Authors: Kshitij Ansingkar, Yohei Hoshino, Liangliang Yang

Abstract:

Advances in algorithms related to autonomous systems have made it possible to research on improving the accuracy of estimation of a train’s location. This has the capability of increasing the throughput of a railway network without the need to create additional infrastructure. To develop such a system, the railway industry requires data to test sensor fusion theories or implement simultaneous localization and mapping (SLAM) algorithms. Though, such simulation data and ground truth datasets are available for testing automation algorithms of vehicles, however, due to regulations and economic considerations there is a dearth of such datasets in the railway industry. Thus, there is a need for the creation of a simulation environment that can generate realistic synthetic datasets. This paper proposes (1) to leverage the capabilities of open-source 3D graphic rendering software to create a visualization of the environment; (2) to utilize open-source 3D geospatial data for accurate visualization; and (3) integrate the graphic rendering software with a programming language and numerical computing platform. To develop such an integrated platform this paper utilizes the computing platform’s advanced sensor models like LIDAR, camera, IMU or GPS and merges it with the 3D rendering of the game engine to generate high quality synthetic data. Further, these datasets can be used to train Railway models and improve accuracy of estimation algorithm of a train’s location.

Keywords: 3D game engine, 3D geospatial data, dataset generation, railway simulator, sensor fusion, SLAM, simultaneous localization and mapping.

Authors: Hadi Rouhi Belvirdi

Abstract:

With the increasing demand for sustainable development, recycled materials are receiving more attention in construction projects. To promote sustainable development, this paper evaluates the feasibility of using recycled concrete in construction projects from an economic and environmental perspective. The results show that making concrete using recycled concrete is a suitable strategy for sustainable development. A complete examination of the physical and chemical properties of these recycled materials also provides important information about their suitability for use in the construction industry. Most of the studies do not show surprising results of the compressive or bending strength of these materials, and only the aspect of sustainable development of these materials is of interest. Their application can be investigated more in masonry and joinery works, but among them, some studies sometimes obtained more compressive and bending strength than the control sample, which can be used in concrete structures. Most of the cases introduced and discussed in this study can be implemented and help the country and the people of Iran preserve the environment and discuss sustainable development.

Keywords: Environmental recycling, sustainable development, recycled materials, construction management.

Authors: Jianghua Chen

Abstract:

Sustainable urban development requires the coordination of economic growth and resource environment, and industrial transformation and upgrading is the key link. Based on the decoupling theory and the improved decoupling decomposition model, the quantitative evaluation and comprehensive analysis of the degree of decoupling between resource consumption and economic development in Jiangsu Province are conducted using the data related to the resource consumption and economic growth of the secondary and tertiary industries in Jiangsu Province from 2013 to 2018, as a way of exploring the sustainable development path of industrial transformation in Jiangsu Province. The results show that from the decoupling status, the degree of decoupling of economic development of the secondary industry to the required coke resources, water resources, and human resources is gradually deepening, and the decoupling index of economic development of the tertiary industry to the required water resources is fluctuating; from the perspective of the driving factors, the technological effect is the main driving factor to decouple the economic growth of Jiangsu Province to the resources, and the structural effect has a significant impact on the driving index of the decoupling of economic growth of the secondary and tertiary industries to the consumption of resources in Jiangsu Province. Finally, we discuss the difficulties of industrial transformation and layout faced by Jiangsu Province at present.

Keywords: Resource consumption, economic growth, industrial transformation, decoupling theory.

Authors: Ulemu Chidyaonga Maseko

Abstract:

This study critically examines the empowerment of Muslim women in Malawi, focusing on the Muslim Women’s Organization (MWO) and its impact on gender equality within Islamic communities. It explores MWO's interpretation of Islamic women's rights, the stereotypes Muslim women face, and the factors limiting their rights. Utilizing qualitative methods, including interviews, focus groups, and participant observations, the research adopts phenomenological and feminist frameworks. Findings indicate that since its establishment in 1985, MWO has significantly advocated for gender equality by leveraging Islamic teachings and policy to support women’s empowerment, enabling Muslim women to participate in social change. However, entrenched cultural traditions, patriarchal structures, and systemic poverty remain barriers to empowerment.

Keywords: Islam, women empowerment, Malawi, Islamic feminism.

Authors: C. H. Rodrigues de Oliveira, L. C. Costa Fonseca, C. de Castro Torres, D. Gusmão Pereira, L. R. Souza Ripardo, M. Castro Moraes, A. P. Ferreira Costa, L. C. C. Lima Junior, A. A. da Cunha, R. da Silva Sousa Lima

Abstract:

VoIP University is a communication solution based on the IP protocol. This solution was proposed to modernize and save on communication, which required the development of Android, iOS, and Windows applications and a web service server. This solution allows integration with management system databases to create and manage a list of user extensions. VoIP UEMA was the first deployed project of VoIP University. A mean opinion score test was done, and the results indicated good quality. A financial analysis revealed that annual spending on telephone bills decreased by more than 97%.

Keywords: VoIP eTec, VoIP UEMA, VoIP University, VoIP Valen.

Authors: Hadi Rouhi Belvirdi

Abstract:

Reinforced concrete structures, in addition to bearing service loads and seismic effects, may also be subjected to impact loads resulting from unforeseen incidents. Understanding the behavior of these structures is crucial, as they serve to protect against such sudden loads and can significantly reduce damage and destruction. In examining the behavior of structures under such loading conditions, a total of eight specimens of single-layer reinforced concrete slabs were subjected to impact loading through the free fall of weights from specified heights. The weights and dimensions of the specimens were uniform, and the amount of reinforcement was consistent. By altering the slabs' overall shape and the reinforcement details, efforts were made to optimize the behavior of the slabs against impact loads. The results indicated that utilizing ductile features in the slabs increased their resistance to impact loading. However, the compressive strength of the reinforcement did not significantly enhance the flexural resistance. Assuming a constant amount of longitudinal steel, changes in the placement of tensile reinforcement led to a decrease in resistance. With a fixed amount of transverse steel, merely adjusting the angle of the transverse reinforcement could help control cracking and mitigate premature failures. An increase in compressive resistance beyond a certain limit resulted in local buckling of the compressive zone, subsequently decreasing the impact resistance.

Keywords: Reinforced concrete slab, high-strength concrete, impact loading, seismic efforts, sudden loads, concrete compressive resistance.

Authors: Mars Burke, Alvin Gatto

Abstract:

Rotary-wing aircrafts serve as indispensable components in the advancement of aviation, valued for their ability to operate in diverse and challenging environments without the need for conventional runways. This versatility makes them ideal for applications such as environmental conservation, precision agriculture, emergency medical support, and rapid-response operations in rugged terrains. However, although highly manoeuvrable, rotary-wing platforms generally have lower aerodynamic efficiency than fixed-wing aircraft. This study aims to improve aerodynamic performance by examining a 1/7th-scale rotor blade model equipped with a NACA0012 airfoil using CROTOR software. The analysis focuses on optimal spanwise locations for separating morphing and fixed blade sections at 85%, 90%, and 95% of the blade radius with up to +20 degrees of twist incorporated into the design. Key performance metrics assessed in this investigation include lift coefficient (CL), drag coefficient (CD), lift-to-drag ratio (CL/CD), Mach number, power, thrust coefficient, and Figure of Merit (FOM). Results indicate that the 0.90 r/R position is optimal for dividing the morphing and fixed sections, achieving a significant improvement of over 7% in both lift-to-drag ratio and FOM. These findings underscore the substantial impact on the overall performance of the rotor system and rotational aerodynamics that geometric modifications through the inclusion of a morphing capability can ultimately realise.

Keywords: Helicopter, rotor blade, rotary morphing, rotational aerodynamics, twist morphing, adaptive structures.

Authors: Md. Ashfaqur Rahman

Abstract:

Hallucination in Large Language Models (LLMs) poses a significant challenge in chatbot reliability, especially in critical domains such as healthcare, finance, and education. This paper presents a multi-agent approach to hallucination detection and mitigation using Llama2, integrating retrieval-based verification, fact-checking mechanisms, and response correction strategies. The proposed framework consists of specialized agents, including a Web Retrieval Agent that fetches factual information from external sources (e.g., Wikipedia, DuckDuckGo & Google Serper), Fact-Checking Agents that evaluate response accuracy using semantic similarity scoring, a Correction Agent that refines outputs when hallucinations are detected, and a Monitoring Agent that logs hallucination scores and calculates truthfulness metrics. Experimental results demonstrate that incorporating retrieval-augmented generation (RAG) and multi-agent verification significantly reduces hallucination rates. The study highlights the effectiveness of using Llama2 alongside external knowledge sources and multi-agent collaboration to improve chatbot reliability and factual accuracy. Future research will explore reinforcement learning for dynamic agent optimization and enhancing real-time fact verification methods.

Keywords: Hallucination detection, Llama2, multi-agent systems, retrieval-augmented generation, fact-checking, chatbot reliability, truth scoring, large language models, response correction, semantic similarity.

Authors: O. E. Obayemi, M. A. Ayoade, O. O. Komolafe, O. V. Ekundare

Abstract:

Water pollution is a critical global issue and is mostly caused by human activities and poses significant threats to aquatic ecosystems around the world. Fish collection was carried out for an annual cycle covering both rainy and dry seasons using cast nets, gill nets and traps at two different reservoirs. The objective was to examined the fish assemblages of the reservoirs and provide more additional information on the reservoir. The fish species in the reservoirs comprised of 12 species of six families. The results of the study also showed that five species of fish were caught in reservoir five while 10 fish species were captured in reservoir six. Species such as Malapterurus electricus, Ctenopoma kingsleyae, Mormyrus rume, Parachanna obscura, Sarotherodon galilaeus, Tilapia mariae, Chromidotilapia guntheri, Clarias macromystax, Coptodon zilii and Clarias gariepinus were caught during the sampling period. There was a significant difference (p = 0.014, t = 1.711) in the abundance of fish species in the two reservoirs. Seasonally, reservoirs five (p = 0.221, t = 1.859) and six (p = 0.453, t = 1.734) showed no significant difference in their fish populations. Also, despite being impacted with gold mining, the diversity indices were high when compared to less disturbed waterbodies. The study concluded that the environments recorded low abundant fish species which suggests the influence of mining on the abundance and diversity of fish species.

Keywords: Igun, fish, Shannon-Wiener Index, Simpson index.

Authors: Tsion Tekalegn

Abstract:

In Ethiopia, improved potato varieties are essential for food security, but smallholders' adoption of improved technologies limits their productivity. For this study, data were collected based on a structured questionnaire randomly collected from the 329 sample farmers (158 adopters and 171 non-adopters). We estimate the adoption of improved variety and causal impact using Endogenous Switching Regression (ESR) and a Propensity Score Matching (PSM) was used to test the treatment effect. This helps us estimate the effect of improved potato variety on smallholder farmer income by controlling for the role of the selection bias problem stemming from both observed and unobserved heterogeneity. According to the result, key determinants influencing adoption include livestock ownership, access to extension services, and farming experience, which positively affect the likelihood of adopting improved varieties. In contrast, access to irrigation negatively correlates with adoption, suggesting that farmers with reliable water sources perceive less need for improved varieties. The ESR model result confirmed that improved potato variety adoption increases the smallholder farmer income with an estimated gain of 8.77%. Thus, to improve the potato variety of the farming households, the government should give due emphasis to potato production, and the extension services need to be strengthened.

Keywords: Adoption, improved Potato varieties, endogenous switching regression, Ethiopia.

Authors: Hadi Rouhi Belvirdi

Abstract:

The construction of underground structures using the traditional method begins with excavation and the implementation of the foundation. The process then continues with the construction of the main structure from the ground up. Finally, it concludes with the completion of the final ceiling. This method is known as the Bottom-Up Method. In contrast to this method, there is an advanced technique called the Top-Down Method, which has practically replaced the traditional construction method in large projects in industrialized countries in recent years. Unlike the traditional approach, this method starts with the construction of surrounding walls, columns, and the final ceiling and is completed with the excavation and construction of the foundation of the underground structure. Some of the most significant advantages of this method include the elimination or minimization of formwork surfaces, the removal of temporary bracing during excavation, the creation of some traffic facilities during the construction of the structure, and the possibility of using it in limited and high-traffic urban spaces. Despite these numerous advantages, unfortunately, there is still insufficient awareness of this method in Iran, to the extent that it can be confidently stated that most stakeholders in the construction industry are unaware of the existence of such a construction method. However, it can be utilized as a critical execution option alongside other conventional methods in constructing underground structures. Therefore, due to the extensive practical capabilities of this method, this article aims to present a methodology for constructing underground structures based on the aforementioned advanced method to the scientific community of the country, examine the advantages and limitations of this method and their impacts on time and costs, and discuss its application in urban spaces.

Keywords: Top-Down Method, Bottom-Up Method, underground structure, construction method.

Authors: S. M. Khairul Halim, Mojtaba Ghodsi, Morteza Mohammadzaheri

Abstract:

Gait analysis is essential for diagnosing musculoskeletal and neurological conditions. However, current methods are often complex and expensive. This paper presents a methodology for analysing gait parameters using a smart insole with a built-in accelerometer. The system measures stance time, swing time, step count, and cadence and wirelessly transmits data to a user-friendly IoT dashboard for centralized processing. This setup enables remote monitoring and advanced data analytics, making it a versatile tool for medical diagnostics and everyday usage. Integration with IoT enhances the portability and connectivity of the device, allowing for secure, encrypted data access over the Internet. This feature supports telemedicine and enables personalized treatment plans tailored to individual needs. Overall, the approach provides a cost-effective (almost 25 GBP), accurate, and user-friendly solution for gait analysis, facilitating remote tracking and customized therapy.

Keywords: Gait Analysis, IoT, Smart Insole, accelerometer sensor.

Authors: Shohoni Mahabub

Abstract:

Health Information Systems (HIS) are essential to contemporary healthcare; nonetheless, they frequently encounter challenges such as data fragmentation, inefficiencies, and an absence of real-time analytics. The advent of Machine Learning (ML) and Artificial Intelligence (AI) provides a revolutionary potential to address these difficulties via AI-driven data fusion ecosystems. These ecosystems integrate many health data sources, including Electronic Health Records (EHRs), wearable devices, and genetic data, with sophisticated ML techniques such as Natural Language Processing (NLP) and predictive analytics to produce actionable insights. Through the integration of strong data intake layers, secure interoperability protocols, and privacy-preserving models, these ecosystems provide individualized treatment, early illness diagnosis, and enhanced operational efficiency. This paradigm change enhances clinical decision-making and rectifies systemic inefficiencies in healthcare delivery. Nonetheless, adoption presents problems such as data privacy concerns, ethical considerations, and scalability constraints. The study examines options such as federated learning for safe, decentralized data sharing, explainable AI for transparency, and cloud-based infrastructure for scalability to address these issues. These ecosystems aim to address health equity disparities, particularly in resource-limited environments, and improve public health surveillance, notably in pandemic response initiatives. This article emphasizes the revolutionary potential of AI-driven data fusion ecosystems in redefining HIS by providing an implementation roadmap and showcasing successful deployment case studies. The suggested method promotes a cooperative initiative among legislators, healthcare professionals, and technology to establish a cohesive, efficient, and patient-centric healthcare model.

Keywords: AI-powered healthcare systems, data fusion ecosystem, predictive analytics, digital health interoperability.

Authors: Elhadi I. Elhadi A, Ayad A. Abdalla, Esam A. Alabeedy

Abstract:

This study is part of a series of research papers investigating shape functions for non-standard intervals (i.e. intervals other than the standard [0, 1] or [-1, 1]) within the Totally Volume Integrals Discontinuous-Galerkin (TVI-DG) framework. In this paper, the shape functions for the interval [0, √π] are explored. The numerical results confirm that the shape functions constructed from polynomials within [0, √π] achieve the expected order of accuracy. Furthermore, a comparison between the shape functions for [0, √π] and those for the standard interval [0,1] demonstrates their equivalence in numerical results at T=√π.

Keywords: Discontinuous-Galerkin, Non-standard interval, shape function, Totally Volume Integrals.

Authors: Barenten Suciu, Sota Karimine

Abstract:

In this paper, photos of the nodal lines forming on plates made in copper and bronze in the range of low frequencies are shown in correlation with the recorded vibration spectra. Resonant peaks corresponding to the rigid plate mode of vibration, as well as the lowest bending plate mode of vibration, are identified. Then, the damping ratio at these resonant frequencies is evaluated by using the Q-factor technique. In order to achieve agreement between the predicted and measured frequencies, the mean wavelength is accurately evaluated by taking into account the rounding of the actual nodal lines near the plate edges. Additionally, a frequency correction factor multiplying the core of the frequency expression is introduced to precisely fit the measured values of the frequency. The relationship between the proposed frequency correction factor and the well-known modal overlap factor is explicitly illustrated. In this way, a reliable model able to correlate the excitation frequency with the shape of the actual nodal lines can be achieved, and based on it, control of the micro-particles motion on the vibrating plates via frequency adjustment can be successfully implemented.

Keywords: Nodal lines shape control, frequency adjustment, frequency correction factor, modal overlap factor, square metallic plates.

Authors: Beate Weyland

Abstract:

The interdisciplinary laboratory EDEN (Educational Environments with Nature), established in 2020 at the Faculty of Education of the Free University of Bolzano, continues a research path initiated in 2012 on the relationship between pedagogy and architecture in the design process of school buildings. Between 2016 and 2018, an advisory support activity for schools emerged, combining the need to enhance the physical spaces of schools with the need to update teaching practices and develop school organization, aiming to improve the well-being of both pupils and teachers. The goal of accompanying the development of school communities through research-training paths involves designing pedagogical-didactic and architectural environments collaboratively, involving professionals from education, educational research, architecture, design, and local administration. From 2019 to 2024, more than 30 schools and educational communities across Italy have entered into research-training agreements with the university, focusing increasingly on creating spaces and teaching methods that envision educational spaces as places of well-being and cultural development. This paper presents the research path and the mixed methods used to support schools and educational communities: identifying the research question, developing the research objective, experimentation, and data collection for analysis and reflection. School and educational communities are involved in a participative and active manner. The quality of the action-research work is enriched by a special focus on the relationship with plants and nature in general. Plants are seen as mediators of processes that challenge traditional didactics and invite teachers, students, parents, and administrators to think about the quality of learning spaces and relationships based on well-being. The contribution is characterized by a particular focus on research methodologies and tools developed together with teachers to address the issues raised and measure the impact of the actions undertaken.

Keywords: School development, learning space, wellbeing, plants and nature.

Authors: Nasrudin Andi Mappaware, Muh. Wirawan Harahap, Erlin Syahril, Farah Ekawati Mulyadi, Susdiaman

Abstract:

The increasing trend of cesarean sections, especially those performed on self-request without medical indications, presents complex dilemmas related to medical aspects, professionalism, and bioethics. This study aims to investigate the medical, professional, and bioethical considerations surrounding anesthesia in cesarean sections performed on self-request without medical indications. We report the case of a 27-year-old woman, G1P0A0 gravid 38 weeks, admitted to the hospital for a planned cesarean section on request for the reason that she could not tolerate pain and requested on a date that corresponded to the date and month of her mother's birth. Cesarean section on patient request fulfills the principle of autonomy, which states that patients have the right to themselves. However, this medical procedure is still considered no safer and riskier even though medical technology has developed rapidly. Furthermore, anesthesia during cesarean section at self-request without medical indications is a dilemma for anesthesiologists considering the risks and complications of anesthesia for both the fetus and the mother. The trend in increasing the number of cesarean sections is influenced by patient reasons such as not being able to tolerate pain, trust factors, and worry about damage to the birth canal.

Keywords: Anesthesia, bioethics, cesarean section, self-request, professionalism.

Authors: Gabriel Andrade

Abstract:

Ramadan is a time of intense religious salience in the Islamic world. Apart from ritual engagement, it is also a time for reflection on devotion and shared humanity. This prompts the issue if Ramadan has an effect on moral integrity and decision-making. The present study seeks to answer that question. A group of Muslim students in the United Arab Emirates (UAE) were assessed on moral integrity both during and after Ramadan. Results came out showing that Ramadan has no significant effect on participants’ moral integrity. It is concluded that Ramadan has no effect on participants’ moral behavior, and this is potentially explained by the UAE’s increased secularization in recent decades.

Keywords: Ramadan, United Arab Emirates, moral integrity, secularization, Trolley Dilemmas.

Authors: Praniil Nagaraj

Abstract:

This paper proposes a method to collect homelessness data using crowdsourcing and presents an approach to analyze the data, demonstrating its potential to strengthen existing and future policies aimed at promoting socio-economic equilibrium. This paper's contributions can be categorized into three main areas. Firstly, a method for collecting homelessness data is presented, utilizing a user-friendly smartphone app (currently available for Android). The app enables the general public to quickly record information about homeless individuals, including the number of people and details about their living conditions. The collected data, including date, time, and location, are anonymized and securely transmitted to the cloud. It is anticipated that an increasing number of users motivated to contribute to society will adopt the app, thus expanding the data collection efforts. Duplicate data are addressed through simple classification methods, and historical data are utilized to fill in missing information. The second contribution of this paper is the description of data analysis techniques applied to the collected data. By combining these new data with existing information, statistical regression analysis is employed to gain insights into various aspects, such as distinguishing between unsheltered and sheltered homeless populations, as well as examining their correlation with factors like unemployment rates, housing affordability, and labor demand. Initial data are collected in San Francisco, while pre-existing information is drawn from three cities: San Francisco, New York City, and Washington D.C., facilitating the conduction of simulations. The third contribution focuses on demonstrating the practical implications of the data processing results. The challenges faced by key stakeholders, including charitable organizations and local city governments, are taken into consideration. Two case studies are presented as examples. The first case study explores improving the efficiency of food and necessities distribution, as well as medical assistance, driven by charitable organizations. The second case study examines the correlation between micro-geographic budget expenditure by local city governments and homelessness information to justify budget allocation and expenditures. The ultimate objective of this endeavor is to enable the continuous enhancement of the quality of life for the underprivileged. It is hoped that through increased crowdsourcing of data from the public, the Generosity Curve and the Need Curve will intersect, leading to a better world for all.

Keywords: Crowdsourcing, homelessness, socio-economic policies, statistical analysis.

Authors: Andrea Treviño Gavito, Diego Klabjan, Sanjiv J. Shah

Abstract:

Echocardiograms serve as pivotal resources for clinicians in diagnosing cardiac conditions, offering non-invasive insights into a heart’s structure and function. When echocardiographic studies are conducted, no standardized labeling of the acquired views is performed. Employing machine learning algorithms for automated echocardiogram view detection has emerged as a promising solution to enhance efficiency in echocardiogram use for diagnosis. However, existing approaches predominantly rely on supervised learning, necessitating labor-intensive expert labeling. In this paper, we present a fully unsupervised echocardiographic view detection framework that leverages convolutional autoencoders to obtain lower dimensional representations and the K-means algorithm for clustering them into view-related groups. Our approach focuses on discriminative patches from echocardiographic frames. Additionally, we propose a trainable inverse average layer to optimize decoding of average operations. By integrating both public and proprietary datasets, we obtain a marked improvement in model performance when compared to utilizing a proprietary dataset alone. Our experiments show boosts of 15.5% in accuracy and 9.0% in the F-1 score for frame-based clustering, and 25.9% in accuracy and 19.8% in the F-1 score for view-based clustering. Our research highlights the potential of unsupervised learning methodologies and the utilization of open-sourced data in addressing the complexities of echocardiogram interpretation, paving the way for more accurate and efficient cardiac diagnoses.

Keywords: Artificial intelligence, echocardiographic view detection, echocardiography, machine learning, self-supervised representation learning, unsupervised learning.

Authors: Abigail R. Gardner

Abstract:

With the rise of big budget television comes a demand for more nuanced characters. However, female characters are often underdeveloped, especially those who do not fit neatly into societal norms. This study examines how Ted Lasso’s Rebecca Welton challenges this idea by using her on-screen fashion to mirror her motivations and character development. Through detailed analysis, this research explores how Rebecca’s wardrobe adds depth to her character, contrasting traditional strategies of costuming female characters in mainstream movies and television. While women, especially older women, are getting more screen time, very few have been given a wardrobe to reflect their dynamic characters. Rebecca’s costumes represent a form of visual storytelling typically reserved for film, but with the rise of single-camera television, there is an opportunity to redefine the relationship between women and fashion on screen.

Keywords: Costume design, gender and media, visual storytelling, women in television.

Authors: M. Benbaghdad, F. Betouche, M. Semmani

Abstract:

Image processing become more sophisticated over time due to technological advances especially an artificial intelligence (AI) technology. Currently, AI image processing is used in many areas, including surveillance, industry, science and medicine. AI in medical image processing can help doctors to diagnose diseases faster, with minimal mistakes, and with less effort. Among these diseases is the malaria which remains a major public health challenge in many parts of the world. It affects millions of people every year, particularly in tropical and subtropical regions. Early detection of malaria is essential to prevent serious complications and reduce the burden of the disease. In this paper, we propose and implement a scheme based on AI image processing to enhance malaria disease diagnostic through automated analysis of blood smear images. The scheme is based on the convolutional neural network (CNN) method. So, we have developed a model that classifies infected and uninfected single red cells using images available on Kaggle, as well as real blood smear images obtained from the Central Laboratory of Medical Biology EHS Laadi Flici (formerly El Kettar) in Algeria. The real images were segmented into individual cells using the watershed algorithm in order to match the images from the Kaagle dataset. The model was trained and tested, achieving an accuracy of 99% and 97% accuracy for new real images. This validates that the model performs well with new real images, although with slightly lower accuracy. Additionally, the model has been embedded on a Raspberry Pi4, and a graphical user interface (GUI) was developed to visualize the malaria diagnostic results and facilitate user interaction.

Keywords: Medical Image Processing, Malaria parasite, classification, CNN, Artificial Intelligence.

Authors: Yifan Hou, Haibo Liu, Le Jiang, Wandong Su, Binqing Wang

Abstract:

Road defect detection is crucial for modern urban management and infrastructure maintenance. Traditional road defect detection methods mostly rely on manual labor, which is not only inefficient but also difficult to ensure their reliability. However, existing deep learning-based road defect detection models have poor detection performance in complex environments and lack robustness to multi-scale targets. To address this challenge, this paper proposes an innovative detection framework based on the One Stage algorithm network structure. This article designs a deep feature extraction network based on RCSDarknet, which applies channel shuffling to enhance information fusion between tensors. Through repeated stacking of RCS (Reparametrized Convolution based on channel Shuffle) modules, the information flow between different channels of adjacent layer features is enhanced to improve the model's ability to capture target spatial features. In addition, a multi-scale feature fusion mechanism with weighted dual flow paths was adopted to fuse spatial features of different scales, thereby further improving the detection performance of the model at different scales. To validate the performance of the proposed algorithm, we tested it using the RDD2022 dataset. The experimental results show that the enhancement algorithm achieved 84.14% mAP, which is 1.06% higher than the currently advanced YOLOv8 algorithm. Through visualization analysis of the results, it can also be seen that our proposed algorithm has good performance in detecting targets of different scales in complex scenes. The above experimental results demonstrate the effectiveness and superiority of the proposed algorithm, providing valuable insights for advancing real-time road defect detection methods.

Keywords: Road defect detection, defect detection, visualization, deep learning.

Authors: Rui Yin, Ming Yin, Yang Wang

Abstract:

This paper presents a comprehensive analysis of the strong interactions among protons within atomic nuclei, leveraging the principles of special relativity and Lorentz transformations. We introduce empirical evidence supporting the Lorentz transformation for rotational frames, distinguishing between the Inside Critical Cylinder (ICC) and Outside Critical Cylinder (OCC) configurations. Our experimental setup, involving the precession of electrons, has revealed a natural law where the electric field force of precessing electrons exhibits opposite directions in ICC versus OCC, a phenomenon we term the Critical Cylinder Effect (CCE). Through detailed mathematical derivations, we extend the CCE to explain the strong nuclear force, demonstrating that protons can experience an attractive force when positioned at the OCC of each other's spin and precession reference frames. This force, which we find to be 469 times stronger than the electrostatic force at distances on the order of 10-15 meters, provides a mechanism for the stability of multi-proton systems. Our analysis covers systems from two to seven protons, elucidating the dynamics and stability of these configurations. The findings suggest that the strong interaction is fundamentally electromagnetic in nature, modified by the CCE, offering a potential bridge between electromagnetic and strong nuclear forces within a unified theoretical framework.

Keywords: Special relativity, Lorentz transformation. strong interactions, particle spin.

Authors: Mustafa M. Donma

Abstract:

Fat mass/percentage is a valuable parameter commonly used in obesity studies. Muscle mass, a component of lean body mass, is negatively associated with the degree of obesity. These features make body’s fat mass-to-skeletal muscle mass ratio is a strong anthropometric marker, providing information about two opposing body compartments. Combing this ratio with the alanine aminotransferase-to-aspartate aminotransferase (ALT/AST) ratio may offer meaningful insights during the evaluation of morbid obese children, both from a cardiometabolic risk perspective and regarding their tendency towards MetS development. The aim of this study was to determine the possible involvement of fat-to-muscle ratio (FMR) as a predictor in studies aimed at preventing the development of MetS in children with morbid obesity. 132 children, comprising children with normal body mass index (N-BMI) (n = 35) and morbid obesity (n = 97), participated in the study. Institutional Ethical Committee approval as well as informed consent forms were taken. World Health Organization tables were used for the constitution of two groups. None of the participants had MetS findings. Anthropometric measurements, blood pressure measurements, biochemical analysis, bioelectrical impedance analysis were performed. Within this context, body weight, height, waist/hip/head/neck circumferences, systolic (SBP) and diastolic (DBP) blood pressures, alanine transaminase (ALT), aspartate transaminase (AST), insulin, fasting blood glucose, triglycerides, high density lipoprotein cholesterol, as well as fat and muscle percentages were determined. FMR, body mass index, ALT/AST ratio, and advanced diagnostic obesity notation model assessment cardiac index (ADCI) were calculated. Statistical analysis was performed using SPSS software. There were twofold increases for FMRs obtained from total, trunk, and upper and lower extremities in the MO group compared to the values given for children with N-BMI. A significant performance difference was observed between the ALT/AST ratio and ADCI. In the MO group, twice and five times the values were obtained for the ALT/AST ratio and ADCI, respectively, in comparison to the values found for the N-BMI group. Positive correlations were calculated between total FMR and SBP, DBP as well as ADCI. In conclusion, these findings may signify the possible danger of future MetS and cardiovascular diseases in children with morbid obesity.

Keywords: Cardiac index, diastolic blood pressure, metabolic syndrome, morbid obese children, systolic blood pressure.

Authors: Mustafa M. Donma, Ahsen Yilmaz, Savas Guzel, Orkide Donma

Abstract:

The aim of the study was to evaluate possible relations among biochemical parameters thought to be related to adiposity-related mechanisms in children with normal body mass index (N-BMI), obese (OB) children, and children with metabolic syndrome (MetS). Weight, height, as well as waist, hip, head, and neck circumference values were taken and BMI values were calculated. Children aged 6-18 years were investigated. 176 children (44 children whose BMI percentiles between the 15th and 85th were classified as the control, 88 children with BMI percentiles above the 95th percentile were categorized as the OB group, and 44 children meeting the criteria for MetS were included in the MetS group) were surveyed in total. Serum leptin, ghrelin, adiponectin, asprosin, liver-expressed antimicrobial peptide 2 (Leap2), obestatin, and interleukin-6 levels were determined using ELISA. Statistical analyses were performed using SPSS for Windows. Leptin concentrations increased with the progression of obesity, while ghrelin concentrations showed a corresponding decrease. Interleukin-6, as a proinflammatory marker, increased in obese groups compared to those in the control group. Adiponectin appeared to be a parameter, did not significant potential for distinguishing OB children from those with MetS. In the MetS group, interleukin-6 was correlated with fasting blood glucose (FBG). Strong correlations were obtained among asprosin, Leap2, and obestatin in all groups. Strong correlations among asprosin, Leap2, and obestatin, suggesting their potential interrelation through various mechanisms in different metabolic phases. The association between FBG, as a key predictor of MetS, and interleukin-6 in the MetS group highlights the potential importance of including interleukin-6 in the list of MetS components.

Keywords: Adiposity, interleukin-6, metabolic syndrome, obese children.

Authors: Paria Pourmohammad , Iman Hajirad *

Abstract:

Urban hydrology and water conservation have become critical concerns due to rapid urbanization, climate change, and increasing water scarcity. This review explores the role of green infrastructure and stormwater management in addressing these challenges, emphasizing their effectiveness in mitigating urban water-related issues and enhancing sustainability. Green infrastructure, incorporating strategies such as green roofs, permeable pavements, and urban wetlands, offers a sustainable alternative to conventional stormwater management. These approaches reduce runoff, improve water quality, and support biodiversity while also providing social and economic benefits, including enhanced public spaces, improved air quality, and increased property values. The integration of green infrastructure with stormwater management presents a comprehensive solution for urban water conservation, aiding in flood mitigation and climate resilience. However, challenges such as high initial costs, limited public awareness, and maintenance requirements hinder widespread adoption. To overcome these barriers, supportive policy frameworks, financial incentives, and public engagement initiatives are necessary. Collaboration among policymakers, urban planners, engineers, and private stakeholders is essential to ensuring the successful implementation of green infrastructure. This review highlights the long-term economic advantages of green infrastructure, including cost savings in maintenance and infrastructure replacement. It underscores the need for continuous research, technological innovation, and interdisciplinary cooperation to optimize these strategies for global application. Ultimately, integrating green infrastructure and stormwater management into urban planning can contribute to more sustainable, resilient, and livable cities.

Keywords: Urban hydrology, green infrastructure, stormwater management, water conservation, climate resilience, sustainable urban planning.

Authors: Brehme D’napoli Reis de Mesquita

Abstract:

This study examines the influence of robotics competitions on the career aspirations and skill development of middle and high school students. The program adopts an innovative approach by focusing on the students’ perceptions. The responses of 66 students who participated in a robotics competition were analyzed, encompassing individuals from various age groups and genders. The results indicate that the majority of students perceive robotics to be a significant aspect of their learning experience. Specifically, 81.8% of the participants classified robotics as ”very important,” while 16.7% rated it as ”important.” Furthermore, 63.6% of students indicated that their participation in robotics competitions has influenced their career decisions in fields such as engineering and information technology. The current data demonstrate that robotics competitions facilitate the development of essential skills, including logical thinking, creativity, emotional intelligence, teamwork, discipline, and self-confidence. Students’ perceptions suggest that robotics serves as an effective educational tool, fostering increased motivation and engagement and preparing them for the challenges of the 21st-century job market. This study contributes to the extant literature by putting the students’ perspective at the forefront rather than the traditional view of teachers. Moreover, it emphasizes the importance of targeted initiatives to promote inclusion and gender equity in robotics competitions. Practical implications of integrating robotics activities into the school curriculum suggest that it can enhance students academic performance and personal development. Subsequent studies could further enhance this research by incorporating a more substantial sample size and examining the long-term ramifications of robotics competitions on students’ career trajectories.

Keywords: Educational robotics, robotics competitions, skills development, career choice.

Authors: Roghaye Behroozi

Abstract:

The development of functionalized metal complexes and nanomaterials has gained significant attention due to their potential in catalyzing selective oxidation and coupling reactions. These catalysts play a crucial role in various industrial and pharmaceutical processes, enhancing the efficiency, selectivity, and sustainability of chemical reactions. This research aims to design and synthesize new functionalized metal complexes and nanomaterials to explore their catalytic applications in the selective oxidation of alcohols and coupling reactions, focusing on improving yield, selectivity, and catalyst reusability. The study involves the synthesis of a nickel Schiff base complex stabilized within Mobil Composition of Matter No. 41 (MCM-41) as a heterogeneous catalyst. A Schiff base ligand derived from glycine was used to create a tin(IV) metal complex characterized through spectroscopic techniques and computational analysis. Additionally, iron-based magnetic nanoparticles functionalized with melamine were synthesized for catalytic evaluation. Lastly, a palladium(IV) complex was prepared, and its oxidative stability was analyzed. The nickel Schiff base catalyst showed high selectivity in converting primary and secondary alcohols to aldehydes and ketones, with yields ranging from 73% to 90%. The tin(IV) complex demonstrated accurate structural and electronic properties, with consistent results between experimental and computational data. The melamine-functionalized iron nanoparticles exhibited efficient catalytic activity in producing triazoles, with enhanced reaction speed and reusability. The palladium(IV) complex displayed remarkable stability and low reactivity towards C–C bond formation due to its symmetrical structure. The synthesized metal complexes and nanomaterials demonstrated significant potential as efficient, selective, and reusable catalysts for oxidation and coupling reactions. These findings pave the way for developing environmentally friendly and cost-effective catalytic systems for industrial applications.

Keywords: Catalysts, Schiff base complexes, metal-organic frameworks, oxidation reactions, nanoparticles, reusability.

Authors: Al. I. Diwedar, M. Iskander, M. Yossef, A. ElKut, N. Fouad, R. Fathy, M. M. Almaghraby, A. Samir, A. Romya, N. Hassan, A. Abo Zed, B. Reijmerink, J. Groenenboom, M. Salah

Abstract:

Coastal problems are stressing on the coastal environment due to its complexity. The dynamic interaction between the sea and the land results in serious problems that threaten coastal areas worldwide, in addition to human interventions and activities. This makes the coastal environment highly vulnerable to natural processes like flooding, erosion, and the impact of human activities as pollution. Protecting and preserving this vulnerable coastal zone with its valuable ecosystems calls for addressing the coastal problems. This, in the end, will support the sustainability of the coastal communities and maintain the current and future generations. Consequently, applying suitable management strategies and sustainable development that consider the unique characteristics of the coastal system is a must. The coastal management philosophy aims to solve the conflicts of interest between human development activities and this dynamic nature. Modeling emerges as a successful tool that provides support to decision-makers, engineers, and researchers for better management practices. Modeling tools proved that it is accurate and reliable in prediction. With its capability to integrate data from various sources such as bathymetric survey, satellite images, and meteorological data, it offers the possibility for engineers and scientists to understand this complex dynamic system, and get in-depth into the interaction between both the natural and human-induced factors. This enables decision makers to make informed choices and develop effective strategies for sustainable development and risk mitigation of the coastal zone. The application of modeling tools supports the evaluation of various scenarios by affording the possibility to simulate and forecast different coastal processes from the hydrodynamic and wave actions and the resulting flooding, and erosion. The state-of-the-art application of modeling tools in coastal management allows for better understanding and predicting coastal processes, optimizing infrastructure planning and design, supporting ecosystem-based approaches, assessing climate change impacts, managing hazards, and finally facilitating stakeholder engagement. This paper emphasizes the role of hydraulic models in enhancing the management of coastal problems by discussing the diverse applications of modeling in coastal management. It highlights the modelling role in understanding complex coastal processes, and predicting outcomes. The importance of informing decision-makers with modeling results which gives technical and scientific support to achieve sustainable coastal development and protection.

Keywords: Coastal problems, coastal management, hydraulic model, numerical model, physical model.

Authors: Erietta-Christina Arnaoutaki, Stelios-Elion Bousi, Marinos Zachiotis, Simoni Zarkada, Alexandra Chrysagi, Mamdouh Fahad Alenazi, Dimitri Aristotle Raptis

Abstract:

This paper aimed to evaluate the mental health status of patients with esophageal cancer following surgical treatment, as well as the role of psychological distress screening tests in this patient population. Studies reporting psychometric screening tools used in esophageal cancer patients after esophagectomy, published before January 2024 on PubMed, Scopus, and CENTRAL databases, were searched and analyzed. Six non-randomized control trials were selected for inclusion in this study which involved 1059 patients undergoing esophagectomy for esophageal cancer. Among the included studies, five employed the Hospital Anxiety and Depression Scale (HADS) for anxiety and/or depression screening, while one used the MD Anderson Symptom Inventory for gastrointestinal cancer (MDASI-GI) for sadness screening. A range of time points was used to evaluate these patients: 102 patients were evaluated at 1 month, 230 patients at 3 months, 218 patients at 6 months, 653 patients at 12 months, and 154 patients at 24 months postoperatively. Analysis of data pooled from three studies employing the HADS revealed a prevalence of 19.45% for anxiety and 17.92% for depression at the 12-month follow-up and mean scores of 3.91 (3.12) and 3.56 (3.12) for the HADS anxiety (HADS-A) and depression (HADS-D) subscales respectively, at any time postoperatively. The findings show a neglected concern regarding the mental health of esophageal cancer survivors following surgical treatment. The use of psychometric screening tools is essential to address psychological distress and improve the quality of life of these patients.

Keywords: Esophageal cancer, esophagectomy, psychological distress, anxiety, depression, psychometric tests, HADS, MDASI-GI.

Authors: Sachi Nandan Mohanty, Shreya Sinha, Sweeti Sah, Shweta Sharma

Abstract:

The advancement of natural language processing has majorly influenced the field of medical transcript classification, providing a robust framework for enhancing the accuracy of clinical data processing. It has enormous potential to transform healthcare and improve people's livelihoods. This research focuses on improving the accuracy of medical transcript categorization using Bidirectional Encoder Representations from Transformers (BERT) and its specialized variants, including BioBERT, ClinicalBERT, SciBERT, and BlueBERT. The experimental work employs Optuna, an optimization framework, for hyperparameter tuning to identify the most effective variant, concluding that BioBERT yields the best performance. Furthermore, various optimizers, including Adam, RMSprop, and Layerwise adaptive large batch optimization (LAMB), were evaluated alongside BERT's default AdamW optimizer. The findings show that the LAMB optimizer achieves a performance that is equally good as AdamW's. Synthetic data generation techniques from Gretel were utilized to augment the dataset, expanding the original dataset from 5,000 to 10,000 rows. Subsequent evaluations demonstrated that the model maintained its performance with synthetic data, with the LAMB optimizer showing marginally better results. The enhanced dataset and optimized model configurations improved classification accuracy, showcasing the efficacy of the BioBERT variant and the LAMB optimizer. It resulted in an accuracy of up to 98.2% and 90.8% for the original and combined datasets.

Keywords: BioBERT, clinical data, healthcare AI, transformer models.

Authors: Narges Ghobadi, Witold Kinsner, Tony Szturm, Nariman Sepehri

Abstract:

Accurate joint localization is essential for effective hand rehabilitation using soft robotic gloves. This paper presents a real-time joint localization system that employs a lightweight object detection model (YOLOv8n), deployed on a micro-computer integrated with an AI accelerator. Visual tags embedded on the glove enable precise detection and tracking of finger movements. The model, trained to recognize these tags with 99% precision, achieves low-latency inference of approximately 3 ms per frame and operates at an average frame rate of 60 frames per second (fps). This corresponds to a Nyquist frequency of 30 Hz, ensuring accurate capture of human hand movements without aliasing. The system's compact and energy-efficient design supports portability and ease of use for patients, while real-time data processing enables responsive feedback during rehabilitation exercises. Performance evaluations confirm the system’s effectiveness under the computational constraints of embedded platforms.

Keywords: Embedded system, vision system, marker-based detection, rehabilitation, joint localization.

Authors: Etienne Mfoumou, Mo Shamma, Martin Tango, Michael Locke

Abstract:

Water literacy is essential for addressing emerging water management challenges in southwest Nova Scotia (SWNS), where growing concerns over water scarcity and sustainability have highlighted the need for improved educational frameworks. Current approaches often fail to fully represent the complexity of water systems, focusing narrowly on the water cycle while neglecting critical aspects such as groundwater infiltration and the interconnectedness of surface and subsurface water systems. To address these gaps, this paper proposes a comprehensive framework for water literacy that integrates the physical dimensions of water systems with key aspects of understanding, including processes, energy, scale, and human dependency. Moreover, a suggested tool to enhance this framework is a real-time hydrometric data map supported by a network of water level monitoring devices deployed across the province. These devices, particularly for monitoring dug wells, would provide critical data on groundwater levels and trends, offering stakeholders actionable insights into water availability and sustainability. These real-time data would facilitate deeper understanding and engagement with local water issues, complementing the educational framework and empowering stakeholders to make informed decisions. By integrating this tool, the proposed framework offers a practical, interdisciplinary approach to improving water literacy and promoting sustainable water management in SWNS.

Keywords: Water education, water literacy, water management, water systems, southwest Nova Scotia.

Authors: Fulufhelo Khangale, Ednah Onyari, Idowu Bodunrin

Abstract:

With the climate change phenomena upon us, the changes in the decadal hydrological cycles must be analysed. Water availability must be analysed to obtain sustainable water supply and future water management planning. The hydrological components, such as evaporation, streamflow, and rainfall, were used to analyse and predict the decadal hydrological cycles in the basin. The streamflow results for the Mann-Kendall test indicate a decreasing trend and an increasing trend is insignificant. For evaporation, the results indicate a decreasing trend in significance and insignificance, whereas the results for rainfall indicate a decreasing trend in both significance and insignificance. The trend analysis for historical data from 1993 to 2023 and future predictions for 2023 to 2063 indicate a decreasing trend for rainfall and evaporation and a decreasing and increasing trend for streamflow. There is a correlation between streamflow and rainfall, but there is no correlation between evaporation and either rainfall or streamflow.

Keywords: Decadal hydrological cycle, Mann Kendall, trend analysis, water availability.

Authors: C. Cruz, A. Breda

Abstract:

In the early years, until children’s entrance at the elementary school, they are stimulated by educators, through rich and attractive contexts, to explore and develop skills from different domains. Many of these contexts trigger real or imaginary situations, through resources or pedagogical practices that incite curiosity, questioning, expression of ideas or emotions, social interaction, among others. Later, when children enter elementary school, the educational activity becomes more focused on cognitive domain and critical thinking. Children are naturally curious; they have an intrinsic desire to know more and question what is unknown. Thus, it is important to bring to the classroom issues that seduce or captivate children’s attention, trigger curiosity, and allow them to be heard. The storytelling´s narrative exploration is a pedagogical practice that stimulates children’s insights supported into their experiences, emotions, learnings and personality, and promote opportunities for children express freely their feelings and thoughts, being a promise tool to develop critical, creative and caring thinking. In this work, the "From the Outside In and From the Inside Out" book and a task developed with children in the 3rd year from a Portuguese 1st Cycle Basic School, promoted in a presentation book session, are analyzed from the perspective of philosophical inquiry. This study aims to verify how promising the children’s book "From the outside in and from the inside out" is to develop philosophical thought in children.

Keywords: Children’s literature, non-formal learning contexts, philosophical inquiry, thinking components.

Authors: Michael James, Michael Bewong, Yeslam Al-Saggaf, Jason Howarth

Abstract:

The shortage of cybersecurity professionals is a widely recognised and documented issue. However, the role of higher education institutions in addressing this challenge is less understood. While universities are key contributors to workforce development, a notable gap in the literature is the lack of emphasis on soft skills, essential for effective collaboration on complex problems. This study used a qualitative document analysis approach, examining current and past postgraduate cybersecurity program curricula from Australian university handbooks. Postgraduate education plays a vital role in professional development, offering pathways to advanced capabilities and specialised knowledge. Our analysis reviewed 41 programs across 27 Australian universities, with the findings consolidated into a map of commonly offered courses and core technical and soft skills contained within them. Our findings show that most programs lack opportunities for industry-based projects, a critical component for easing students' transition into the workforce. Additionally, some programs do not offer elective courses, limiting students' ability to address specific skill gaps in their learning. These shortcomings highlight the need for curricula that better integrate industry engagement and provide more flexible learning pathways.

Keywords: Australia, cybersecurity, cybersecurity qualifications, postgraduate.