Authors: D. M. O. Silva, F. L. P. Pessoa, A. L. H. Costa

Abstract:

The use of the simulation results can be limited due to the uncertainties associated with the input variables and parameters of the actual process. In this context, the main objective of this study was the use of stochastic optimization techniques in the analysis of a biodiesel production process in relation to the uncertainties involved. The stochastic simulation procedures were applied associated with Hysys process simulator. The characterization of the system behavior before the uncertainties was performed using a set of simulations, where in each, the values of one or more input variables/parameters were randomly chosen according to an appropriate probability density function. The studied cases were: the disturbance of the vacuum system pressure drop in the purification step and the influence of changes in the molar ratio of the reactants on the yield of the reaction. In conclusion it could be seen that the stochastic analysis helped to choose the best simulation tracks to the input variables/parameters studied, since it helps to know the probabilities of the proposed conditions do not meet specifications.

Keywords: Biodiesel, stochastic, thermodynamic, transesterification.

Authors: Heather K. Caldwell

Abstract:

In 1915, Colorado State Superintendent Mary C.C. Bradford introduced a statewide standardization plan to improve educational quality and equity. Facing the challenges of Colorado’s vast and varied geography, state leaders partnered with local schools to reform curriculum, enhance teacher development, and strengthen school-community ties. These efforts led to notable improvements in consistency and access to education. This paper explores the origins, implementation, outcomes, and legacy of Bradford’s initiative, highlighting its impact on students, educators, and communities. It also places Colorado’s plan within the broader national context of early 20th-century education reform, examining both its alignment with and divergence from national trends.

Keywords: History of education, standardization, curriculum, state superintendent, women in education.

Authors: Lillie Beiting, Nell Watson

Abstract:

This paper presents the Mentorship-Driven Agile Process (MDAP), an approach to software development that addresses the limitations of traditional agile methodologies. MDAP reimagines software development with a focus on human capital, efficient knowledge transfer, and developer empowerment while strategically integrating artificial intelligence to enhance productivity and quality. The framework is built around small, cross-functional "Skill Cells" that combine traditionally separate roles such as development, code review, Quality Assurance (QA), and DevOps. This structure facilitates rapid skill transfer, enhances code quality, and improves system understanding. MDAP leverages modern tools and practices, including managed software environments, advanced monitoring systems, and AI-assisted processes, to streamline development cycles and reduce overhead. The paper outlines the structure, operational dynamics, and key practices of MDAP, including its unique approach to ceremonies, code review, and DevOps. It also discusses the benefits, prerequisites for success, and potential challenges of implementing MDAP, as well as the ethical considerations of AI integration. By fostering a more collaborative and fulfilling work environment augmented by AI, MDAP aims to create better software, happier teams, and more successful companies, potentially reshaping the landscape of software development.

Keywords: Agile Software Development, mentorship, skill cells, DevOps, AI in software development, organizational psychology.

Authors: Mohsen Rahimi, Giulia De Poli, Andrea Masella, Matteo Bregonzio

Abstract:

Interpretability is an important aspect of natural language processing (NLP) models and refers to the ability to understand how a model makes its predictions and why it makes certain decisions; furthermore, it is crucial for a trustworthy approach. Interpretability is particularly important in NLP since it can help to identify potential biases and errors in the model, and can also help to improve the overall performance of the model. This paper discusses the increasing complexity of NLP models and the need for interpretability to ensure their reliability, impartiality, and accuracy. The paper proposes an interpretability system that can analyse and interpret the predictions of black-box NLP models using adversarial examples. The system uses a hybrid approach that combines both local and global interpretability methods to achieve a more comprehensive understanding of the model's behaviour. The proposed system provides a state-of-art solution to the challenge of understanding how NLP models arrive at their decisions and when they can fail, thus improving trust between humans and machines in real-world applications. They can be used to identify potential biases and errors and to build more robust, trustworthy, and accurate models.

Keywords: Interpretability, trustworthy AI, XAI, Explainable AI, NLP, Natural Language Processing.

Authors: Logan Miller, Wisam Bukaita

Abstract:

The research focuses on the design and construction of an image-based disease classification model based on deep learning techniques. The model aims to classify plants' leaves and stems into one of the five categories: black spots, downy mildew, powdery mildew, healthy, and other diseases. A dataset downloaded from a public sourced datasets and preprocessing techniques involving resizing, color normalization, and augmentation techniques, like rotation and flip, are used to enhance the model's efficiency. The deep model, built from the use of TensorFlow and the Keras API, performs image-based extraction of the images through the use of the convolution layer, enhancing the model's ability to distinguish between different health states in plants. Evaluation results show that the model achieves around 40% confidence when identifying one of the three diseases, indicating reasonable performance with room for future improvement. The model's efficiency in terms of classification measurement confirms the model's reliability in disease prediction. The current research identifies the use of deep models in the future in the field of agriculture to offer scalable and automatable means to disease prediction and monitoring in plants.

Keywords: Black spots, computer vision, downy mildew, feature extraction, Keras, plant disease, powdery mildew.

Authors: Mohsen Soori, Behrooz Arezoo

Abstract:

In the context of Industry 4.0, cloud computing offers the scalability, flexibility, and wide range of services required to facilitate big data analytics. This enables enterprises to extract meaningful insights from the vast amounts of data produced by intelligent and networked production processes. Industry 4.0 demands real-time decision-making as cloud-based analytics enable quick processing of streaming data for immediate insights. The combination of big data analytics and cloud computing is driving the digital age in order to expand the processing power. Organizations can expand their computer capabilities according to the amount of data and processing demands thanks to cloud platforms. Cloud computing increases productivity and enables predictive maintenance by analyzing equipment data in real-time and reducing downtime. By evaluating data from several sources, cloud-based analytics enhance supply chain operations and facilitate better inventory control and logistics. Furthermore, real-time processing at the point of data production is made possible by the developing combination of edge computing and cloud analytics, which lowers latency. The present assessment underscores the revolutionary effect of merging big data analytics and cloud computing within the framework of Industry 4.0, stressing the benefits, obstacles, applications, and forthcoming patterns in this ever-evolving domain. The goal of this in-depth analysis is to further our knowledge of how important it will be for Industry 4.0 to integrate Big Data analytics with cloud computing.

Keywords: Big Data Analytics, Cloud Computing, Industry 4.0, Manufacturing management.

Authors: K. Korkmaz Onat

Abstract:

Decision making significantly influences outcomes and shapes future actions, making it a crucial aspect of both personal and professional life. This study examines various decision-making approaches, focusing on their procedures and applications. The rational decision-making model is highlighted for its systematic approach and reliance on data analysis and logical reasoning. Additionally, the study explores consensus, weighted scoring, voting and brainstorming analysis methods. Key findings indicate that each method has unique strengths and is best suited for specific contexts. The article concludes by offering practical guidance for how to choose the appropriate decision-making approach based on the circumstances.

Keywords: Decision-making, decision-making process, decision-making methods, group decision-making.

Authors: Priya Iyer, Jay Iyer

Abstract:

Electric vehicles (EVs) are heavier than Internal Combustion Engine (ICE) vehicles of the same size and class. Embracing EVs can impact the structural integrity of multi-level parking garages, particularly those built decades ago for a much lighter fleet than now. Using the Gumbel probability distribution, extreme live loads on a parking slab in a parking garage are analyzed for forward-looking periods until 2075. Results show that EVs have 50% higher maximum live load bending moments than ICE vehicles. Further, the dead weight loads of the parking slab and the increased live loads from EVs are applied to an initiated crack. The resulting stress intensity factor of the crack exceeds the critical value. This indicates parking slab collapse across all return periods.

Keywords: Aging parking structures, American Concrete Institute, electric vehicles, parking garage, structural failure.

Authors: Md. Ashfaqur Rahman

Abstract:

Fine-tuning large language models (LLMs) for question-answering (QA) tasks often incurs high computational costs, demanding significant memory and leading to increased inference latency. In this study, we investigate the effectiveness of Low-Rank Adaptation (LoRA) in optimizing the Llama2 7B model by reducing trainable parameters, minimizing memory consumption, and enhancing processing efficiency. Experimental results reveal that LoRA reduces memory usage from 8.49 GB to 4.52 GB and lowers trainable parameters from 6.73 billion to 4.19 million. Additionally, we analyze inference latency across different input configurations, where single-question, two-question, three-question, and four-question inputs are evaluated separately. The fine-tuned model consistently outperforms the base model, achieving latency reductions of 6.23s, 11.66s, 23.97s, and 43.11s, respectively. These results indicate that LoRA enhances efficiency without compromising model performance. To assess overall effectiveness, we employ multiple evaluation metrics, including memory usage, parameter pruning, inference latency, and human evaluation, ensuring a balanced trade-off between computational efficiency and accuracy. The dataset, sourced from the Hugging Face Library, is partitioned into 9.85k training samples and 518 testing samples. Our findings establish LoRA-based fine-tuning as a robust method for improving LLM performance in QA applications while reducing computational overhead, making large-scale QA systems more practical and resource-efficient.

Keywords: Large Language Models, fine-tuning, Low-Rank Adaptation, Llama2, question answering, inference latency, memory optimization, parameter pruning, computational efficiency, hugging face dataset.

Authors: Jacob J. Shila, Xiaoyu O. Wu

Abstract:

As the applications of unmanned aerial vehicles (UAV) continue to increase across the world, it is critical to understand the factors that contribute to incidents and accidents associated with these systems. Given the variety of daily applications that could utilize the operations of the UAV (e.g., medical, security operations, construction activities, landscape activities), the main discussion has been how to safely incorporate the UAV into the national airspace system. The types of UAV incidents being reported range from near sightings by other pilots to actual collisions with aircraft or UAV. These incidents have the potential to impact the rest of aviation operations in a variety of ways including human lives, liability costs, and delay costs. One of the largest causes of these incidents cited is the human factor; other causes cited include maintenance, aircraft, and others. This work investigates the key human factors associated with the UAV incidents. To that end, the data related to UAV incidents that have occurred in the Australia are both reviewed and analyzed to identify key human factors related UAV incidents. The data utilized in this work are gathered from the Australia Transportation Safety Board (ATSB) database. This study adapts the Human Factor Analysis and Classification System (HFACS) to identify key human factors that have contributed to some of the UAV failures to date. The uniqueness of this work is the incorporation of UAV incident data from a variety of applications and not just the military data. In addition, identifying the specific human factors is crucial towards developing safety operational models and human factor guidelines for the UAV. The findings of these common human factors are also compared to similar studies in other countries to determine whether these factors are common internationally.

Keywords: Human factors, incidents and accidents, safety, UAS, UAV.

Authors: Kuo-Dung Chiou, Yi-Zhen Chen, Shin-Hau Chiou, Chia-Ying Chang

Abstract:

This study presents the development of an AI-driven expert system for evaluating the appearance and ripeness of Atemoya fruit. A comprehensive fruit appearance database was constructed by collecting images focused on surface defects and ripeness classification. Deep learning techniques were employed to detect fruit locations in real time, enabling automated assessment of surface imperfections and maturity. Additionally, hyperspectral imaging was utilized to analyze light reflection across different spectral bands, identifying key wavelengths associated with pectin softening in post-ripened fruit. A large-scale multispectral image collection and data analysis process was conducted to establish a robust Atemoya fruit database. The dataset includes high-resolution color images, hyperspectral images covering the 377–1020 nm spectrum, and multispectral images at five specific wavelengths (450, 500, 670, 720, and 800 nm). In total, 4,896 labeled images with ground truth annotations were collected, along with hyperspectral scans of 26 Atemoya fruits (each containing 520 images) and multispectral data from 168 Atemoya fruits (each containing five images). These findings contribute to the advancement of precision agriculture by enhancing fruit quality assessment and post-harvest management.

Keywords: Deep learning, hyperspectral imaging, fruit quality assessment, ripeness detection.

Authors: Mustafa M. Donma

Abstract:

Metabolic syndrome (MetS) is characterized, in the first instance, by central obesity accompanied by disturbed blood pressure, glucose and certain lipid fractions. Pediatric MetS is the predictor of MetS in adulthood commonly associated with cardiovascular diseases (CVDs). Since MetS is such a complicated health problem, attempts have been made to eliminate this problem. For this, many formulas have been developed. However, most of them require sophisticated mathematical expressions, which make their usage in clinics difficult. Out of the overall complications, the most important problem, which draws attention was the possibility of developing CVDs. Alanine aminotransferase-to-aspartate aminotransferase (ALT/AST) ratio is proven to be linked to cardiometabolic risk. The aim of this study was to introduce the possible association of an easily applicable formula with ALT/AST. The study population was composed of 88 morbid obese (MO) children. The first group was MO group, second group was MetS group. Each group consisted of 44 children. Ethics Committee Approval and Informed Consent Forms were taken. World Health Organization Obesity Criteria and International Diabetes Federation Metabolic Syndrome Criteria were used for the selection of the participants in groups. Blood pressure values were determined. Routine laboratory tests were performed. ALT/AST, homeostatic model assessment for insulin resistance (HOMA-IR), metabolic syndrome insulin resistance index (MetS-IR), diagnostic obesity notation model assessment metabolic syndrome index (DMetSI) were calculated. Statistical evaluation of the data was performed. All indices exhibited increased values in MetS group compared to those calculated for MO group. Increase in the median values for MetS-IR in MetS group was less than two-fold. On the other hand, about a three-fold increase was observed for DMetSI in MetS group in comparison with the value obtained in MO group. Correlation between ALT/AST ratio and DMetSI was stronger than correlation between ALT/AST and MetS-IR. Besides, DMetSI had no sophisticated mathematical expressions in the formula. In conclusion, two indices, MetS-IR and DMetSI, which were found to be much higher in MetS group and associations of both with ALT/AST ratio have indicated their potential as the tools for use in differential diagnosis of MO and MetS. However, much higher association with ALT/AST, both an insulin resistance as well as a cardiometabolic ratio, easily applicable nature of the equation and striking difference between median values of groups suggested that DMetSI showed much higher performance during the selection of MetS cases from MO population of children than the other index.

Keywords: Cardiometabolic risk, childhood, metabolic syndrome, metabolic syndrome index, morbid obesity.

Authors: Manar Gamal, Sayed Hemeda, Tadahiro Kin, Hany Helal, Ayman Mahrous

Abstract:

Muography leverages cosmic ray muons to visualize internal structures and identify hidden cavities within ancient constructions. The Scintillator Imaging Detector for the Egyptian Pyramids (SciDep) project specifically targets the detection of concealed chambers in the Khafre Pyramid, utilizing a polyvinyl toluene (PVT) scintillator detector. To ensure accurate and reliable detector performance in field conditions analogous to the pyramid interior, this study employs the Particle and Heavy Ion Transport code System (PHITS) combined with the PHITS-based Analytical Radiation Model in the Atmosphere (PARMA). Simulations replicate the muon interactions within a testing environment situated 11 meters underground in the tunnels at Egypt-Japan University of Science and Technology (E-JUST). Two distinct scenarios, a void environment and a concrete-air environment, were analyzed to evaluate muon flux, energy deposition, and detection efficiency. The simulation results confirm the detector’s capability for reliable performance, informing the optimal configuration for its forthcoming deployment in field tests at the Khafre Pyramid.

Keywords: Muography, underground imaging, scintillator detector, cosmic rays.

Authors: Aydin Homay, Mario de Sousa, Martin Wollschlaeger

Abstract:

Axiomatic design is a system design methodology that systematically analyses the transformation of customer needs into functional requirements, design parameters, and process variables. This approach aims to create high-quality product or system designs by adhering to specific design principles or axioms, namely, the independence and information axiom. The application of axiomatic design in the design of industrial control and automation software systems could be challenging due to the high flexibility exposed by the software system and the coupling enforced by the hardware part. This paper aims to present how to use axiomatic design for designing industrial control and automation software systems and how to satisfy the independence axiom within these tightly coupled systems.

Keywords: Axiomatic design, decoupling, uncoupling, automation.

Authors: Alexander A. Kharlamov, Maria Pilgun

Abstract:

This paper presents the validation results of a text mining algorithm applied to urban system development in the transportation sector, leveraging user-generated content from social media platforms. The study employed sentiment analysis, aggression detection, semantic network and core formation, associative search, associative network development, and word association analysis. Data collection was conducted using the Brand Analytics social media and media monitoring system. Data analysis and interpretation utilized TextAnalyst 2.32, GPT-3.5, GPT-4, and GPT-4o, while Tableau was used for interactive visualization and analytics. Social tension levels were assessed through calculated indices of social stress and well-being. Based on the findings, recommendations were proposed to improve project effectiveness by integrating residents' perspectives.

Keywords: Social media, text mining, neural network technologies, large-scale data.

Authors: Emmanuel Ekwueme, Anwar Ali, Saeed Ur Rehman

Abstract:

As the Internet of Things (IoT) continues to expand, the demand for battery-free devices is increasing, which is crucial for the efficiency of 5G networks and eco-friendly industrial systems. The solution is a device that operates indefinitely, requires no maintenance, and has no negative impact on the ambient environment. One promising approach to achieve this is energy harvesting, which involves capturing energy from the ambient environment and transferring it to power devices. This method can revolutionize industries; such as manufacturing, agriculture, and healthcare by enabling real-time data collection and analysis, reducing maintenance costs, improving efficiency, and contributing to a future with lower carbon emissions. This research explores various energy harvesting techniques, focusing on radio frequencies (RF) and multiple energy sources. It examines RF-based and solar methods for powering battery-free sensors, low-power circuits, and IoT devices. The study investigates a hybrid RF-solar harvesting circuit designed for remote sensing devices. The proposed system includes distinct RF and solar energy harvester circuits, with the RF harvester operating at 2.45 GHz and the solar harvester utilizing a maximum power point tracking (MPPT) algorithm to maximize efficiency.

Keywords: Radio frequency, energy harvesting, Internet of Things, IoT, multisource, solar energy.

Authors: K. Suresh, M. Padmavathi, N. Darga Kumar, P. S. Harsha

Abstract:

The objective of this study is to investigate the suitability of coal mine waste (CMW) and improved clay soil as materials used for a fill and embankment construction. To determine the bearing capacities of CMW and improved clay soil, laboratory experiments and bearing capacity tests are conducted. PLAXIS 2D software is used to simplify the analysis and determine the load-bearing capacities of the materials under various conditions. Load carrying capacities are determined for CMW, clay, and their combinations by altering their height ratios, with clay (H2) at the bottom and CMW (H1) on top, for three different cases H = 0.25H1 + 0.75H2, 0.5H1 + 0.5H2, 0.75H1 + 0.25H2. Additionally, the bearing capacity of improved clay soil (by replacing clay with 10% CMW, 30% CMW, and 50% CMW, along with Polycom) is determined. The safe height of the embankment that can be constructed with the improved clay for different slopes, i.e., for 1:1, 1.5: 1, 2: 1 is also determined by using PLAXIS 2D software by limiting the Factor of Safety to 1.5.

Keywords: Cohesion, angle of shearing resistance, elastic modulus, coefficient of consolidation, coal mine waste.

Authors: Lingxia Li, Frédéric Schnell, Thibault Lachard, Anne-Charlotte Dupont, Shuzhe Ding, Solène Le Douairon Lahaye

Abstract:

Prolonged intensive endurance exercise is associated with cardiovascular adaptations in athletes. However, the sex differences in electrocardiographic (ECG) performance in triathletes are poorly understood. ECG results of male and female triathletes registered on the French ministerial lists of high-level athletes between 2015 and 2021 were involved. The ECG was evaluated according to commonly accepted criteria. 86 triathletes (50 male, 36 female) participated, with an average age of 19.9 ± 4.8 years. The training volume was 21 ± 6 hours per week in males and 19 ± 6 hours per week in females (p > 0.05). Despite the relatively larger P wave (96.0 ± 12.0 vs. 89.9 ± 11.5 ms, p = 0.02) and longer QRS complex (96.6 ± 11.1 vs. 90.3 ± 8.6 ms, p = 0.005) in males than in females, all indicators were within normal ranges in both sexes. The most common electrical manifestations were early repolarization (46.5%) and incomplete right bundle branch block (39.5%). No difference was found between sexes in electrical manifestations (p > 0.05). All ECG patterns were within normal limits under similar training volumes, but male triathletes were more susceptible to cardiovascular changes than females. The most common ECG manifestations in triathletes were early repolarization and incomplete right bundle branch block, with no disparity between males and females. Large samples involving both sexes are required.

Keywords: Cardiovascular remodeling, electrocardiography, triathlon, elite athletes.

Authors: Ali Heidari, Hosain Ardalan

Abstract:

A CFD model was developed for a desander on the waterway of the Madyan Hydro Power Plant. An underground desander was designed to settle the sediments before the headrace tunnel, which is 14 km long. The desander chamber consists of two caverns, each including two basins with a flushing-type desander, adopted in the feasibility design on the left bank of the river. Eleven scenarios were defined for different configurations of the desander, including the transition vertical slope, symmetric and asymmetric entrance, the basin net length, and tranquilizer racks specifications. The CFD results confirmed the symmetric entrance configuration, desander net length of 135 m, and a transition angle of 34° to the horizon as the optimum configuration. The configuration provides a removal efficiency of 97% for a particle size of 0.2 mm. The CFD results also show that horizontal tranquilizing racks are risky and do not help sediment trapping in the basin. 

Keywords: CFD, Computational Fluid Dynamic, sediment, desander, Madyan.

Authors: Manoj Sood, Upendra Bajpai, Sunil Kumar Singal

Abstract:

Extracting kinetic energy from small streams and canals in off-grid and remote areas offers a modern approach to energy generation using water current turbines. The Savonius water current turbine, a drag-based cross-flow turbine, is particularly suited for its self-starting characteristics at low flow velocities. This study numerically examines the impact of rotor alignment (horizontal vs. vertical) on the performance of the Savonius rotor and its impact on flow condition across velocities ranging from 0.5 to 2.0 m/s, with the tip speed ratio (TSR) maintained between 0.2 and 1.6. The Unsteady Reynolds-Averaged Navier-Stokes (URANS) equations, along with a scalable wall function, has been solved to account for wall effects. Additionally, velocity and pressure contours are generated to analyze and discuss the wake recovery distance. The turbine’s performance is evaluated in terms of power coefficient and its influence on flow velocity. Results indicate that the horizontally aligned turbine demonstrates superior performance compared to the vertically aligned configuration. These findings provide valuable insights for researchers and industry professionals in selecting optimal turbine orientation for site-specific installations.

Keywords: Flow velocity, Savonius rotor, tip speed ratio, wake recovery distance.

Authors: Abbas Khosravi

Abstract:

This paper discusses the use of cooperation without communication in a multi-agent, unpredictable, dynamic real-time environment. The architecture of the Persian Gulf agent consists of three layers: fixed rule, low level, and high level layers, allowing for cooperation without direct communication. A scenario is presented to each agent in the form of a file, specifying each player's role and actions in the game. The scenario helps in cases of miscommunication, improving team performance. Cooperation without communication enhances reliability and coordination among agents, leading to better results in challenging situations.

Keywords: Multi-agent systems, cooperation without communication, Robocop, software engineering.

Authors: Á. L. Bendes

Abstract:

A cautious and questioning approach to the growing impact of digital technologies on society—often referred to as digital skepticism—raises complex issues when explored through the lens of legal philosophy. This paper aims to explore the intersection of digital skepticism and legal philosophy, emphasizing the implications for justice, rights, and the rule of law in the digital age. Digital skepticism arises from concerns about privacy, security, and the ethical implications of digital technology. It questions the extent to which digital advancements enhance or undermine fundamental human values. Legal philosophy, which interrogates the foundations and purposes of law, provides a framework for examining these concerns critically. One key area where digital skepticism and legal philosophy intersect is in the realm of privacy. Digital technologies, particularly data collection and surveillance mechanisms, pose substantial threats to individual privacy. Legal philosophers must grapple with questions about the limits of state power and the protection of personal autonomy. They must consider how traditional legal principles, such as the right to privacy, can be adapted or reinterpreted in light of emerging technological realities. Security is another critical concern. Digital skepticism highlights vulnerabilities in cybersecurity and the potential for malicious activities, such as hacking and cybercrime, to disrupt legal systems and societal order. Legal philosophy must address how laws can evolve to protect against these emerging forms of threats while balancing security with civil liberties. Ethics plays a central role in this discourse. Digital technologies raise ethical dilemmas, such as the development and use of artificial intelligence and machine learning algorithms that may perpetuate biases or make decisions without human oversight. Legal philosophers must evaluate the moral responsibilities of those who design and implement these technologies and consider the implications for justice and fairness. Furthermore, digital skepticism prompts a reevaluation of the concept of the rule of law. In an increasingly digital world, maintaining transparency, accountability, and fairness becomes more complex. Legal philosophers must explore how legal frameworks can ensure that digital technologies serve the public good and do not entrench power imbalances or erode democratic principles. Finally, the intersection of digital skepticism and legal philosophy has practical implications for policy-making. Legal scholars and practitioners must work collaboratively to develop regulations and guidelines that address the challenges posed by digital technology. This includes crafting laws that protect individual rights, ensure security, and promote ethical standards in technology development and deployment. In conclusion, digital skepticism provides a crucial lens for examining the impact of digital technology on law and society. A legal philosophical approach offers valuable insights into how legal systems can adapt to protect fundamental values in the digital age. By addressing privacy, security, ethics, and the rule of law, legal philosophers can help shape a future where digital advancements enhance, rather than undermine, justice and human dignity.

Keywords: Data privacy, cybersecurity, digital transformation, ethical considerations, regulatory compliance.

Authors: A. Shoiynbek, D. Kuanyshbay, P. Menezes, A. Bekarystankyzy, A. Mukhametzhanov, T. Shoiynbek

Abstract:

Speech emotion recognition (SER) has received increasing research interest in recent years. It is a common practice to utilize emotional speech collected under controlled conditions recorded by actors imitating and artificially producing emotions in front of a microphone. There are three issues related to that approach: emotions are not natural, meaning that machines are learning to recognize fake emotions; emotions are very limited in quantity and poor in variety of speaking; there is some language dependency in SER; consequently, each time researchers want to start work with SER, they need to find a good emotional database in their language. This paper proposes an approach to create an automatic tool for speech emotion extraction based on facial emotion recognition and describes the sequence of actions involved in the proposed approach. One of the first objectives in the sequence of actions is the speech detection issue. The paper provides a detailed description of the speech detection model based on a fully connected deep neural network for Kazakh and Russian. Despite the high results in speech detection for Kazakh and Russian, the described process is suitable for any language. To investigate the working capacity of the developed model, an analysis of speech detection and extraction from real tasks has been performed.

Keywords: Deep neural networks, DNN, speech detection, speech emotion recognition, SER, Mel-frequency cepstrum coefficients, collecting speech emotion corpus, collecting speech emotion dataset, Kazakh speech dataset.

Authors: Gao Fangting

Abstract:

With the accelerated pace of industrialization and urbanization, the economic level has been significantly improved, and at the same time, the air quality situation has also become a focus of attention, which not only affects people's health, but also has certain impacts on the economy and ecology. As the capital city of China, the air quality situation in Beijing has attracted much attention. In this paper, based on the day-by-day PM2.5, PM10, CO, NO2, SO2, and O3 conditions in Beijing from 2018 to 2022, the characterization of pollutants is launched, and the seasonal decomposition and prediction of the main pollutants, PM2.5, PM10, and O3, are performed in Season-Trend decomposition using LOESS (STL). The results of the study show that (1) the overall air quality of Beijing has significantly improved from 2018 to 2022, and the main pollutants are PM2.5, PM10, and O3; (2) the seasonal intensities of the main pollutants are higher, and they are influenced by seasonal factors; and (3) it is predicted that the O3 concentration will have a trend of slowly increasing from 2023 to 2026, and the PM10 and PM2.5 pollution situation slowly improves.

Keywords: Air pollution, Beijing, characteristic analysis, STL.

Authors: Jonathan W. Shute

Abstract:

Truancy – unexcused absences from school and class – continues to challenge educators throughout the world, including in Oceania. Traditionally, the focus of attendance issues has been students, parents, and social factors. For this study, 2,536 Junior and senior high school students were surveyed from six high schools in the following island nations: Tonga, Samoa, Kiribati, Fiji. From these data, patterns and characteristics emerged which seems to influence truancy from the truant’s perspective, and which may enlighten teachers in their practice. For lasting solutions to an age-old challenge, the 21st century educational community should consider sharing the responsibility for truancy by focusing on pedagogy practices as a possible explanation for truancy. Specific ideas are suggested to inform teacher effectiveness and therefore keep our students in school and class. This research does not focus on the effectiveness of Teacher Education Programs from which teachers arrive in their careers or the potential cultural, linguistic, political trends, and policies that may or may not influence truancy. While these are critical topics to be researched, the focus of this research is to better understand the students’ opinions and perspectives of why they make the rational choice to truant. 

Keywords: Truancy, truant, student engagement, active learning, pedagogy, curriculum, teacher education, attendance, teaching and learning.

Authors: Bruno Nascimento, Cristina Wanzeller, Jorge Silva, João A. Dias, André Barbosa, José Ribeiro

Abstract:

The increasing complexity of logistics operations stems from evolving business needs, such as the shift from mass production to mass customisation, which demands greater efficiency and flexibility. In response, Industry 4.0 and 5.0 technologies provide improved solutions to enhance operational agility and better meet market demands. The management of kitting zones, combined with the use of Autonomous Mobile Robots, faces challenges related to coordination, resource optimisation, and rapid response to customer demand fluctuations. Additionally, implementing Lean Manufacturing practices in this context must be carefully orchestrated by intelligent systems and human operators to maximise efficiency without sacrificing the agility required in an advanced production environment. This paper proposes and implements a microservices-based architecture integrating principles from Industry 4.0 and 5.0 with Lean Manufacturing practices. The architecture enhances communication and coordination between autonomous vehicles and kitting management systems, allowing more efficient resource utilization and increased scalability. The proposed architecture focuses on the modularity and flexibility of operations, enabling seamless flexibility to change demands and efficiently allocate resources in real-time. Conducting this approach is expected to significantly improve logistics operations’ efficiency and scalability by reducing waste and optimising resource use while improving responsiveness to demand changes. The implementation of this architecture provides a robust foundation for the continuous evolution of kitting management and process optimisation. Designed to adapt to dynamic environments marked by rapid shifts in production demands and real-time decision-making. It also ensures seamless integration with automated systems, aligning with Industry 4.0 and 5.0 needs while reinforcing Lean Manufacturing principles.

Keywords: Microservices, event-driven, kitting, lean manufacturing, industry 4.0, industry 5.0.

Authors: Kung Wong Lau

Abstract:

On August 20, 2024, the global launch of the Wukong game generated significant enthusiasm within the gaming community. This game provides gamers with an immersive experience and some digital twins (the location) that effectively bridge cultural heritage and contemporary gaming, thereby facilitating heritage tourism to some extent. Travel websites highlight locations featured in the Wukong game, encouraging visitors to explore these sites. However, this area remains underexplored in cultural and communication studies, both locally and internationally. This pilot study aims to explore the potential of in-game cultural communication in Wukong for promoting Chinese culture and heritage tourism. An exploratory research methodology was employed, utilizing a focus group of non-Chinese active gamers on an online discussion platform. The findings suggest that the use of digital twins as a means to facilitate cultural communication and heritage tourism for non-Chinese gamers shows promise. While this pilot study cannot generalize its findings due to the limited number of participants, the insights gained could inform further discussions on the influential factors of cultural communication through gaming.

Keywords: Digital game, game culture, heritage tourism, cultural communication, non-Chinese gamers.

Authors: Shan Wang, Fahad Alhathal, Hari Krishhnaa Subramanian

Abstract:

User-centred design skills play an important role in crafting a positive and intuitive user experience for software applications. Embracing a user-centric design approach involves understanding the needs, preferences, and behaviours of the end-users throughout the design process. This mindset not only enhances the usability of the software but also fosters a deeper connection between the digital product and its users. This paper encompasses a 6-month knowledge exchange collaboration project between an academic institution and an external industry in 2023 in the UK; it aims to improve the user experience of a digital platform utilized for a knowledge management tool, to understand users' preferences for features, identify sources of frustration, and pinpoint areas for enhancement. This research conducted one of the most effective methods to implement user-centred design through co-design workshops for testing user onboarding experiences that involve the active participation of users in the design process. More specifically, in January 2023, we organized eight co-design workshops with a diverse group of 11 individuals. Throughout these co-design workshops, we accumulated a total of 11 hours of qualitative data in both video and audio formats. Subsequently, we conducted an analysis of user journeys, identifying common issues and potential areas for improvement within three insights. This analysis was pivotal in guiding the knowledge management software in prioritizing feature enhancements and design improvements. Employing a user-centred design thinking process, we developed a series of graphic design solutions in collaboration with the software management tool company. These solutions were targeted at refining onboarding user experiences, workplace interfaces, and interactive design. Some of these design solutions were translated into tangible interfaces for the knowledge management tool. By actively involving users in the design process and valuing their input, developers can create products that are not only functional but also resonate with the end-users, ultimately leading to greater success in the competitive software landscape. In conclusion, this paper not only contributes insights into designing onboarding user experiences for software within a co-design approach but also presents key theories on leveraging the user-centred design process in software design to enhance overall user experiences.

Keywords: User experience design, user-centred design, co-design approach, knowledge management tool.

Authors: Jiahao Zhou, Lei Wang

Abstract:

In heterogeneous supercomputing clusters, designing an efficient scheduling strategy is pivotal for enhancing both energy efficiency and workflow execution performance. Dynamic allocation and reclamation of computing resources are critical to improving resource utilization. However, existing approaches often rely on fixed resource allocation for jobs prior to execution, retaining these resources until job completion. This static scheduling paradigm fails to account for the dynamic nature of job execution, leading to suboptimal performance. To address these challenges, this paper introduces the Heterogeneous Hierarchical Fine-grained Scheduling algorithm (HeHiFiS), leveraging Graph Convolutional Networks (GCN) and Proximal Policy Optimization (PPO). The proposed algorithm aims to mitigate prolonged workflow completion times and improve resource utilization in heterogeneous supercomputing environments. Specifically, GCNs are employed to extract task dependency features, which are integrated into state representations, while the PPO reinforcement learning algorithm is used to train a scheduling policy. This policy dynamically adjusts scheduling decisions in real-time based on the evolving states of tasks and resources. To evaluate the effectiveness of HeHiFiS, a heterogeneous scheduling simulation platform was developed. Experimental results demonstrate that HeHiFiS, through the incorporation of resource inheritance and intra-task parallelism mechanisms, significantly enhances resource utilization. Compared to existing scheduling algorithms, HeHiFiS achieves over a 50% improvement in both job completion time and response performance metrics, showcasing its efficacy in dynamic and heterogeneous computing environments.

Keywords: Heterogeneous, Dynamic Scheduling, Graph Convolutional Networks, Proximal Policy Optimization.

Authors: Shehar Bano

Abstract:

This research presents the theoretical framework and real-world empirical implementations of Islamic public policy on present-day governments. The study explores how the principles of Islamic Shariah which are derived from the Quran and Sunnah may be applied in the modern states with a view to achieving justice and progress in present-day governance system. Thus, the present qualitative approach used secondary data through text analysis, as based on the conventional, classical Islamic sources and peer-reviewed articles and case studies. This study undertakes a critical evaluation of both the historical and contemporary dimensions of Islamic public policy, employing content analysis as the primary methodological approach. Further, it was observed that although the Islamic governance offers a full ethical agenda based on justice, equity and public interest yet it faces certain shortcomings in its practice, some of them are how can we explain the revealed truths in the light of modern world and the deficiency of competent scholars in the sphere of policy making. Such challenges were discovered to be tractable with a dynamic approach that accommodates ijtihad and partnerships between scientists and economist. Based on this study, it was found that the incorporation of Islamic ethics into policy system could offer a workable solution to current challenges of governance, not simply restricted to the Islamic countries only, but as part of the discussion on justice and governance in the international level as well. Islamic public policy was present as a model for contemporary management of the state pointing out social justice, economic justice and the public good as its rationales.

Keywords: Islamic public policy, modern governance, justice, equity, ijtihād, maqāṣid al-sharī‘ah, public welfare, Islamic ethics, collaboration, contemporary applications.

Authors: Fengguang Lyu, Hongbo Zhang, Chiheng Dang

Abstract:

The significance of water resource management has grown in response to the growing concerns of worldwide water shortages and water environmental degradation, necessitating more scientific theory as well as more advanced management practices. Most water resource management methods still focus on water management and use traditional water quantity evaluation indicators to analyze water resources, which cannot be combined with modern water resource management methods and are of little significance for realizing regional efficient water saving, responding to the construction of a water rights market, and improving the level of fine water resources management. Therefore, this paper proposes a management system that blends accounting management with the water cycle process and the creation of the water rights market. This water accounting system for water consumption management is built by designing a regional target allocation method, a factor transformation system, and an account setting method for water consumption management, as well as incorporating economic analysis and auditing indexes into the water resources management process, and the application is carried out using typical cities in the Hebei Plain as examples. According to the findings, the system successfully increases water consumption management and control, and it can fulfill the regional management goal of water conservation on both sides (water usage side and water consumption side). Regional water consumption is managed by more appropriate goal allocation, accurate factor accounting, scientific analytical methods, and reasonable management strategies, and the water accounting system proposed is more suitable for the development needs of building a water-saving society.

Keywords: Water resource management, water consumption, water accounting, water rights.

Authors: Francis Padi, Solomon Nunoo, John Kojo Annan

Abstract:

This paper presents a comprehensive survey on the deployment of fiber optic networks for telecommunications operators in Ghana. It addresses the challenges encountered by operators using microwave transmission systems for backhauling traffic and emphasizes the advantages of deploying fiber optic networks. The study delves into the coverage gap, provides recommendations, and outlines research directions to enhance the telecommunications infrastructure in Ghana. Additionally, it evaluates next-generation optical access technologies and architectures tailored to operators' needs. The paper also investigates current technological solutions and regulatory, technical, and economical dimensions related to sharing mobile telecommunication networks in emerging countries. Overall, this paper offers valuable insights into fiber optic network deployment for telecommunications operators in Ghana and suggests strategies to meet the increasing demand for data and mobile applications.

Keywords: Fiber optic deployment, coverage gap, telecommunications operator, network expansion strategies, coverage challenges.

Authors: Abdelaziz Nasr

Abstract:

This work conducted numerical simulations to examine the impact of the static electric field on a falling-film evaporation system. A constant electric field can alter the dynamics of a liquid film by modifying the heat and mass transfer properties of the system. The geometry problem consists of two parallel plates in a vertical channel, with the left plate experiencing a constant heat flux and the liquid flowing downward over it, while the right plate remains dry and maintains a constant temperature. The gaseous component consists of dry air and water vapor, whilst the liquid component comprises a thin coating of water. The results suggest that the electric field's impact on heat and mass transport, as well as the evaporation of the liquid sheet, is minimal. Experimental evidence demonstrates that the electric field exerts a minor influence on heat, mass transport, and liquid film evaporation at elevated electric field intensities.

Keywords: Electric field, evaporation, liquid film, heat and mass transfer.