bluuebunny/crossref_metadata_2025 huggingface dataset:
On the Global Practical Exponential Stability of h-Manifolds for Impulsive Reaction–Diffusion Cohen–Grossberg Neural Networks with Time-Varying Delays
Authors: Stamov Gani , Stamov Trayan , Stamova Ivanka , Spirova Cvetelina
Published: February 2025

In this paper, we focus on h-manifolds related to impulsive reaction–diffusion Cohen–Grossberg neural networks with time-varying delays. By constructing a new Lyapunov-type function and a comparison principle, sufficient conditions that guarantee the global practical exponential stability of specific states are established. The states of interest are determined by the so-called h-manifolds, i.e., manifolds defined by a specific function h, which is essential for various applied problems in imposing constraints on their dynamics. The established criteria are less restrictive for the variable domain and diffusion coefficients. The effect of some uncertain parameters on the stability behavior is also considered and a robust practical stability analysis is proposed. In addition, the obtained h-manifolds’ practical stability results are applied to a bidirectional associative memory (BAM) neural network model with impulsive perturbations and time-varying delays. Appropriate examples are discussed.
The Role of Assessment in Improving Education and Promoting Educational Equity
Authors: Levy-Feldman Irit
Published: February 2025

Assessment is an integral core component of the educational process (learning–teaching–assessment), influencing learning and teaching. Its impact extends beyond these core elements, shaping decisions that affect students, teachers, administrators, policymakers, and the wider community. Recognizing the critical role and broad impact of assessment on educational practices, we must leverage its power to foster better education that caters to diverse multicultural learners and promotes a more equitable society. In this theoretical paper, we comprehensively explore the transformative potential of assessment focusing on culturally responsive assessment, on both teaching and learning practices, emphasizing the needs of diverse learners. We discuss possible solutions to enhance educational outcomes through assessment redesign, critically examine the barriers to implementing these solutions, and propose viable strategies to mitigate these challenges, ensuring all learners reach their full potential.
Low-Power Silicon-Based Frequency Dividers: An Overview
Authors: Badiali Alessandro , Borgarino Mattia
Published: February 2025

Frequency divider circuits divide the frequency of an input signal by a specified ratio. They are critical components in analog, digital, and mixed-signal microelectronics. In power-constrained environments, such as cryogenic electronics or implanted biomedical devices, minimizing power consumption is crucial. This paper reviews operational principles, benefits, trade-offs, and circuit solutions of three main typologies of frequency divider: Current Mode Logic (CML), Injection-Locking (IL), and True Single-Phase Clock (TSPC). Distinct trade-offs between operation speed, power efficiency, complexity, and integration make each of them suitable for specific applications. Nevertheless, hybrid circuit solutions combining different typologies could potentially balance performance and energy efficiency. This paper thus also reports and discusses examples of hybrid frequency dividers. Examples of frequency dividers implemented in emerging technologies, such as the FinFETs CMOS, are addressed, as well. The purpose of this paper is to guide designers in selecting frequency divider solutions that best meet the design-specific requirements.
Age-Aware Scheduling for Federated Learning with Caching in Wireless Computing Power Networks
Authors: Zhuang Xiaochong , Luo Chuanbai , Xie Zhenghao , Li Yu , Jiang Li
Published: February 2025

With the rapid development of Wireless Computing Power Networks (WCPNs), the urgent need for data privacy protection and communication efficiency has led to the emergence of the federated learning (FL) framework. However, the time delay leads to dragging problems and reduces the convergence performance of FL in the training process. In this article, we propose an FL resource scheduling strategy based on information age perception in WCPNs, which can effectively reduce the time delay and enhance the convergence performance of FL. Moreover, a data cache buffer and a model cache buffer are set up at the user end and the central server, respectively. Next, we formulate the parametric age-aware problem to simultaneously minimize the global parameter age, energy consumption, and FL service delays. Considering the dynamic WCPN environment, the optimization target is modeled as a Markov decision process (MDP), and the Proximal Policy Optimization (PPO) algorithm is used to achieve the optimal solution. Numerical simulation results demonstrate that the proposed method significantly outperforms baseline schemes across critical metrics. Specifically, the proposed approach reduces FL service delays by 25.2%. It also decreases the global parameter age by 45.5% through the joint optimization of the data collection frequency, computation frequency, and bandwidth allocation. The method attains a reward value of 65 at convergence, 18.2% higher than the WithoutAnyCache scheme and 8.3% higher than the OnlyLocalCache scheme. FL accuracy improves to 98.2% with a 0.08 final loss. Finally, numerical simulation results further confirm the superiority and outstanding performance of the proposed method.
A Pixel Shift Estimation Approach Using Spectral Information
Authors: Koukiou Georgia
Published: February 2025

This research paper presents a robust image registration algorithm tailored for the accurate estimation of image displacements. Image registration is a fundamental task in computer vision and image processing, with applications ranging from medical imaging to motion tracking in surveillance systems. The algorithm’s efficacy is explored through a series of experiments conducted on image pairs, both in scenarios without noise and those affected by additive noise. The algorithm’s core methodology involves a combination of techniques, including Fourier transforms, phase correlation, and subpixel estimation. By leveraging these techniques, the algorithm can simultaneously compute both the integer and subpixel components of image displacement. This capability is particularly valuable in scenarios demanding precise alignment and motion analysis. In the experiments, the algorithm’s performance is assessed using the Mean Estimation Error (MEE), which quantifies the accuracy of displacement estimation. The results reveal that the algorithm consistently achieves high precision and accuracy, even in the presence of uniform white noise with a mean of 25 and standard deviation of 15. This robustness to noise underscores its suitability for real-world applications where images are often affected by various sources of interference. The comparative analysis between noise-free and noisy scenarios demonstrates the algorithm’s resilience to adverse conditions, making it a versatile tool for image registration tasks in practical environments. Its potential applications encompass computer vision, medical imaging, security and surveillance, and high-precision image processing. The robustness of the algorithm to noise and sub-pixel accuracy makes it an asset for a wide range of applications, promising enhanced capabilities in image alignment and motion analysis.
RETRACTED: Ortiz-Garces et al. Development of a Platform for Learning Cybersecurity Using Capturing the Flag Competitions. Electronics 2023, 12, 1753
Authors: Ortiz-Garces Iván , Gutierrez Rommel , Guerra David , Sanchez-Viteri Santiago , Villegas-Ch. William
Published: February 2025

The journal retracts the article, Development of a Platform for Learning Cybersecurity Using Capturing the Flag Competitions [...]
Engineering Terahertz Light–Matter Interaction with Quantum Electronic Metamaterials
Authors: Smolyaninov Igor I. , Smolyaninova Vera N.
Published: February 2025

While electromagnetic metamaterials completely revolutionized optics and radio frequency engineering, recent progress in the development of conceptually related electronic metamaterials was more slow. Similar to electromagnetic metamaterials, which engineer material response to the electromagnetic field of a photon, the purpose of electronic metamaterials is to affect electron propagation and its wave function by changing material response to its electric field. This makes electronic metamaterials an ideal tool for engineering light–matter interaction in semiconductors and superconductors. Here, we propose the use of Fermi’s quantum refraction, which was previously observed in the terahertz spectroscopy of Rydberg atoms and two-dimensional surface electronic states, as a novel tool in quantum electronic metamaterial design. In particular, we demonstrate several potential applications of this concept in two-dimensional metamaterial superconductors and “universal quantum dots” designed for operation in the terahertz frequency range.
Speech Enhancement Algorithm Based on Microphone Array and Multi-Channel Parallel GRU-CNN Network
Authors: Xi Ji , Xu Zhe , Zhang Weiqi , Xie Yue , Zhao Li
Published: February 2025

This paper presents an improved speech enhancement algorithm based on microphone arrays to improve speech enhancement performance in complex settings. The algorithm’s model consists of two key components: the feature extraction module and the speech enhancement module. The feature extraction module processes the speech amplitude spectral features derived from STFT (short-time Fourier transform). It employs parallel GRU-CNN (Gated Recurrent Units and CNN Convolutional Neural Network) structures to capture unique channel information, and skip connections are utilized to enhance the model’s convergence speed. The speech enhancement module focuses on obtaining cross-channel spatial information. By introducing an attention mechanism and applying a global hybrid pooling strategy, it reduces feature loss. This strategy dynamically assigns weights to each channel, emphasizing features that are most beneficial for speech signal restoration. Experimental results on the CHIME3 dataset show that the proposed model effectively suppresses diverse types of noise and outperforms other algorithms in improving speech quality and comprehension.
Shallow Learning-Based Intrusion Detection System for In-Vehicle Network: ASIC Implementation
Authors: Choi Minseok , Lee Myeongjin , Im Hyungchul , Lee Joosock , Lee Seongsoo
Published: February 2025

This paper presents an Application-Specific Integrated Circuit (ASIC) implementation and Field-Programmable Gate Array (FPGA) verification of a Convolutional Neural Network (CNN)-based Intrusion Detection System (IDS) designed to enhance the security of an in-vehicle Controller Area Network (CAN) BUS and detect malicious messages. The CNN model employs a lightweight architecture with a single convolution layer using a 2 × 2 kernel and integrates a filter algorithm optimized for Fuzzy and Spoofing attacks to improve the performance. The IDS is implemented on an Electronic Control Unit platform powered by an ARM Cortex-M3 core and uses SRAM to store the parameters utilized by the CNN model and filter algorithm, targeting ASIC implementation with TSMC 180 nm technology. Functional verification was conducted by configuring a simplified CAN bus environment using the Xilinx Nexys Video FPGA and PEAK-System PCAN-USB, which was validated in real-time against DoS, Spoofing, and Fuzzy attack scenarios. The proposed lightweight CNN-based IDS achieved a fast detection speed of 0.0233 ms and an average accuracy of 99.6879%, thereby demonstrating its potential to enhance the security of in-vehicle CAN BUS.
Digital Twin-Enabled Multi-Service Task Offloading in Vehicular Edge Computing Using Soft Actor-Critic
Authors: Liu Hengwei , Tian Ni , Song Deng-Ao , Zhang Long
Published: February 2025

With the rapid development of vehicular networks, the computational capabilities and application scenarios of vehicles are becoming increasingly diverse, leading to a continuous emergence of complex computational tasks. Facing these tasks, a single vehicle node often struggles to handle them effectively; thus, it is necessary to offload tasks to other vehicles with computational resources through Vehicle-to-Vehicle (V2V) communication. However, due to the mobility of vehicles and the limitations of computing and communication resources, efficiently completing these complex computational tasks presents a significant challenge. To address this, this paper proposes an innovative optimization scheme that combines Digital Twin (DT) technology with vehicular edge computing. It constructs digital twins of vehicles through Roadside Units (RSUs) and utilizes these digital twins to optimize task offloading strategies. The scheme aims to jointly optimize transmission power, task offloading ratios, and computational resource allocation to minimize the impact of communication constraints and vehicle mobility on task completion delay. The paper models the wireless communication channel between vehicles using the Nakagami-m fading model, taking into account both transmission delay and computation delay in the overall task completion time. To solve this non-convex optimization problem, we introduce a joiSACnt optimization framework based on the Soft Actor-Critic (SAC) algorithm for efficient task allocation and dynamic transmission power adjustment. The simulation results show that the proposed scheme significantly reduces the maximum task delay and improves overall communication efficiency, particularly when compared with baseline schemes without power optimization and digital twin modules, as well as the DQN and DDPG algorithms. It demonstrates better task processing efficiency and communication performance, providing an effective solution for task handling in vehicular networks.
Modeling the Energy and Heating Efficiency of 3D Printing for Composite Materials with Dispersed Volumetric Particles
Authors: Grakov Teodor , Mateev Valentin , Marinova Iliana
Published: February 2025

Additive manufacturing, such as the 3D printing of composite materials for electronics is rapidly evolving, enabling the production of advanced electric and magnetic composites with tailored properties. These materials require special printing conditions and advanced control to maintain the desired material properties during the 3D printing process and in the final product design. Hence, determining the heating and energy consumption and estimating the efficiency of 3D printing is essential. This work modeled the fused filament fabrication 3D printing of composite materials with a polymer carrier matrix. A 3D time-dependent thermal model of a 3D printer extruder was developed and implemented using the finite element method to study and improve the efficiency of 3D printing. As the filler content influences the operational parameters and process energy consumption of the 3D printing process, the transient heating process parameters were estimated using different composite modifier contents. Two types of modifiers were considered: Fe2O3 and CaO, both mixed in a PLA carrier material. The volumetric fill ratio of the two modifiers did not exceed 45%, as the mixing dependency of the material properties is linear in this range. The power fluxes and power efficiency were estimated. The results provide new possibilities for better control methodologies and advanced additive manufacturing for new materials in electronics. Operational control can accelerate the 3D printing process, speeding up the heating of 3D-printed composite materials and reducing the printing time and total energy consumption. Furthermore, this research provides directions for new advanced 3D printing extruder designs with better power and energy heating efficiency.
Research on a Network Diagnosis Method for a Train Control Center and Interlocking Integrated System Based on a Fuzzy Broad Learning System Model
Authors: Yuan Lei , Li Yinghui , Wei Guodong , Guo Wenzhang
Published: February 2025

In high-speed railway signaling systems, the network structure of the Train Control Center and Inter-locking Integrated System (TIS) is highly complex, with a large number of interfaces, numerous redundant channels, and forwarding components such as switches. These factors result in challenges such as insufficient accuracy, low efficiency, and poor real-time performance in terms of network monitoring and fault diagnosis. As the scale of railway yards continues to expand, these issues are becoming increasingly prominent. To address these challenges, this paper proposes a network fault propagation model based on the Fuzzy Broad Learning System (FBLS). By leveraging nonlinear transformations and feature mapping techniques, FBLS can efficiently extract and analyze network fault features, even with a relatively small amount of data. Experimental results show that the FBLS-based diagnostic model achieves higher accuracy and faster response speed in fault identification and propagation path analysis compared to traditional graph theory and fuzzy reasoning methods. Further comparisons with existing methods validate the advantages of FBLS in handling multi-source data, improving noise tolerance, and adapting to large-scale railway yard network systems, demonstrating its broad application prospects in railway signaling systems.
Integrating Artificial Intelligence Agents with the Internet of Things for Enhanced Environmental Monitoring: Applications in Water Quality and Climate Data
Authors: Miller Tymoteusz , Durlik Irmina , Kostecka Ewelina , Kozlovska Polina , Łobodzińska Adrianna , Sokołowska Sylwia , Nowy Agnieszka
Published: February 2025

The integration of artificial intelligence (AI) agents with the Internet of Things (IoT) has marked a transformative shift in environmental monitoring and management, enabling advanced data gathering, in-depth analysis, and more effective decision making. This comprehensive literature review explores the integration of AI and IoT technologies within environmental sciences, with a particular focus on applications related to water quality and climate data. The methodology involves a systematic search and selection of relevant studies, followed by thematic, meta-, and comparative analyses to synthesize current research trends, benefits, challenges, and gaps. The review highlights how AI enhances IoT’s data collection capabilities through advanced predictive modeling, real-time analytics, and automated decision making, thereby improving the accuracy, timeliness, and efficiency of environmental monitoring systems. Key benefits identified include enhanced data precision, cost efficiency, scalability, and the facilitation of proactive environmental management. Nevertheless, this integration encounters substantial obstacles, including issues related to data quality, interoperability, security, technical constraints, and ethical concerns. Future developments point toward enhancements in AI and IoT technologies, the incorporation of innovations like blockchain and edge computing, the potential formation of global environmental monitoring systems, and greater public involvement through citizen science initiatives. Overcoming these challenges and embracing new technological trends could enable AI and IoT to play a pivotal role in strengthening environmental sustainability and resilience.
Life Cycle Evaluation of Substations with Carbon Reduction Based on Analytic Hierarchy Process Considering Multiple Performance Measures
Authors: Wu Hongshan , Jiang Xue , Song Kun , Xiu Ce , Lin Junru , Xu Qiangsheng , Chen Heng
Published: February 2025

As an important part of power transmission and conversion, it is of great significance to establish a scientific and comprehensive evaluation system for substations. However, the current research on substation evaluation focuses on economy and energy efficiency, ignoring the carbon emission and environmental impact of the substation, which leads to an inadequate evaluation of the substation. For this reason, a comprehensive substation evaluation system model containing economic, energy-efficiency, safety, environmental, and low-carbon indicators is established by adopting the concept of a life cycle, using hierarchical analysis to determine the weights of the indicators, introducing the ideal-solution approximation ranking method to analyze the actual substations, and providing scoring and ranking of the substation performance. The results show that the AHP-TOPSIS-based comprehensive evaluation index and model of the whole life cycle of the substation can reflect the performance of the substation and provide help for the optimization of the substation.
Microbial Fuel Cells as CO2 Source in the Autotrophic Cultivation of the Green Microalgae Tetraselmis subcordiformis: Impact on Biomass Growth, Nutrient Removal, and Hydrogen Production
Authors: Zieliński Marcin , Barczak Łukasz , Rusanowska Paulina , Nowicka Anna , Dębowski Marcin
Published: February 2025

Carbon dioxide (CO2) is often a limiting factor for the growth of microalgal biomass. Consequently, the search for new CO2 sources that do not contain components inhibitory to microalgal metabolism remains a priority. An alternative to the solutions tested thus far may involve the use of CO2-rich gas derived from microbial fuel cells (MFCs). This concept served as the basis for the original experimental work described in this study. The objective of the research was to evaluate the effect of using gases from the anode chamber of an MFC as a CO2 source in the autotrophic cultivation of Tetraselmis subcordiformis. The highest biomass growth efficiency was observed when the CO2 concentration in the culture medium was maintained at 220.0 ± 8.0 mg/L. Under these conditions, the microalga proliferation rate reached 0.52 ± 0.03 g VS/(L∙day) and 11.54 ± 0.42 mg chl-a/(L∙day), with a final biomass concentration of 2.68 ± 0.10 g VS/L and 63.53 ± 2.44 mg chl-a/L at the end of the cultivation cycle. Moreover, the highest total hydrogen (H2) production of 312 ± 38 mL was achieved in the same experimental variant, corresponding to an H2 production rate of 62.4 ± 6.1 mL/day. The removal efficiency of ammonium nitrogen (N-NH4) was notably high in experimental variants using MFC-derived biogas, ranging from 97.0 ± 2.2% to 98.2 ± 1.8%. Additionally, the growing microalgal biomass effectively utilized phosphate phosphorus (P-PO4) and iron, further highlighting its potential for nutrient recovery.
Lifestyle Behaviors and Gestational Diabetes Mellitus: A Narrative Review
Authors: Taousani Eleftheria , Papaioannou Konstantinos-Georgios , Mintziori Gesthimani , Grammatikopoulou Maria G. , Antonakou Angeliki , Tzitiridou-Chatzopoulou Maria , Veneti Stavroula , Goulis Dimitrios G.
Published: February 2025

Gestational diabetes mellitus (GDM) is a prevalent condition impacting approximately 14% of pregnancies globally, posing significant health risks to mother and child. This review explores the role of diet, physical activity (PA), and sedentary behavior (SB) in preventing and managing GDM. Consumption of fish, fruits, vegetables, and legumes, and adherence to healthy dietary patterns, like the Mediterranean diet, are linked to lower GDM risk. Higher levels of PA and structured exercise consistently show protective effects against GDM, enhancing glucose metabolism and insulin sensitivity. Conversely, SB is a risk factor for GDM; prolonged sedentary periods detrimentally affect glucose regulation. The review emphasizes the need for a combined approach integrating healthy dietary habits, regular PA, and reduced SB to mitigate GDM risk effectively. Future research should prioritize standardized assessment methods and personalized lifestyle interventions to optimize GDM prevention strategies, ultimately informing public health guidelines and clinical recommendations for healthier pregnancies and better long-term outcomes.
Predicting Acute Oral Toxicity in Bobwhite Quail: Development of QSAR Models for LD50
Authors: Iovine Nadia , Roncaglioni Alessandra , Benfenati Emilio
Published: February 2025

The development of a predictive model for estimating oral acute toxicity (LD50) in wildlife species is essential for environmental risk assessments. In this study, a quantitative structure–activity relationship (QSAR) model was developed to predict the acute oral toxicity of pesticides toward Bobwhite quail, categorizing them into three toxicity classes: low, moderate, and high. This model was built using the SARpy softwareA dataset of pesticides collected from OpenFoodTox and the ECOTOX database was used to identify training and test datasets, while data collected from the PPDB were used as an external validation. The model’s performance was evaluated using these three sets. The accuracy achieved on the training set was 0.75, indicating good performance during model development. However, the model’s accuracy dropped to 0.55 for the test set, suggesting some overfitting. The external validation accuracy was 0.69, reflecting the model’s ability to generalize to new, unseen data. While these results demonstrate the potential of the QSAR models for predicting toxicity in Bobwhite quail, they also highlight the need for further refinement to improve predictive accuracy, particularly for unseen compounds. This work contributes to the development of computational tools for wildlife risk assessment and toxicological predictions.
Repeatability of SOPRANOISE Quick Method for Measuring Sound Reflection and Sound Insulation of Noise Barriers
Authors: Guidorzi Paolo , Garai Massimo
Published: February 2025

The measurement method employed for the estimation of the intrinsic acoustic characteristics of noise barriers installed along European highways is outlined in the EN 1793-5 and 1793-6 standards, commonly named the “Adrienne method”. This method has been shown to have repeatability and reproducibility comparable to or better than laboratory methods. However, its correct application requires skilled operators managing with great care the equipment on site, thus limiting the number of measurements made in a working day on different positions of the noise barrier under test. To overcome this limitation and perform fast measurements in the field, the Quick Method, a simplified version of the Adrienne measurement method, was developed in the context of the European SOPRANOISE project. The Quick Method needs only lightweight and easy-to-use equipment, called the Quick System; this allows extensive measurement campaigns to be carried out at many points of the noise barrier under test. However, the repeatability of the Quick Method has not yet been assessed. This article reports and analyses a series of repeatability tests of the Quick Method conducted in the laboratory and on site; moreover, comparisons with the Adrienne method are systematically presented. These results can be considered the first validation of the Quick Method and its measuring equipment.
Unveiling the Non-Market Value of a Fragile Coastal Wetland: A CVM Approach in the Amvrakikos Gulf, Greece
Authors: Pappa Dimitra , Kaliampakos Dimitris
Published: February 2025

Wetlands are highly productive ecosystems with multidimensional value and significant social and economic impacts. Estimating the economic value of their non-marketed goods and services—benefits not traded in conventional markets—can provide essential insights to guide protection, restoration, and sustainable management strategies for these sensitive ecosystems. The present study employs environmental economics tools, specifically the Contingent Valuation Method (CVM), to assess the value of the Amvrakikos Gulf in northwestern Greece. This semi-enclosed wetland system is particularly fragile due to its low water renewal rate, while being a primary source of income and an integral component of local cultural identity. Despite its high ecological importance, the Amvrakikos Gulf has experienced substantial environmental degradation stemming from its geomorphological characteristics and external anthropogenic pressures. This investigation was designed to explore residents’ perceptions of the wetland’s value and its correlation with the need for restoration. In total, 383 coastal area residents participated in this study. Data analysis was conducted using appropriate econometric methods based on both parametric and non-parametric models. Approximately 46.2% of respondents expressed willingness to pay, and the environmental restoration of the Amvrakikos Gulf was valued at EUR 715,968.36. Additionally, this study examined potential associations between willingness to pay and various socio-cultural and demographic factors recorded during the interviews. In conclusion, the need for the restoration and preservation of the Amvrakikos Gulf’s natural wealth was made evident, affirming the contribution of the CVM in valuing wetlands and enriching the existing literature, while explicitly recognizing the subjectivity inherent in WTP assessments.
The Phytoremediation of Arsenic-Contaminated Waste by Poa labillardieri, Juncus pauciflorus, and Rytidosperma caespitosum
Authors: Huslina Feizia , Khudur Leadin S. , Besedin Julie A. , Nahar Kamrun , Shah Kalpit , Surapaneni Aravind , Netherway Pacian , Ball Andrew S.
Published: February 2025

Phytoremediation represents a potentially effective and environmentally friendly technology to remediate arsenic (As) in mine waste soils. However, soil amendments are often required to improve phytoremediation due to depleted nutrients in mine waste. This study aims to assess the effect of biosolids biochar, applied at different rates (0%, 5%, and 10%) on As phytoremediation using three plant species: Poa labillardieri, Rytidosperma caespitosum, and Juncus pauciflorus. This study was conducted in a replicated greenhouse pot study using soil from an abandoned mine site. Dry plant biomass, As concentration in plants and soil, and soil microbial abundance were investigated. Juncus pauciflorus produced eight times more root and shoot biomass than R. caespitosum in soils amended with 10% biochar. The highest As uptake was also observed in J. pauciflorus grown in soils amended with 10% biochar (7.10 mg/plant), while R. caespitosum had the lowest As uptake in soils without biochar (0.16 mg/plant). In soils amended with 10% biochar, the total bacterial community decreased to approximately 8.50 log10 copies/g, compared to the initial soil (9.05 log10 copies/g), while the number of gene copies of the nifH gene increased, suggesting the importance of nitrogen-fixing bacteria to promote plant growth. Taguchi analysis confirmed that plant species was the key factor for As phytoremediation, followed by biochar application dose. This study showed that J. pauciflorus and the addition of 10% biochar was the best treatment for remediating As-contaminated mine waste, offering the potential for use commercially. Moreover, the utilisation of biochar derived from biosolids as a soil amendment for enhancing phytoremediation represents good circular economy practice to manage excessive biosolids production.
Unlocking the Main Factors Affecting the Strength and Stability of Yellow Poplar (Liriodendron tulipifera) After a Mild and Environmentally Friendly Densification Process
Authors: Bencsik Balazs , Denes Levente , McNeel Joseph , Chaddock Luke , Oporto Gloria S.
Published: February 2025

The densification of yellow poplar (Liriodendron tulipifera) has emerged as a critical area of research, driven by its desirable properties and broad potential applications. This study investigated the effects of four densification parameters using a 24 full factorial design to evaluate their impact on physical and mechanical properties. Analysis of variance (ANOVA) and Pareto analyses identified the compression ratio as the most influential factor, significantly affecting bending strength, compression strength, hardness, and spring-back behavior. Pressing temperature was the second most significant factor, with higher levels positively impacting mechanical properties. However, increasing the pre-steaming treatment duration from 30 to 60 min at 130 °C had a detrimental effect on strength and spring-back performance, particularly at a 50% compression ratio. Pressing time showed no significant effect on strength properties but contributed positively to hardness and spring-back behavior at higher levels. Several significant factor interactions were observed, further influencing the outcomes. Differences in density profiles were notable across compression ratios, with higher ratios producing more uniform distributions. Under optimal parameters, densification increased compression strength by 117%, bending strength by 60%, and hardness by 154% compared to undensified control samples, demonstrating the potential of densified yellow poplar (Liriodendron tulipifera) for high-performance applications.
Framework of Best Practices to Drive the Digital Transition: Towards a 4.0 Paradigm Based on Evidence from Case Studies
Authors: Bastos Tiago , Guimarães Cármen , Teixeira Leonor
Published: February 2025

In a technology-driven world, the concepts of digitalization and Industry 4.0 (I4.0) are an ever more present reality for companies. The benefits which these advancements offer, coupled with the market-driven pressure to reduce response times, have placed businesses in a technological “wave”, where it is imperative to adapt to avoid falling behind. While numerous studies focus on technological developments—such as definitions, capabilities, and potential benefits—few provide a systematic approach to best practices and recommendations for accelerating digital transformation while mitigating adverse impacts. In this study, case studies from 10 companies are analyzed to identify and systematize the best practices implemented in their digital transitions. The findings culminate in a best-practice framework for digital transition (DT), highlighting critical aspects such as understanding a company’s current state, acquiring external knowledge, and addressing the importance of cybersecurity and skilled staff. Concerning originality, this work presents an artifact based on manufacturing industry case studies, emphasizing the practical vision concerning DT.
Secure Tiny Machine Learning on Edge Devices: A Lightweight Dual Attestation Mechanism for Machine Learning
Authors: Baciu Vlad-Eusebiu , Braeken An , Segers Laurent , Silva Bruno da
Published: February 2025

Emerging edge devices are transforming the Internet of Things (IoT) by enabling more responsive and efficient interactions between physical objects and digital networks. These devices support diverse applications, from health-monitoring wearables to environmental sensors, by moving data processing closer to the source. Traditional IoT systems rely heavily on centralized servers, but advances in edge computing and Tiny Machine Learning (TinyML) now allow for on-device processing, enhancing battery efficiency and reducing latency. While this shift improves privacy, the distributed nature of edge devices introduces new security challenges, particularly regarding TinyML models, which are designed for low-power environments and may be vulnerable to tampering or unauthorized access. Since other IoT entities depend on the data generated by these models, ensuring trust in the devices is essential. To address this, we propose a lightweight dual attestation mechanism utilizing Entity Attestation Tokens (EATs) to validate the device and ML model integrity. This approach enhances security by enabling verified device-to-device communication, supports seamless integration with secure cloud services, and allows for flexible, authorized ML model updates, meeting modern IoT systems’ scalability and compliance needs.
Investigation of Various Fire Dynamics Simulator Approaches to Modelling Airflow in Road Tunnel Induced by Longitudinal Ventilation
Authors: Weisenpacher Peter , Glasa Jan , Valasek Lukas
Published: February 2025

The ability of computer simulations to model airflows in a tunnel can significantly contribute to the effectiveness of fire safety precautions. This study examines two ways of modelling the Polana tunnel (Slovakia) and its influence on the airflow created via longitudinal ventilation using a fire dynamics simulator. The first class of studied models is based on the assumption that the airflow in the tunnel is influenced to a large extent by the supporting structures and other installations under the tunnel ceiling. Due to the resolution of the computational grid, the constructions are modelled using a system of cuboids distributed along the tunnel at regular distances. The second class of models combines this approach with the previous one, in which tunnel drag is modelled by increased roughness of the tunnel walls. Unlike the previous model, the roughness values are not constant but reflect the curvature of the tunnel walls. The simulations results are compared against on-site measurements during a full-scale ventilation test conducted in 2017 by a grid of five anemometers, as well as with the results of the previous model. The results agree well with the experimental data with relative errors below 2% for bulk velocities and with mean absolute percentage deviations of 3, 6, and 10% for velocities measured using individual grid anemometers for three ventilation modes. The new models achieve several improvements in accuracy compared to the previous one.
Experimental Investigation of the Effects of Fire Size on Burning Characteristics in a Well-Confined Military Vehicle Compartment
Authors: Junjunan Soleh Fajar , Chetehouna Khaled , Gascoin Nicolas , Sellami Ilyas , Oger Antoine
Published: February 2025

This study presents an experimental investigation into the effects of fire size on burning characteristics within well-confined military vehicle engine compartments. The research evaluates burning duration, self-extinguishing phenomena, heat release rates, pressure dynamics, and flame morphology using heptane pool fires of varying pan diameters (8 cm, 16 cm, and 24 cm). Key findings include the proportional relationship between fire size and heat release rate, with larger pans causing higher oxygen consumption, elevated pressure differences, and increased total heat flux. Self-extinguishment was observed for larger pans due to oxygen depletion, with extinction time linked to the ratio of compartment volume to heat release rate. Temperature measurements revealed significantly higher ceiling temperatures and heat flux levels for larger fires, emphasizing the structural and thermal risks. These results contribute to understanding fire behavior in confined spaces, offering practical implications for designing fire protection systems tailored to military vehicles.
Citrobacter freundii Caused Head Ulcer Disease and Immune Response in Juvenile Procambarus clarkii
Authors: He Yong , Yuan Xiudan , Li Jinlong , Tian Xing , He Zhigang , Zeng Chunfang , Xie Yukun , Liu Li , Deng Shiming , Wang Dongwu
Published: February 2025

Red swamp crayfish (Procambarus clarkii) is one of the most important aquaculture species in China. Frequent outbreaks of diseases seriously threatened the sustainable development of the industry. It is necessary to understand the causes of disease and study the mechanism of disease resistance in P. clarkii. In this paper, the pathogenic bacteria causing head ulcers in juvenile P. clarkii were found and identified as Citrobacter freundii, which can cause severe pathological changes in the hepatopancreas and intestines of juvenile P. clarkii. Detection of humoral immune factors revealed that PO activity and lysozyme activity of infected P. clarkii were significantly enhanced at 15 and 20 dpi, respectively. Transcriptome analysis was conducted of hepatopancreas from normal and diseased P. clarkii after C. freundii injection, as well as bacteria-free control of P. clarkii. It was found that DEGs are rich in NF-κB, oxidative phosphorylation, JAK/STAT, Leukocyte transendothelial migration, MAPK, and PPAR signaling pathway. These pathways are related to immune modulation, metabolism, and pathogen clearance. Meanwhile, immune-related genes such as Gip, nfyA, psmD13, and FGFR were significantly highly expressed in the normal group, which was verified by qRT-PCR results, suggesting that they may be the key regulatory genes for juvenile P. clarkii resistance to C. freundii. This study will help to elucidate the molecular mechanism of the immune response of P. clarkii to C. freundii. The results are instructive for the prevention and treatment of P. clarkii diseases and for further understanding of the invertebrate immune system.
Molecular Immune Responses of Ruditapes decussatus to Vibrio Infection at Different Temperatures: Expression of Key Genes and Resilience-Associated Single Nucleotide Polymorphisms
Authors: Papadopoulos Dimitrios K. , Giantsis Ioannis A. , Lattos Athanasios , Bitchava Konstantina , Michaelidis Basile
Published: February 2025

The impact of pathogenic Vibrio on bivalves is expected to be aggravated by global warming, posing a growing threat to aquaculture. Clam production has been particularly vulnerable, with significant losses attributed to the lack of pathogen-resistant strains. In this study, the mantle cavity of the grooved carpet shell clam Ruditapes decussatus (Linnaeus, 1758) was injected with Vibrio splendidus at 18 °C, 22 °C, and 24 °C and the transcription of the phagelysozyme, Cu-Zn superoxide dismutase (Cu-Zn sod), tumor necrosis factor receptor associated factor 6 (traf6), inhibitor of NF-κB (IκB), and Fas-associated protein with death domain (fadd) genes were assessed during a 20-day period. Additionally, the coding sequences of lysozyme, fadd, and IκB in Ruditapes decussatus were characterized for the first time, and SNPs were identified. Some SNPs showed significantly different distributions between infection-resistant and infection-susceptible individuals. Infected clams experienced increased mortality at elevated temperatures. Lysozyme mRNA was upregulated in infected groups across all temperatures. The sustained increase on day 20, coinciding with elevated traf6 mRNA, suggests a prolonged activation of the immune response. Cu-Zn sod transcription at 18 °C and 22 °C peaked on day 7 and returned to control levels by day 20, indicating an effective immune response, while at 24 °C, infected animals showed a continuously increased transcription. IκΒ and traf6 transcription, reflecting NF-κB pathway activity, varied with temperature and showed transient stimulation at higher temperatures. The pattern of fadd expression indicated a late induction of apoptosis, particularly at 18 °C and 24 °C. Overall, this study illustrates the involvement of five key genes in host–pathogen interactions and identifies potential markers for selection for Vibrio resistance in Ruditapes decussatus. However, given the weak correlation observed, further research is needed on the link between these polymorphisms and pathogen resilience.
Computational Analysis of Flow Separation in Non-Transferred Plasma Torch: Causes, Impacts and Control Methods
Authors: Siddanathi Sai Likitha , Westerberg Lars-Göran , Åkerstedt Hans O. , Gren Per , Wiinikka Henrik , Sepman Alexey
Published: February 2025

In a non-transferred plasma torch, the working gas becomes ionized and forms plasma as it interacts with the electric arc at the cathode tip. However, in certain cathode shapes, particularly flat ones, and under specific conditions, the gas flow can separate at the cathode tip, forming a vortex region. While this flow separation is influenced by geometric factors, it occurs in the critical zone where plasma is generated. Understanding the causes of this separation is essential, as it may significantly impact torch performance. If the separation proves detrimental, it is important to identify ways to mitigate it. This paper presents a computational analysis of a non-transferred plasma torch to investigate the physics behind flow separation. The results highlight the location and causes of the separation, as well as its potential advantages and disadvantages. Finally, the paper explores theoretical approaches to address flow separation in plasma torches, offering practical insights for enhancing their design and efficiency.
Determination of Polar Heterocyclic Aromatic Amines in Meat Thermally Treated in a Roasting Bag with Dried Fruits
Authors: Bulanda Sylwia , Szumska Magdalena , Nowak Agnieszka , Janoszka Beata , Damasiewicz-Bodzek Aleksandra
Published: February 2025

Frequent consumption of processed meat has been classified as carcinogenic to humans by the International Agency for Research on Cancer (Group 1), while red meat has been classified as probably carcinogenic (Group 2A). Mutagenic and carcinogenic compounds formed by heating in protein-rich food include, among others, heterocyclic aromatic amines (HAAs). Modifying the heat treatment of meat and using natural additives with antioxidant properties can lead to a reduction in their formation. The aim of this study was to determine polar HAAs (imidazoquinolines, IQ and MeIQ; imidazoquinoxalines, 8-MeIQx and 4,8-DiMeIQx; and phenylimidazopyridine, PhIP) in pork loin prepared without additives and with three types of dried fruit (apricots, cranberries, and prunes), baked in a roasting bag. HAAs were isolated from meat samples by solid-phase extraction. Quantitative analysis was performed by high-performance liquid chromatography with fluorescence detection (FLD) and a diode array detector (DAD). Only two HAAs, 8-MeIQx and PhIP, were detected in extracts isolated from meat samples. The total content of these compounds in meat roasted without additives was 5.9 ng/g. Using a dried fruit stuffing content of 200 g/kg of meat reduced these concentrations in dishes prepared with prunes, apricots, and cranberries by 42%, 47%, and 77%, respectively.
Measuring Minerals in Pseudocereals Using Inductively Coupled Plasma Optical Emission Spectrometry: What Is the Optimal Digestion Method?
Authors: Nascimento Ana C. , Motta Carla , Rego Andreia , Delgado Inês , Santiago Susana , Assunção Ricardo , Matos Ana Sofia , Santos Mariana , Castanheira Isabel
Published: February 2025

Pseudocereals have gained attention due to their adaptability to different climates, high nutritional value, and suitability for gluten-free and plant-based diets. However, a challenge lies in the necessary adaptations in the diet pathways, mainly due to the lack of matrix-matching metrological tools. To address this problem, we developed a classification system to support laboratory decisions without shaped Proficiency Testing (PT) or Certified/Standard References Material. This system evaluates method performance through limit of detection (LOD), maximum uncertainty, and statistical comparison. For that matter, the mineral contents (Cu, Mn, Fe, Zn, Mg, P, Ca, K, and Na) of quinoa (Chenopodium quinoa), amaranth (Amaranthus caudatus), and buckwheat (Fagopyrum esculentum) were determined, using three different digestion methods, including dry-ashing, microwave, and graphite block acid digestion. A decision was reached concerning the optimal digestion method to be employed, with the results classified into three categories: (i) “rejected if results failed in two categories; (ii) “use with caution” if results were not satisfactory in one category; or (iii) “accepted”, if the results passed in all the categories. The system efficacy was exemplified by the effectiveness of dry-ashing and graphite block acid digestion by comparison with microwave digestion. Neither dry-ashing nor graphite block acid digestion can be recommended as an alternative method to the microwave digestion method when all the prioritized nutrient minerals are understudied. Although the microwave method is preferable for multi-elemental analysis, it is possible to obtain, with caution, comparable results from all the digestion methods if a higher relative combined uncertainty is defined (target uncertainty < 11%) under the assumption that this is suitable for the study.