bluuebunny/crossref_metadata_2025 huggingface dataset:
A Sentiment Analysis Model Based on User Experiences of Dubrovnik on the Tripadvisor Platform
Authors: Zakarija Ivona , Škopljanac-Mačina Frano , Marušić Hrvoje , Blašković Bruno
Published: September 2024

Emerging research indicates that sentiment analyses of Dubrovnik focus mainly on hotel accommodations and restaurants. However, little attention has been paid to attractions, even though they are an important aspect of destinations and require more care and investment than amenities. This study examines how visitors experience Dubrovnik based on the reviews published on the Tripadvisor platform. Data were collected by implementing a web-scraping script to retrieve reviews of the tourist attraction “Old Town” from Tripadvisor, while data augmentation and random oversampling techniques were applied to address class imbalances. A sentiment analysis model, based on the pre-trained RoBERTa, was also developed and evaluated. In particular, a sentiment analysis was performed to compare reviews from 2022 and 2023. Overall, the results of this study are promising and demonstrate the effectiveness of this model and its potential applicability to other attractions. These findings provide valuable insights for decision makers to improve services and to increase visitor engagement.
Physicochemical, Antimicrobial Properties and Mineral Content of Several Commercially Available Honey Samples
Authors: Yaman Kerem , Nicolescu Alexandru , Tepe Onur , Cornea-Cipcigan Mihaiela , Aydoğan-Çoşkun Burcu , Mărgăoan Rodica , Şenoğul Dilek , Topal Erkan , Bouari Cosmina Maria
Published: September 2024

Ensuring food safety and protecting consumers are major aspects for commercialized products. Honey, the most prominent in the class of bee products, requires special regulations due to its origin as a natural product. Mislabeling, imitation, and adulteration represent a source of risks for human health. Specific determinations and analyses are essential for controlling the sector and preventing unfair competition. To compare and establish the correct labeling of several different honeys, melissopalynological, physicochemical, mineral content, and microbiological analyses were carried out on 18 samples commercially available in different countries, namely Türkiye, Romania, Bulgaria, and Northern Cyprus. The honey labels were in accordance with the determined pollen content. The physiochemical parameters showed high variability: 4.07–5.25 (pH), 79.95–83.45 (°Brix), 0.262–1.452 µS/cm (electrical conductivity), and 14.6–18.4% (moisture). The samples were quantitatively high in K, P, Na, and Ca, with the highest cumulative mineral content being found for honeys containing Fagaceae pollen. Additionally, the antimicrobial potential of the various honey samples was evaluated against selected bacteria, employing the disk diffusion and serial dilution methods. Results revealed that the honey samples exhibited increased antibacterial activity against Gram-negative bacteria, with notable activity against S. typhimurium, and moderate activity against Gram-positive S. aureus.
A Quantitative Assessment Approach to Implement Pneumatic Waste Collection System Using a New Expert Decision Matrix Related to UN SDGs
Authors: Molina-Jorge Óscar , Terrón-López María-José , Latorre-Dardé Ricardo
Published: September 2024

An innovative decision matrix has been developed to guide the selection and implementation of Pneumatic Urban Solid Waste Collection Systems (PUSWCS) in smart city projects. This study comprehensively collects and analyzes data on the advantages and disadvantages of pneumatic collection systems from technical, economic, and social perspectives. A decision-making tool was created to address the complexities of evaluating the desirability of incorporating PUSWCS in municipalities or specific areas, using a holistic approach. The tool assesses the technical, economic, and social feasibility of implementing PUSWCS, aligning it with the United Nations’ Sustainable Development Goals (SDGs). Specific variables are measured to assess compliance with the SDGs, distinguishing technical aspects from economic and social aspects. The methodology includes surveys of system users and technicians, expert assessments, and the development of a decision matrix that cross-references study variables and SDGs. The matrix assigns numerical values to the Magnitude (M) and Impact (I) of each variable, enabling quantitative interpretation. This holistic approach accommodates the complexities of waste management and diverse stakeholder perspectives. The results demonstrate the matrix’s effectiveness in accurately assessing the desirability of implementing PUSWCS. This confirms the matrix’s ability to optimally integrate with innovative smart city concepts and align with long-term sustainability goals. The study concludes that the design of the decision matrix allows the collection of information from experts, users, and stakeholders about economic, social, and environmental variables and relates them to the SDGs, to obtain a numerical result that allows to decide whether in a given urban environment it is advisable to implement a PUSWCS.
Machine Learning Framework for Classifying and Predicting Depressive Behavior Based on PPG and ECG Feature Extraction
Authors: Alzate Mateo , Torres Robinson , De la Roca José , Quintero-Zea Andres , Hernandez Martha
Published: September 2024

Depression is a significant risk factor for other serious health conditions, such as heart failure, dementia, and diabetes. In this study, a quantitative method was developed to detect depressive states in individuals using electrocardiogram (ECG) and photoplethysmogram (PPG) signals. Data were obtained from 59 people affiliated with the high-specialized medical center of Bajio T1, which consists of medical professionals, administrative personnel, and service workers. Data were analyzed using the Beck Depression Inventory (BDI-II) to discern potential false positives. The statistical analyses performed elucidated distinctive features with variable behavior in response to diverse physical stimuli, which were adeptly processed through a machine learning classification framework. The method achieved an accuracy rate of up to 92% in the identification of depressive states, substantiating the potential of biophysical data in increasing the diagnostic process of depression. The results suggest that this method is innovative and has significant potential. With additional refinements, this approach could be utilized as a screening tool in psychiatry, incorporated into everyday devices for preventive diagnostics, and potentially lead to alarm systems for individuals with suicidal thoughts.
Applied Research on Atopic Dermatitis with Special Emphasis on the Role of Emollients in This Disorder: A Review
Authors: Kowalska Małgorzata Katarzyna , Orłowska Sara Małgorzata , Bednarczyk Łukasz
Published: September 2024

Atopic dermatitis is a chronic and multifactorial inflammatory dermatosis. Recurrent eczematous lesions and intense pruritus very often reduce the quality of life of patients, affecting their mental health. For this reason, it is necessary to undertake treatment. Treatment should be characterized by an individual approach to the patient, taking into account the predominant pathogenetic factors in the development of atopic dermatitis and systematic skin care. Soothing the typical symptoms of AD, i.e., dry skin and persistent itching, involves emollients, which counteract xerosis and reduce the feeling of itching. Studies confirm that the regular use of emollients in patients with AD prolongs the period between relapses and alleviates the intensity of symptoms during periods of disease severity. This review paper aims to highlight the challenges that patients with atopic dermatitis face. This work will also present an indication of the rationale for the use of emollients in this condition, as well as an indication of the forms of their application in therapeutic and care preparations.
Augmented Reality- and Geographic Information System-Based Inspection of Brick Details in Heritage Warehouses
Authors: Shih Naai-Jung , Wu Yu-Chen
Published: September 2024

Brick warehouses represent interdisciplinary heritage sites developed by social, cultural, and economic impacts. This study aimed to connect warehouse details and GIS maps in augmented reality (AR) based on the former Camphor Refinery Workshop Warehouse. AR was applied as an innovation interface to communicate the differences between construction details, providing a feasible on-site solution for articulating historical brick engineering technology. A complex warehouse cluster was georeferenced by the AR models of brick details. The map was assisted by a smartphone-based comparison of the details of adjacent warehouses. Sixty AR models of warehouse details exemplified the active and sustainable preservation of the historical artifacts. The side-by-side allocation of warehouse details in AR facilitated cross-comparisons of construction differences. We found that a second reconstructed result integrated AR and reality in a novel manner based on the use of a smartphone AR. GIS and AR facilitated a management effort using webpages and cloud access from a remote site. The vocabulary of building details can be enriched and better presented in AR.
Upgrading In Vitro Digestion Protocols with Absorption Models
Authors: Antal Otilia , Dalmadi István , Takács Krisztina
Published: September 2024

Intestinal digestion and absorption are complex processes; thus, it is a challenge to imitate them realistically. There are numerous approaches available, with different disadvantages and advantages. The simplest methods to mimic absorption are the non-cell-based transport models but these lack important characteristics of enterocytes of the intestine. Therefore, the most often used method is to measure absorption through viable mammalian cells (most commonly Caco-2 cells, cultured on membrane insert plates), which not only assures the incorporation of brush border enzymes (responsible for the final digestion of peptides and disaccharides), it also simulates the absorption process. This means that influx/efflux transporter-facilitated transport, carrier-mediated transport, endocytosis, and transcytosis is also imitated besides passive diffusion. Still, these also lack the complexity of intestinal epithelium. Organoids or ex vivo models are a better approach if we want to attain precision but the highest accuracy can be achieved with microfluidic systems (gut-on-a-chip models). We propose that more research is necessary, and food absorption should also be studied on gut-on-a-chips, especially with fragmented organoids. Our review supports the choices of a proper intestinal epithelium model, which may have a key role in functional food development, nutrition studies, and toxicity assessment.
Safety of 3D-Printed Acrylic Resins for Prosthodontic Appliances: A Comprehensive Cytotoxicity Review
Authors: Arossi Guilherme Anziliero , Abdou Nauera Abou , Hung Benjamin , Garcia Isadora Martini , Zimmer Roberto , Melo Mary Anne
Published: September 2024

Additive manufacturing resins used in dental prosthetics may retain uncured monomers post-polymerization, posing potential long-term patient exposure risks. Understanding the biological safety of these materials is crucial, particularly for 3D-printed acrylic-based prosthodontic devices such as occlusal nightguards, complete and partial dentures, and temporary fixed prostheses. This paper reviews the literature evaluating the cytotoxicity of such materials. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, we conducted a scoping review using the MESH keywords related to population (P), intervention (I), comparison (C), and outcome (O) across databases, including OVID Medline, EMBASE, and SCOPUS. Our search, limited to peer-reviewed English language articles from 2015 to 2023, resulted in 22 papers. These studies, utilizing digital light processing (DLP) or stereolithography (SLA) printing methods, varied in examining different 3D-printed materials, as well as washing and post-curing protocols. The primary experimental cells used were human gingival fibroblasts (HGF) and mouse fibroblasts (L929). There are no statistical differences in biocompatibility regarding different commercially available resins, washing solutions, or methods. Improvements in cell viability were related to an increase in washing time, as well as post-curing time. After the polishing procedure, 3D resin-based printed occlusal devices perform similarly to milled and conventionally processed ones. Our findings underline the importance of appropriate washing and post-curing protocols in minimizing the cytotoxic risks associated with these 3D-printed resin-based devices.
Evaluation of Occupational Exoskeletons: A Comprehensive Protocol for Experimental Design and Analysis
Authors: Perini Matteo , Bacchetta Adriano Paolo , Cavazza Nicoletta , Khamaisi Riccardo Karim , Melloni Riccardo , Morganti Alessio , Peruzzini Margherita , Botti Lucia
Published: September 2024

This paper proposes a modular protocol for the designing of experimental studies to analyze exoskeletons used in industrial settings to support manual material handling (MMH). Despite exoskeleton technologies starting to be highly commercialized and present in workplaces, research still lacks a standardized procedure for analyzing the impact of these devices on workers. The protocol presented in this paper outlines a step-by-step procedure, including the parameters to be collected and analyzed in a research study. Moreover, the approach could be easily adapted to meet the specificity of a wide range of exoskeletons. The main novelty of the protocol is thus to support the experimental design and analysis of studies assessing the overall impact of exoskeletons on workers. To implement the protocol, the selected case study concerned a palletizing task involving the MMH of 12 cardboard boxes, weighing 10 kg. The results from physiological signals and pressure insoles show that the protocol is comprehensive and can be utilized by researchers evaluating different occupational exoskeletons for assistance during MMH (both active and passive), with modifications to specific parts based on the type of exoskeleton being assessed or the variables of interest.
Radar-Based Target Tracking Using Deep Learning Approaches with Unscented Kalman Filter
Authors: Patrick Uwigize , Rao S. Koteswara , Jagan B. Omkar Lakshmi , Rai Hari Mohan , Agarwal Saurabh , Pak Wooguil
Published: September 2024

Machine learning, a rapidly growing field, has attracted numerous researchers for its ability to automatically learn from and make predictions based on data. This manuscript presents an innovative approach to estimating the covariance matrix of noise in radar measurements for target tracking, resulting from collaborative efforts. Traditionally, researchers have assumed that the covariance matrix of noise in sonar measurements is present in the vast majority of literature related to target tracking. On the other hand, this research aims to estimate it by employing deep learning algorithms with noisy measurements in range, bearing, and elevation from radar sensors. This collaborative approach, involving multiple disciplines, provides a more precise and accurate covariance matrix estimate. Additionally, the unscented Kalman filter was combined with the gated recurrent unit, multilayer perceptron, convolutional neural network, and long short-term memory to accomplish the task of 3D target tracking in an airborne environment. The quantification of the results was achieved through the use of Monte Carlo simulations, which demonstrated that the convolutional neural network performed better than any other approach. The system was simulated using a Python program, and the proposed method offers higher accuracy and faster convergence time than conventional target tracking methods. This is a demonstration of the potential that collaboration can have in research.
CEEMDAN-RIME–Bidirectional Long Short-Term Memory Short-Term Wind Speed Prediction for Wind Farms Incorporating Multi-Head Self-Attention Mechanism
Authors: Yang Wenlu , Zhang Zhanqiang , Meng Keqilao , Wang Kuo , Wang Rui
Published: September 2024

Accurate wind speed prediction is extremely critical to the stable operation of power systems. To enhance the prediction accuracy, we propose a new approach that integrates bidirectional long short-term memory (BiLSTM) with fully adaptive noise ensemble empirical modal decomposition (CEEMDAN), the RIME optimization algorithm (RIME), and a multi-head self-attention mechanism (MHSA). First, the historical data of wind farms are decomposed via CEEMDAN to extract the change patterns and features on different time scales, and different subsequences are obtained. Then, the parameters of the BiLSTM model are optimized using the frost ice optimization algorithm, and each subsequence is input into the neural network model containing the MHSA for prediction. Finally, the predicted values of each component are weighted and reconstructed to obtain the predicted values of wind speed time series. According to the experimental results, the method can predict the short-term wind speeds of wind farms more accurately. We verified the effectiveness of the method by comparing it with different models.
Online Trajectory Replanning for Avoiding Moving Obstacles Using Fusion Prediction and Gradient-Based Optimization
Authors: Fu Qianyi , Zhao Wenjie , Fang Shiyu , Zhu Yiwen , Li Jun , Chen Qili
Published: September 2024

In this study, we introduce a novel method for an online trajectory replanning approach for fixed-wing Unmanned Aerial Vehicles (UAVs). Our method integrates moving obstacle predictions within a gradient-based optimization framework. The trajectory is represented by uniformly discretized waypoints, which serve as the optimization variables within the cost function. This cost function incorporates multiple objectives, including obstacle avoidance, kinematic and dynamic feasibility, similarity to the reference trajectory, and trajectory smoothness. To enhance prediction accuracy, we combine physics-based and pattern-based methods for predicting obstacle movements. These predicted movements are then integrated into the online trajectory replanning framework, significantly enhancing the system’s safety. Our approach provides a robust solution for navigating dynamic environments, ensuring both optimal and secure UAV operation.
Analysis of Mechanical Properties of Steep Surrounding Rock and Failure Process with Countermeasures for Tunnel Bottom Structures
Authors: Fan Rong , Chen Tielin , Yin Xuexuan , Wang Gujian , Li Man , Wang Shunyu
Published: September 2024

Steep surrounding rock significantly challenges tunnel stability by affecting the stress distribution and deformation behavior. The angle of dip in surrounding rock greatly influences these factors, heightening the risk of instability along bedding planes, particularly under high ground stress conditions. This paper presents a comprehensive analysis of steep rock strata mechanical properties based on a railway tunnel in Yunnan Province, China. It incorporates long-term field monitoring and various laboratory tests, including point load, triaxial, and loose circle tests. Using experimental data, this study simulated the failure processes of steep surrounding rock and tunnel structures with a custom finite element method (FEM) integrated with the volume of fluid (VOF) approach. The analysis summarized the deformation patterns, investigated the causes of inverted arch deformation and failure, and proposed countermeasures. The findings reveal that increasing the rock dip angle results in greater deformation and accelerated failure rates, with the surrounding rock’s loose zone stabilizing at approximately 8 m once deformation stabilizes. At a surface deformation of 8 cm, the failure zone extends to 6 m; however, this extension occurs more rapidly with higher lateral pressure coefficients. Additionally, failure zones develop more quickly in thin, soft rock on steep slopes compared to uniform rock formations. The rise of the tunnel floor is attributed to the steeply inclined, thin surrounding rock. To enhance bottom structure stiffness, this study recommends incorporating an inverted arch structure and increasing both the number and strength of the anchor bolts.
A Framework for Agricultural Intelligent Analysis Based on a Visual Language Large Model
Authors: Yu Piaofang , Lin Bo
Published: September 2024

Smart agriculture has become an inevitable trend in the development of modern agriculture, especially promoted by the continuous progress of large language models like chat generative pre-trained transformer (ChatGPT) and general language model (ChatGLM). Although these large models perform well in general knowledge learning, they still have certain limitations and errors when facing agricultural professional knowledge about crop disease identification, growth stage judgment, and so on. Agricultural data involves images and texts and other modalities, which play an important role in agricultural production and management. In order to better learn the characteristics of different modal data in agriculture, realize cross-modal data fusion, and thus understand complex application scenarios, we propose a framework AgriVLM that uses a large amount of agricultural data to fine-tune the visual language model to analyze agricultural data. It can fuse multimodal data and provide more comprehensive agricultural decision support. Specifically, it utilizes Q-former as a bridge between an image encoder and a language model to achieve a cross-modal fusion of agricultural images and text data. Then, we apply a Low-Rank adaptive to fine-tune the language model to achieve an alignment between agricultural image features and a pre-trained language model. The experimental results prove that AgriVLM demonstrates great performance in crop disease recognition and growth stage recognition, with recognition accuracy exceeding 90%, demonstrating its capability to analyze different modalities of agricultural data.
Engagement and Brand Recall in Software Developers: An Eye-Tracking Study on Advergames
Authors: Akcan Duygu , Yilmaz Murat , Güleç Ulaş , Ilgın Hüseyin Emre
Published: September 2024

Advergames represent a novel product placement strategy that surpasses traditional advertising methods by fostering interaction between brands and their target audiences. This study investigates the unique engagement opportunities provided by video games, focusing mainly on the ‘flow experience’, an intensified state of immersion frequently encountered by players of computer games. Such immersive experiences have the potential to significantly influence a player’s perception, offering a new avenue for advertisements to impact and engage audiences effectively. The primary objective of this research was to examine the influence of advergames on players who are deeply immersed in the gaming experience, with a specific focus on the subsequent effects on brand recognition over time. The study involved 44 software developers, who were evenly divided into two groups for the experiment. Both groups were exposed to an identical gaming environment with the task of locating a designated product within the game. However, one group interacted with an enhanced version of the game, which included additional stimuli—such as dynamic music, an engaging narrative, time constraints, a competitive leaderboard, and immersive voice acting—to intensify the gaming experience. The experiment strategically placed various products within the game, and their detectability was assessed using eye-tracking technology. Following gameplay, participants completed questionnaires that measured their experience with flow state and brand recall. The data were analyzed using the Mann–Whitney U test and correlation analysis to facilitate comparisons. The findings indicated that the product associated with the primary task achieved the highest recall rate between both groups. Furthermore, eye-tracking technology identified the areas in the game that attracted the most attention, revealing a preference for mid- and high-level placements over lower-level ones.
Assessment of a Portable Air Cleaning Device Performance in Eliminating Indoor Air Contaminants by Considering Particle Transport Characteristics
Authors: Gür Miray , Kılıç Muhsin
Published: September 2024

Human health is significantly impacted by the quality of the air in living areas. Numerous factors, such as the kind of particle, clean air delivery rate, room geometry, surface features, and thermal plume produced by people or other equipment, all have an impact on indoor particle movement. This work uses computational fluid dynamics to quantitatively examine the performance of a portable air purifier that is routinely used to improve the indoor air quality of a room. The volumetric flow rate, particle diameter, and placement of the air cleaner device were considered in the assessment of the particle transport characteristics. The temperature, velocity, and age of the air distribution in the room were computed in three-dimensional simulations, and the effectiveness of the air cleaning device (ACD) in eliminating contaminants was then investigated. Clean air delivery rate (CADR), collection efficiency, and particle concentration rate values were also computed for every case that was taken into consideration. It is found that CADR and collection efficiency values for larger particles are about 2–7% better than those for smaller particles. The collection efficiency of ACD with different operating conditions is in the range of 71% to 87%. Better collection performance parameters are observed with higher flow rates.
TPSQLi: Test Prioritization for SQL Injection Vulnerability Detection in Web Applications
Authors: Yang Guan-Yan , Wang Farn , Gu You-Zong , Teng Ya-Wen , Yeh Kuo-Hui , Ho Ping-Hsueh , Wen Wei-Ling
Published: September 2024

The rapid proliferation of network applications has led to a significant increase in network attacks. According to the OWASP Top 10 Projects report released in 2021, injection attacks rank among the top three vulnerabilities in software projects. This growing threat landscape has increased the complexity and workload of software testing, necessitating advanced tools to support agile development cycles. This paper introduces a novel test prioritization method for SQL injection vulnerabilities to enhance testing efficiency. By leveraging previous test outcomes, our method adjusts defense strength vectors for subsequent tests, optimizing the testing workflow and tailoring defense mechanisms to specific software needs. This approach aims to improve the effectiveness and efficiency of vulnerability detection and mitigation through a flexible framework that incorporates dynamic adjustments and considers the temporal aspects of vulnerability exposure.
Numerical Study on the Lateral Load Response of Offshore Monopile Foundations in Clay: Effect of Slenderness Ratio
Authors: Khezri Ali , Park Hongbae , Lee Daeyong
Published: September 2024

To meet growing energy demands, offshore wind turbines (OWTs) with higher energy outputs are being developed, presenting increased challenges for their foundation design. Over the past decade, extensive research on the design optimization of OWT support structures has significantly reduced the anticipated costs of offshore wind farm development. Various design methods have been developed and applied in practice, each with its own advantages and limitations. In this study, 3D finite element (FE) modeling, validated against the measured response of a large-scale test monopile, is used to investigate the lateral load response of monopiles with different geometries and slenderness ratios in smaall and large displacements. The results are compared to the standard p–y method, and specific behavioral and design aspects of large-diameter monopiles, such as the moment contribution ratio from different resisting components and the minimum embedment length criteria, are evaluated and discussed. The results showed that the maximum and minimum differences between the 3D FE modeling and one-dimensional (1D) DNV p–y method are 41% and 11% for large displacements, and 32.5% and 13.3% for small displacements, respectively. As the slenderness ratio increases, the discrepancy between the finite element (FE) modeling results and the 1D DNV p–y method decreases, with an average difference of about 13% across all monopile diameters at an L/D ratio of 10, in both small and large displacements, indicating the reasonable accuracy of the 1D method for slenderness ratios of 10 and above. Among the three minimum embedment length criteria examined, the DNV recommended and vertical-tangent criteria offered shorter embedment lengths. The primary resisting moment across all slenderness ratios comes from the distributed lateral load along the monopile shaft (MCRp−y), which increases as the L/D ratio increases.
The Effectiveness of Exercise Programs on Balance, Functional Ability, Quality of Life, and Depression in Progressive Supranuclear Palsy: A Case Study
Authors: Papamichail Panagiotis , Michalas Michail , Krokos Dimitris , Balamoutsou Maria , Karkoula Panagiota , Lyros Epameinondas , Sakellari Vasiliki , Christakou Anna
Published: September 2024

Progressive supranuclear palsy is a form of atypical Parkinsonism. People living with Progressive Supranuclear Palsy have various symptoms, such as movement and cognitive disorders, which mainly affect balance and functional ability with an increased risk of falls, dexterity, and dementia. The role of exercise at the early stage of progressive supranuclear palsy remains unclear. The aim of the present study was to examine the effectiveness of an exercise program at the early stage of progressive supranuclear palsy. A patient with a diagnosis of progressive supranuclear palsy within the past year followed a supervised 12-week exercise program (two times per week) by a physiotherapist, with a session lasting about 40 min at a private physiotherapy clinic. Functional status, balance, quality of life, anxiety, and depression were assessed four times with valid instruments and tests. The results from the timed-up-and-go test demonstrated an improvement in performance (MCID value = 3.4). Improvements were observed in the scores of the Parkinson’s Disease Questionnaire-39 (MCID value = 0.6). Finally, an improvement was reported in the score of the anxiety factor of the hospital anxiety and depression scale (MCID value = 1.5). Physiotherapy appears to improve functional capacity, quality of life, and mental health. Further research is needed to confirm these results with a large sample size in combination with other complementary therapies such as mental imagery.
HumanEnerg Hotspot: Conceptual Design of an Agile Toolkit for Human Energy Reinforcement in Industry 5.0
Authors: Onyemelukwe Ifeoma Chukwunonso , Ferreira José Antonio Vasconcelos , Ramos Ana Luísa , Direito Inês
Published: September 2024

This paper presents the conceptual design of the HumanEnerg Hotspot, an agile toolkit aimed at addressing the human energy crisis in the context of Industry 5.0. The toolkit has been developed using a blend of Design Science Research (DSR) and Human-Centered Design (HCD) methodologies, enabling a comprehensive human-centered problem identification and solution-seeking approach. The toolkit includes a variety of strategies, techniques, frameworks, and resource recommendations for industry use and has been designed to be easily adaptable for use in diverse industry settings. The toolkit is intended to support the European Union’s goal for industry to influence society through a human-centric approach to Industry 5.0 by prioritizing human energy reinforcement and creating a more resilient and productive workforce. The toolkit provides a valuable resource for employees and managers alike and offers a promising solution for addressing the human energy crisis in the era of Industry 5.0.
Identifying Key Factors for Securing a Champions League Position in French Ligue 1 Using Explainable Machine Learning Techniques
Authors: Plakias Spyridon , Kokkotis Christos , Mitrotasios Michalis , Armatas Vasileios , Tsatalas Themistoklis , Giakas Giannis
Published: September 2024

Introduction: Performance analysis is essential for coaches and a topic of extensive research. The advancement of technology and Artificial Intelligence (AI) techniques has revolutionized sports analytics. Aim: The primary aim of this article is to present a robust, explainable machine learning (ML) model that identifies the key factors that contribute to securing one of the top three positions in the standings of the French Ligue 1, ensuring participation in the UEFA Champions League for the following season. Materials and Methods: This retrospective observational study analyzed data from all 380 matches of the 2022–23 French Ligue 1 season. The data were obtained from the publicly-accessed website “whoscored” and included 34 performance indicators. This study employed Sequential Forward Feature Selection (SFFS) and various ML algorithms, including XGBoost, Support Vector Machine (SVM), and Logistic Regression (LR), to create a robust, explainable model. The SHAP (SHapley Additive Explanations) model was used to enhance model interpretability. Results: The K-means Cluster Analysis categorized teams into groups (TOP TEAMS, 3 teams/REST TEAMS, 17 teams), and the ML models provided significant insights into the factors influencing league standings. The LR classifier was the best-performing classifier, achieving an accuracy of 75.13%, a recall of 76.32%, an F1-score of 48.03%, and a precision of 35.17%. “SHORT PASSES” and “THROUGH BALLS” were features found to positively influence the model’s predictions, while “TACKLES ATTEMPTED” and “LONG BALLS” had a negative impact. Conclusions: Our model provided satisfactory predictive accuracy and clear interpretability of results, which gave useful information to stakeholders. Specifically, our model suggests adopting a strategy during the ball possession phase that relies on short passes (avoiding long ones) and aiming to enter the attacking third and the opponent’s penalty area with through balls.
A bi-Gamma Distribution Model for a Broadband Non-Gaussian Random Stress Rainflow Range Based on a Neural Network
Authors: Wang Jie , Chen Huaihai
Published: September 2024

A bi-Gamma distribution model is proposed to determine the probability density function (PDF) of broadband non-Gaussian random stress rainflow ranges during vibration fatigue. A series of stress Power Spectral Densities (PSD) are provided, and the corresponding Gaussian random stress time histories are generated using the inverse Fourier transform and time-domain randomization methods. These Gaussian random stress time histories are then transformed into non-Gaussian random stress time histories. The probability density values of the stress ranges are obtained using the rainflow counting method, and then the bi-Gamma distribution PDF model is fitted to these values to determine the model’s parameters. The PSD parameters and the kurtosis, along with their corresponding model parameters, constitute the neural network input–output dataset. The neural network model established after training can directly provide the parameter values of the bi-Gamma model based on the input PSD parameters and kurtosis, thereby obtaining the PDF of the stress rainflow ranges. The predictive capability of the neural network model is verified and the effects of non-Gaussian random stress with different kurtosis on the structural fatigue life are compared for the same stress PSD. And all life predicted results were within the second scatter band.
An Effective Training Method for Counterfactual Multi-Agent Policy Network Based on Differential Evolution Algorithm
Authors: Qu Shaochun , Guo Ruiqi , Cao Zijian , Liu Jiawei , Su Baolong , Liu Minghao
Published: September 2024

Due to the advantages of a centralized critic to estimate the Q-function value and decentralized actors to optimize the agents’ policies, counterfactual multi-agent (COMA) stands out in most multi-agent reinforcement learning (MARL) algorithms. The sharing of policy parameters can improve sampling efficiency and learning effectiveness, but it may lead to a lack of policy diversity. Hence, to balance parameter sharing and diversity among agents in COMA has been a persistent research topic. In this paper, an effective training method for a COMA policy network based on a differential evolution (DE) algorithm is proposed, named DE-COMA. DE-COMA introduces individuals in a population as computational units to construct the policy network with operations such as mutation, crossover, and selection. The average return of DE-COMA is set as the fitness function, and the best individual of policy network will be chosen for the next generation. By maintaining better parameter sharing to enhance parameter diversity, multi-agent strategies will become more exploratory. To validate the effectiveness of DE-COMA, experiments were conducted in the StarCraft II environment with 2s_vs_1sc, 2s3z, 3m, and 8m battle scenarios. Experimental results demonstrate that DE-COMA significantly outperforms the traditional COMA and most other multi-agent reinforcement learning algorithms in terms of win rate and convergence speed.
Advancing Phantom Fabrication: Exploring 3D-Printed Solutions for Abdominal Imaging Research
Authors: Becircic Muris , Delibegovic Samir , Sehic Adnan , Julardzija Fuad , Beganovic Adnan , Ljuca Kenana , Pandzic Adi , Jusufbegovic Merim
Published: September 2024

Background: The development of novel medical imaging technologies and treatment procedures hinges on the availability of accurate and versatile phantoms. This paper presents a cost-effective approach for creating anthropomorphic abdominal phantoms. Methods: This study proposes a cost-effective method using 3D printing and readily available materials (beeswax, plaster, and epoxy resin) to create high-fidelity anthropomorphic abdominal phantoms. The three-dimensionally printed phantoms exhibited X-ray attenuation properties closely matching those of human tissues, with measured Hounsfield unit (HU) values of −115.41 ± 20.29 HU for fat, 65.61 ± 18.06 HU for muscle, and 510 ± 131.2 HU for bone. These values were compared against patient images and a commercially available phantom, and no statistically significant difference was observed in fat tissue simulation (p = 0.428). Differences were observed for muscle and bone tissues, in which the 3D-printed phantom demonstrated higher HU values compared with patient images (p < 0.001). The 3D-printed phantom’s bone simulation was statistically like that of the commercially available phantom (p = 0.063). Conclusion: This method offers a cost-effective, accessible, and customizable alternative for abdominal phantoms. This innovation has the potential to accelerate advancements in abdominal imaging research, leading to improved diagnostic tools and treatment options for patients. These phantoms could be used to develop and test new imaging techniques with high accuracy.
Influence of Foam Content and Concentration on the Physical and Mechanical Properties of Foam Concrete
Authors: Shill Sukanta Kumer , Garcez Estela Oliari , Al-Deen Safat , Subhani Mahbube
Published: September 2024

Foam concrete has been used in various real-life applications for decades. Simple manufacturing methods, lightweight, high flowability, easy transportability, and low cost make it a useful construction material. This study aims to develop foam concrete mixtures for various civil and geotechnical engineering applications, such as in-fill, wall backfill and soil replacement work. A blended binder mix containing cement, fly ash and silica fume was produced for this study. Its compressive strength performance was compared against conventional general purpose (GP) cement-based foam concrete. Polypropylene (PP) fibre was used for both mixtures and the effect of various percentages of foam content on the compressive strength was thoroughly investigated. Additionally, two types of foaming agents were used to examine their impact on density, strength and setting time. One foaming agent was conventional, whereas the second foaming agent type can be used to manufacture permeable foam concrete. Results indicate that an increase in foam content significantly decreases the strength; however, this reduction is higher in GP mixes than in blended mixes. Nevertheless, the GP mixes attained two times higher compressive strength than the blended mix’s compressive strengths at any foam content. It was also found that the foaming agent associated with creating permeable foam concrete lost its strength (reduced by more than half), even though the density is comparable. The compressive stress–deformation behaviour showed that densification occurs in foam concrete due to its low density, and fibres contributed significantly to crack bridging. These two effects resulted in a long plateau in the compressive stress–strain behaviour of the fibre-reinforced foam concrete.
Characterization of Seismic Dynamic Response of Uranium Tailings Dams Based on Discrete Element Method
Authors: Lan Ming , Huang Hongyu , He Yan
Published: September 2024

Tailings dams play a critical role in ensuring the safety of mining operations. However, earthquakes can cause breaches in these dams, resulting in significant casualties and property damage. This study investigates the dynamic response characteristics of uranium tailings dams subjected to seismic loading, employing the discrete element method. It specifically analyzes how seismic wave amplitude, frequency, and the friction angle of tailings sand affect the dams’ dynamic response. The results reveal that the peak ground acceleration ratio (PGAR) exhibits an increasing–decreasing–increasing pattern with elevation. When the friction angle of the tailings sand exceeds 35°, the overall stability of the dam improves. Conversely, a friction angle below 25° significantly increases the risk of dam failure. Additionally, the dam shows a reduced dynamic response to seismic waves with frequencies exceeding 15 Hz. At lower frequencies, deformation and damage are primarily concentrated on the slope face, while at higher frequencies, damage is predominantly located at the bottom of the model. These findings provide a theoretical foundation and reference for the safe operation of tailings dams, highlighting their practical significance.
Optimisation of Not-from-Concentrate Goji Juice Processing Using Fuzzy Mathematics and Response Surface Methodology and Its Quality Assessment
Authors: Meng Xintao , Ye Duoduo , Pan Yan , Zhang Ting , Liang Lixian , Liu Yiming , Ma Yan
Published: September 2024

Not-from-concentrate (NFC) juice effectively retains the original characteristics of the fruit. Despite the various health benefits of Goji berries, reports on the processing technology and quality changes of NFC goji juice are lacking. We optimised the processing technology of NFC goji juice. Employing fuzzy mathematics evaluation combined with response surface methodology based on single-factor experiments, the physicochemical, nutritional, and microbiological indicators of the juice were determined. Gas chromatography-ion mobility spectroscopy was used to analyse changes in volatile compounds before and after processing. The optimal processing parameters were: times for homogenisation, ultrasonication, and sterilisation of 2 min, 3 min, and 85 s, respectively, and sterilisation temperature of 102 °C. The resulting product had a sensory evaluation score of 85.5 and a rich, pleasant taste. The ratio of total soluble solids to titratable acidity and turbidity were 12.8 and 1420 NTU, respectively. Post-processing, the contents of β-carotene, polysaccharides, and betaine increased by 57.3%, 26.7%, and 31.5%, respectively. Microbiological indicators met Chinese national limits for food pollutants and microorganisms. The total relative content of volatile substances in NFC goji juice decreased by 19.86% after processing. This study provides a theoretical reference for the intensive processing and development of high-value goji berries.
Space Efficiency of Tall Buildings in Singapore
Authors: Aslantamer Özlem Nur , Ilgın Hüseyin Emre
Published: September 2024

Space efficiency in Singaporean tall buildings results from a complex interplay of historical, architectural, engineering, technological, socioeconomic, and environmental factors. The city-state’s innovative and adaptive approach has enabled it to overcome the challenges associated with skyscraper construction, leading to the development of some of the most advanced and sustainable high-rise structures in the world. However, there is currently a lack of detailed analysis on space utilization in Singaporean high-rise buildings. This study addresses this gap by examining 63 cases. The main findings of this research: 1. Residential functions, central core layouts, and prismatic shapes are the most frequent. 2. Concrete material with a shear-walled frame system is the preferred structural choice. 3. Average spatial efficiency is 80%, and the core-to-GFA (Gross Floor Area) ratio averages 17%. These metrics vary from a minimum of 68% and 5% to a maximum of 91% and 32%, respectively. These insights offer valuable guidance for Singaporean construction professionals, particularly architects, helping them make informed design decisions for high-rise projects.
Legacy ICS Cybersecurity Assessment Using Hybrid Threat Modeling—An Oil and Gas Sector Case Study
Authors: Badawy Mohamed , Sherief Nada H. , Abdel-Hamid Ayman A.
Published: September 2024

As security breaches are increasingly widely reported in today’s culture, cybersecurity is gaining attention on a global scale. Threat modeling methods (TMM) are a proactive security practice that is essential for pinpointing risks and limiting their impact. This paper proposes a hybrid threat modeling framework based on system-centric, attacker-centric, and risk-centric approaches to identify threats in Operational Technology (OT) applications. OT is made up of software and hardware used to manage, secure, and control industrial control systems (ICS), and its environments include factories, power plants, oil and gas refineries, and pipelines. To visualize the “big picture” of its infrastructure risk profile and improve understanding of the full attack surface, the proposed framework builds on several threat modeling methodologies: PASTA modeling, STRIDE, and attack tree components. Nevertheless, the continuity and stability of vital infrastructure will continue to depend heavily on legacy equipment. Thus, protecting the availability, security, and safety of industrial environments and vital infrastructure from cyberattacks requires operational technology (OT) cybersecurity. The feasibility of the proposed approach is illustrated with a case study from a real oil and gas production plant control system where numerous significant cyberattacks in recent years have targeted OT networks more frequently as hackers realized the possibility of disruption due to insufficient OT security, particularly for outdated systems. The proposed framework achieved better results in detecting threats and severity in the design of the case study system, helping to increase security and support cybersecurity assessment of legacy control systems.