of semantic technologies can be realized for this domain. First, many semantic technologies operate exclusively at the level of the word. However, texts convey meanings in ways beyond lexical semantics. For example, transitivity patterns (distributions of active or passive voice) and modality patterns (configurations of modal constituents like may, might, could, would, should) convey experiential and epistemic meanings that are not captured by single words. Language users also naturally associate stretches of text with discrete meanings, so that whole sentences can be ascribed senses similar to the senses of words (so-called ‘discourse topics’). Second, natural language processing systems tend to operate according to the principle of ‘one token, one tag’. For instance, occurrences of the word sound must be disambiguated for part of speech: in context, is sound a noun or a verb or an adjective? In syntactic analysis, deterministic annotation methods may be acceptable. But because natural language utterances are typically characterized by polyvalency and ambiguities of all kinds (including intentional ambiguities), such methods leave the meanings of texts highly impoverished. Third, ontologies tend to be disconnected from everyday language use and so struggle in cases where single concepts are captured through complex lexicalizations that involve profile shifts or other embodied representations. More problematically, concept graphs tend to capture ‘expert’ technical models rather than ‘folk’ models of knowledge and so may not match users’ common-sense intuitions about the organization of concepts in prototypical structures rather than Aristotelian categories. Fourth, and finally, most ontologies do not recognize the pervasively figurative character of human language. However, since the time of Galen the widespread use of metaphor in the linguistic usage of both medical professionals and lay persons has been recognized. In particular, metaphor is a well-documented linguistic tool for communicating experiences of pain. Because semantic medical knowledge-bases are designed to help capture variations within technical vocabularies – rather than the kinds of conventionalized figurative semantics that practitioners as well as patients actually utilize in clinical description and diagnosis – they fail to capture this dimension of linguistic usage. The failure of semantic technologies in these respects degrades the efficiency and efficacy not only of medical research, where information retrieval inefficiencies can lead to direct financial costs to organizations, but also of care provision, especially in contexts of patients’ self-management of complex medical conditions.

Keywords: ambiguity, bioinformatics, language, meaning, metaphor, ontology, semantic web, semantics

Procedia PDF Downloads 132

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

Abstract:

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 78

Authors: Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Kensuke Nakamura, Dongeun Choi, Byung-Woo Hong

Abstract:

Artefacts are commonly encountered in the imaging process of clinical computed tomography (CT) where the artefact refers to any systematic discrepancy between the reconstructed observation and the true attenuation coefficient of the object. It is known that CT images are inherently more prone to artefacts due to its image formation process where a large number of independent detectors are involved, and they are assumed to yield consistent measurements. There are a number of different artefact types including noise, beam hardening, scatter, pseudo-enhancement, motion, helical, ring, and metal artefacts, which cause serious difficulties in reading images. Thus, it is desired to remove nuisance factors from the degraded image leaving the fundamental intrinsic information that can provide better interpretation of the anatomical and pathological characteristics. However, it is considered as a difficult task due to the high dimensionality and variability of data to be recovered, which naturally motivates the use of machine learning techniques. We propose an image restoration algorithm based on the deep neural network framework where the denoising auto-encoders are stacked building multiple layers. The denoising auto-encoder is a variant of a classical auto-encoder that takes an input data and maps it to a hidden representation through a deterministic mapping using a non-linear activation function. The latent representation is then mapped back into a reconstruction the size of which is the same as the size of the input data. The reconstruction error can be measured by the traditional squared error assuming the residual follows a normal distribution. In addition to the designed loss function, an effective regularization scheme using residual-driven dropout determined based on the gradient at each layer. The optimal weights are computed by the classical stochastic gradient descent algorithm combined with the back-propagation algorithm. In our algorithm, we initially decompose an input image into its intrinsic representation and the nuisance factors including artefacts based on the classical Total Variation problem that can be efficiently optimized by the convex optimization algorithm such as primal-dual method. The intrinsic forms of the input images are provided to the deep denosing auto-encoders with their original forms in the training phase. In the testing phase, a given image is first decomposed into the intrinsic form and then provided to the trained network to obtain its reconstruction. We apply our algorithm to the restoration of the corrupted CT images by the artefacts. It is shown that our algorithm improves the readability and enhances the anatomical and pathological properties of the object. The quantitative evaluation is performed in terms of the PSNR, and the qualitative evaluation provides significant improvement in reading images despite degrading artefacts. The experimental results indicate the potential of our algorithm as a prior solution to the image interpretation tasks in a variety of medical imaging applications. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).

Keywords: auto-encoder neural network, CT image artefact, deep learning, intrinsic image representation, noise reduction, total variation

Procedia PDF Downloads 190

Authors: Zahra Shakoori, Farid Salmanpour

Abstract:

Pollination services are a vital service for the ecosystem to maintain environmental stability. The decline of pollinators can disrupt the ecological balance by affecting components of biodiversity. Bumble bees are crucial pollinators, playing a vital role in maintaining plant diversity. This study investigated the temporal patterns of their visitation to flowers in Kiasar National Park, Iran. Observations were conducted in Jun 2024, totaling 442 person-minutes of observation. Five species of bumble bees were identified. The study revealed that they consistently visited an average of 12-15 flowers per minute, regardless of species. The findings highlight the importance of protecting natural habitats, where their populations are thriving in the absence of human-induced stressors. This study was conducted in Kiasar National Park, located in the southeast of Mazandaran, northern Iran. The surveyed area, at an altitude of 1800-2200 meters, includes both forest and pasture. Bumble bee surveys were carried out on sunny days from June 2024, starting at dawn and ending at sunset. To avoid double-counting, we systematically searched for foraging habitats on low-sloping ridges with high mud density, frequently moving between patches. We recorded bumble bee visits to flowers and plant species per minute using direct observation, a stopwatch, and a pre-prepared form. We used statistical analysis of variance (ANOVA) with a confidence level of 95% to examine potential differences in foraging rates across different bumble bee species, flowers, plant bases, and plant species visited. Bumble bee identification relied on morphological indicators. A total of 442 person-minutes of bumble bee observations were recorded. Five species of bumble bees (Bombus fragrans, Bombus haematurus, Bombus lucorum, Bombus melanurus, Bombus terrestris) were identified during the study. The results of this study showed that the visits of bumble bees to flower sources were not different from each other. In general, bumble bees visit an average of 12-15 flowers every 60 seconds. In addition, at the same time they visit between 3-5 plant bases. Finally, they visit an average of 1 to 3 plant species per minute. While many taxa contribute to pollination, insects—especially bees—are crucial for maintaining plant diversity and ecosystem functions. As plant diversity increases, the stopping rate of pollinating insects rises, which reduces their foraging activity. Bumble bees, therefore, stop more frequently in natural areas than in agricultural fields due to higher plant diversity. Our findings emphasize the need to protect natural habitats like Kiasar National Park, where bumble bees thrive without human-induced stressors like pesticides, livestock grazing, and pollution. With bumble bee populations declining globally, further research is essential to understand their behavior in different environments and develop effective conservation strategies to protect them.

Keywords: bumble bees, pollination, pollinator, plant diversity, Iran

Procedia PDF Downloads 30

Authors: Dongping Fang, Lian Duan, Xiaojing Yuan, Mike Xu, Allyn Klunder, Kevin Tan, Suiting Cao, Yeqing Ji

Abstract:

Accurate prediction of a medical condition with straight clinical evidence is a long-sought topic in the medical management and health insurance field. Although great progress has been made with machine learning algorithms, the medical community is still, to a certain degree, suspicious about the model's accuracy and interpretability. This paper presents an innovative hierarchical attention deep learning model to achieve good prediction and clear interpretability that can be easily understood by medical professionals. This deep learning model uses a hierarchical attention structure that matches naturally with the medical history data structure and reflects the member’s encounter (date of service) sequence. The model attention structure consists of 3 levels: (1) attention on the medical code types (diagnosis codes, procedure codes, lab test results, and prescription drugs), (2) attention on the sequential medical encounters within a type, (3) attention on the medical codes within an encounter and type. This model is applied to predict the occurrence of stage 3 chronic kidney disease (CKD3), using three years’ medical history of Medicare Advantage (MA) members from a top health insurance company. The model takes members’ medical events, both claims and electronic medical record (EMR) data, as input, makes a prediction of CKD3 and calculates the contribution from individual events to the predicted outcome. The model outcome can be easily explained with the clinical evidence identified by the model algorithm. Here are examples: Member A had 36 medical encounters in the past three years: multiple office visits, lab tests and medications. The model predicts member A has a high risk of CKD3 with the following well-contributed clinical events - multiple high ‘Creatinine in Serum or Plasma’ tests and multiple low kidneys functioning ‘Glomerular filtration rate’ tests. Among the abnormal lab tests, more recent results contributed more to the prediction. The model also indicates regular office visits, no abnormal findings of medical examinations, and taking proper medications decreased the CKD3 risk. Member B had 104 medical encounters in the past 3 years and was predicted to have a low risk of CKD3, because the model didn’t identify diagnoses, procedures, or medications related to kidney disease, and many lab test results, including ‘Glomerular filtration rate’ were within the normal range. The model accurately predicts members A and B and provides interpretable clinical evidence that is validated by clinicians. Without extra effort, the interpretation is generated directly from the model and presented together with the occurrence date. Our model uses the medical data in its most raw format without any further data aggregation, transformation, or mapping. This greatly simplifies the data preparation process, mitigates the chance for error and eliminates post-modeling work needed for traditional model explanation. To our knowledge, this is the first paper on an interpretable deep-learning model using a 3-level attention structure, sourcing both EMR and claim data, including all 4 types of medical data, on the entire Medicare population of a big insurance company, and more importantly, directly generating model interpretation to support user decision. In the future, we plan to enrich the model input by adding patients’ demographics and information from free-texted physician notes.

Keywords: deep learning, interpretability, attention, big data, medical conditions

Procedia PDF Downloads 91

Authors: David A. Olasehinde, Peter I. Olasehinde, Segun M. A. Adelana, Dapo O. Olasehinde

Abstract:

An investigation was carried out on underground water system dynamics within Ilorin metropolis to monitor the subsurface flow and its corresponding pollution. Africa population growth rate is the highest among the regions of the world, especially in urban areas. A corresponding increase in waste generation and a change in waste composition from predominantly organic to non-organic waste has also been observed. Percolation of leachate from non-engineered landfills, the chief means of waste disposal in many of its cities, constitutes a threat to the underground water bodies. Ilorin city, a transboundary town in southwestern Nigeria, is a ready microcosm of Africa’s unique challenge. In spite of the fact that groundwater is naturally protected from common contaminants such as bacteria as the subsurface provides natural attenuation process, groundwater samples have been noted to however possesses relatively higher dissolved chemical contaminants such as bicarbonate, sodium, and chloride which poses a great threat to environmental receptors and human consumption. The Geographic Information System (GIS) was used as a tool to illustrate, subsurface dynamics and the corresponding pollutant indicators. Forty-four sampling points were selected around known groundwater pollutant, major old dumpsites without landfill liners. The results of the groundwater flow directions and the corresponding contaminant transport were presented using expert geospatial software. The experimental results were subjected to four descriptive statistical analyses, namely: principal component analysis, Pearson correlation analysis, scree plot analysis, and Ward cluster analysis. Regression model was also developed aimed at finding functional relationships that can adequately relate or describe the behaviour of water qualities and the hypothetical factors landfill characteristics that may influence them namely; distance of source of water body from dumpsites, static water level of groundwater, subsurface permeability (inferred from hydraulic gradient), and soil infiltration. The regression equations developed were validated using the graphical approach. Underground water seems to flow from the northern portion of Ilorin metropolis down southwards transporting contaminants. Pollution pattern in the study area generally assumed a bimodal pattern with the major concentration of the chemical pollutants in the underground watershed and the recharge. The correlation between contaminant concentrations and the spread of pollution indicates that areas of lower subsurface permeability display a higher concentration of dissolved chemical content. The principal component analysis showed that conductivity, suspended solids, calcium hardness, total dissolved solids, total coliforms, and coliforms were the chief contaminant indicators in the underground water system in the study area. Pearson correlation revealed a high correlation of electrical conductivity for many parameters analyzed. In the same vein, the regression models suggest that the heavier the molecular weight of a chemical contaminant of a pollutant from a point source, the greater the pollution of the underground water system at a short distance. The study concludes that the associative properties of landfill have a significant effect on groundwater quality in the study area.

Keywords: dumpsite, leachate, groundwater pollution, linear regression, principal component

Procedia PDF Downloads 117

Authors: Kuladip Jana

Abstract:

Benzo(a)pyrene [B(a)P] is an environmental toxicant present mostly in cigarette smoke and car exhaust, is an aryl hydrocarbon receptor (AhR) ligand that exerts its toxic effects on both male and female reproductive systems. In this study, the effect of B(a)P at different doses (0.1, 0.25, 0.5, 1 and 5 mg /kg body weight) was studied on male reproductive system of rat. A significant decrease in cauda epididymal sperm count and motility along with the presence of sperm head abnormalities and altered epididymal and testicular histology were documented following B(a)P treatment. B(a)P treatment resulted apoptotic sperm cells as observed by TUNEL and Annexin V-PI assay with increased ROS, altered sperm mitochondrial membrane potential (ΔΨm) with a simultaneous decrease in the activity of antioxidant enzymes and GSH status. TUNEL positive apoptotic cells also observed in testis as well as isolated germ and Leydig cells following B(a)P exposure. Western Blot analysis revealed the activation of p38MAPK, cytosolic translocation of cytochrome-c, up-regulation of Bax and inducible nitric oxide synthase (iNOS) with cleavage of PARP and down-regulation of BCl2 in testis upon B(a)P treatment. The protein and mRNA levels of testicular key steroidogenesis regulatory proteins like StAR, cytochrome P450 IIA1 (CYPIIA1), 3β HSD, 17β HSD showed a significant decrease in a dose dependent manner while an increase in the expression of cytochrome P450 1A1 (CYP1A1), Aryl hydrocarbon Receptor (AhR), active caspase- 9 and caspase- 3 following B(a)P exposure. We conclude that exposure of benzo(a)pyrene caused testicular gamatogenic and steroidogenic disorders by induction of oxidative stress, inhibition of StAR and other steroidogenic enzymes along with activation of p38MAPK and initiated caspase-3 mediated germ and Leydig cell apoptosis.The possible protective role of naturally occurring phytochemicals against B(a)P induced testicular toxicity needs immediate consideration. Curcumin and resveratrol separately were found to protect against B(a)P induced germ cell apoptosis, and their combinatorial effect was more significant. Our present study in isolated testicular germ cell population from adult male Wistar rats, highlighted their synergistic protective effect against B(a)P induced germ cell apoptosis. Curcumin-resveratrol co-treatment decreased the expression of pro-apoptotic proteins like cleaved caspase 3,8,9, cleaved PARP, Apaf1, FasL, tBid. Curcumin-resveratrol co-treatment decreased Bax/Bcl2 ratio, mitochondria to cytosolic translocation of cytochrome c and activated the survival protein Akt. Curcumin-resveratrol decreased the expression of p53 dependent apoptotic genes like Fas, FasL, Bax, Bcl2, Apaf1.Curcumin-resveratrol co-treatment thus prevented B(a)P induced germ cell apoptosis. B(a)P induced testicular ROS generation and oxidative stress were significantly ameliorated with curcumin and resveratrol. Curcumin-resveratrol co-treatment prevented B(a)P induced nuclear translocation of AhR and CYP1A1 production. The combinatorial treatment significantly inhibited B(a)P induced ERK 1/2, p38 MAPK and JNK 1/2 activation. B(a)P treatment increased the expression of p53 and its phosphorylation (p53 ser 15). Curcumin-resveratrol co-treatment significantly decreased p53 level and its phosphorylation (p53 ser 15). The study concludes that curcumin-resveratrol synergistically modulated MAPKs and p53, prevented oxidative stress, regulated the expression of pro and anti-apoptotic proteins as well as the proteins involved in B(a)P metabolism thus protected germ cells from B(a)P induced apoptosis.

Keywords: benzo(a)pyrene, germ cell, apoptosis, oxidative stress, resveratrol, curcumin

Procedia PDF Downloads 260

Authors: Jeremie Coullon, Robert J. Webber

Abstract:

We introduce a Markov chain Monte Carlo (MCMC) sam-pler for infinite-dimensional inverse problems. Our sam-pler is based on the affine invariant ensemble sampler, which uses interacting walkers to adapt to the covariance structure of the target distribution. We extend this ensem-ble sampler for the first time to infinite-dimensional func-tion spaces, yielding a highly efficient gradient-free MCMC algorithm. Because our ensemble sampler does not require gradients or posterior covariance estimates, it is simple to implement and broadly applicable. In many Bayes-ian inverse problems, Markov chain Monte Carlo (MCMC) meth-ods are needed to approximate distributions on infinite-dimensional function spaces, for example, in groundwater flow, medical imaging, and traffic flow. Yet designing efficient MCMC methods for function spaces has proved challenging. Recent gradi-ent-based MCMC methods preconditioned MCMC methods, and SMC methods have improved the computational efficiency of functional random walk. However, these samplers require gradi-ents or posterior covariance estimates that may be challenging to obtain. Calculating gradients is difficult or impossible in many high-dimensional inverse problems involving a numerical integra-tor with a black-box code base. Additionally, accurately estimating posterior covariances can require a lengthy pilot run or adaptation period. These concerns raise the question: is there a functional sampler that outperforms functional random walk without requir-ing gradients or posterior covariance estimates? To address this question, we consider a gradient-free sampler that avoids explicit covariance estimation yet adapts naturally to the covariance struc-ture of the sampled distribution. This sampler works by consider-ing an ensemble of walkers and interpolating and extrapolating between walkers to make a proposal. This is called the affine in-variant ensemble sampler (AIES), which is easy to tune, easy to parallelize, and efficient at sampling spaces of moderate dimen-sionality (less than 20). The main contribution of this work is to propose a functional ensemble sampler (FES) that combines func-tional random walk and AIES. To apply this sampler, we first cal-culate the Karhunen–Loeve (KL) expansion for the Bayesian prior distribution, assumed to be Gaussian and trace-class. Then, we use AIES to sample the posterior distribution on the low-wavenumber KL components and use the functional random walk to sample the posterior distribution on the high-wavenumber KL components. Alternating between AIES and functional random walk updates, we obtain our functional ensemble sampler that is efficient and easy to use without requiring detailed knowledge of the target dis-tribution. In past work, several authors have proposed splitting the Bayesian posterior into low-wavenumber and high-wavenumber components and then applying enhanced sampling to the low-wavenumber components. Yet compared to these other samplers, FES is unique in its simplicity and broad applicability. FES does not require any derivatives, and the need for derivative-free sam-plers has previously been emphasized. FES also eliminates the requirement for posterior covariance estimates. Lastly, FES is more efficient than other gradient-free samplers in our tests. In two nu-merical examples, we apply FES to challenging inverse problems that involve estimating a functional parameter and one or more scalar parameters. We compare the performance of functional random walk, FES, and an alternative derivative-free sampler that explicitly estimates the posterior covariance matrix. We conclude that FES is the fastest available gradient-free sampler for these challenging and multimodal test problems.

Keywords: Bayesian inverse problems, Markov chain Monte Carlo, infinite-dimensional inverse problems, dimensionality reduction

Procedia PDF Downloads 154

Authors: Anshu Anshu

Abstract:

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

Procedia PDF Downloads 259

Authors: Evren Aysev Denec

Abstract:

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

Procedia PDF Downloads 198

Authors: Ronald Orth, Johanna Haunschild, Sara Tsog

Abstract:

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

Procedia PDF Downloads 125

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

Abstract:

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

Procedia PDF Downloads 277

Authors: Inna Kornienko, Svetlana Guryeva, Natalia Danilova, Elena Petersen

Abstract:

Drug toxicity often goes undetected until clinical trials, the most expensive and dangerous phase of drug development. Both human cell culture and animal studies have limitations that cannot be overcome by improvements in drug testing protocols. Tissue engineering is an emerging alternative approach to creating models of human malignant tumors for experimental oncology, personalized medicine, and drug discovery studies. This new generation of bioengineered tumors provides an opportunity to control and explore the role of every component of the model system including cell populations, supportive scaffolds, and signaling molecules. An area that could greatly benefit from these models is cancer research. Recent advances in tissue engineering demonstrated that decellularized tissue is an excellent scaffold for tissue engineering. Decellularization of donor organs such as heart, liver, and lung can provide an acellular, naturally occurring three-dimensional biologic scaffold material that can then be seeded with selected cell populations. Preliminary studies in animal models have provided encouraging results for the proof of concept. Decellularized Organs preserve organ microenvironment, which is critical for cancer metastasis. Utilizing 3D tumor models results greater proximity of cell culture morphological characteristics in a model to its in vivo counterpart, allows more accurate simulation of the processes within a functioning tumor and its pathogenesis. 3D models allow study of migration processes and cell proliferation with higher reliability as well. Moreover, cancer cells in a 3D model bear closer resemblance to living conditions in terms of gene expression, cell surface receptor expression, and signaling. 2D cell monolayers do not provide the geometrical and mechanical cues of tissues in vivo and are, therefore, not suitable to accurately predict the responses of living organisms. 3D models can provide several levels of complexity from simple monocultures of cancer cell lines in liquid environment comprised of oxygen and nutrient gradients and cell-cell interaction to more advanced models, which include co-culturing with other cell types, such as endothelial and immune cells. Following this reasoning, spheroids cultivated from one or multiple patient-derived cell lines can be utilized to seed the matrix rather than monolayer cells. This approach furthers the progress towards personalized medicine. As an initial step to create a new ex vivo tissue engineered model of a cancer tumor, optimized protocols have been designed to obtain organ-specific acellular matrices and evaluate their potential as tissue engineered scaffolds for cultures of normal and tumor cells. Decellularized biomatrix was prepared from animals’ kidneys, urethra, lungs, heart, and liver by two decellularization methods: perfusion in a bioreactor system and immersion-agitation on an orbital shaker with the use of various detergents (SDS, Triton X-100) in different concentrations and freezing. Acellular scaffolds and tissue engineered constructs have been characterized and compared using morphological methods. Models using decellularized matrix have certain advantages, such as maintaining native extracellular matrix properties and biomimetic microenvironment for cancer cells; compatibility with multiple cell types for cell culture and drug screening; utilization to culture patient-derived cells in vitro to evaluate different anticancer therapeutics for developing personalized medicines.

Keywords: 3D models, decellularization, drug discovery, drug toxicity, scaffolds, spheroids, tissue engineering

Procedia PDF Downloads 301

Authors: Dan Kanmegne

Abstract:

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

Procedia PDF Downloads 145

Authors: Asif Ali, Daud Salim Faruquie

Abstract:

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

Procedia PDF Downloads 401

Authors: Dibyendu Rakshit, Santosh K. Sarkar

Abstract:

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

Procedia PDF Downloads 234

Authors: Simrat Kaur, Fatema Diwan, Brad Reddersen

Abstract:

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

Procedia PDF Downloads 100

Authors: Bitewulign Mekonnen

Abstract:

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

Procedia PDF Downloads 94

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

Abstract:

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

Procedia PDF Downloads 95

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

Abstract:

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

Procedia PDF Downloads 204

Authors: Xingyu Gao, Qiang Wu

Abstract:

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

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

Procedia PDF Downloads 50

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

Abstract:

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

Procedia PDF Downloads 148

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

Procedia PDF Downloads 69

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 157

Authors: Pinzhe Zhao

Abstract:

This study focuses on predicting university students' cheating tendencies using psychological traits and machine learning techniques. Academic dishonesty is a significant issue that compromises the integrity and fairness of educational institutions. While much research has been dedicated to detecting cheating behaviors after they have occurred, there is limited work on predicting such tendencies before they manifest. The aim of this research is to develop a model that can identify students who are at higher risk of engaging in academic misconduct, allowing for earlier interventions to prevent such behavior. Psychological factors are known to influence students' likelihood of cheating. Research shows that traits such as test anxiety, moral reasoning, self-efficacy, and achievement motivation are strongly linked to academic dishonesty. High levels of anxiety may lead students to cheat as a way to cope with pressure. Those with lower self-efficacy are less confident in their academic abilities, which can push them toward dishonest behaviors to secure better outcomes. Students with weaker moral judgment may also justify cheating more easily, believing it to be less wrong under certain conditions. Achievement motivation also plays a role, as students driven primarily by external rewards, such as grades, are more likely to cheat compared to those motivated by intrinsic learning goals. In this study, data on students’ psychological traits is collected through validated assessments, including scales for anxiety, moral reasoning, self-efficacy, and motivation. Additional data on academic performance, attendance, and engagement in class are also gathered to create a more comprehensive profile. Using machine learning algorithms such as Random Forest, Support Vector Machines (SVM), and Long Short-Term Memory (LSTM) networks, the research builds models that can predict students’ cheating tendencies. These models are trained and evaluated using metrics like accuracy, precision, recall, and F1 scores to ensure they provide reliable predictions. The findings demonstrate that combining psychological traits with machine learning provides a powerful method for identifying students at risk of cheating. This approach allows for early detection and intervention, enabling educational institutions to take proactive steps in promoting academic integrity. The predictive model can be used to inform targeted interventions, such as counseling for students with high test anxiety or workshops aimed at strengthening moral reasoning. By addressing the underlying factors that contribute to cheating behavior, educational institutions can reduce the occurrence of academic dishonesty and foster a culture of integrity. In conclusion, this research contributes to the growing body of literature on predictive analytics in education. It offers a approach by integrating psychological assessments with machine learning to predict cheating tendencies. This method has the potential to significantly improve how academic institutions address academic dishonesty, shifting the focus from punishment after the fact to prevention before it occurs. By identifying high-risk students and providing them with the necessary support, educators can help maintain the fairness and integrity of the academic environment.

Keywords: academic dishonesty, cheating prediction, intervention strategies, machine learning, psychological traits, academic integrity

Procedia PDF Downloads 20

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 90