Search results for: climate network

Authors: Yenni Anggrayni

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The lack of awareness of the significant prevalence of school absenteeism in Indonesia, which ultimately results in high rates of school dropouts, is an unresolved issue. Therefore, this study aims to investigate the root causes of chronic absenteeism qualitatively and quantitatively using the bioecological systems paradigm in secondary schools for any reason. This study used an open-ended questionnaire to collect data from 1,148 students in six West Java Province districts/cities. Univariate and stepwise multiple logistic regression analyses produced a prediction model for the components. Analysis results show that poor teacher-student relationships, bullying by peers or teachers, negative perception of education, and lack of parental involvement in learning activities are the leading causes of chronic absenteeism. Another finding is to promote home-school partnerships to improve school climate and parental involvement in learning to address chronic absenteeism.

Keywords: bullying, chronic absenteeism, dropout of school, home-school partnerships, parental involvement

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Authors: Adebayo J. Julius, Oluremi N. Iluyomade

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Livelihood in Nigeria is a paradox of poverty amidst plenty. The Country is endowed with a good climate for agriculture, naturally growing fruit trees and vegetables, and undomesticated water resources. In spite of all its endowment, Nigeria continues to live in poverty year in year out. This thus raises a very important question as to how can there be so much poverty in Nigeria with all its natural endowments. This study focused comparative analysis of the utilization of community informatics for sustainable livelihoods through agriculture. The idea projected in this study is that small strategic changes in the modus operandi of social informatics can have a significant impact on sustainability of livelihoods. This paper carefully explored the theories of community informatics and its efficacies in dealing with sustainability issues. This study identified, described and evaluates the roles of community informatics in some sectors of the economy, different analytical tools to benchmark the influence of social informatics in agriculture against what is obtainable in agricultural sectors of the economy were used. It further employed comparative analysis to build a case model for sustainable livelihood in agriculture through community informatics.

Keywords: informatics , model, rural community, livelihoods sustainability, Nigeria

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Authors: Priti Kumari, Tricha Anjali

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Body Area Networks (BANs) have emerged as the most promising technology for pervasive health care applications. Since they facilitate communication of very sensitive health data, information leakage in such networks can put human life at risk, and hence security inside BANs is a critical issue. Safe distribution and periodic refreshment of cryptographic keys are needed to ensure the highest level of security. In this paper, we focus on the key distribution techniques and how they are categorized for BAN. The state-of-art pre-shared key distribution algorithms are surveyed. Possible attacks on algorithms are demonstrated with examples.

Keywords: attacks, body area network, key distribution, key refreshment, pre-shared keys

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Authors: Ying Bo Xie, Hisa Yuki Kurokawa

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Due to the fast development, China's express delivery industry has involved in a dilemma that the service quality are keeping decreasing while the construction rate of delivery network cannot meet the customers’ demand. In order to get out of this dilemma and enjoy a succession development rate, it is necessary to understand the current situation of China's express delivery industry. Firstly, the evolution of China's express delivery industry was systematical presented. Secondly, according to the number of companies and the amount of parcels they has dealt each year, the merits and faults of tow kind of operating pattern was analyzed. Finally, based on the characteristics of these express companies, the problems of China's express delivery industry was divided into several types and the countermeasures were given out respectively.

Keywords: China, express delivery industry, status, problem

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Authors: M. Chumburidze, N. Salia, T. Namchevadze

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This work deal with case processing problems in business. The task of strategic credit requirements management of cases portfolio is discussed. The information model of credit requirements in a binary tree diagram is considered. The algorithms to solve issues of prioritizing clusters of cases in business have been investigated. An implementation of priority queues to support case management operations has been presented. The corresponding pseudo codes for the programming application have been constructed. The tools applied in this development are based on binary tree ordering algorithms, optimization theory, and business management methods.

Keywords: credit network, case portfolio, binary tree, priority queue, stack

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Authors: N. Zerzouri, N. Benalia, N. Bensiali

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This work is devoted to an analysis of the operation of a doubly fed induction generator (DFIG) integrated with a wind system. The power transfer between the stator and the network is carried out by acting on the rotor via a bidirectional signal converter. The analysis is devoted to the study of a fault in the converter due to an interruption of the control of a semiconductor. Simulation results obtained by the MATLAB / Simulink software illustrate the quality of the power generated at the default.

Keywords: doubly fed induction generator (DFIG), wind power generation, back to back PWM converter, default switching

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Authors: Xuemei Jiang, Kunfu Zhu, Shouyang Wang

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In this paper, we selected China as a case and employ a time-series of unique input-output tables distinguishing firm ownership and processing exports, to evaluate the role of foreign-invested enterprises (FIEs) in China’s rapid carbon dioxide emission growth. The results suggested that FIEs contributed to 11.55% of the economic outputs’ growth in China between 1992-2010, but accounted for only 9.65% of the growth of carbon dioxide emissions. In relative term, until 2010 FIEs still emitted much less than Chinese-owned enterprises (COEs) when producing the same amount of outputs, although COEs experienced much faster technology upgrades. In an ideal scenario where we assume the final demands remain unchanged and COEs completely mirror the advanced technologies of FIEs, more than 2000 Mt of carbon dioxide emissions would be reduced for China in 2010. From a policy perspective, the widespread FIEs are very effective and efficient channel to encourage technology transfer from developed to developing countries.

Keywords: carbon dioxide emissions, foreign-invested enterprises, technology transfer, input–output analysis, China

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Authors: Milad Nooshadi, Alessandro Manzardo

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CO₂ emissions in the atmosphere continue to rise, mostly as a result of the combustion of fossil fuels. CO₂ injection into the oceans and geological formation as a process of physical carbon capture are two of the most promising emerging strategies for mitigating climate change and global warming. The purpose of this research is to evaluate the two mentioned methods of CO₂ sequestration and to assess information on previous and current advancements, limitations, and uncertainties associated with carbon sequestration in order to identify possible prospects for ensuring the timely implementation of the technology, such as determining how governments and companies can gain a better understanding of CO₂ storage in terms of which media have the most applicable capacity, which type of injection has the fewer environmental impact, and how much carbon sequestration and storage will cost. The behavior of several forms is characterized as a near field, a far field, and a see-floor in ocean storage, and three medias in geological formations as an oil and gas reservoir, a saline aquifer, and a coal bed. To determine the capacity of various forms of media, an analysis of some models and practical experiments are necessary. Additionally, as a major component of sequestration, the various injection methods into diverse media and their monitoring are associated with a variety of environmental impacts and financial consequences.

Keywords: carbon sequestration, ocean storage, geologic storage, carbon transportation

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Authors: Sakshi Dhumale, Madhushree C., Amba Shetty

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The effect of station density on the geostatistical prediction of groundwater levels is of critical importance to ensure accurate and reliable predictions. Monitoring station density directly impacts the accuracy and reliability of geostatistical predictions by influencing the model's ability to capture localized variations and small-scale features in groundwater levels. This is particularly crucial in regions with complex hydrogeological conditions and significant spatial heterogeneity. Insufficient station density can result in larger prediction uncertainties, as the model may struggle to adequately represent the spatial variability and correlation patterns of the data. On the other hand, an optimal distribution of monitoring stations enables effective coverage of the study area and captures the spatial variability of groundwater levels more comprehensively. In this study, we investigate the effect of station density on the predictive performance of groundwater levels using the geostatistical technique of Ordinary Kriging. The research utilizes groundwater level data collected from 121 observation wells within the semi-arid Berambadi watershed, gathered over a six-year period (2010-2015) from the Indian Institute of Science (IISc), Bengaluru. The dataset is partitioned into seven subsets representing varying sampling densities, ranging from 15% (12 wells) to 100% (121 wells) of the total well network. The results obtained from different monitoring networks are compared against the existing groundwater monitoring network established by the Central Ground Water Board (CGWB). The findings of this study demonstrate that higher station densities significantly enhance the accuracy of geostatistical predictions for groundwater levels. The increased number of monitoring stations enables improved interpolation accuracy and captures finer-scale variations in groundwater levels. These results shed light on the relationship between station density and the geostatistical prediction of groundwater levels, emphasizing the importance of appropriate station densities to ensure accurate and reliable predictions. The insights gained from this study have practical implications for designing and optimizing monitoring networks, facilitating effective groundwater level assessments, and enabling sustainable management of groundwater resources.

Keywords: station density, geostatistical prediction, groundwater levels, monitoring networks, interpolation accuracy, spatial variability

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Authors: Ahmet Faruk Akyuz, Hasan Sakir Bilge

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Depth estimation using stereo images is a challenging problem in computer vision. Many different studies have been carried out to solve this problem. With advancing machine learning, tackling this problem is often done with neural network-based solutions. The images used in these studies are mostly in the visible spectrum. However, the need to use the Infrared (IR) spectrum for depth estimation has emerged because it gives better results than visible spectra in some conditions. At this point, we recommend using thermal-thermal (IR) image pairs for depth estimation. In this study, we used two well-known networks (PSMNet, FADNet) with minor modifications to demonstrate the viability of this idea.

Keywords: thermal stereo matching, deep neural networks, CNN, Depth estimation

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Authors: Sarah Nauwelaerts, Koen De Cremer, Alfred Bernard, Meredith Verlooy, Kristel Heremans, Natalia Bustos Sierra, Katrien Tersago, Tim Nawrot, Jordy Vercauteren, Christophe Stroobants, Sigrid C. J. De Keersmaecker, Nancy Roosens

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Projected climate changes could lead to exacerbation of respiratory disorders associated with reduced air quality. Air pollution and climate changes influence each other through complex interactions. The poor air quality in urban and rural areas includes high levels of particulate matter (PM), ozone (O3) and nitrogen oxides (NOx), representing a major threat to public health and especially for the most vulnerable population strata, and especially young children. In this study, we aim to develop generic standardized policy supporting tools and methods that allow evaluating in future follow-up larger scale epidemiological studies the risks of the combined short-term effects of O3 and PM on the cardiorespiratory system of children. We will use non-invasive indicators of airway damage/inflammation and of genetic or epigenetic variations by using urine or saliva as alternative to blood samples. Therefore, a multi-phase field study will be organized in order to assess the sensitivity and applicability of these tests in large cohorts of children during episodes of air pollution. A first test phase was planned in March 2018, not yet taking into account ‘critical’ pollution periods. Working with non-invasive samples, choosing the right set-up for the field work and the volunteer selection were parameters to consider, as they significantly influence the feasibility of this type of study. During this test phase, the selection of the volunteers was done in collaboration with medical doctors from the Centre for Student Assistance (CLB), by choosing a class of pre-pubertal children of 9-11 years old in a primary school in Flemish Brabant, Belgium. A questionnaire, collecting information on the health and background of children and an informed consent document were drawn up for the parents as well as a simplified cartoon-version of this document for the children. A detailed study protocol was established, giving clear information on the study objectives, the recruitment, the sample types, the medical examinations to be performed, the strategy to ensure anonymity, and finally on the sample processing. Furthermore, the protocol describes how this field study will be conducted in relation with the prevision and monitoring of air pollutants for the future phases. Potential protein, genetic and epigenetic biomarkers reflecting the respiratory function and the levels of air pollution will be measured in the collected samples using unconventional technologies. The test phase results will be used to address the most important bottlenecks before proceeding to the following phases of the study where the combined effect of O3 and PM during pollution peaks will be examined. This feasibility study will allow identifying possible bottlenecks and providing missing scientific knowledge, necessary for the preparation, implementation and evaluation of federal policies/strategies, based on the most appropriate epidemiological studies on the health effects of air pollution. The research leading to these results has been funded by the Belgian Science Policy Office through contract No.: BR/165/PI/PMOLLUGENIX-V2.

Keywords: air pollution, biomarkers, children, field study, feasibility study, non-invasive

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Authors: Chico A. E. Hindarto

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Knowledge management practices are more contextual when they combine with the corporate culture. Each entity has a specific cultural climate that enables knowledge sharing in both functional and individual levels. The interactions between people within organization can be influenced by the culture and how the knowledge is transmitted. On the other hand, these interactions have impacts in culture modification as well. Storytelling is one of the methods in delivering the knowledge throughout the organization. This paper aims to explore the possibility in using a quasi-folklore in the family-based business. Folklore is defined as informal tradition culture that spreading through a word-of-mouth, without knowing the source of the story. In this paper, the quasi-folklore term is used to differentiate it with the original term of folklore. The story is created by somebody in the organization, not like the folklore with unknown source. However, the source is not disclosed, in order to avoid the predicted interest from the story origin. The setting of family-based business is deliberately chosen, since the kinship is considerably strong in this type of entity. Through a thorough literature review that relates to knowledge management, storytelling, and folklore, this paper determines how folklore can be an option for knowledge sharing within the organization.

Keywords: folklore, family business, organizational culture, knowledge management, storytelling

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Authors: Richard Joseph, Nerey Mvungi

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An electric power system includes a generating, a transmission, a distribution and consumers subsystems. An electrical power network in Tanzania keeps growing larger by the day and become more complex so that, most utilities have long wished for real-time monitoring and remote control of electrical power system elements such as substations, intelligent devices, power lines, capacitor banks, feeder switches, fault analyzers and other physical facilities. In this paper, the concept of automation of management of power systems from generation level to end user levels was determined by using Power System Simulator for Engineering (PSS/E) version 30.3.2.

Keywords: automation, distribution subsystem, generating subsystem, PSS/E, TANESCO, transmission subsystem

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Authors: Ebrahim Alrashed, Yousef Rafique

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Mobile devices have drastically changed the way we do things on the move. They are being extremely relied on to perform tasks that are analogous to desktop computer capability. There has been a rapid increase of computational power on these devices; however, battery technology is still the bottleneck of evolution. The primary modern approach day approach to tackle this issue is offloading computation to the cloud, proving to be latency expensive and requiring high network bandwidth. In this paper, we explore efforts to perform barter-based mobile-to-mobile offloading. We present define a protocol and present an architecture to facilitate the development of such a system. We further highlight the deployment and security challenges.

Keywords: computational offloading, power conservation, cloud, sandboxing

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Authors: Muhammad Nauman Ahmad, Patrick Büker, Sofia Khalid, Leon Van Den Berg, Hamid Ullah Shah, Abdul Wahid, Lisa Emberson, Sally A. Power, Mike Ashmore

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Although ozone is well-documented to affect crop yields in the densely populated Indo-Gangetic Plain, there is little knowledge of its effects around cities in more remote areas of South Asia. We surveyed crops around the city of Peshawar, Pakistan for visible injury, linking this to passive measurements of ozone concentrations. Foliar injury was found in the field on potato, onion and cotton when the mean monthly ozone concentration reached 35-55ppb. The symptoms on onion were reproduced in ozone fumigation experiments, which also showed that daytime ozone concentrations of 60ppb and above significantly reduce the growth of Pakistani varieties of both spinach (Beta vulgaris) and onion. Aphid infestation on spinach was also reduced at these elevated ozone concentrations. The ozone concentrations in Peshawar are comparable to those through many parts of northern south Asia, where ozone may therefore be a significant threat to sensitive vegetable crops in peri-urban regions.

Keywords: ozone, air pollution, vegetable crops, peshawar, south asia

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Authors: Yangzhi Li

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Network transfer of information and performance customization is now a viable method of digital industrial production in the era of Industry 4.0. Robot platforms and network platforms have grown more important in digital design and construction. The pressing need for novel building techniques is driven by the growing labor scarcity problem and increased awareness of construction safety. Robotic approaches in construction research are regarded as an extension of operational and production tools. Several technological theories related to robot autonomous recognition, which include high-performance computing, physical system modeling, extensive sensor coordination, and dataset deep learning, have not been explored using intelligent construction. Relevant transdisciplinary theory and practice research still has specific gaps. Optimizing high-performance computing and autonomous recognition visual guidance technologies improves the robot's grasp of the scene and capacity for autonomous operation. Intelligent vision guidance technology for industrial robots has a serious issue with camera calibration, and the use of intelligent visual guiding and identification technologies for industrial robots in industrial production has strict accuracy requirements. It can be considered that visual recognition systems have challenges with precision issues. In such a situation, it will directly impact the effectiveness and standard of industrial production, necessitating a strengthening of the visual guiding study on positioning precision in recognition technology. To best facilitate the handling of complicated components, an approach for the visual recognition of parts utilizing machine learning algorithms is proposed. This study will identify the position of target components by detecting the information at the boundary and corner of a dense point cloud and determining the aspect ratio in accordance with the guidelines for the modularization of building components. To collect and use components, operational processing systems assign them to the same coordinate system based on their locations and postures. The RGB image's inclination detection and the depth image's verification will be used to determine the component's present posture. Finally, a virtual environment model for the robot's obstacle-avoidance route will be constructed using the point cloud information.

Keywords: robotic construction, robotic assembly, visual guidance, machine learning

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Authors: Turuganti Venkateswarlu, Jagadeesh Anmala

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Excessive runoffs from various non-point source land uses, and other point sources are rapidly contaminating the water quality of streams in the Upper Green River watershed, Kentucky, USA. It is essential to maintain the stream water quality as the river basin is one of the major freshwater sources in this province. It is also important to understand the water quality parameters (WQPs) quantitatively and qualitatively along with their important features as stream water is sensitive to climatic events and land-use practices. In this paper, a model was developed for predicting one of the significant WQPs, Fecal Coliform (FC) from precipitation, temperature, urban land use factor (ULUF), agricultural land use factor (ALUF), and forest land-use factor (FLUF) using Random Forest (RF) algorithm. The RF model, a novel ensemble learning algorithm, can even find out advanced feature importance characteristics from the given model inputs for different combinations. This model’s outcomes showed a good correlation between FC and climate events and land use factors (R2 = 0.94) and precipitation and temperature are the primary influencing factors for FC.

Keywords: water quality, land use factors, random forest, fecal coliform

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Authors: Cai J. T. Ladd, Mollie F. Duggan-Edwards, Tjeerd J. Bouma, Jordi F. Pages, Martin W. Skov

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Salt marshes are valued for their role in coastal flood protection, carbon storage, and for supporting biodiverse ecosystems. As a biogeomorphic landscape, marshes evolve through the complex interactions between sea level rise, sediment supply and wave/current forcing, as well as and socio-economic factors. Climate change and direct human modification could lead to a global decline marsh extent if left unchecked. Whilst the processes of saltmarsh erosion and expansion are well understood, empirical evidence on the key drivers of long-term lateral marsh dynamics is lacking. In a GIS, saltmarsh areal extent in 25 estuaries across Great Britain was calculated from historical maps and aerial photographs, at intervals of approximately 30 years between 1846 and 2016. Data on the key perceived drivers of lateral marsh change (namely sea level rise rates, suspended sediment concentration, bedload sediment flux rates, and frequency of both river flood and storm events) were collated from national monitoring centres. Continuous datasets did not extend beyond 1970, therefore predictor variables that best explained rate change of marsh extent between 1970 and 2016 was calculated using a Partial Least Squares Regression model. Information about the spread of Spartina anglica (an invasive marsh plant responsible for marsh expansion around the globe) and coastal engineering works that may have impacted on marsh extent, were also recorded from historical documents and their impacts assessed on long-term, large-scale marsh extent change. Results showed that salt marshes in the northern regions of Great Britain expanded an average of 2.0 ha/yr, whilst marshes in the south eroded an average of -5.3 ha/yr. Spartina invasion and coastal engineering works could not explain these trends since a trend of either expansion or erosion preceded these events. Results from the Partial Least Squares Regression model indicated that the rate of relative sea level rise (RSLR) and availability of suspended sediment concentration (SSC) best explained the patterns of marsh change. RSLR increased from 1.6 to 2.8 mm/yr, as SSC decreased from 404.2 to 78.56 mg/l along the north-to-south gradient of Great Britain, resulting in the shift from marsh expansion to erosion. Regional differences in RSLR and SSC are due to isostatic rebound since deglaciation, and tidal amplitudes respectively. Marshes exposed to low RSLR and high SSC likely leads to sediment accumulation at the coast suitable for colonisation by marsh plants and thus lateral expansion. In contrast, high RSLR with are likely not offset deposition under low SSC, thus average water depth at the marsh edge increases, allowing larger wind-waves to trigger marsh erosion. Current global declines in sediment flux to the coast are likely to diminish the resilience of salt marshes to RSLR. Monitoring and managing suspended sediment supply is not common-place, but may be critical to mitigating coastal impacts from climate change.

Keywords: lateral saltmarsh dynamics, sea level rise, sediment supply, wave forcing

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Authors: Pei Luen Jiang

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Oysters (Ostreidae) and hard clams (Meretrix lusoria) are important shallow sea-cultured shellfish in Taiwan, and are mainly farmed in Changhua, Yunlin, Chiayi and Tainan. As these shellfish are fed primarily on natural plankton, the artificial feed is not required, leading to high economic value in aquatic farming. However, in recent years, though mariculture production areas have expanded steadily, large-scale deaths of farmed shellfish have also become increasingly common due to climate change and human factors. Through studies over the past few years, our research team has determined the impact of nitrogen deprivation on growth and morphological variations in algae and sea anemones (Actiniaria) and identified the target genes affected by adverse environmental factors. In mariculture, high-density farming is commonly adopted, which results in elevated concentrations of nitrogenous waste in the water. In addition, excessive carbon dioxide from the atmosphere also dissolves in seawater, causing a steady decrease in the pH of seawater, leading to acidification. This study to observe the impact of high concentrations of nitrogen sources and carbon dioxide on algae.

Keywords: algae, shellfish, nitrogen, acidification

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Authors: Mohammed Taleb

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Morocco is characterized by an important genetic diversity represented by a rich and varied flora with 5211 species and subspecies and many natural ecosystems. Biodiversity and natural ecosystems provide the local population with highly diversified services represented by aromatic and medicinal plants, forage plants, melliferous plants, firewood, lumber, mushrooms, etc. Ecosystem services are currently subject to many pressures: overgrazing and deforestation, climate change, including increased drought, urbanization and forest fire. Conscious of the risks that weigh on biodiversity and ecosystem services, Morocco had made an important effort to reverse the tendencies by developing a consistent biodiversity conservation strategy focused on in-situ and ex-situ conservation. This presentation will be focused on the current state of biodiversity and ecosystem services and their role for the human development and their decline under the action of different pressures (grazing, timber harvest, harvesting of medicinal and aromatic plants, charcoal making...) while emphasizing efforts constructed by Morocco to conserve and sustainably manage biodiversity and ecosystem services.

Keywords: morocco, biodiversity, ecosystem services, local population

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Authors: John Hang Leung, Shyh-Yau Wang, Hei-Tung Yip, Fion, Ho Tsung-chin, Agnes LF Chan

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Objectives: Platinum-resistant ovarian cancer has a short overall survival of 9–12 months and limited treatment options. The combination of immunotherapy and targeted therapy appears to be a promising treatment option for patients with ovarian cancer, particularly to patients with platinum-resistant recurrent ovarian cancer (PRrOC). However, there are no direct head-to-head clinical trials comparing their efficacy and toxicity. We, therefore, used a network to directly and indirectly compare seven newer immunotherapies or targeted therapies combined with chemotherapy in platinum-resistant relapsed ovarian cancer, including antibody-drug conjugates, PD-1 (Programmed death-1) and PD-L1 (Programmed death-ligand 1), PARP (Poly ADP-ribose polymerase) inhibitors, TKIs (Tyrosine kinase inhibitors), and antiangiogenic agents. Methods: We searched PubMed (Public/Publisher MEDLINE), EMBASE (Excerpta Medica Database), and the Cochrane Library electronic databases for phase II and III trials involving PRrOC patients treated with immunotherapy or targeted therapy plus chemotherapy. The quality of included trials was assessed using the GRADE method. The primary outcomes compared were progression-free survival, the secondary outcomes were overall survival and safety. Results: Seven randomized controlled trials involving a total of 2058 PRrOC patients were included in this analysis. Bevacizumab plus chemotherapy showed statistically significant differences in PFS (Progression-free survival) but not OS (Overall survival) for all interested targets and immunotherapy regimens; however, according to the heatmap analysis, bevacizumab plus chemotherapy had a statistically significant risk of ≥grade 3 SAEs (Severe adverse effects), particularly hematological severe adverse events (neutropenia, anemia, leukopenia, and thrombocytopenia). Conclusions: Bevacizumab plus chemotherapy resulted in better PFS as compared with all interested regimens for the treatment of PRrOC. However, statistical differences in SAEs as bevacizumab plus chemotherapy is associated with a greater risk for hematological SAE.

Keywords: platinum-resistant recurrent ovarian cancer, network meta-analysis, immune checkpoint inhibitors, target therapy, antiangiogenic agents

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Authors: Shervin Alaei, Reza Moradi

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Trading in financial markets is subject to risks due to their high volatilities. Here, using an LSTM neural network, and by doing some risk-based feature engineering tasks, we developed a method that can accurately predict trends of the Tehran stock exchange market index from a few days ago. Our test results have shown that the proposed method with an average prediction accuracy of more than 94% is superior to the other common machine learning algorithms. To the best of our knowledge, this is the first work incorporating deep learning and risk factors to accurately predict market trends.

Keywords: deep learning, LSTM, trend prediction, risk management, artificial neural networks

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Authors: Furkan Ozkan, M. Selcuk Arslan, Hatice Mercan

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Electric vehicles are becoming increasingly popular asa sustainable alternative to traditional combustion engine vehicles. However, to fully realize the potential of EVs in reducing environmental impact and energy consumption, efficient control strategies are essential. This study explores the application of green wave control using model predictive control for electric vehicles, coupled with energy consumption modeling using neural networks. The use of MPC allows for real-time optimization of the vehicles’ energy consumption while considering dynamic traffic conditions. By leveraging neural networks for energy consumption modeling, the EV's performance can be further enhanced through accurate predictions and adaptive control. The integration of these advanced control and modeling techniques aims to maximize energy efficiency and range while navigating urban traffic scenarios. The findings of this research offer valuable insights into the potential of green wave control for electric vehicles and demonstrate the significance of integrating MPC and neural network modeling for optimizing energy consumption. This work contributes to the advancement of sustainable transportation systems and the widespread adoption of electric vehicles. To evaluate the effectiveness of the green wave control strategy in real-world urban environments, extensive simulations were conducted using a high-fidelity vehicle model and realistic traffic scenarios. The results indicate that the integration of model predictive control and energy consumption modeling with neural networks had a significant impact on the energy efficiency and range of electric vehicles. Through the use of MPC, the electric vehicle was able to adapt its speed and acceleration profile in realtime to optimize energy consumption while maintaining travel time objectives. The neural network-based energy consumption modeling provided accurate predictions, enabling the vehicle to anticipate and respond to variations in traffic flow, further enhancing energy efficiency and range. Furthermore, the study revealed that the green wave control strategy not only reduced energy consumption but also improved the overall driving experience by minimizing abrupt acceleration and deceleration, leading to a smoother and more comfortable ride for passengers. These results demonstrate the potential for green wave control to revolutionize urban transportation by enhancing the performance of electric vehicles and contributing to a more sustainable and efficient mobility ecosystem.

Keywords: electric vehicles, energy efficiency, green wave control, model predictive control, neural networks

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Authors: Fathi Abid, Bilel Kaffel

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The olive tree, the olive harvest during winter season and the production of olive oil better known by professionals under the name of the crushing operation have interested institutional traders such as olive-oil offices and private companies such as food industry refining and extracting pomace olive oil as well as export-import public and private companies specializing in olive oil. The major problem facing producers of olive oil each winter campaign, contrary to what is expected, it is not whether the harvest will be good or not but whether the sale price will allow them to cover production costs and achieve a reasonable margin of profit or not. These questions are entirely legitimate if we judge by the importance of the issue and the heavy complexity of the uncertainty and competition made tougher by a high level of indebtedness and the experience and expertise of speculators and producers whose objectives are sometimes conflicting. The aim of this paper is to study the formation mechanism of olive oil prices in order to learn about speculators’ behavior and expectations in the market, how they contribute by their industry knowledge and their financial alliances and the size the financial challenge that may be involved for them to build private information hoses globally to take advantage. The methodology used in this paper is based on two stages, in the first stage we study econometrically the formation mechanisms of olive oil price in order to understand the market participant behavior by implementing ARMA, SARMA, GARCH and stochastic diffusion processes models, the second stage is devoted to prediction purposes, we use a combined wavelet- ANN approach. Our main findings indicate that olive oil market participants interact with each other in a way that they promote stylized facts formation. The unstable participant’s behaviors create the volatility clustering, non-linearity dependent and cyclicity phenomena. By imitating each other in some periods of the campaign, different participants contribute to the fat tails observed in the olive oil price distribution. The best prediction model for the olive oil price is based on a back propagation artificial neural network approach with input information based on wavelet decomposition and recent past history.

Keywords: olive oil price, stylized facts, ARMA model, SARMA model, GARCH model, combined wavelet-artificial neural network, continuous-time stochastic volatility mode

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Authors: Bibhuti Bhusan Sahoo, Ramakar Jha

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Mapping of the extreme events (droughts) is one of the adaptation strategies to consequences of increasing climatic inconsistency and climate alterations. There is no operational practice to forecast the drought. One of the suggestions is to update mapping of drought prone areas for developmental planning. Drought indices play a significant role in drought mitigation. Many scientists have worked on different statistical analysis in drought and other climatological hazards. Many researchers have studied droughts individually for different sub-divisions or for India. Very few workers have studied district wise probabilities over large scale. In the present study, district wise drought probabilities over KBK (Kalahandi-Balangir-Koraput) districts of Odisha, India, Which are seriously prone to droughts, has been established using Hydrological drought index and Meteorological drought index along with the remote sensing drought indices to develop a multidirectional approach in the field of drought mitigation. Mapping for moderate and severe drought probabilities for KBK districts has been done and regions belonging different class intervals of probabilities of drought have been demarcated. Such type of information would be a good tool for planning purposes, for input in modelling and better promising results can be achieved.

Keywords: drought indices, KBK districts, proposed drought severity index, SPI

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Authors: M. Pramila Devi, N. V. N. Indra Kiran

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Control charts, predominantly in the form of X-bar chart, are important tools in statistical process control (SPC). They are useful in determining whether a process is behaving as intended or there are some unnatural causes of variation. A process is out of control if a point falls outside the control limits or a series of point’s exhibit an unnatural pattern. In this paper, a study is carried out on four training algorithms for CCPs recognition. For those algorithms optimal structure is identified and then they are studied for type I and type II errors for generalization without early stopping and with early stopping and the best one is proposed.

Keywords: control chart pattern recognition, neural network, backpropagation, generalization, early stopping

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Authors: Masho Mebrahtom Gebrehiwot, Gidey Yirga

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Tigray highlands in northern Ethiopia, is characterized by a wide range of ecological conditions and climate. The siege of Tigray is believed to cause the deaths of nearly 600,000 civilians mainly due to starvation and lack of medicine. In this study, the most important edible and medicinal plants used during the siege of Tigray were investigated. Semi-structured interviews, observation and guided field walks were used in 500 informants (300 males and 200 females) selected randomly from two districts. A total of 25 species of medicinal plants were collected and identified for treating 30 human ailments. Furthermore, a total of 21 edible plants were also collected and identified. Nearly 68.75% of these species were wild and harvested mainly for their leaves and the remedies were administered through dermal, nasal and oral routes. Oral and dermal applications were the highest and most used route of application. Famen foods significantly saved thousands of human lives during the siege of Tigray both in urban and rural communities. We suggest domestication of some of the wild medicinal plants for long term conservation of the species. Documentation of farmers’ knowledge, attitude and practices of ethnobotany would be very important before the indigenous knowledge is lost forever.

Keywords: ethnobotany, tigray, siege, application

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Authors: A. S. CohenMiller, Jenifer Lewis, Gwen McEvoy, Kristy Kelly

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This international, interdisciplinary study presents the first stage of a gender mainstreaming project within one university as a microcosm of society in Kazakhstan to make concrete policy recommendations and set up the potential for new research to monitor change over time. Local, regional, and UN representatives have noted the critical need and interest in gender related issues in Kazakhstan. Gender mainstreaming has been noted as a strategy to understand and address gender equality and equity such as within the academy in exploring and examining organizational/management issues, university decision-making and leadership, assessing the overall academic climate, discrimination issues, hiring and promotion, and student recruitment and retention. This presentation provides preliminary findings from the university gender audit, highlighting key elements for moving forward in gender mainstreaming. The full study analyzes findings from the full gender audit including interview with key stakeholders, time-use surveys, participant-observations and interviews with female students, staff and faculty, and reviews of formal organizational policies and practices.

Keywords: academia, equity, Eurasia, gender audit, gender mainstreaming, Kazakhstan, policy, time-use survey

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Authors: Huang Yajing

Abstract:

Context: Computer-aided language learning (CALL) in China has undergone significant development over the past few decades, with distinct stages marking its evolution. This paper aims to provide a comprehensive review of the practice and research in this field in China, tracing its journey from the early stages of audio-visual education to the current multimedia network integration stage. Research Aim: The study aims to analyze the historical progression of CALL in China, identify key developments in the field, and provide recommendations for enhancing CALL practices in the future. Methodology: The research employs document analysis and literature review to synthesize existing knowledge on CALL in China, drawing on a range of sources to construct a detailed overview of the evolution of CALL practices and research in the country. Findings: The review highlights the significant advancements in CALL in China, showcasing the transition from traditional audio-visual educational approaches to the current integrated multimedia network stage. The study identifies key milestones, technological advancements, and theoretical influences that have shaped CALL practices in China. Theoretical Importance: The evolution of CALL in China reflects not only technological progress but also shifts in educational paradigms and theories. The study underscores the significance of cognitive psychology as a theoretical underpinning for CALL practices, emphasizing the learner's active role in the learning process. Data Collection and Analysis Procedures: Data collection involved extensive review and analysis of documents and literature related to CALL in China. The analysis was carried out systematically to identify trends, developments, and challenges in the field. Questions Addressed: The study addresses the historical development of CALL in China, the impact of technological advancements on teaching practices, the role of cognitive psychology in shaping CALL methodologies, and the future outlook for CALL in the country. Conclusion: The review provides a comprehensive overview of the evolution of CALL in China, highlighting key stages of development and emerging trends. The study concludes by offering recommendations to further enhance CALL practices in the Chinese context.

Keywords: English education, educational technology, computer-aided language teaching, applied linguistics

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Authors: Jamaludin Sallim, Rozlina Mohamed, Roslina Abdul Hamid

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In this paper, we proposed an Ant Colony Optimization (ACO) algorithm together with Traveling Salesman Problem (TSP) approach to investigate the clustering problem in Protein Interaction Networks (PIN). We named this combination as ACOPIN. The purpose of this work is two-fold. First, to test the efficacy of ACO in clustering PIN and second, to propose the simple generalization of the ACO algorithm that might allow its application in clustering proteins in PIN. We split this paper to three main sections. First, we describe the PIN and clustering proteins in PIN. Second, we discuss the steps involved in each phase of ACO algorithm. Finally, we present some results of the investigation with the clustering patterns.

Keywords: ant colony optimization algorithm, searching algorithm, protein functional module, protein interaction network

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