Search Results (181)

Dictyostelium discoideum (Dicty) is a type of unicellular amoeba, but when starved, a large number of amoebas gather together to form a multicellular organism. In this review, we first introduce our cellular dynamics method for Dicty, including intracellular biochemical reactions. We then introduce a number of hidden roles of receptors revealed by our simulation studies. Of particular note is that receptor–receptor interactions are strengthened under starvation conditions, resulting in diverse dynamic functions that cannot be predicted from the action of a single receptor, such as intercellular synchronization. Furthermore, we introduce a mathematical generalization of Dicty’s receptor function and demonstrate its potential applications not only in the biological field but also in the engineering field. Full article

Amid growing environmental concerns and an increasing push for sustainable development, countries in the Middle East and North Africa (MENA) region have taken proactive steps toward green growth, energy transition, and technological innovation. As a result, this study examines the effects of green growth, energy transition, technological innovation, financial inclusion, and urbanization on environmental sustainability in the Middle East and North Africa (MENA) region. Moreover, this study breaks new ground by exposing the hidden environmental costs of financial inclusion, urbanization, and technological innovation in the MENA region’s development trajectory, thereby providing compelling evidence for rethinking sustainability through an integrated approach that aligns economic ambition with ecological responsibility. Data for the studied variables were sourced from the World Bank database covering the period 1990 to 2021. The results show that green growth and energy transition significantly reduce CO₂ emissions, supporting the idea that economic expansion aligned with environmental priorities can contribute to ecological improvement. However, the impact of technological innovation is statistically insignificant, indicating that innovation in the region has not yet translated into meaningful environmental gains, possibly due to the dominance of non-green or industrial-focused innovation. Financial inclusion is found to increase CO₂ emissions, likely by facilitating greater access to credit and financial services that fuel energy-intensive consumption and production activities. Similarly, urbanization also contributes to rising emissions, reflecting the unsustainable nature of urban growth in many MENA region. Based on this study, we advocate for a coordinated regional approach to climate and energy policy, underpinned by shared governance and collective action. Full article

A fuzzy extractor is a foundational cryptographic component that enables the extraction of reproducible and uniformly random strings from sources with inherent noise, such as biometric traits. Reusable fuzzy extractor guarantees the security of multiple extractions from the same noisy source. In addition, although isogeny-based cryptography has become an important branch in post-quantum cryptography, the study of fuzzy extractors based on isogeny assumptions is still in its early stages and holds much room for improvement. In this paper, we give two reusable fuzzy extractor schemes derived from isogeny-based assumptions: one is based on the linear hidden shift assumption over group actions, while the other is built upon the group-action decisional Diffie–Hellman assumption within the isogeny framework. Both proposed constructions achieve post-quantum security and are capable of correcting a linear proportion of errors. They rely solely on fundamental cryptographic primitives, which ensure simplicity and efficiency. Additionally, the second construction is based on restricted effective group action, which is weaker than the effective group action used in the first construction, thereby offering greater practical applicability. Full article

This review contains a compilation of data about the occurrence, mining, refining, and biological actions of lithium, without claiming completeness of knowledge. This should give a baseline for judging future pollutions of environmental and agricultural items and human nutrition and may show still existing gaps of screening. Emerging electromobility and use of computers leads to a steep increase in Li-based batteries, which are a source of hazardous waste unless recycled. Lack of recovery methods from effluents and sewage, however, will increase pollution with soluble Li-salts from increasing mining and waste in the future; therefore, biochemical effects of levels out of ambient range have been included. Many published data are hidden in multi-element tables, including the data of the author. Mobile fractions of soils and soil-to-plant transfer, as well as retainment in animal tissues, are low. A lot of data, starting from geology via soils, plants, water, and human nutrition, lead to a largely unknown average daily intake for men. With respect to nutrition of dairy cows, the contribution of Li from water was highest among all elements investigated, but only 4% of intake. Main sources for human nutrition are mineral water and table salt. Li is not labelled on mineral water bottles, nor table salt, which are the main sources. Though some data have been gathered, for human nutrition, the average daily intake is uncertain to estimate because some mineral waters are quite high in Li. Full article

This study proposes a dual-level analytical approach to observing human–robot interactions in a real-world public setting, specifically a science museum. Observation plays a crucial role in human–robot interaction research by enabling the capture of nuanced and context-sensitive behaviors that are often missed by post-interaction surveys or controlled laboratory experiments. Public environments such as museums pose particular challenges due to their dynamic and open-ended nature, requiring methodological approaches that balance ecological validity with analytical rigor. To address these challenges, we introduce a dual-level approach for behavioral observation, integrating statistical analysis across demographic groups with time-series modeling of individual engagement dynamics. At the group level, we analyzed engagement patterns based on age and gender, revealing significantly higher interaction levels among children and adolescents compared to adults. At the individual level, we employed temporal behavioral analysis using a Hidden Markov Model to identify sequential engagement states—low, moderate, and high—derived from time-series behavioral patterns. This approach offers both broad and detailed insights into visitor engagement, providing actionable implications for designing adaptive and socially engaging robot behaviors in complex public environments. Furthermore, it can facilitate the analysis of social robot interactions in everyday contexts and contribute to building a practical foundation for their implementation in real-world settings. Full article

Interpretability is widely recognized as essential in machine learning, yet optimization models remain largely opaque, limiting their adoption in high-stakes decision-making. While optimization provides mathematically rigorous solutions, the reasoning behind these solutions is often difficult to extract and communicate. This lack of transparency is particularly problematic in fields such as energy planning, healthcare, and resource allocation, where decision-makers require not only optimal solutions but also a clear understanding of trade-offs, constraints, and alternative options. To address these challenges, we propose a framework for interpretable optimization built on three key pillars. First, simplification and surrogate modeling reduce problem complexity while preserving decision-relevant structures, allowing stakeholders to engage with more intuitive representations of optimization models. Second, near-optimal solution analysis identifies alternative solutions that perform comparably to the optimal one, offering flexibility and robustness in decision-making while uncovering hidden trade-offs. Last, rationale generation ensures that solutions are explainable and actionable by providing insights into the relationships among variables, constraints, and objectives. By integrating these principles, optimization can move beyond black-box decision-making toward greater transparency, accountability, and usability. Enhancing interpretability strengthens both efficiency and ethical responsibility, enabling decision-makers to trust, validate, and implement optimization-driven insights with confidence. Full article

Higher education can promote environmental awareness and action through hidden curricula. This study at the Witten/Herdecke University examined the impact of the Human–Animal Studies course on students’ environmental awareness and behavior, comparing participants with the general student population. A cross-sectional and longitudinal survey was conducted using a 12-question Likert-scale questionnaire. Course participants were surveyed three times, while the general student body was surveyed once. In addition, reflective writing was qualitatively analyzed to assess changes in attitudes and behaviors. The results showed that both groups exhibited high levels of environmental awareness and behavior, exceeding the German population average. Female students showed greater commitment than male students. While no significant differences were found between course participants and other students, reflections indicated that the course promoted personal awareness and behavioral change and that the course encouraged participants to think about changes in their attitudes and behaviors toward the environment. These findings suggest that courses such as Human–Animal Studies can promote environmental awareness and self-reflection among students. Full article

Clustering plays a vital role in process mining as it organizes complex event logs into meaningful groups, helping to identify common patterns, outliers, and inefficiencies. This simplification enables organizations to detect bottlenecks and optimize workflows by uncovering trends and variations that might otherwise remain hidden. Fuzzy clustering addresses the challenge of overlapping behaviors, providing actionable insights for targeted improvements and enhanced operational efficiency. Nevertheless, conventional clustering algorithms for process mining focus either on activity sequences or cycle times, resulting in incomplete insights due to the neglect of temporal or structural variations. This work introduces a new fuzzy clustering methodology that incorporates both activity sequences and cycle times through a weighted distance metric. The proposed approach balances the weights of similarity in sequences as well as time variation flexibly using the parameter $\alpha$, enabling clusters to represent both structural as well as performance-based process attributes. Through using fuzzy C-means clustering, the method allows cases to have multiple memberships with different membership degrees, providing flexibility regarding overlapping process behavior. An experimental evaluation using real-life event logs demonstrates the effectiveness of the method in discerning process variants. It yields superior results compared to conventional methods that account for only sequence-based clustering scenarios, as well as time-based clustering methods. The results describe the significant importance of optimizing clustering results by varying $\alpha$, where a balanced weighting ($\alpha =0.5$) gives more meaningful clusters. Ultimately, the framework enhances process mining by offering detailed insights for analyzing operational inefficiencies, bottlenecks, and resource allocation mismatches, providing substantial real-world benefits for industries that demand effective process improvement. Full article

Identifying sustainable and efficient methods for the degradation of plastic waste in landfills is critical for the implementation of the Saudi Green Initiative, the European Union’s Strategic Plan, and the 2030 United Nations Action Plan, all of which are aimed at achieving a sustainable environment. This study assesses the influence of palm fronds (PFR) on the thermal degradation of polypropylene plastic (PP) using TGA/FTIR experimental measurements, thermo-kinetics, and machine learning convolutional deep learning neural networks (CDNN). Thermal degradation operations were conducted on pure materials (PFR and PP) as well as mixed (blended) materials containing 25% and 50% PFR, across degradation temperatures ranging from 25 to 600 °C and heating rates of 10, 20, and 40 °C·min⁻¹. The TGA data indicated a synergistic interaction between the agricultural waste (PFR) and PP plastic, with decreased thermal stability at temperatures below 500 °C, attributed to the hemicellulose and cellulose present in the PFR biomass. In contrast, at temperatures exceeding 500 °C, the presence of lignin retards the degradation of the PFR biomass and blends. Activation energy values between 81.92 and 299.34 kJ·mol⁻¹ were obtained through the application of the Flynn–Wall–Ozawa (FWO) and Kissinger–Akahira–Sunose (KAS) model-free methods. The application of CDNN facilitated the extraction of significant features and labels, which were crucial for enhancing modeling accuracy and convergence. This modeling and simulation approach reduced the overall cost function from 41.68 to 0.27, utilizing seven hidden neurons, and 673,910 epochs in 13.28 h. This method effectively bridged the gap between modeling and experimental data, achieving an R² value of approximately 0.992, and identified sample composition as the most critical parameter influencing the thermolysis process. It is hoped that such findings may facilitate an energy-efficient pathway necessary for the thermal decomposition of plastic materials in landfills. Full article

Due to the construction damage of high-density polyethylene film (HDPE) during the early stages of landfill construction and missed or faulty welding, this paper proposes a method based on the synchronous characteristic temperature differences between defective and intact areas of HDPE film. An image feature-edge-picking algorithm was used to detect various defects. First, under the action of a continuous heat source, infrared images of different types of defects on the surface of HDPE films were collected, and we recorded the temperature of different areas on the film surface. We also analyzed the changes in the temperatures of the complete and defect areas over time and extracted the temperature characteristic curves. Second, the contour characteristics of hidden defects in the weld area were analyzed. The image with the most substantial temperature difference resolution was selected and preliminary noise reduction was performed. Further enhancement of the edges was carried out using the guided image-filtering (GIF) algorithm, which was improved by using the edge-aware weighting in weighted guided image filtering (WGIF) and the weighted aggregation mechanism in weighted aggregated guided image filtering (WAGIF). Finally, the Canny operator was used to detect the edges of the processed images to recognize the contour of the welding defect. The best pixel image was extracted, the pixel comparison relationship was used to quantitatively detect the defect size of the HDPE film and the error between the image defect size and the actual size was analyzed. The experimental results show that the model could identify the surface defects on HDPE film during construction and could obtain the approximate outline and size of the hidden defects in the welding area. Full article

The consumption of ultra-processed foods (UPFs) has become a central topic in discussions surrounding public health, nutrition, and consumer behaviour. This study aimed to investigate the key factors shaping customer perceptions and attitudes towards UPFs and explore their impact on purchase decisions. A total of 290 completed questionnaires from an online survey were analysed to identify the drivers influencing consumer actions and habits. Users’ opinions were systematised based on their attitudes towards UPFs, considering factors such as health consciousness, knowledge, subjective norms, and environmental concerns. Participants were then categorised using both traditional and advanced data analysis methods. Structural equation modelling (SEM), machine learning (ML), and multi-criteria decision-making (MCDM) techniques were applied to identify hidden dependencies between variables from the perspective of UPF consumers. The developed models reveal the underlying relationships that influence acceptance or rejection mechanisms for UPFs. The results provide specific recommendations for stakeholders across the food production and marketing value chain. Public health authorities can use these insights the findings to design targeted interventions that promote healthier food choices. Manufacturers and marketers can leverage the findings to optimise product offerings and communication strategies with a focus on less harmful options, aligning more closely with consumer expectations and health considerations. Consumers benefit from enhanced product transparency and tailored information that reflects their preferences and concerns, fostering informed and balanced decision-making. As attitudes toward UPFs evolve alongside changing nutrition and consumption patterns, stakeholders should regularly assess consumer feedback to mitigate the impact of these harmful foods on public health. Full article

Enterprise performance in real-world markets is shaped by dynamic factors, including competitors, collaborators, and hidden associates. Existing models struggle to capture the interplay between time-varying network dynamics and financial asset price movements. Traditional energy cascading models rely on static network assumptions, while deep learning approaches lack the incorporation of key network science principles such as structural balance and assortativity degree. To address these gaps, we propose the Dynamic Energy Cascading Model (DECM), a framework that models the propagation of business influence within dynamic enterprise networks. This method first constructs a dynamic enterprise association network, then applies an energy cascading mechanism to this network, utilizing the propagated energy metrics as predictive indicators for stock price forecasting. Unlike existing approaches, DECM uniquely integrates dynamic network properties and knowledge structures, such as structural balance and assortativity degree, to model the cascading effects of business influences on stock prices. Through extensive evaluations using data from S&P 500 companies, we demonstrate that DECM significantly outperforms conventional models in predictive precision. A key innovation of our work lies in identifying the critical role of assortativity degree in predicting stock price movements, which surpasses the impact of structural balance. These findings not only advance the theoretical understanding of enterprise performance dynamics but also provide actionable insights for policymakers and practitioners from a network science perspective. Full article

Pedestrians, as the most vulnerable road users in traffic crashes, prompt transportation researchers and urban planners to prioritize pedestrian safety due to the elevated risk and growing incidence of injuries and fatalities. Thorough pedestrian crash data are indispensable for safety research, as the most detailed descriptions of crash scenes and pedestrian actions are typically found in crash narratives and diagrams. However, extracting and analyzing this information from police crash reports poses significant challenges. This study tackles these issues by introducing innovative image-processing techniques to analyze crash diagrams. By employing cutting-edge technological methods, the research aims to uncover and extract hidden features from pedestrian crash data in Michigan, thereby enhancing the understanding and prevention of such incidents. This study evaluates the effectiveness of three Convolutional Neural Network (CNN) architectures—VGG-19, AlexNet, and ResNet-50—in classifying multiple hidden features in pedestrian crash diagrams. These features include intersection type (three-leg or four-leg), road type (divided or undivided), the presence of marked crosswalk (yes or no), intersection angle (skewed or unskewed), the presence of Michigan left turn (yes or no), and the presence of nearby residentials (yes or no). The research utilizes the 2020–2023 Michigan UD-10 pedestrian crash reports, comprising 5437 pedestrian crash diagrams for large urbanized areas and 609 for rural areas. The CNNs underwent comprehensive evaluation using various metrics, including accuracy and F1-score, to assess their capacity for reliably classifying multiple pedestrian crash features. The results reveal that AlexNet consistently surpasses other models, attaining the highest accuracy and F1-score. This highlights the critical importance of choosing the appropriate architecture for crash diagram analysis, particularly in the context of pedestrian safety. These outcomes are critical for minimizing errors in image classification, especially in transportation safety studies. In addition to evaluating model performance, computational efficiency was also considered. In this regard, AlexNet emerged as the most efficient model. This understanding is precious in situations where there are limitations on computing resources. This study contributes novel insights to pedestrian safety research by leveraging image processing technology, and highlights CNNs’ potential use in detecting concealed pedestrian crash patterns. The results lay the groundwork for future research, and offer promise in supporting safety initiatives and facilitating countermeasures’ development for researchers, planners, engineers, and agencies. Full article

Background/Objective In every surgical environment, the prevention of surgical site infections is not merely desirable but essential, given their profound impact on patient health and healthcare costs. To optimize patient care during surgery, a thorough exploration and assessment of all intraoperative nursing practices are necessary, guided by empirical evidence. The aim of this study was to explore nurses’ experiences with surgical site infection prevention practices in the intraoperative setting. Methods Twenty-one nurses working in clinical settings in Hong Kong participated in semi-structured interviews for this qualitative study. Data were analyzed using Colaizzi’s seven-step method. Results Four main themes were identified from the interview data: ensuring safety and minimizing threats; facing silent, intangible, and hidden risks; team collaboration in eliminating risks; and persistent knowledge acquisition. Conclusions Nurses encountered diverse obstacles tied to teamwork, updated knowledge, communication, and patient safety. Enhanced quality of care in clinical settings can be achieved through strategic implementations. Focusing on quality improvement initiatives, establishing consistent teams, and recognizing the vital role of nurses strengthen care delivery. These actions contribute significantly to preventing surgical site infections and ensuring patient safety during intraoperative nursing practices. Full article

This article describes solutions to control problems using fuzzy logic, which facilitates the development of decision support systems across various fields. However, addressing this task through the manual creation of rules in specific fields necessitates significant expert knowledge. Machine learning methods can identify hidden patterns. A key novelty of this approach is the algorithm for generating fuzzy rules for a fuzzy controller, derived from interpreting a decision tree. The proposed algorithm allows the quality of the control actions in organizational and technical systems to be enhanced. This article presents an example of generating a set of fuzzy rules through the analysis of a decision tree model. The proposed algorithm allows for the creation of a set of fuzzy rules for constructing fuzzy rule-based systems (FRBSs). Additionally, it autogenerates membership functions and linguistic term labels for all of the input and output parameters. The machine learning model and the FRBS obtained were assessed using the coefficient of determination ($R^2$). The experimental results demonstrated that the constructed FRBS performed on average 2% worse than the original decision tree model. While the quality of the FRBS could be enhanced by optimizing the membership functions, this topic falls outside the scope of the current article. Full article