Search results for: mixed forest

Authors: Majid Khalili, Hamed Tayebi

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This paper considers a stochastic flexible job-shop scheduling (SFJSS) problem in the presence of production disruptions, and reactive scheduling is implemented in order to find the optimal solution under uncertainty. In this problem, there are two main disruptions including machine failure which influences operation time, and modification or cancellation of the order delivery date during production. In order to decrease the negative effects of these difficulties, two derived strategies from reactive scheduling are used; the first one is relevant to being able to allocate multiple machine to each job, and the other one is related to being able to select the best alternative process from other job while some disruptions would be created in the processes of a job. For this purpose, a Mixed Integer Linear Programming model is proposed.

Keywords: flexible job-shop scheduling, reactive scheduling, stochastic environment, mixed integer linear programming

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Authors: Sahar Dehghan, Yeganeh Mousavi Jahromi, Ghahraman Abdoli

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Since tax revenues are one of the most important sources of government revenue, it is essential to consider increasing taxpayers' compliance. One of the factors that can affect the taxpayers' compliance is the structure of the crimes and incentives envisaged in the tax law. In this research, by using the 'prospect theory', the effects of changes in the rate of crimes and the tax incentive in the direct tax law on the taxpayer’s compliance behavior have been investigated. To determine the preferences and preferences of taxpayer’s in the business sector and their degree of sensitivity to fines and incentives, a questionnaire with mixed gamble structure is designed. Estimated results using the Hierarchical Bayesian method indicate that the taxpayer’s that have been tested in this study are more sensitive to the incentives in the direct tax law, and the tax administration can use this to increase the level of collected tax and increase the level of compliance.

Keywords: tax compliance, prospect theory, value function, mixed gamble

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Authors: Muwaffaq Usman Adam, Isaac Quaye, Jim Anbazu, Yetimoni Kpeebi, Michael Osei-Assibey

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Urban informality, characterized by spontaneous and self-organized practices, plays a significant but often overlooked role in shaping the development of cities, particularly in the context of mixed-use and compact urban environments. This paper aims to explore the invisible planning processes inherent in informal practices and their influence on the urban form of Ghanaian cities. By examining the dynamic interplay between informality and formal planning, the study will discuss the ways in which informal actors shape and plan for mixed-use and compact development. Drawing on the synthesis of relevant secondary data, the research will begin by defining urban informality and identifying the factors that contribute to its prevalence in Ghanaian cities. It will delve into the concept of mixed-use and compact development, highlighting its benefits and importance in urban areas. Drawing on case studies, the paper will uncover the hidden planning processes that occur within informal settlements, showcasing their impact on the physical layout, land use, and spatial arrangements of Ghanaian cities. The study will also uncover the challenges and opportunities associated with informal planning. It examines the constraints faced by informal planners (actors) while also exploring the potential benefits and opportunities that emerge when informality is integrated into formal planning frameworks. By understanding the invisible planner, the research will offer valuable insights into how informal practices can contribute to sustainable and inclusive urban development. Based on the findings, the paper will present policy implications and recommendations. It highlights the need to bridge the policy gaps and calls for the recognition of informal planning practices within formal systems. Strategies are proposed to integrate informality into planning frameworks, fostering collaboration between formal and informal actors to achieve compact and mixed-use development in Ghanaian cities. This research underscores the importance of recognizing and leveraging the invisible planner in Ghanaian cities. By embracing informal planning practices, cities can achieve more sustainable, inclusive, and vibrant urban environments that meet the diverse needs of their residents. This research will also contribute to a deeper understanding of the complex dynamics between informality and planning, advocating for inclusive and collaborative approaches that harness the strengths of both formal and informal actors. The findings will likewise contribute to advancing our understanding of informality's role as an invisible yet influential planner, shedding light on its spatial planning implications on Ghanaian cities.

Keywords: informality, mixed-uses, compact development, land use, ghana

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Authors: Muhtasim Shafi Kader, Fizar Ahmed, Annesha Acharjee

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Machine learning and data mining are crucial in health care, as well as medical information and detection. Machine learning approaches are now being utilized to improve awareness of a variety of critical health issues, including diabetes detection, neuron cell tumor diagnosis, COVID 19 identification, and so on. Parkinson’s disease is basically a disease for our senior citizens in Bangladesh. Parkinson's Disease indications often seem progressive and get worst with time. People got affected trouble walking and communicating with the condition advances. Patients can also have psychological and social vagaries, nap problems, hopelessness, reminiscence loss, and weariness. Parkinson's disease can happen in both men and women. Though men are affected by the illness at a proportion that is around partial of them are women. In this research, we have to get out the accurate ML algorithm to find out the disease with a predictable dataset and the model of the following machine learning classifiers. Therefore, nine ML classifiers are secondhand to portion study to use machine learning approaches like as follows, Naive Bayes, Adaptive Boosting, Bagging Classifier, Decision Tree Classifier, Random Forest classifier, XBG Classifier, K Nearest Neighbor Classifier, Support Vector Machine Classifier, and Gradient Boosting Classifier are used.

Keywords: naive bayes, adaptive boosting, bagging classifier, decision tree classifier, random forest classifier, XBG classifier, k nearest neighbor classifier, support vector classifier, gradient boosting classifier

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Authors: Tareq Oshan

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Facility location and size decisions are important to any company because they affect profitability and success. However, warehouses are exposed to various risks of failure that affect their activity. This paper presents a mixed-integer non-linear mathematical model that can be used to determine optimal warehouse locations and sizes, which warehouses to fortify, and which branches should be assigned to specific warehouses when there is a risk of warehouse failure. Every branch is assigned to a fortified primary warehouse or a nonfortified primary warehouse and a fortified backup warehouse. The standard method and an introduced method, based on the average probabilities, for linearizing this mathematical model were used. A Canadian case study was used to demonstrate the developed mathematical model, followed by some sensitivity analysis.

Keywords: supply chain network design, fortified warehouse, mixed-integer mathematical model, warehouse failure risk

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Authors: Irene Capecchi, Tommaso Borghini, Iacopo Bernetti

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One of the most common pieces of equipment still used today for pruning, felling, and processing trees is the chainsaw in forestry. However, chainsaw use highlights dangers and one of the highest rates of accidents in both professional and non-professional work. Felling is proportionally the most dangerous phase, both in severity and frequency, because of the risk of being hit by the plant the operator wants to cut down. To avoid this, a correct sequence of chainsaw cuts must be taught concerning the different conditions of the tree. Virtual reality (VR) makes it possible to virtually simulate chainsaw use without danger of injury. The limitations of the existing applications are as follow. The existing platforms are not symmetrical collaborative because the trainee is only in virtual reality, and the trainer can only see the virtual environment on a laptop or PC, and this results in an inefficient teacher-learner relationship. Therefore, most applications only involve the use of a virtual chainsaw, and the trainee thus cannot feel the real weight and inertia of a real chainsaw. Finally, existing applications simulate only a few cases of tree felling. The objectives of this research were to implement and test a symmetrical collaborative training application based on VR and mixed reality (MR) with the overlap between real and virtual chainsaws in MR. The research and training platform was developed for the Meta quest 2 head-mounted display. The research and training platform application is based on the Unity 3D engine, and Present Platform Interaction SDK (PPI-SDK) developed by Meta. PPI-SDK avoids the use of controllers and enables hand tracking and MR. With the combination of these two technologies, it was possible to overlay a virtual chainsaw with a real chainsaw in MR and synchronize their movements in VR. This ensures that the user feels the weight of the actual chainsaw, tightens the muscles, and performs the appropriate movements during the test allowing the user to learn the correct body posture. The chainsaw works only if the right sequence of cuts is made to felling the tree. Contact detection is done by Unity's physics system, which allows the interaction of objects that simulate real-world behavior. Each cut of the chainsaw is defined by a so-called collider, and the felling of the tree can only occur if the colliders are activated in the right order simulating a safe technique felling. In this way, the user can learn how to use the chainsaw safely. The system is also multiplayer, so the student and the instructor can experience VR together in a symmetrical and collaborative way. The platform simulates the following tree-felling situations with safe techniques: cutting the tree tilted forward, cutting the medium-sized tree tilted backward, cutting the large tree tilted backward, sectioning the trunk on the ground, and cutting branches. The application is being evaluated on a sample of university students through a special questionnaire. The results are expected to test both the increase in learning compared to a theoretical lecture and the immersive and telepresence of the platform.

Keywords: chainsaw, collaborative symmetric virtual reality, mixed reality, operator training

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Authors: Saeed Ahmed, Tamoor Abbas, M. Amir, M. S. Iqbal, D. Hussain

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This study was conducted to evaluate the effect of supplementation of different levels of yeast culture (Saccharomyces cerevisiae) in Beetal male goats diets on growth performance, digestibility of nutrients and selected blood metabolites. Another objective was to determine the inclusion level of yeast culture for optimal growth performance of Beetal male goats. Eighteen (n=18) Beetal male goats were randomly assigned to three total mixed ration treatments (n=6 goats/treatment): T1, T2 and T3 containing 0gm, 3gm and 6gm/day yeast culture (YC) mixed with total mixed ration (TMR). The diets were iso-nitrogenous and iso-caloric having crude protein 15.2% and ME 2.6Mcal/kg. The total duration of the experiment was 8 weeks. Beetal bucks were fed on TMR diets (T1, T2 and T3) having blend of oat silage, Lucerne hay and concentrate mixed with yeast culture (YC). Bucks were housed individually and feed was offered @ 4% of body weight on dry matter basis. Samples of fresh feed and refusal were collected twice weekly of moisture percentage using hot air oven. Data for daily dry matter intake, body weight gain, nutrient digestibility and selected blood metabolites were analyzed through one-way ANOVA technique under Complete randomised design (SAS Institute Inc, 2002-03). Results were declared significant at P≤0.05. Overall, DMI was not affected (P≥0.05) by dietary treatments. Body weight gain, digestibility of crude protein and crude fibre were improved. Blood glucose concentration was detected higher in the group having supplementation of yeast culture (YC) 6gm/day compared to other two dietary treatments. This study suggested the positive impact of inclusion of yeast culture (YC) up to 6gm/day in the TMR diet for optimal growth performance and digestibility of nutrients in Beetal male goats.

Keywords: yeast culture, growth performance, digestibility, beetle goat

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Authors: Krishang Surapaneni

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The goal of this project is to determine how to predict important aspects of options, including the ask price. We want to compare different machine learning models to learn the best model and the best hyperparameters for that model for this purpose and data set. Option pricing is a relatively new field, and it can be very complicated and intimidating, especially to inexperienced people, so we want to create a machine learning model that can predict important aspects of an option stock, which can aid in future research. We tested multiple different models and experimented with hyperparameter tuning, trying to find some of the best parameters for a machine-learning model. We tested three different models: a Random Forest Regressor, a linear regressor, and an MLP (multi-layer perceptron) regressor. The most important feature in this experiment is the ask price; this is what we were trying to predict. In the field of stock pricing prediction, there is a large potential for error, so we are unable to determine the accuracy of the models based on if they predict the pricing perfectly. Due to this factor, we determined the accuracy of the model by finding the average percentage difference between the predicted and actual values. We tested the accuracy of the machine learning models by comparing the actual results in the testing data and the predictions made by the models. The linear regression model performed worst, with an average percentage error of 17.46%. The MLP regressor had an average percentage error of 11.45%, and the random forest regressor had an average percentage error of 7.42%

Keywords: finance, linear regression model, machine learning model, neural network, stock price

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Authors: Masoud Rafiee

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To study the quality response of forage to chickpea-barley mixed cropping under drought stress and vermicompost consumption, an experiment was carried out under well watered and %70 water requirement (stress condition) in RCBD as split plot with four replications in temperate condition of Khorramabad in 2013. Chickpea-barley mix cropping (%100 chickpea, %75:25 chickpea:barley, %50:50 chickpea:barley, %25:75 chickpea:barley, and %100 barley) was studied. Results showed that wet and dry forage yield were significantly affected by environment and decreased in stress condition. Also, crude protein content decreased from %26.2 in well watered to %17.3 in stress condition.

Keywords: crude protein, wet forage yield, dry forage yield, water stress condition, well watered

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Authors: Muhammad Akhtaruzzaman

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The impact of corporate environmental social and governance (ESG) on financial performance is often difficult to quantify despite the ESG related theories predict that ESG performance improves financial performance of a company. This research examines the link between corporate ESG performance and the financial performance of the NZX (New Zealand Stock Exchange) listed companies. For this purpose, this research utilizes mixed methods approaches to examine and understand this link. While quantitative results found no robust evidence of such a link, however, the qualitative analysis of content data suggests a strong cooccurrence exists between ESG performance and financial performance. The findings of this research have important implications for policymakers to support higher ESG-performing companies and for management practitioners to develop ESG-related strategies.

Keywords: ESG, financial performance, New Zealand firms, thematic analysis, mixed methods

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Authors: Viacheslav Shkuratskyy, Aminu Bello Usman, Michael O’Dea, Saifur Rahman Sabuj

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This paper examines relationships between solar activity and earthquakes; it applied machine learning techniques: K-nearest neighbour, support vector regression, random forest regression, and long short-term memory network. Data from the SILSO World Data Center, the NOAA National Center, the GOES satellite, NASA OMNIWeb, and the United States Geological Survey were used for the experiment. The 23rd and 24th solar cycles, daily sunspot number, solar wind velocity, proton density, and proton temperature were all included in the dataset. The study also examined sunspots, solar wind, and solar flares, which all reflect solar activity and earthquake frequency distribution by magnitude and depth. The findings showed that the long short-term memory network model predicts earthquakes more correctly than the other models applied in the study, and solar activity is more likely to affect earthquakes of lower magnitude and shallow depth than earthquakes of magnitude 5.5 or larger with intermediate depth and deep depth.

Keywords: k-nearest neighbour, support vector regression, random forest regression, long short-term memory network, earthquakes, solar activity, sunspot number, solar wind, solar flares

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Authors: Chun-Ying Lee, Kun-Hsien Lee, Bor-Wei Wang

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The process of post-supercritical CO₂ electroplating uses the electrolyte solution after being mixed with supercritical CO₂ and released to atmospheric pressure. It utilizes the microbubbles that form when oversaturated CO₂ in the electrolyte returns to gaseous state, which gives the similar effect of pulsed electroplating. Under atmospheric pressure, the CO₂ bubbles gradually diffuse. Therefore, the introduction of ultrasound and/or other agitation can potentially excite the CO₂ microbubbles to achieve an electroplated surface of even higher quality. In this study, during the electroplating process, three different modes of agitation: magnetic stirrer agitation, ultrasonic agitation and a combined mode (magnetic + ultrasonic) were applied, respectively, in order to obtain an optimal surface morphology and mechanical properties for the electroplated Ni coating. It is found that the combined agitation mode at a current density of 40 A/dm² achieved the smallest grain size, lower surface roughness, and produced an electroplated Ni layer that achieved hardness of 320 HV, much higher when compared with conventional method, which were usually in the range of 160 to 300 HV. However, at the same time, the electroplating with combined agitation developed a higher internal stress of 320 MPa due to the lower current efficiency of the process and finer grain in the coating. Moreover, a new control methodology for tailoring the coating’s mechanical property through its thickness was demonstrated by the timely introduction of ultrasonic agitation during the electroplating process with post supercritical CO₂ mixed electrolyte.

Keywords: nickel electroplating, micro-bubbles, supercritical carbon dioxide, ultrasonic agitation

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Authors: Baba John, Abdullahi Mohammed

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The aim of this research is to compare the analysis of biodegradation on polythene and plastics buried in fadama soil amended with Organic and Inorganic fertilizer. Physico- chemical properties of the samples were determined. Bacteria and Fungi implicated in the biodegradation were identified and enumerated. Physico- chemical properties before the analysis indicated pH range of the samples from 4.28 – 5.80 , While the percentage Organic carbon and Organic matter was highest in cow dung samples with 3.89% and 6.69% respectively. The total Nitrogen percentage was recorded to be highest in Chicken dropping (0.68), while the availability of Phosphorus (P), Sodium (Na), Pottasium (K), and Magnessium (mg) was recorded to be highest in F – soil (Control), with values to be 37ppm, 1.63 Cmolkg-1, 0.35 Cmolkg-1 and 1.18 Cmolkg-1 respectively, except for calcium which was recorded to be highest in Cow dung (5.80 Cmolkg-1). However, physico – chemical properties of the samples after analysis indicated pH range of 4.6 – 5.80, Percentage Organic carbon and Organic matter was highest in Fadama soil mixed with fertilizer, having 0.7% and 1.2% respectively. Total Percentage Nitrogen content was found to be highest (0.56) in Fadama soil mixed with poultry dropping. Availability of Sodium (Na), Pottasium (K), and Calcium (Ca) was recorded to be highest in Fadama Soil mixed with Cow dung with values to be 0.64 Cmolkg-1, 2.07 Cmolkg-1 and 3.36 Cmolkg-1 respectively. The percentage weight loss of polythene and plastic bags after nine months in fadama soil mixed with poultry dropping was 11.9% for polythene and 6.0% for plastics. Weight loss in fadama soil mixed with cow dung was 18.1% for polythene and 4.7% for plastics. Weight loss of polythene and plastic in fadama soil mixed with fertilizer (NPK) was 7.4% for polythene and 3.3% for plastics. While, the percentage weight loss of polythene and plastics after nine months of burial in fadama soil (control) was 3.5% and 0.0% respectively. The bacteria species isolated from Fadama soil, organic and inorganic fertilizers before amendments include: S. aureus, Micrococcus sp, Streptococcus. pyogenes, Psuedomonas aeruginosa Bacillus subtilis and Bacillus cereus. The fungi species include: Aspergillus niger, Aspergillus fumigatus, Aspergillus flavus, Fusarium sp, Mucor sp Penicillium sp and Candida sp. The bacteria species isolated and characterized after nine months of seeding include: S. aureus, Micrococcus sp, S. pyogenes, P. aeruginosa and B. subtilis. The fungi species are: A. niger A. flavus, A. fumigatus, Mucor sp, Penicillium sp and Fusarium sp. The result of this study indicated that plastic materials can be degraded in the fadama soil irrespective of whether the soil is amended or not. The Period of composting also has a significant impact on the rate at which polythene and plastics are degraded.

Keywords: Fadama, fertilizer, plastic and polythene, biodegradation

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Authors: Ella Sèdé Maforikan

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Accurate land cover mapping is essential for effective environmental monitoring and natural resources management. This study focuses on assessing the classification performance of two satellite datasets and evaluating the impact of different input feature combinations on classification accuracy in the Beterou catchment, situated in the northern part of Benin. Landsat-8 and Sentinel-2 images from June 1, 2020, to March 31, 2021, were utilized. Employing the Random Forest (RF) algorithm on Google Earth Engine (GEE), a supervised classification categorized the land into five classes: forest, savannas, cropland, settlement, and water bodies. GEE was chosen due to its high-performance computing capabilities, mitigating computational burdens associated with traditional land cover classification methods. By eliminating the need for individual satellite image downloads and providing access to an extensive archive of remote sensing data, GEE facilitated efficient model training on remote sensing data. The study achieved commendable overall accuracy (OA), ranging from 84% to 85%, even without incorporating spectral indices and terrain metrics into the model. Notably, the inclusion of additional input sources, specifically terrain features like slope and elevation, enhanced classification accuracy. The highest accuracy was achieved with Sentinel-2 (OA = 91%, Kappa = 0.88), slightly surpassing Landsat-8 (OA = 90%, Kappa = 0.87). This underscores the significance of combining diverse input sources for optimal accuracy in land cover mapping. The methodology presented herein not only enables the creation of precise, expeditious land cover maps but also demonstrates the prowess of cloud computing through GEE for large-scale land cover mapping with remarkable accuracy. The study emphasizes the synergy of different input sources to achieve superior accuracy. As a future recommendation, the application of Light Detection and Ranging (LiDAR) technology is proposed to enhance vegetation type differentiation in the Beterou catchment. Additionally, a cross-comparison between Sentinel-2 and Landsat-8 for assessing long-term land cover changes is suggested.

Keywords: land cover mapping, Google Earth Engine, random forest, Beterou catchment

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Authors: Asmit Neupane, Narayan Prasad Gautam, Prabin Bhusal

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Indian pangolin, locally called as ‘Salak’, ‘Sal machha’, ‘Pakho machha’, is a globally endangered species, nationally categorized as a critically endangered species, protected under the National Parks and Wildlife Conservation (NPWC) Act 1973 and appended in Appendix I of CITES. Indian pangolins occur in the tropical areas of Terai region and Chure foothills of eastern Nepal, and India, Bangladesh, Pakistan, and Sri Lanka. They utilize a wide range of habitats, including primary and secondary tropical forest, limestone forest, bamboo forest, grassland, and agricultural lands. So, in regard to this fact, this research is aimed to provide detailed information regarding the current distribution pattern, status, habitat preference, prevailing threats and attitude of local people towards species conservation in Madhesh Province, Nepal. The study was conducted in four CFs, two from Bara district and two from Dhanusha district. The study area comprised of Churia range and foothills with tropical and sub-tropical vegetation. A total of 24 transects were established, each of 500*50 m2, where indirect signs of Indian pangolin, including active/old burrows, pugmarks and scratches, were found. Altogether 93 burrows were found, where only 20 were active burrows. Similarly, a vegetation survey and social survey was also conducted. The data was analyzed using Stata 16 and SPSS software. Distance from settlement, ground cover, aspect, presence/absence of ants/termites and human disturbance were the important habitat parameters having statistically significant relationship with the distribution of Indian pangolin in the area. The species was found to prefer an elevation of 360 to 540m, 0-15º slope, red soil, North-east aspect, moderate crown and ground cover, without fire and rocks, vicinity of water, roads, settlement, Sal dominated forest and minimum disturbed by human activities. Similarly, the attitude of local people towards Indian pangolin conservation was found to be significantly different with respect to age, sex and education level. The study concludes that majority of active burrows were found in Churia hills, which indicates that Indian pangolin population is gradually moving uphill towards higher elevation as hilly area supports better prey availability and also less human disturbance. Further studies are required to investigate microhabitat preferences, seasonal variability and impacts of climate change on the distribution, habitat and prey availability of Indian pangolin for the sustainable conservation of this species.

Keywords: conservation, IUCN red list, local participation, small mammal, status, threats

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Authors: Magda Gorczyca, Zofia Nocoń

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Past and current studies from different countries showed that river engineering leads to environmental degradation and extinction of many species - often those protected by local and international wildlife conservation laws. Through the years, the main focus of rivers utilization has shifted from industrial applications to recreation and wildlife preservation with a focus on keeping the biodiversity which plays a significant role in preventing climate changes. Thus an opportunity appeared to recreate flooding areas and natural habitats, which are very rare in the scale of Europe. Additionally, river restoration helps to avoid floodings and periodic droughts, which are usually very damaging to the economy. In this research, the biodiversity of dragonflies and ground beetles was analyzed in the context of plant communities and forest stands structure. Results were enriched with data from past and current literature. A comparison was made between two parts of the Odra river. A part where oxbow lake and riparian forest were separated from the river bed by embankment and a part of the river with floodplains left intact. Validity assessment of embankments relocation was made based on the research results. In the period between May and September, insects were collected, phytosociological analysis were taken, and forest stand structure properties were specified. In the part of the river not separated by the embankments, rare and protected species of plants were spotted (e.g., Trapanatans, Salvinianatans) as well as greater species and quantitive diversity of dragonfly. Ground beetles fauna, though, was richer in the area separated by the embankment. Even though the research was done during only one season and in a limited area, the results can be a starting point for further extended research and may contribute to acquiring legal wildlife protection and restoration of the researched area. During the research, the presence of invasive species Impatiens parviflora, Echinocystislobata, and Procyonlotor were observed, which may lead to loss of the natural values of the researched areas.

Keywords: carabidae, floodplains, middle Odra river, Odonata, oxbow lakes, riparian forests

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Authors: Nidhal Jamia, Sami El-Borgi

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In this paper, the problem of a mixed-Mode crack embedded in an infinite medium made of a functionally graded piezoelectric material (FGPM) with crack surfaces subjected to electro-mechanical loadings is investigated. Eringen’s non-local theory of elasticity is adopted to formulate the governing electro-elastic equations. The properties of the piezoelectric material are assumed to vary exponentially along a perpendicular plane to the crack. Using Fourier transform, three integral equations are obtained in which the unknown variables are the jumps of mechanical displacements and electric potentials across the crack surfaces. To solve the integral equations, the unknowns are directly expanded as a series of Jacobi polynomials, and the resulting equations solved using the Schmidt method. In contrast to the classical solutions based on the local theory, it is found that no mechanical stress and electric displacement singularities are present at the crack tips when nonlocal theory is employed to investigate the problem. A direct benefit is the ability to use the calculated maximum stress as a fracture criterion. The primary objective of this study is to investigate the effects of crack length, material gradient parameter describing FGPMs, and lattice parameter on the mechanical stress and electric displacement field near crack tips.

Keywords: functionally graded piezoelectric material (FGPM), mixed-mode crack, non-local theory, Schmidt method

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Authors: Manavjit Singh Dhindsa, Ursula Das, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

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These days, wildland fires, also known as forest fires, are more prevalent than ever. Wildfires have major repercussions that affect ecosystems, communities, and the environment in several ways. Wildfires lead to habitat destruction and biodiversity loss, affecting ecosystems and causing soil erosion. They also contribute to poor air quality by releasing smoke and pollutants that pose health risks, especially for individuals with respiratory conditions. Wildfires can damage infrastructure, disrupt communities, and cause economic losses. The economic impact of firefighting efforts, combined with their direct effects on forestry and agriculture, causes significant financial difficulties for the areas impacted. This research explores different forest fire spread models and presents a comprehensive review of various techniques and methodologies used in the field. A forest fire spread model is a computational or mathematical representation that is used to simulate and predict the behavior of a forest fire. By applying scientific concepts and data from empirical studies, these models attempt to capture the intricate dynamics of how a fire spreads, taking into consideration a variety of factors like weather patterns, topography, fuel types, and environmental conditions. These models assist authorities in understanding and forecasting the potential trajectory and intensity of a wildfire. Emphasizing the need for a comprehensive understanding of wildfire dynamics, this research explores the approaches, assumptions, and findings derived from various models. By using a comparison approach, a critical analysis is provided by identifying patterns, strengths, and weaknesses among these models. The purpose of the survey is to further wildfire research and management techniques. Decision-makers, researchers, and practitioners can benefit from the useful insights that are provided by synthesizing established information. Fire spread models provide insights into potential fire behavior, facilitating authorities to make informed decisions about evacuation activities, allocating resources for fire-fighting efforts, and planning for preventive actions. Wildfire spread models are also useful in post-wildfire mitigation strategies as they help in assessing the fire's severity, determining high-risk regions for post-fire dangers, and forecasting soil erosion trends. The analysis highlights the importance of customized modeling approaches for various circumstances and promotes our understanding of the way forest fires spread. Some of the known models in this field are Rothermel’s wildland fuel model, FARSITE, WRF-SFIRE, FIRETEC, FlamMap, FSPro, cellular automata model, and others. The key characteristics that these models consider include weather (includes factors such as wind speed and direction), topography (includes factors like landscape elevation), and fuel availability (includes factors like types of vegetation) among other factors. The models discussed are physics-based, data-driven, or hybrid models, also utilizing ML techniques like attention-based neural networks to enhance the performance of the model. In order to lessen the destructive effects of forest fires, this initiative aims to promote the development of more precise prediction tools and effective management techniques. The survey expands its scope to address the practical needs of numerous stakeholders. Access to enhanced early warning systems enables decision-makers to take prompt action. Emergency responders benefit from improved resource allocation strategies, strengthening the efficacy of firefighting efforts.

Keywords: artificial intelligence, deep learning, forest fire management, fire risk assessment, fire simulation, machine learning, remote sensing, wildfire modeling

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Authors: York Neudel

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The phenomenon of tourism has been considered as tool to overcome cultural frontiers, to comprehend the other and to cope with mutual mistrust and suspicion. Well, that has been a myth, at least when it comes to mass-tourism. Other approaches, like community-based tourism, still are based on the idea of embracing the other in order to help or to understand the cultural difference. In 1997, two American NGOs incentivized a tourism-project in a community in the highlands of Ecuador, in order to protect the cloud forest from destructive exploitation of its own inhabitants. Nineteen years after that, I analyze in this investigation the interactions between the Ecuadorian hosts in the mestizo-community of Yunguilla and the foreign tourist in the quest for “authentic life” in the Ecuadorian cloud forest. As a sort of “contemporary pilgrim” the traveller tries to find authenticity in other times and places far away from their everyday life in Europe or North America. Therefore, tourists are guided by stereotypes and expectations that are produced by the touristic industry. The host, on the other hand, has to negotiate this pre-established imaginary. That generates a kind of theatre-play with front- and backstage in organic gardens, little fabrics and even private housing, since this alternative project offers to share the private space of the host with the tourist in the setting the community-based tourism. In order to protect their privacy, the community creates new hybrid spaces that oscillate between front- and backstages that culminates in a game of hide and seek – a phenomenon that promises interesting frictions for an anthropological case-study.

Keywords: Tourism, Authenticity, Community-based tourism, Ecuador, Yunguilla

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Authors: S. S. P. M. Isa, N. M. Arifin, R. Nazar, N. Bachok, F. M. Ali, I. Pop

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A theoretical study has been presented to describe the boundary layer flow and heat transfer on an exponentially shrinking sheet with a variable wall temperature and suction, in the presence of magnetic field. The governing nonlinear partial differential equations are converted into ordinary differential equations by similarity transformation, which are then solved numerically using the shooting method. Results for the skin friction coefficient, local Nusselt number, velocity profiles as well as temperature profiles are presented through graphs and tables for several sets of values of the parameters. The effects of the governing parameters on the flow and heat transfer characteristics are thoroughly examined.

Keywords: exponentially shrinking sheet, magnetic field, mixed convection, suction

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Authors: A. Fazilah, I. Darah, I. Noraznawati

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Three different bacteria capable of degrading phenanthrene were isolated from hydrocarbon contaminated site. In this study, the phenanthrene-degrading activity by defined monoculture was determined and mixed culture was identified as Acinetobacter sp. P3d, Bacillus sp. P4a and Pseudomonas sp. P6. All bacteria were able to grow in a minimal salt medium saturated with phenanthrene as the sole source of carbon and energy. Phenanthrene degradation efficiencies by different combinations (consortia) of these bacteria were investigated and their phenanthrene degradation was evaluated by gas chromatography. Among the monocultures, Pseudomonas sp. P6 exhibited 58.71% activity compared to Acinetobacter sp. P3d and Bacillus sp. P4a which were 56.97% and 53.05%, respectively after 28 days of cultivation. All consortia showed high phenanthrene elimination which were 95.64, 79.37, 87.19, 79.21% for Consortia A, B, C and D, respectively. The results indicate that all of the bacteria isolated may effectively degrade target chemical and have a promising application in bioremediation of hydrocarbon contaminated soil purposes.

Keywords: phenanthrene, consortia, acinetobacter sp. P3d, bacillus sp. P4a, pseudomonas sp. P6

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Authors: Tarul Garg, P. N. Agrawal

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We propose to define the q-analogue of the α-Bernstein Kantorovich operators and then introduce the q-bivariate generalization of these operators to study the approximation of functions of two variables. We obtain the rate of convergence of these bivariate operators by means of the total modulus of continuity, partial modulus of continuity and the Peetre’s K-functional for continuous functions. Further, in order to study the approximation of functions of two variables in a space bigger than the space of continuous functions, i.e. Bögel space; the GBS (Generalized Boolean Sum) of the q-bivariate operators is considered and degree of approximation is discussed for the Bögel continuous and Bögel differentiable functions with the aid of the Lipschitz class and the mixed modulus of smoothness.

Keywords: Bögel continuous, Bögel differentiable, generalized Boolean sum, K-functional, mixed modulus of smoothness

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Authors: A. A. Md. Ryhan Uddin, Swarup Das, Rajesh Barua, Joheb Hasan, Rashedul Islam

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Background: The herbicide has toxicological importance because some of them are associated with high mortality rates due to respiratory failure. Organophosphate poisoning (OPC) & Paraquat self-poisoning is a major clinical and public health problems in low and middle-income countries across much of South Asia. Paraquat was not used as a common suicidal agent previously in Bangladesh. We report a case of 15 years old female admitted to the ER with a history of nausea & vomiting after ingestion of an unknown substance in a suicidal attempt, later identified as mixed poisoning- OPC & Paraquat. She was initially asymptomatic but later developed renal shutdown & lung injuries as well as pneumothorax, referred to as Daisley Barton Syndrome. Objective: This case report aims to alert spontaneous pneumothorax in mixed poisoning on uncommon forms of presentation. Pneumothorax in a patient with paraquat poisoning is a less unusual but underdiagnosed finding. It has a high index of early mortality. Case history: The patient's attendant complained about nausea followed by vomiting, which was nonprojectile & contains undigested food materials first, then gastric juice later. After a few hours, she also complains of urinary retention. Her family members treated her with some home remedies for her initial symptoms, but all attempts failed. After admission, the patient was initially asymptomatic. Through repeated history taking, her attendant showed a bottle of OPC in liquid form, which they suspected that she may have ingested some of the liquid from that bottle accidentally or attempted Suicide. So, management started for OPC poisoning. She responded well initially, but on 4th day of admission, the patient's condition became deteriorating. After the workout with the family member, 2nd bottle of Pesticide was discovered, which was Paraquat. Conclusion: Physicians should be aware of the symptoms of mixed poisoning and the timely use of urine dithionate testing for early detection and treatment. Pneumothorax is an early predictor of mortality in patients with paraquat poisoning.

Keywords: pneumothorax, suicide, dithionate, OPC, herbicide

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Authors: D. Karibasavaraja, Ramesh M.R., Sufiyan Ahmed, Noyonika M.R., Sameeksha A. V., Mamatha J., Samiksha S. Urs

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This research paper gives an overview of the experimental analysis of natural fibers with polymer composite. The whole world is concerned about conserving the environment. Henceforth, the demand for natural and decomposable materials is increasing. The application of natural fibers is widely used in aerospace for manufacturing aircraft bodies, and ship construction in navy fields. Based on the literature review, researchers and scientists are replacing synthetic fibers with natural fibers. The selection of these fibers mainly depends on lightweight, easily available, and economical and has its own physical and chemical properties and many other properties that make them a fine quality fiber. The pineapple fiber has desirable properties of good mechanical strength, high cellulose content, and fiber length. Hybrid composite was prepared using different proportions of pineapple fiber and banana fiber, and their ratios were varied in 90% polypropylene mixed with 5% banana fiber and 5% pineapple fiber, 85% polypropylene mixed with 7.5% banana fiber and 7.5% pineapple fiber and 80% polypropylene mixed with 10% banana fiber and 10% pineapple fiber. By impact experimental analysis, we concluded that the combination of 90% polypropylene and 5% banana fiber and 5% pineapple fiber exhibits a higher toughness value with mechanical strength. We also conducted scanning electron microscopy (SEM) analysis which showed better fiber orientation bonding between the banana and pineapple fibers with polypropylene composites. The main aim of the present research is to evaluate the properties of pineapple fiber and banana fiber reinforced with hybrid polypropylene composites.

Keywords: toughness, fracture, impact strength, banana fibers, pineapple fibers, tensile strength, SEM analysis

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Authors: Tahammal Hussain, Khuram Zia, Mumammad Jalal Arif, Megha Parajulee, Abdul Hakeem

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The Bioassay on neonate, 2nd and 3rd instar larvae of Helicoverpa armigera (Hubner) were conducted against Bacillus thuringiensis proteins Cry1Ac. Cry1Ac was incorporated into an artificial diet and was serially diluted with distilled water and then mixed with diet at an appropriate temperature of diet. Toxins incorporated prepared diet was poured into Petri-dishes. For controls, distilled water was mixed with the diet. Five toxin doses 0.25, 0.5, 1, 2, and 4 ug / ml and one control were used for each instars of H. armigera 20 larvae were used in each replication and each treatment is replicated four times. LC50 of Cry1Ac against neonate, 2nd and 3rd instar larvae of H. armigera were 0.34, 0.81 and 1.46 ug / ml. So Cry1Ac is more effective against neonate larvae of H .armigera as compared to 2nd and 3rd instar larvae under laboratory conditions.

Keywords: B. thuringiensis, Cry1Ac, H. armigera, toxicity

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Authors: Hyppolite Aignon, Souleymane Yorou, Martin Ryberg, Anneli Svanholm

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Climate change and human actions cause the extinction of wild mushrooms. In Benin, the diversity of fungi is large and may still contain species new to science but the inventory effort remains low and focuses on particularly edible species (Russula, Lactarius, Lactifluus, and also Amanita). In addition, inventories have started recently and some groups of fungi are not sufficiently sampled, however, the degradation of fungal habitat continues to increase and some species are already disappearing. (Yorou and De Kesel, 2011), however, the degradation of fungi habitat continues to increase and some species may disappear without being known. This genus (Inocybe) overlooked has a worldwide distribution and includes more than 700 species with many undiscovered or poorly known species worldwide and particularly in tropical Africa. It is therefore important to orient the inventory to other genera or important families such as Inocybe (Fungi, Agaricales) in order to highlight their diversity and also to know their phylogenetic positions with a combined approach of gene regions. This study aims to evaluate the species richness and phylogenetic position of Inocybe species and affiliated taxa in West Africa. Thus, in North Benin, we visited the Forest Reserve of Ouémé Supérieur, the Okpara forest and the Alibori Supérieur Forest Reserve. In the center, we targeted the Forest Reserve of Toui-Kilibo. The surveys have been carried during the raining season in the study area meaning from June to October. A total of 24 taxa were collected, photographed and described. The DNA was extracted, the Polymerase Chain Reaction was carried out using primers (ITS1-F, ITS4-B) for Internal transcribed spacer (ITS), (LROR, LWRB, LR7, LR5) for nuclear ribosomal (LSU), (RPB2-f5F, RPB2-b6F, RPB2- b6R2, RPB2-b7R) for RNA polymerase II gene (RPB2) and sequenced. The ITS sequences of the 24 collections of Inocybaceae were edited in Staden and all the sequences were aligned and edited with Aliview v1.17. The sequences were examined by eye for sufficient similarity to be considered the same species. 13 different species were present in the collections. In addition, sequences similar to the ITS sequences of the thirteen final species were searched using BLAST. The nLSU and RPB2 markers for these species have been inserted in a complete alignment, where species from all major Inocybaceae clades as well as from all continents except Antarctica are present. Our new sequences for nLSU and RPB2 have been manually aligned in this dataset. Phylogenetic analysis was performed using the RAxML v7.2.6 maximum likelihood software. Bootstrap replications have been set to 100 and no partitioning of the dataset has been performed. The resulting tree was viewed and edited with FigTree v1.4.3. The preliminary tree resulting from the analysis of maximum likelihood shows us that these species coming from Benin are much diversified and are distributed in four different clades (Inosperma, Inocybe, Mallocybe and Pseudosperma) on the seven clades of Inocybaceae but the phylogeny position of 7 is currently known. This study marks the diversity of Inocybe in Benin and the investigations will continue and a protection plan will be developed in the coming years.

Keywords: Benin, diversity, Inocybe, phylogeny placement

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Authors: Naduni Ranasinghe

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E-learning environments are getting more popular than any other due to the impact of COVID19. Even though e-learning is one of the best solutions for the teaching-learning process in the academic process, it’s not without major challenges. Nowadays, machine learning approaches are utilized in the analysis of how behavioral factors lead to better adoption and how they related to better performance of the students in eLearning environments. During the pandemic, we realized the academic process in the eLearning approach had a major issue, especially for the performance of the students. Therefore, an approach that investigates student behaviors in eLearning environments using a data-intensive machine learning approach is appreciated. A hybrid approach was used to understand how each previously told variables are related to the other. A more quantitative approach was used referred to literature to understand the weights of each factor for adoption and in terms of performance. The data set was collected from previously done research to help the training and testing process in ML. Special attention was made to incorporating different dimensionality of the data to understand the dependency levels of each. Five independent variables out of twelve variables were chosen based on their impact on the dependent variable, and by considering the descriptive statistics, out of three models developed (Random Forest classifier, SVM, and Decision tree classifier), random forest Classifier (Accuracy – 0.8542) gave the highest value for accuracy. Overall, this work met its goals of improving student performance by identifying students who are at-risk and dropout, emphasizing the necessity of using both static and dynamic data.

Keywords: academic performance prediction, e learning, learning analytics, machine learning, predictive model

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Authors: Elvira Mustikawati P.H., Iis Dewi Ratih, Dita Amelia

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Bojonegoro has proclaimed a policy of zero malnutrition. Therefore, as an effort to solve the cases of malnutrition children in Bojonegoro, this study used the approach geographically Mixed Weighted Regression (MGWR) to determine the factors that influence the percentage of malnourished children under five in which factors can be divided into locally influential factor in each district and global factors that influence throughout the district. Based on the test of goodness of fit models, R2 and AIC values in GWR models are better than MGWR models. R2 and AIC values in MGWR models are 84.37% and 14.28, while the GWR models respectively are 91.04% and -62.04. Based on the analysis with GWR models, District Sekar, Bubulan, Gondang, and Dander is a district with three predictor variables (percentage of vitamin A, the percentage of births assisted health personnel, and the percentage of clean water) that significantly influence the percentage of malnourished children under five.

Keywords: GWR, MGWR, R2, AIC

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Authors: Ramesh Chandra Majhi

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Saturation flow is a very important input variable for the design of signalized intersections. Saturation flow measurement is well established for homogeneous traffic. However, saturation flow measurement and modeling is a challenging task in heterogeneous characterized by multiple vehicle types and non-lane based movement. Present study focuses on proposing a field procedure for Saturation flow measurement and the effect of typical mixed traffic behavior at the signal as far as non-lane based traffic movement is concerned. Data collected during peak and off-peak hour from five intersections with varying approach width is used for validating the saturation flow model. The insights from the study can be used for modeling saturation flow and delay at signalized intersection in heterogeneous traffic conditions.

Keywords: optimization, passenger car unit, saturation flow, signalized intersection

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Authors: Neha Gupta

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Landslide vulnerability is considered as the crucial parameter for the assessment of landslide risk. The term vulnerability defined as the damage or degree of elements at risk of different dimensions, i.e., physical, social, economic, and environmental dimensions. Himalaya region is very prone to multi-hazard such as floods, forest fires, earthquakes, and landslides. With the increases in fatalities rates, loss of infrastructure, and economy due to landslide in the Himalaya region, leads to the assessment of vulnerability. In this study, a methodology to measure the combination of vulnerability dimension, i.e., social vulnerability, physical vulnerability, and environmental vulnerability in one framework. A combined result of these vulnerabilities has rarely been carried out. But no such approach was applied in the Indian Scenario. The methodology was applied in an area of east Sikkim Himalaya, India. The physical vulnerability comprises of building footprint layer extracted from remote sensing data and Google Earth imaginary. The social vulnerability was assessed by using population density based on land use. The land use map was derived from a high-resolution satellite image, and for environment vulnerability assessment NDVI, forest, agriculture land, distance from the river were assessed from remote sensing and DEM. The classes of social vulnerability, physical vulnerability, and environment vulnerability were normalized at the scale of 0 (no loss) to 1 (loss) to get the homogenous dataset. Then the Multi-Criteria Analysis (MCA) was used to assign individual weights to each dimension and then integrate it into one frame. The final vulnerability was further classified into four classes from very low to very high.

Keywords: landslide, multi-criteria analysis, MCA, physical vulnerability, social vulnerability

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