Search results for: cloud service models

Authors: Catherine P. Bradshaw, Elise T. Pas, Katrina J. Debnam, Jessika H. Bottiani, Michael Rosenberg

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Worldwide, ethnically and culturally diverse students are at increased risk for school failure, discipline problems, and dropout. Despite decades of concern about this issue of disparities in education and other fields (e.g., 'school to prison pipeline'), there has been limited empirical examination of models that can actually reduce these gaps in schools. Moreover, few studies have examined the effectiveness of in-service teacher interventions and supports specifically designed to reduce discipline disparities and improve student engagement. This session provides an overview of the evidence-based Double Check model which serves as a framework for teachers to use culturally-responsive strategies to engage ethnically and culturally diverse students in the classroom and reduce discipline problems. Specifically, Double Check is a school-based prevention program which includes three core components: (a) enhancements to the school-wide Positive Behavioral Interventions and Supports (PBIS) tier-1 level of support; (b) five one-hour professional development training sessions, each of which addresses five domains of cultural competence (i.e., connection to the curriculum, authentic relationships, reflective thinking, effective communication, and sensitivity to students’ culture); and (c) coaching of classroom teachers using an adapted version of the Classroom Check-Up, which intends to increase teachers’ use of effective classroom management and culturally-responsive strategies using research-based motivational interviewing and data-informed problem-solving approaches. This paper presents findings from a randomized controlled trial (RCT) testing the impact of Double Check, on office discipline referrals (disaggregated by race) and independently observed and self-reported culturally-responsive practices and classroom behavior management. The RCT included 12 elementary and middle schools; 159 classroom teachers were randomized either to receive coaching or serve as comparisons. Specifically, multilevel analyses indicated that teacher self-reported culturally responsive behavior management improved over the course of the school year for teachers who received the coaching and professional development. However, the average annual office discipline referrals issued to black students were reduced among teachers who were randomly assigned to receive coaching relative to comparison teachers. Similarly, observations conducted by trained external raters indicated significantly more teacher proactive behavior management and anticipation of student problems, higher student compliance, less student non-compliance, and less socially disruptive behaviors in classrooms led by coached teachers than classrooms led teachers randomly assigned to the non-coached condition. These findings indicated promising effects of the Double Check model on a range of teacher and student outcomes, including disproportionality in office discipline referrals among Black students. These results also suggest that the Double Check model is one of only a few systematic approaches to promoting culturally-responsive behavior management which has been rigorously tested and shown to be associated with improvements in either student or staff outcomes indicated significant reductions in discipline problems and improvements in behavior management. Implications of these findings are considered within the broader context of globalization and demographic shifts, and their impacts on schools. These issues are particularly timely, given growing concerns about immigration policies in the U.S. and abroad.

Keywords: ethnically and culturally diverse students, student engagement, school-based prevention, academic achievement

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Authors: Kamalendu Pal

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Supply chains are the spinal cord of trade and commerce. Their logistics use different transport corridors on regular basis for operational purpose. The international supply chain transport corridors include different infrastructure elements (e.g. weighbridge, package handling equipment, border clearance authorities, and so on) in supply chains. This paper presents the use of multi-agent systems (MAS) to model and simulate some aspects of transportation corridors, and in particular the area of weighbridge resource optimization for operational profit generation purpose. An underlying multi-agent model provides a means of modeling the relationships among stakeholders in order to enable coordination in a transport corridor environment. Simulations of the costs of container unloading, reloading, and waiting time for queuing up tracks have been carried out using data sets. Results of the simulation provide the potential guidance in making decisions about optimal service resource allocation in a trade corridor.

Keywords: multi-agent systems, simulation, supply chain, transport corridor, weighbridge

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Authors: M. Mansouri, P. Jaklitsch, E. Teiniker

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SQL injection on web applications is a very popular kind of attack. There are mechanisms such as intrusion detection systems in order to detect this attack. These strategies often rely on techniques implemented at high layers of the application but do not consider the low level of system calls. The problem of only considering the high level perspective is that an attacker can circumvent the detection tools using certain techniques such as URL encoding. One technique currently used for detecting low-level attacks on privileged processes is the tracing of system calls. System calls act as a single gate to the Operating System (OS) kernel; they allow catching the critical data at an appropriate level of detail. Our basic assumption is that any type of application, be it a system service, utility program or Web application, “speaks” the language of system calls when having a conversation with the OS kernel. At this level we can see the actual attack while it is happening. We conduct an experiment in order to demonstrate the suitability of system call analysis for detecting SQL injection. We are able to detect the attack. Therefore we conclude that system calls are not only powerful in detecting low-level attacks but that they also enable us to detect high-level attacks such as SQL injection.

Keywords: Linux system calls, web attack detection, interception, SQL

Procedia PDF Downloads 360

Authors: Besma Khalfoun

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In many current Human Activity Recognition (HAR) applications, users' data is frequently shared and centrally stored by third parties, posing a significant privacy risk. This practice makes these entities attractive targets for extracting sensitive information about users, including their identity, health status, and location, thereby directly violating users' privacy. To tackle the issue of centralized data storage, a relatively recent paradigm known as federated learning has emerged. In this approach, users' raw data remains on their smartphones, where they train the HAR model locally. However, users still share updates of their local models originating from raw data. These updates are vulnerable to several attacks designed to extract sensitive information, such as determining whether a data sample is used in the training process, recovering the training data with inversion attacks, or inferring a specific attribute or property from the training data. In this paper, we first introduce PUR-Attack, a parameter-based user re-identification attack developed for HAR applications within a federated learning setting. It involves associating anonymous model updates (i.e., local models' weights or parameters) with the originating user's identity using background knowledge. PUR-Attack relies on a simple yet effective machine learning classifier and produces promising results. Specifically, we have found that by considering the weights of a given layer in a HAR model, we can uniquely re-identify users with an attack success rate of almost 100%. This result holds when considering a small attack training set and various data splitting strategies in the HAR model training. Thus, it is crucial to investigate protection methods to mitigate this privacy threat. Along this path, we propose SAFER, a privacy-preserving mechanism based on adaptive local differential privacy. Before sharing the model updates with the FL server, SAFER adds the optimal noise based on the re-identification risk assessment. Our approach can achieve a promising tradeoff between privacy, in terms of reducing re-identification risk, and utility, in terms of maintaining acceptable accuracy for the HAR model.

Keywords: federated learning, privacy risk assessment, re-identification risk, privacy preserving mechanisms, local differential privacy, human activity recognition

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Authors: Sarah Reker, Christiane H. Kellner

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From the perspective of people with disabilities, inequalities can also emerge from spatial segregation, the lack of social contacts or limited economic resources. In order to reduce or even eliminate these disadvantages and increase general well-being, community-based participation as well as decentralisation efforts within exclusively residential homes is essential. Therefore, the new research project “Index for participation development and quality of life for persons with disabilities”(TeLe-Index, 2014-2016), which is anchored at the Technische Universität München in Munich and at a large residential complex and service provider for persons with disabilities in the outskirts of Munich aims to assist the development of community-based living environments. People with disabilities should be able to participate in social life beyond the confines of the institution. Since a diverse society is a society in which different individual needs and wishes can emerge and be catered to, the ultimate goal of the project is to create an environment for all citizens–regardless of disability, age or ethnic background–that accommodates their daily activities and requirements. The UN-Convention on the Rights of Persons with Disabilities, which Germany also ratified, postulates the necessity of user-centered design, especially when it comes to evaluating the individual needs and wishes of all citizens. Therefore, a multidimensional approach is required. Based on this insight, the structure of the town-like center will be remodeled to open up the community to all people. This strategy should lead to more equal opportunities and open the way for a much more diverse community. Therefore, macro-level research questions were inspired by quality of life theory and were formulated as follows for different dimensions: •The user dimension: what needs and necessities can we identify? Are needs person-related? Are there any options to choose from? What type of quality of life can we identify? The economic dimension: what resources (both material and staff-related) are available in the region? (How) are they used? What costs (can) arise and what effects do they entail? •The environment dimension: what “environmental factors” such as access (mobility and absence of barriers) prove beneficial or impedimental? In this context, we have provided academic supervision and support for three projects (the construction of a new school, inclusive housing for children and teenagers with disabilities and the professionalization of employees with person-centered thinking). Since we cannot present all the issues of the umbrella-project within the conference framework, we will be focusing on one project more in-depth, namely “Outpatient Housing Options for Children and Teenagers with Disabilities”. The insights we have obtained until now will enable us to present the intermediary results of our evaluation. The most central questions pertaining to this part of the research were the following: •How have the existing network relations been designed? •What meaning (or significance) does the existing service offers and structures have for the everyday life of an external residential group? These issues underpinned the environmental analyses as well as the qualitative guided interviews and qualitative network analyses we carried out.

Keywords: decentralisation, environmental analyses, outpatient housing options for children and teenagers with disabilities, qualitative network analyses

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Authors: F. Uriel, M. M. Fernandez Liporace

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In the last decade, academic achievement in higher education has become a topic of agenda in Argentina, regarding the high figures of adjustment problems, academic failure and dropout, and the low graduation rates in the context of massive classes and traditional teaching methods. Psychological variables, such as perceived social support, academic motivation and learning styles and strategies have much to offer since their measurement by tests allows a proper diagnose of their influence on academic achievement. Framed in a major research, several studies analysed multiple samples, totalizing 5135 students attending Argentinean public universities. The first goal was aimed at the identification of statistically significant differences in psychological variables -perceived social support, learning styles, learning strategies, and academic motivation- by age, gender, and degree of academic advance (freshmen versus sophomores). Thus, an inferential group differences study for each psychological dependent variable was developed by means of student’s T tests, given the features of data distribution. The second goal, aimed at examining associations between the four psychological variables on the one hand, and academic achievement on the other, was responded by correlational studies, calculating Pearson’s coefficients, employing grades as the quantitative indicator of academic achievement. The positive and significant results that were obtained led to the formulation of different predictive models of academic achievement which had to be tested in terms of adjustment and predictive power. These models took the four psychological variables above mentioned as predictors, using regression equations, examining predictors individually, in groups of two, and together, analysing indirect effects as well, and adding the degree of academic advance and gender, which had shown their importance within the first goal’s findings. The most relevant results were: first, gender showed no influence on any dependent variable. Second, only good achievers perceived high social support from teachers, and male students were prone to perceive less social support. Third, freshmen exhibited a pragmatic learning style, preferring unstructured environments, the use of examples and simultaneous-visual processing in learning, whereas sophomores manifest an assimilative learning style, choosing sequential and analytic processing modes. Despite these features, freshmen have to deal with abstract contents and sophomores, with practical learning situations due to study programs in force. Fifth, no differences in academic motivation were found between freshmen and sophomores. However, the latter employ a higher number of more efficient learning strategies. Sixth, freshmen low achievers lack intrinsic motivation. Seventh, models testing showed that social support, learning styles and academic motivation influence learning strategies, which affect academic achievement in freshmen, particularly males; only learning styles influence achievement in sophomores of both genders with direct effects. These findings led to conclude that educational psychologists, education specialists, teachers, and universities must plan urgent and major changes. These must be applied in renewed and better study programs, syllabi and classes, as well as tutoring and training systems. Such developments should be targeted to the support and empowerment of students in their academic pathways, and therefore to the upgrade of learning quality, especially in the case of freshmen, male freshmen, and low achievers.

Keywords: academic achievement, academic motivation, coping, learning strategies, learning styles, perceived social support

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Authors: Heiko Diefenbach, Christoph H. Glock

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Order picking, i.e., the order-based retrieval of items in a warehouse, is an important time- and cost-intensive process for many logistic systems. Despite the ongoing trend of automation, most order picking systems are still manual picker-to-parts systems, where human pickers walk through the warehouse to collect ordered items. Human work in warehouses is not free from errors, and order pickers may at times pick the wrong or the incorrect number of items. Errors can cause additional costs and significant correction efforts. Moreover, age might increase a person’s likelihood to make mistakes. Hence, the negative impact of picking errors might increase for an aging workforce currently witnessed in many regions globally. A significant amount of research has focused on making order picking systems more efficient. Among other factors, storage assignment, i.e., the assignment of items to storage locations (e.g., shelves) within the warehouse, has been subject to optimization. Usually, the objective is to assign items to storage locations such that order picking times are minimized. Surprisingly, there is a lack of research concerned with picking errors and respective prevention approaches. This paper hypothesize that the storage assignment of items can affect the probability of pick errors. For example, storing similar-looking items apart from one other might reduce confusion. Moreover, storing items that are hard to count or require a lot of counting at easy-to-access and easy-to-comprehend self heights might reduce the probability to pick the wrong number of items. Based on this hypothesis, the paper discusses how to incorporate error-prevention measures into mathematical models for storage assignment optimization. Various approaches with respective benefits and shortcomings are presented and mathematically modeled. To investigate the newly developed models further, they are compared to conventional storage assignment strategies in a computational study. The study specifically investigates how the importance of error prevention increases with pickers being more prone to errors due to age, for example. The results suggest that considering error-prevention measures for storage assignment can reduce error probabilities with only minor decreases in picking efficiency. The results might be especially relevant for an aging workforce.

Keywords: an aging workforce, error prevention, order picking, storage assignment

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Authors: I. Touaїbia, E. Azzag, O. Narjes

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Power supply networks are growing continuously and their reliability is getting more important than ever. The complexity of the whole network comprises numerous components that can fail and interrupt the power supply for the end user. Underground distribution systems are normally exposed to permanent faults, due to specific construction characteristics. In these systems, visual inspection cannot be performed. In order to enhance service restoration, accurate fault location techniques must be applied. This paper describes the different faults that affect the underground distribution system of SONELGAZ (National Society of Electricity and Gas of Algeria), and cable fault location procedure with impulse reflection method (TDR), based in the analyses of the cable response of the electromagnetic impulse, allows cable fault prelocation. The results are obtained from real test in the underground distribution feeder from electrical network of energy distribution company of Souk-Ahras, in order to know the influence of cable characteristics in the types and frequency of faults.

Keywords: distribution networks, fault location, TDR, underground cable

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Authors: Syeda Afsana Azad

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Climate change, due to the release of greenhouse gases into the atmosphere has been recognized as one of the biggest threats to the present world. The condition of the earth is getting worse day by day due to climate change. Bangladesh is considered to be one of the most vulnerable countries to climate change due to large population, sharp urbanization, etc. Construction of green building is a very good solution to reduce the greenhouse effect. Green building technology refers to that kind of structures which are environmentally friendly and resource-efficient throughout a building’s service life. This technology can provide at least 50% energy saving opportunity to the nation. The necessity of the construction of structures in an environment-friendly way is increasing now. This study shows the scenario of rapid population growth, urbanization, necessity of green building in Bangladesh and also discusses the construction process of green building. As the present climate condition of Bangladesh is not friendly, construction of green building is very much needed. To battle climate change, it is mandatory to construct green building.

Keywords: Bangladesh, climate change, green building, green house effect

Procedia PDF Downloads 383

Authors: Shreya Goyal

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The study aims to build user-friendly software to understand patient arrival patterns and compute the number of potential patients who will visit a particular health facility for a given period by using a machine learning algorithm. The underlying machine learning algorithm used in this study is the Support Vector Machine (SVM). Accurate prediction of patient arrival allows hospitals to operate more effectively, providing timely and efficient care while optimizing resources and improving patient experience. It allows for better allocation of staff, equipment, and other resources. If there's a projected surge in patients, additional staff or resources can be allocated to handle the influx, preventing bottlenecks or delays in care. Understanding patient arrival patterns can also help streamline processes to minimize waiting times for patients and ensure timely access to care for patients in need. Another big advantage of using this software is adhering to strict data protection regulations such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States as the hospital will not have to share the data with any third party or upload it to the cloud because the software can read data locally from the machine. The data needs to be arranged in. a particular format and the software will be able to read the data and provide meaningful output. Using software that operates locally can facilitate compliance with these regulations by minimizing data exposure. Keeping patient data within the hospital's local systems reduces the risk of unauthorized access or breaches associated with transmitting data over networks or storing it in external servers. This can help maintain the confidentiality and integrity of sensitive patient information. Historical patient data is used in this study. The input variables used to train the model include patient age, time of day, day of the week, seasonal variations, and local events. The algorithm uses a Supervised learning method to optimize the objective function and find the global minima. The algorithm stores the values of the local minima after each iteration and at the end compares all the local minima to find the global minima. The strength of this study is the transfer function used to calculate the number of patients. The model has an output accuracy of >95%. The method proposed in this study could be used for better management planning of personnel and medical resources.

Keywords: machine learning, SVM, HIPAA, data

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Authors: Qiao Qiao

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With the development of The Times, the elderly demand for pension service facilities is increasing. Taking 26 street towns in Jiangjin District of Chongqing as examples, ArcGIS spatial analysis method was used to analyze the distribution status of the elderly population, the core density of the elderly population, and the spatial layout characteristics of institutional elderly care facilities in Jiangjin District of Chongqing. The results showed that there were differences in the structure and aging degree of the elderly population in each street town. There is a certain imbalance between the spatial distribution of the elderly population and the planning and construction of elderly care facilities. The accessibility of elderly care facilities is uneven. Therefore, a genetic algorithm is used to optimize the spatial layout of institutional elderly care facilities, improve the accessibility of facilities, strengthen the participation of multiple subjects, and provide a reference for the future construction planning of elderly care facilities.

Keywords: institutional pension facilities, spatial layout, accessibility, ArcGIS

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Authors: R. F. Vieira, D. Lopes, I. Baptista, S. A. Figueiredo, V. F. Domingues, R. Jorge, C. Delerue-matos, O. M. Freitas

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Odours are generated in municipal solid wastes management plants as a result of decomposition of organic matter, especially when anaerobic degradation occurs. Information was collected about the substances and respective concentration in the surrounding atmosphere of some management plants. The main components which are associated with these unpleasant odours were identified: ammonia, hydrogen sulfide and mercaptans. The first is the most common and the one that presents the highest concentrations, reaching values of 700 mg/m3. Biofiltration, which involves simultaneously biodegradation, absorption and adsorption processes, is a sustainable technology for the treatment of these odour emissions when a natural packing material is used. The packing material should ideally be cheap, durable, and allow the maximum microbiological activity and adsorption/absorption. The presence of nutrients and water is required for biodegradation processes. Adsorption and absorption are enhanced by high specific surface area, high porosity and low density. The main purpose of this work is the exploitation of natural waste materials, locally available, as packing media: heather (Erica lusitanica), chestnut bur (from Castanea sativa), peach pits (from Prunus persica) and eucalyptus bark (from Eucalyptus globulus). Preliminary batch tests of ammonia removal were performed in order to select the most interesting materials for biofiltration, which were then characterized. The following physical and chemical parameters were evaluated: density, moisture, pH, buffer and water retention capacity. The determination of equilibrium isotherms and the adjustment to Langmuir and Freundlich models was also performed. Both models can fit the experimental results. Based both in the material performance as adsorbent and in its physical and chemical characteristics, eucalyptus bark was considered the best material. It presents a maximum adsorption capacity of 0.78±0.45 mol/kg for ammonia. The results from its characterization are: 121 kg/m3 density, 9.8% moisture, pH equal to 5.7, buffer capacity of 0.370 mmol H+/kg of dry matter and water retention capacity of 1.4 g H2O/g of dry matter. The application of natural materials locally available, with little processing, in biofiltration is an economic and sustainable alternative that should be explored.

Keywords: ammonia removal, biofiltration, natural materials, odour control

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Authors: German Osma, Gabriel Ordonez

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The production of metal-rubber spares for vehicles is a sequential process that consists in the transformation of raw material through cutting activities and chemical and thermal treatments, which demand electricity and fossil fuels. The energy efficiency analysis for these cases is mostly focused on studying of each machine or production step, but is not common to study of the quality of the production process achieves from aggregated value viewpoint, which can be used as a quality measurement for determining of impact on the environment. In this paper, the theory of exergetic cost is used for determining of aggregated exergy to three metal-rubber spares, from an exergy analysis and thermoeconomic analysis. The manufacturing processing of these spares is based into batch production technique, and therefore is proposed the use of this theory for discontinuous flows from of single models of workstations; subsequently, the complete exergy model of each product is built using flowcharts. These models are a representation of exergy flows between components into the machines according to electrical, mechanical and/or thermal expressions; they determine the demanded exergy to produce the effective transformation in raw materials (aggregated exergy value), the exergy losses caused by equipment and irreversibilities. The energy resources of manufacturing process are electricity and natural gas. The workstations considered are lathes, punching presses, cutters, zinc machine, chemical treatment tanks, hydraulic vulcanizing presses and rubber mixer. The thermoeconomic analysis was done by workstation and by spare; first of them describes the operation of the components of each machine and where the exergy losses are; while the second of them estimates the exergy-aggregated value for finished product and wasted feedstock. Results indicate that exergy efficiency of a mechanical workstation is between 10% and 60% while this value in the thermal workstations is less than 5%; also that each effective exergy-aggregated value is one-thirtieth of total exergy required for operation of manufacturing process, which amounts approximately to 2 MJ. These troubles are caused mainly by technical limitations of machines, oversizing of metal feedstock that demands more mechanical transformation work, and low thermal insulation of chemical treatment tanks and hydraulic vulcanizing presses. From established information, in this case, it is possible to appreciate the usefulness of theory of exergetic cost for analyzing of aggregated value in manufacturing processes.

Keywords: exergy-aggregated value, exergy efficiency, thermoeconomics, exergy modeling

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Authors: Thinh Ngo, Paul Rad, Brian Kelley

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Noise estimation is essential in today wireless systems for power control, adaptive modulation, interference suppression and quality of service. Deep learning (DL) has already been applied in the physical layer for modulation and signal classifications. Unacceptably low accuracy of less than 50% is found to undermine traditional application of DL classification for SNR prediction. In this paper, we use divide-and-conquer algorithm and classifier fusion method to simplify SNR classification and therefore enhances DL learning and prediction. Specifically, multiple CNNs are used for classification rather than a single CNN. Each CNN performs a binary classification of a single SNR with two labels: less than, greater than or equal. Together, multiple CNNs are combined to effectively classify over a range of SNR values from −20 ≤ SNR ≤ 32 dB.We use pre-trained CNNs to predict SNR over a wide range of joint channel parameters including multiple Doppler shifts (0, 60, 120 Hz), power-delay profiles, and signal-modulation types (QPSK,16QAM,64-QAM). The approach achieves individual SNR prediction accuracy of 92%, composite accuracy of 70% and prediction convergence one order of magnitude faster than that of traditional estimation.

Keywords: classification, CNN, deep learning, prediction, SNR

Procedia PDF Downloads 137

Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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Due to increasing cost pressure in global markets, artificial intelligence is becoming a technology that is decisive for competition. Predictive quality enables machinery and plant manufacturers to ensure product quality by using data-driven forecasts via machine learning models as a decision-making basis for test results. The use of cross-process Bosch production data along the value chain of hydraulic valves is a promising approach to classifying the quality characteristics of workpieces.

Keywords: predictive quality, hydraulics, machine learning, classification, supervised learning

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Authors: Mohammad Daud Ali

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For the past decade, marketing ethics has been a central point for academic researchers and practitioners. In particular, the development of frameworks and models to help in the analysis of marketing decisions are the focus of research. The current study aims at finding whether ethical decisions (honesty, fairness, responsibility, and respect) affect organizational marketing decisions. A selection of 250 respondents was purposely made from the telecommunication industry of Pakistan, out of which 204 responses were induced at an acceptable rate of 81.6%. A five-point Likert Scale, itemized with 12 items, was adopted from Taylor-Dunlop & Lester (2000) and used to draw responses regarding ethics.

Keywords: marketing, ethics, decisions making, telecommunication, Pakistan

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Authors: Pablo Oteiza, Ricardo Alvarez, Mehrdad Pirnia, Fuat Can

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To effectively combat climate change, many countries around the world have committed to a decarbonisation of their electricity, along with promoting a large-scale integration of renewable energy sources (RES). While this trend represents a unique opportunity to effectively combat climate change, achieving a sound and cost-efficient energy transition towards low-carbon power systems poses significant challenges for the multi-year Transmission Network Expansion Planning (TNEP) problem. The objective of the multi-year TNEP is to determine the necessary network infrastructure to supply the projected demand in a cost-efficient way, considering the evolution of the new generation mix, including the integration of RES. The rapid integration of large-scale RES increases the variability and uncertainty in the power system operation, which in turn increases short-term flexibility requirements. To meet these requirements, flexible generating technologies such as energy storage systems must be considered within the TNEP as well, along with proper models for capturing the operational challenges of future power systems. As a consequence, TNEP formulations are becoming more complex and difficult to solve, especially for its application in realistic-sized power system models. To meet these challenges, there is an increasing need for developing efficient algorithms capable of solving the TNEP problem with reasonable computational time and resources. In this regard, a promising research area is the use of artificial intelligence (AI) techniques for solving large-scale mixed-integer optimization problems, such as the TNEP. In particular, the use of AI along with mathematical optimization strategies based on decomposition has shown great potential. In this context, this paper presents an efficient algorithm for solving the multi-year TNEP problem. The algorithm combines AI techniques with Column Generation, a traditional decomposition-based mathematical optimization method. One of the challenges of using Column Generation for solving the TNEP problem is that the subproblems are of mixed-integer nature, and therefore solving them requires significant amounts of time and resources. Hence, in this proposal we solve a linearly relaxed version of the subproblems, and trained a binary classifier that determines the value of the binary variables, based on the results obtained from the linearized version. A key feature of the proposal is that we integrate the binary classifier into the optimization algorithm in such a way that the optimality of the solution can be guaranteed. The results of a study case based on the HRP 38-bus test system shows that the binary classifier has an accuracy above 97% for estimating the value of the binary variables. Since the linearly relaxed version of the subproblems can be solved with significantly less time than the integer programming counterpart, the integration of the binary classifier into the Column Generation algorithm allowed us to reduce the computational time required for solving the problem by 50%. The final version of this paper will contain a detailed description of the proposed algorithm, the AI-based binary classifier technique and its integration into the CG algorithm. To demonstrate the capabilities of the proposal, we evaluate the algorithm in case studies with different scenarios, as well as in other power system models.

Keywords: integer optimization, machine learning, mathematical decomposition, transmission planning

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Authors: Brian Bacia, Esther Githaiga, Teresia Kabucho, Paul Moses Ndegwa, Lucy Gichohi

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Purpose: The aim of the study is to ensure an appropriate mix of evidence-based prevention strategies geared towards the reduction of new HIV infections and the incidence of Sexually transmitted Illnesses Background: In Nakuru County, more than 90% of all HIV-infected patients are adults and on a single-dose medication-one pill that contains a combination of several different HIV drugs. Nakuru town has been identified as the hardest hit by HIV/Aids in the County according to the latest statistics from the County Aids and STI group, with a prevalence rate of 5.7 percent attributed to the high population and an active urban center. Method: 2 key studies were carried out to provide evidence for the effectiveness of antiretroviral therapy (ART) when used optimally on preventing sexual transmission of HIV. Discussions based on an examination, assessments of successes in planning, program implementation, and ultimate impact of prevention and treatment were undertaken involving health managers, health workers, community health workers, and people living with HIV/AIDS between February -August 2021. Questionnaires were carried out by a trained duo on ethical procedures at 15 HIV treatment clinics targeting patients on ARVs and caregivers on ARV prevention and treatment of pediatric HIV infection. Findings: Levels of AIDS awareness are extremely high. Advances in HIV treatment have led to an enhanced understanding of the virus, improved care of patients, and control of the spread of drug-resistant HIV. There has been a tremendous increase in the number of people living with HIV having access to life-long antiretroviral drugs (ARV), mostly on generic medicines. Healthcare facilities providing treatment are stressed challenging the administration of the drugs, which require a clinical setting. Women find it difficult to take a daily pill which reduces the effectiveness of the medicine. ART adherence can be strengthened largely through the use of innovative digital technology. The case management approach is useful in resource-limited settings. The county has made tremendous progress in mother-to-child transmission reduction through enhanced early antenatal care (ANC) attendance and mapping of pregnant women Recommendations: Treatment reduces the risk of transmission to the child during pregnancy, labor, and delivery. Promote research of medicines through patients and community engagement. Reduce the risk of transmission through breastfeeding. Enhance testing strategies and strengthen health systems for sustainable HIV service delivery. Need exists for improved antenatal care and delivery by skilled birth attendants. Develop a comprehensive maternal reproductive health policy covering equitability, efficient and effective delivery of services. Put in place referral systems.

Keywords: evidence-based prevention strategies, service delivery, human management, integrated approach

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Authors: Sabina Akter, Osiris Valdez Banda, Pentti Kujala, Jani Romanoff

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This paper examines the relationship between on-board environmental factors and customer overall satisfaction in the context of the cruise on-board experience. The on-board environmental factors considered are ambient, layout/design, social, product/service and on-board enjoyment factors. The study presents a data-driven framework and model for the on-board cruise experience. The data are collected from 893 respondents in an application of a self-administered online questionnaire of their cruise experience. This study reveals the cruise passengers’ on-board experience through the customer decision journey based on the publicly available data. Pearson correlation and regression analysis have been applied, and the results show a positive and a significant relationship between the environmental factors and on-board experience. These data help understand the cruise passengers’ on-board experience, which will be used for the ultimate decision-making process in cruise ship design.

Keywords: cruise behavior, customer activities, on-board environmental factors, on-board experience, user or customer satisfaction

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Authors: Kayne Uriel K. Rodrigo, Jerriane Hillary Heart S. Marcial, Samuel C. Brillo

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Rice leaf diseases significantly impact yield production in rice-dependent countries, affecting their agricultural sectors. As part of precision agriculture, early and accurate detection of these diseases is crucial for effective mitigation practices and minimizing crop losses. Hence, this study proposes an enhancement to the Convolutional Neural Network (CNN), a widely-used method for Rice Leaf Disease Image Classification, by incorporating MobileViTV2—a recently advanced architecture that combines CNN and Vision Transformer models while maintaining fewer parameters, making it suitable for broader deployment on edge devices. Our methodology utilizes a publicly available rice disease image dataset from Kaggle, which was validated by a university structural biologist following the guidelines provided by the Philippine Rice Institute (PhilRice). Modifications to the dataset include renaming certain disease categories and augmenting the rice leaf image data through rotation, scaling, and flipping. The enhanced dataset was then used to train the MobileViTV2 model using the Timm library. The results of our approach are as follows: the model achieved notable performance, with 98% accuracy in both training and validation, 6% training and validation loss, and a Receiver Operating Characteristic (ROC) curve ranging from 95% to 100% for each label. Additionally, the F1 score was 97%. These metrics demonstrate a significant improvement compared to a conventional CNN-based approach, which, in a previous 2022 study, achieved only 78% accuracy after using 5 convolutional layers and 2 dense layers. Thus, it can be concluded that MobileViTV2, with its fewer parameters, outperforms traditional CNN models, particularly when applied to Rice Leaf Disease Image Identification. For future work, we recommend extending this model to include datasets validated by international rice experts and broadening the scope to accommodate biotic factors such as rice pest classification, as well as abiotic stressors such as climate, soil quality, and geographic information, which could improve the accuracy of disease prediction.

Keywords: convolutional neural network, MobileViTV2, rice leaf disease, precision agriculture, image classification, vision transformer

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Authors: Ilef Ketata, Mohamed Khalil Baazaoui, Robert Fromm, Ahmad Fakhfakh, Faouzi Derbel

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The integration of wireless communication, e. g. in real-or quasi-real-time applications, is related to many challenges such as energy consumption, communication range, latency, quality of service, and reliability. To minimize the latency without increasing energy consumption, wake-up receiver (WuRx) nodes have been introduced in recent works. Low-noise amplifiers (LNAs) are introduced to improve the WuRx sensitivity but increase the supply current severely. Different WuRx approaches exist with always-on, power-gated, or duty-cycled receiver designs. This paper presents a comparative study for improving communication range and decreasing the energy consumption of wireless sensor nodes.

Keywords: wireless sensor network, wake-up receiver, duty-cycled, low-noise amplifier, envelope detector, range study

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Authors: Chamseddine Barki, Salam Labidi, Hanen Boussi Rahmouni

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In this article, we present the principle and suitable methodology for the design of a medical ontology that highlights the radiological and dosimetric knowledge, applied in diagnostic radiology and radiation-therapy. Our ontology, which we named «Onto.Rap», is the subject of radiation protection in medical and radiology centers by providing a standardized regulatory oversight. Thanks to its added values of knowledge-sharing, reuse and the ease of maintenance, this ontology tends to solve many problems. Of which we name the confusion between radiological procedures a practitioner might face while performing a patient radiological exam. Adding to it, the difficulties they might have in interpreting applicable patient radioprotection standards. Here, the ontology, thanks to its concepts simplification and expressiveness capabilities, can ensure an efficient classification of radiological procedures. It also provides an explicit representation of the relations between the different components of the studied concept. In fact, an ontology based-radioprotection expert system, when used in radiological center, could implement systematic radioprotection best practices during patient exam and a regulatory compliance service auditing afterwards.

Keywords: knowledge, ontology, radiation protection, radiology

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Authors: Matthew Baucum

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With the increasing popularity of fMRI as an experimental method, psychology and neuroscience can greatly benefit from advanced techniques for summarizing and synthesizing large amounts of data from brain imaging studies. One promising avenue is automated meta-analyses, in which natural language processing methods are used to identify the brain regions consistently associated with certain semantic concepts (e.g. “social”, “reward’) across large corpora of studies. This study builds on this approach by demonstrating how, in fMRI meta-analyses, individual voxels can be treated as vectors in a semantic space and evaluated for their “proximity” to terms of interest. In this technique, a low-dimensional semantic space is built from brain imaging study texts, allowing words in each text to be represented as vectors (where words that frequently appear together are near each other in the semantic space). Consequently, each voxel in a brain mask can be represented as a normalized vector sum of all of the words in the studies that showed activation in that voxel. The entire brain mask can then be visualized in terms of each voxel’s proximity to a given term of interest (e.g., “vision”, “decision making”) or collection of terms (e.g., “theory of mind”, “social”, “agent”), as measured by the cosine similarity between the voxel’s vector and the term vector (or the average of multiple term vectors). Analysis can also proceed in the opposite direction, allowing word cloud visualizations of the nearest semantic neighbors for a given brain region. This approach allows for continuous, fine-grained metrics of voxel-term associations, and relies on state-of-the-art “open vocabulary” methods that go beyond mere word-counts. An analysis of over 11,000 neuroimaging studies from an existing meta-analytic fMRI database demonstrates that this technique can be used to recover known neural bases for multiple psychological functions, suggesting this method’s utility for efficient, high-level meta-analyses of localized brain function. While automated text analytic methods are no replacement for deliberate, manual meta-analyses, they seem to show promise for the efficient aggregation of large bodies of scientific knowledge, at least on a relatively general level.

Keywords: FMRI, machine learning, meta-analysis, text analysis

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Authors: Omer Ozer, Murat Yikilmaz, Ahmet Altinok, Ferhat Bayolu

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There is an increasing number of online counseling services, studies exploring clients’ and counselors’ attitudes toward online counseling services are needed to provide effective and efficient mental health counseling services. The purpose of this study is to investigate counselors’ attitudes toward online counseling in relation to counselors’ genders, their daily usage of computer, their total usage of computer, and their self-efficacy in computer usage. In this study, Personal Information Form, specific items from the Online Counseling Attitudes Scale, and the Face-to-Face Counseling Attitudes Scale were given to 193 counselors to measure attitudes toward online counseling. Data were analyzed by using independent samples t-test and one-way ANOVA. There were no statistically significant differences counselors’ attitudes toward online counseling and counselors’ gender, their daily usage of computer, their total usage of computer, and their self-efficacy in computer usage. The implications of these findings have been discussed in the literature review to provide some suggestions to researchers in the counseling profession.

Keywords: online counseling, counselor, attitude, counseling service

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Authors: Sultan Mahmud, Qamar Zaman, Travis Esau, Young Chang

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Strawberry powdery mildew (PM) is a serious disease that has a significant impact on strawberry production. Field scouting is still a major way to find PM disease, which is not only labor intensive but also almost impossible to monitor disease severity. To reduce the loss caused by PM disease and achieve faster automatic detection of the disease, this paper proposes an approach for detection of the disease, based on image texture and classified with support vector machines (SVMs) and k-nearest neighbors (kNNs). The methodology of the proposed study is based on image processing which is composed of five main steps including image acquisition, pre-processing, segmentation, features extraction and classification. Two strawberry fields were used in this study. Images of healthy leaves and leaves infected with PM (Sphaerotheca macularis) disease under artificial cloud lighting condition. Colour thresholding was utilized to segment all images before textural analysis. Colour co-occurrence matrix (CCM) was introduced for extraction of textural features. Forty textural features, related to a physiological parameter of leaves were extracted from CCM of National television system committee (NTSC) luminance, hue, saturation and intensity (HSI) images. The normalized feature data were utilized for training and validation, respectively, using developed classifiers. The classifiers have experimented with internal, external and cross-validations. The best classifier was selected based on their performance and accuracy. Experimental results suggested that SVMs classifier showed 98.33%, 85.33%, 87.33%, 93.33% and 95.0% of accuracy on internal, external-I, external-II, 4-fold cross and 5-fold cross-validation, respectively. Whereas, kNNs results represented 90.0%, 72.00%, 74.66%, 89.33% and 90.3% of classification accuracy, respectively. The outcome of this study demonstrated that SVMs classified PM disease with a highest overall accuracy of 91.86% and 1.1211 seconds of processing time. Therefore, overall results concluded that the proposed study can significantly support an accurate and automatic identification and recognition of strawberry PM disease with SVMs classifier.

Keywords: powdery mildew, image processing, textural analysis, color co-occurrence matrix, support vector machines, k-nearest neighbors

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Authors: Ali Alshumrani, Nathan Clarke, Bogdan Ghite, Stavros Shiaeles

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Digital forensics has become an essential tool in the investigation of cyber and computer-assisted crime. Arguably, given the prevalence of technology and the subsequent digital footprints that exist, it could have a significant role across almost all crimes. However, the variety of technology platforms (such as computers, mobiles, Closed-Circuit Television (CCTV), Internet of Things (IoT), databases, drones, cloud computing services), heterogeneity and volume of data, forensic tool capability, and the investigative cost make investigations both technically challenging and prohibitively expensive. Forensic tools also tend to be siloed into specific technologies, e.g., File System Forensic Analysis Tools (FS-FAT) and Network Forensic Analysis Tools (N-FAT), and a good deal of data sources has little to no specialist forensic tools. Increasingly it also becomes essential to compare and correlate evidence across data sources and to do so in an efficient and effective manner enabling an investigator to answer high-level questions of the data in a timely manner without having to trawl through data and perform the correlation manually. This paper proposes a Unified Forensic Analysis Tool (U-FAT), which aims to establish a common language for electronic information and permit multi-source forensic analysis. Core to this approach is the identification and development of forensic analyses that automate complex data correlations, enabling investigators to investigate cases more efficiently. The paper presents a systematic analysis of major crime categories and identifies what forensic analyses could be used. For example, in a child abduction, an investigation team might have evidence from a range of sources including computing devices (mobile phone, PC), CCTV (potentially a large number), ISP records, and mobile network cell tower data, in addition to third party databases such as the National Sex Offender registry and tax records, with the desire to auto-correlate and across sources and visualize in a cognitively effective manner. U-FAT provides a holistic, flexible, and extensible approach to providing digital forensics in technology, application, and data-agnostic manner, providing powerful and automated forensic analysis.

Keywords: digital forensics, evidence correlation, heterogeneous data, forensics tool

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Authors: Kartina Sury Kariman

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Brand-to-customer (B2C) engagement has been well established through the traditional platform such as direct sales, advertising, customer service center, customer hotline as well as brand usage experiences. Increasingly, interest to B2C has evolved to include customer-to-customer (C2C) interaction analysis aligned with the vast growth of web 2.0. Hence, discussion on C2C interaction and brand strategy have captured social media as it enables brands and C2C interaction to be connected in various ways, providing opportunities for marketers to shape their brand engagement strategy while reaching C2C as the targeted outcomes. The objective here is to provide a preliminary review of C2C interaction consisting the antecedents and consequences while highlighting areas of research interest within the context from marketers perspective and the business outcomes. This paper discusses how C2C interaction defines marketers’ brand and communication strategy and how social media trend shapes the strategy when promoting the awareness of life insurance industry and educating the target market.

Keywords: social media, brand engagement, customer interaction, customer engagement, brand strategy, life insurance

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Authors: Shafaq Rubab

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The primary reason for dropping out of school is associated with students’ lack of motivation in class, especially in mathematics. Digital game-based learning is an approach that is being actively explored; there are very few learning games based on proven instructional design models or frameworks due to which the effectiveness of the learning games suffers. The purpose of this research was twofold: first, developing an appropriate instructional design model and second, evaluating the impact of the instructional design model on students’ motivation. This research contributes significantly to the existing literature in terms of student motivation and the impact of instructional design model in digital game-based learning. The sample size for this study consists of two hundred out-of-school students between the age of 6 and 12 years. The research methodology used for this research was a quasi-experimental approach and data was analyzed by using the instructional material motivational survey questionnaire which is adapted from the Keller Arcs model. Control and experimental groups consisting of two hundred students were analyzed by utilizing instructional material motivational survey (IMMS), and comparison of result from both groups showed the difference in the level of motivation of the students. The result of the research showed that the motivational level of student in the experimental group who were taught by the game was higher than the student in control group (taught by conventional methodology). The mean score of the experimental group against all subscales (attention, relevance, confidence, and satisfaction) of IMMS survey was higher; however, no statistical significance was found between the motivational scores of control and experimental group. The positive impact of game-based learning on students’ level of motivation, as measured in this study, strengthens the case for the use of pedagogically sound instructional design models in the design of interactive learning applications. In addition, the present study suggests learning from interactive, immersive applications as an alternative solution for children, especially in Third World countries, who, for various reasons, do not attend school. The mean score of experimental group against all subscales of IMMS survey was higher; however, no statistical significance was found between motivational scores of control and experimental group.

Keywords: digital game-based learning, students’ motivation, and instructional designing, instructional material motivational survey

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Authors: Taehyun Kim, Hyunjoo Park, Taehyun Kim

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Since the rapid economic development, Korea has recently entered a period of low growth due to population decline and aging. Due to the urbanization around the metropolitan area and the hollowing of local cities, the ecological capacity of a city is decreasing while ecological footprints are increasing, requiring a compact space plan for maintaining urban functions. The purpose of this study is to analyze the relationship between urban spatial structure and residents' ecological footprints for sustainable spatial planning. To do this, we try to analyze the relationship between intra-urban spatial structure, such as net/gross density and service accessibility, and resident ecological footprints of food, housing, transportation, goods and services through survey and structural equation modeling. The results of the study will be useful in establishing an implementation plan for sustainable development goals (SDGs), especially for sustainable cities and communities (SDG 11) and responsible consumption and production (SDG 12) in the future.

Keywords: ecological footprint, structural equation modeling, survey, sustainability, urban spatial structure

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Authors: Alexander I. Dovgyallo, Evgeniy A. Zinoviev, Svetlana O. Nekrasova

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The most of works applied for the pulse tube converters contain the workflow description implemented through the use of mathematical models on stationary modes. However, the study of the thermoacoustic systems unsteady behavior in the start, stop, and acoustic load changes modes is in the particular interest. The aim of the present study was to develop a mathematical thermal excitation model of acoustic oscillations in pulse tube engine (PTE) as a small-scale scheme of pulse tube engine operating at atmospheric air. Unlike some previous works this standing wave configuration is a fully closed system. The improvements over previous mathematical models are the following: the model allows specifying any values of porosity for regenerator, takes into account the piston weight and the friction in the cylinder and piston unit, and determines the operating frequency. The numerical method is based on the relation equations between the pressure and volume velocity variables at the ends of each element of PTE which is recorded through the appropriate transformation matrix. A solution demonstrates that the PTE operation frequency is the complex value, and it depends on the piston mass and the dynamic friction due to its movement in the cylinder. On the basis of the determined frequency thermoacoustically induced heat transport and generation of acoustic power equations were solved for channel with temperature gradient on its ends. The results of numerical simulation demonstrate the features of the initialization process of oscillation and show that that generated acoustic power more than power on the steady mode in a factor of 3…4. But doesn`t mean the possibility of its further continuous utilizing due to its existence only in transient mode which lasts only for a 30-40 sec. The experiments were carried out on small-scale PTE. The results shows that the value of acoustic power is in the range of 0.7..1.05 W for the defined frequency range f = 13..18 Hz and pressure amplitudes 11..12 kPa. These experimental data are satisfactorily correlated with the numerical modeling results. The mathematical model can be straightforwardly applied for the thermoacoustic devices with variable temperatures of thermal reservoirs and variable transduction loads which are expected to occur in practical implementations of portable thermoacoustic engines.

Keywords: nonlinear processes, pulse tube engine, thermal excitation, standing wave

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