Search Results (878)

Wood is a versatile resource within the circular economy, widely used across various applications. However, in the European Union, demand for wood continues to rise, leading to increased reliance on imports. The pulp and paper industry, closely linked to wood production, is also experiencing supply shortages. To address these challenges, this study explores the use of wood waste (WW) as an alternative feedstock for pulp and glucose production. WW was collected from a mechanical treatment plant in Perugia, Italy, and processed using the organosolv method. This approach yielded a cellulose pulp with improved quality compared to previous research, achieving a cellulose content of 79.33% and a cellulose recovery rate of 94.59%. The optimized pulp was then subjected to enzymatic hydrolysis, producing 20.66 g of glucose per 100 g of initial WW, corresponding to a glucose concentration of 44.08 g/L and a cellulose digestibility of 51.03%. Additionally, a simulation model of a pilot-scale process was developed using Aspen PLUS software, assuming an annual processing capacity of approximately 5500 t of wood waste—equivalent to the quantity managed annually by a local waste treatment company in Perugia. This study highlights the potential of wood waste as a sustainable raw material for pulp and glucose production, supporting circular economy goals and laying the groundwork for future scale-up investigations. Full article

The quality of initial and continuous training for primary-school teachers is essential to fostering science education and building strong scientific foundations. This qualitative case study, conducted over two consecutive school years in Portugal, examines the impact of a continuous professional development program aimed at addressing gaps in primary teachers’ experimental science teaching. The program took place in the municipality of Penafiel and was organized by a university research team in collaboration with local schools. The program combined face-to-face sessions, in-school support from expert monitors, and the provision of teaching resources. Data were drawn from Individual Final Reflective Reports written by 108 teachers, all of whom participated through mandatory enrollment in the local training initiative. The sample was therefore exhaustive, covering the entire population targeted by the municipality. The reports were analyzed using qualitative content analysis, following an inductive coding process supported by peer validation. NVivo (version 14) software was used to assist in the categorization and management of textual data. The analysis revealed that teachers highly valued the training, particularly highlighting the relevance of the content, the effectiveness of the methodologies, and the training’s practical utility in overcoming classroom challenges. The program enhanced teachers’ confidence in implementing experimental activities and improved their teaching practices. The study underscores the importance of continuous professional development in strengthening teacher qualifications and science education. Limitations include reliance on self-reported reflections, the focus on a single municipality, and the absence of triangulation with classroom observations. Nevertheless, the program demonstrates that combining active methodologies, contextualized classroom support, and resource provision is a promising model for teacher professional development. The implications are relevant for policymakers, training centers, and teacher educators designing continuous professional development initiatives. Future research should explore scalability, longitudinal effects, and the comparative effectiveness of different continuous professional development mode. Full article

Introduction: Changes in left ventricular (LV) wall thickness serve as important diagnostic and prognostic indicators in various cardiovascular diseases. To date, no automated software exists for the measurement of myocardial segmental wall thickness in cardiac MRI (CMR), which leads to reliance on manual caliper measurements that carry risks of inaccuracy. Aims: This paper aims to present a new automated segmental wall thickness measurement software, OptiLayer, developed to address this issue and to compare it with the conventional manual measurement method. Methods: In our pilot study, the algorithm of the OptiLayer software was tested on 50 HEALTHY individuals, and 50 excessively trabeculated noncompaction (LVET) subjects with preserved LV function, whose morphology makes it more challenging to measure left ventricular wall thickness, although often occurring with myocardial thinning. Measurements were performed by two independent investigators who assessed LV wall thicknesses in 16 segments, both manually using the Medis Suite QMass program and automatically with the new OptiLayer method, which enables high-density sampling across the distance between the epicardial and endocardial contours. Results: The results showed that the segmental wall thickness measurement values of the OptiLayer algorithm were significantly higher than those of the manual caliper. In comparisons of the HEALTHY and LVET subgroups, OptiLayer measurements demonstrated differences at several points than manual measurements. Between the investigators, manual measurements showed low intraclass correlations (ICC below 0.6 on average), while measurements with OptiLayer gave excellent agreement (ICC above 0.9 in 75% of segments). Conclusions: Our study suggests that OptiLayer, a new automated wall thickness measurement software based on high-precision anatomical segmentation, offers a faster, more accurate, and more reproducible alternative to manual measurements. Full article

The increasing reliance on imported fish products in Kazakhstan raises concerns about the presence of fish-borne parasitic infections, particularly zoonotic helminths that pose risks to public health. This study aimed to assess the diversity and prevalence of helminths in commercially imported marine fish using both traditional and molecular diagnostic methods. A total of 670 specimens representing 17 fish species were collected from retail markets in Astana, Almaty, and Karaganda. Macroscopic inspection and muscle compression techniques were used to detect larval parasites, followed by DNA extraction and PCR amplification targeting the ITS-2, 5.8S, 18S rRNA, and mitochondrial COX gene regions. Sequencing and phylogenetic analysis confirmed the presence of cestodes (Eubothrium crassum, Hepatoxylon trichiuri, Nybelinia surmenicola), acanthocephalans (Echinorhynchus gadi), and nematodes, with a predominance of zoonotic species from the Anisakidae family, including Anisakis simplex, A. pegreffii, Pseudoterranova decipiens, and Contracaecum osculatum. The highest levels of infection were detected in Atka mackerel (97.1%), herring (96.0%), mackerel (92.0%), and blue whiting (88.1%), while the lowest rates were recorded in smelt (6.8%), flounder (10.2%), and haddock (16.0%). This is the first molecular-based survey of fish helminths in Kazakhstan and highlights the need to integrate genetic screening into food safety control systems to better protect consumers and improve parasite monitoring of imported seafood. Full article

Background/objectives: Maternal adiposity is a known risk factor for adverse pregnancy outcomes, yet routine antenatal care primarily relies on body mass index (BMI), which has limitations. This study aimed to explore the acceptability of incorporating a broader range of adiposity measurements into early pregnancy antenatal care, assessing pregnant women’s perceptions to inform implementation strategies. Methods: A qualitative study using semi-structured interviews was conducted with 14 pregnant women purposively sampled to capture variation in BMI, age, and parity. Interviews occurred approximately 4–5 months post-measurement experience. The Theoretical Framework of Acceptability (TFA) guided thematic analysis of transcribed data, with independent coding to ensure rigour. Results: Participants generally viewed the current reliance on BMI as outdated and expressed neutral to positive attitudes toward the use of more detailed adiposity measurements. Most reported little emotional discomfort with the process. However, some reflected likelihood of more body self-consciousness had it been their first pregnancy. Time involved in measurements was not seen as burdensome, however waiting between procedures was a minor inconvenience. Self-assessing body shape was described as difficult. Women emphasised the importance of choice, autonomy, and informed consent, especially in relation to partner involvement, the gender of the anthropometrist, and the nature of the procedures. Clear, advance communication and supportive explanations during appointments were seen as essential to ensuring a positive experience. Conclusions: Expanding adiposity assessments in early pregnancy is acceptable to women if implemented ethically, prioritising consent, privacy, emotional safety, and effective communication. Integration into routine care requires staff training and pre-appointment guidance. Full article

A crucial task in industrial quality control, especially in the food and agriculture sectors, is the quick and precise estimation of an object’s volume. This study combines cutting-edge machine learning and computer vision techniques to provide a comprehensive, non-destructive method for predicting orange volume. We created a reliable pipeline that employs top and side views of every orange to estimate four important dimensions using a calibrated marker. These dimensions are then fed into a machine learning model that has been fine-tuned. Our method uses a range of engineered features, such as complex surface-area-to-volume ratios and new shape-based descriptors, to go beyond basic geometric formulas. Based on a dataset of 150 unique oranges, we show that the Stacking Regressor performs significantly better than other single-model benchmarks, including the highly tuned LightGBM model, achieving an $R^2$ score of 0.971. Because of its reliance on basic physical characteristics, the method is extremely resilient to the inherent variability in fruit and may be used with a variety of produce types. Because it allows for the real-time calculation of density (mass over volume) for automated defect detection and quality grading, this solution is directly applicable to a factory sorting environment. Full article

Ensuring secure and reliable communication in Vehicular Ad hoc Networks (VANETs) is critical for safe transportation systems. This paper presents Vehicular Misbehavior Network (VeMisNet), a deep learning framework for detecting misbehaving vehicles, with primary contributions in systematic feature engineering and scalability analysis. VeMisNet introduces domain-informed spatiotemporal features—including DSRC neighborhood density, inter-message timing patterns, and communication frequency analysis—derived from the publicly available VeReMi Extension Dataset. The framework evaluates Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM architectures across dataset scales from 100 K to 2 M samples, encompassing all 20 attack categories. To address severe class imbalance (59.6% legitimate vehicles), VeMisNet applies SMOTE post train–test split, preventing data leakage while enabling balanced evaluation. Bidirectional LSTM with engineered features achieves 99.81% accuracy and F1-score on 500 K samples, with remarkable scalability maintaining >99.5% accuracy at 2 M samples. Critical metrics include 0.19% missed attack rates, under 0.05% false alarms, and 41.76 ms inference latency. The study acknowledges important limitations, including reliance on simulated data, single-split evaluation, and potential adversarial vulnerability. Domain-informed feature engineering provides 27.5% relative improvement over dimensionality reduction and 22-fold better scalability than basic features. These results establish new VANET misbehavior detection benchmarks while providing honest assessment of deployment readiness and research constraints. Full article

Artisanal and small-scale gold mining (ASGM) constitutes an essential livelihood strategy for marginalized communities, yet it is also associated with severe environmental and social challenges. Persistent inequality and poverty underpin miners’ dependence on informal practices, where access to safer technologies is limited. Mercury use remains critical in ASGM, often mismanaged in processing, applied in amalgamation, and released into air, water, and soils. An estimated 1000–2000 tonnes are emitted annually despite Minamata Convention commitments. This paper examines how mining governance can foster sustainable transitions in ASGM, focusing on the Chilean National Mining Company (ENAMI) as a case study. ENAMI’s model—combining regulatory oversight, institutional support, and inclusive decision-making—shows how public governance can reduce mercury reliance, mitigate environmental degradation, and enhance social inclusion. Findings highlight the importance of regulation, community participation, capacity-building, and addressing structural inequalities as integral components of sustainability. The study argues that sustainable ASGM requires not only technical innovation but also culturally sensitive institutional reforms capable of overcoming resistance and promoting long-term behavioral change. By situating ASGM within the broader framework of sustainable rural development, this research provides insights for policymakers, practitioners, and scholars seeking to reconcile economic inclusion with environmental stewardship in vulnerable contexts. Full article

This study introduces the Machine Learning (ML)-Driven Pattern Synthesis for Digital Forensics in Synthetic Log Analysis (ML-PSDFA) framework to address critical gaps in digital forensics, including the reliance on real-world data, limited pattern diversity, and forensic integration challenges. A key innovation is the introduction of a novel temporal forensics loss ${\mathcal{L}}_{\mathrm{TFL}}$ in the Synthetic Attack Pattern Generator (SAPG), which enhances the preservation of temporal sequences in synthetic logs that are crucial for forensic analysis. The framework employs the SAPG with hybrid seed data (UNSW-NB15 and CICIDS2017) to create 500,000 synthetic log entries using Google Colab, achieving a realism score of 0.96, a temporal consistency score of 0.90, and an entropy of 4.0. The methodology employs a three-layer architecture that integrates data generation, pattern analysis, and forensic training, utilizing TimeGAN, XGBoost classification with hyperparameter tuning via Optuna, and reinforcement learning (RL) to optimize the extraction of evidence. Due to enhanced synthetic data quality and advanced modeling, the results exhibit an average classification precision of 98.5% (best fold 98.7%) 98.5% (best fold 98.7%), outperforming previously reported approaches. Feature importance analysis highlights timestamps (0.40) and event types (0.30), while the RL workflow reduces false positives by 17% over 1000 episodes, aligning with RL benchmarks. The temporal forensics loss improves the realism score from 0.92 to 0.96 and introduces a temporal consistency score of 0.90, demonstrating enhanced forensic relevance. This work presents a scalable and accessible training platform for legally constrained environments, as well as a novel RL-based evidence extraction method. Limitations include a lack of real-system validation and resource constraints. Future work will explore dynamic reward tuning and simulated benchmarks to enhance precision and generalizability. Full article

Breast cancer, a leading global malignancy, exhibits extensive metabolic reprogramming that drives tumorigenesis, therapy resistance, and survival. Ferroptosis, an iron-dependent regulated cell death mechanism characterized by lipid peroxidation, emerges as a promising therapeutic vulnerability, particularly in aggressive subtypes like triple-negative breast cancer (TNBC). This literature review comprehensively explores the metabolic regulation of ferroptosis in breast cancer cells, focusing on how dysregulated pathways modulate sensitivity or resistance. The review will discuss iron homeostasis, including upregulated transferrin receptor 1 (TFR1), diminished ferroportin, mitochondrial dynamics, and ferritinophagy, which catalyze ROS via Fenton reactions. It will examine glutathione (GSH) metabolism through the GPX4-GSH axis, with subtype-specific reliance on cystine import via xCT or de novo cysteine synthesis. Lipid metabolism will be analyzed as the core battleground, highlighting polyunsaturated fatty acid (PUFA) incorporation by ACSL4 promoting peroxidation, contrasted with monounsaturated fatty acid (MUFA) protection via SCD1, alongside subtype adaptations. Further, the review will address tumor microenvironment influences, such as cysteine supply from cancer-associated fibroblasts and oleic acid from adipocytes. Oncogenic signaling (e.g., RAS, mTOR) and tumor suppressors (e.g., p53) will be evaluated for their roles in resistance or sensitivity. Intersections with glucose metabolism (Warburg effect) and selenium-dependent antioxidants will be explored. Therapeutically, the review will consider targeting these nodes with GPX4 inhibitors or iron overload, synergized with immunotherapy for immunogenic cell death. Future directions will emphasize multi-omics integration and patient-derived organoids to uncover subtype-specific strategies for precision medicine in breast cancer. Full article

In Hungary, on-site mixed stabilization of cohesive soil is considered only as soil improvement not a proper pavement layer, therefore its bearing capacity is not taken into account when designing pavement. It was our hypothesis that on low-volume roads built on cohesive soil, lime or lime–cement stabilization can be an alternative to granular base layers. A case study was conducted to obtain initial results and to verify the research methodology. The efficacy of lime stabilization was evaluated across eight experimental road sections, with a view of assessing its structural and economic performance in comparison with crushed stone base layers reinforced with geo-synthetics. The results of the testing demonstrated elastic moduli of 120–180 MPa for the lime-stabilized layers, which closely matched the 200–280 MPa range observed for the crushed stone bases. The results demonstrated that lime stabilization offers a comparable load-bearing capacity while being the most cost-effective solution. Furthermore, this approach enhances sustainability by enabling the utilization of local soils, reducing reliance on imported materials, minimizing transport-related costs, and lowering carbon emissions. Lime stabilization provides a durable, environmentally friendly alternative for road construction, effectively addressing the challenges of material scarcity and rising construction costs while supporting infrastructure resilience. The findings highlight its potential to replace traditional base layers without compromising structural performance or economic viability. Full article

This study employs an improved fractional-order grey multivariable convolution model (FGMC(1,N,2r)) to predict abalone aquaculture output in Fujian, Shandong, and Guangdong. By integrating fractional-order accumulation (r₁, r₂) with a particle-swarm-optimization (PSO) algorithm, the model addresses limitations of handling multivariable interactions and sequence heterogeneity within small-sample regional datasets. Grey relational analysis (GRA) first identified key factors exhibiting the strongest associations with production: abalone production in Fujian and Shandong is predominantly influenced by funding for aquatic-technology extension (GRA degrees of 0.9156 and 0.8357, respectively), while in Guangdong, production was most strongly associated with import volume (GRA degree of 0.9312). Validation confirms that FGMC(1,N,2r) achieves superior predictive accuracy, with mean absolute percentage errors (MAPE) of 0.51% in Fujian, 3.51% in Shandong, and 2.12% in Guangdong, significantly outperforming benchmark models. Prediction of abalone production for 2024–2028 project sustained growth across Fujian, Shandong, and Guangdong. However, risks associated with typhoon disasters (X₆ and import dependency (X₅) require attention. The study demonstrates that the FGMC(1,N,2r) model achieves high predictive accuracy for regional aquaculture output. It identifies the primary drivers of abalone production: technology-extension funding in Fujian and Shandong, and import volume in Guangdong. These findings support the formulation of region-specific strategies, such as enhancing technological investment in Fujian and Shandong, and strengthening seed supply chains while reducing import dependency in Guangdong. Furthermore, by identifying vulnerabilities such as typhoon disasters and import reliance, the study underscores the need for resilient infrastructure and diversified seed sources, thereby providing a robust scientific basis for production optimization and policy guidance towards sustainable and environmentally sound aquaculture development. Full article

This study addresses the growing importance of integrating innovation into corporate sustainability strategies by examining the financial and environmental performance of ten firms listed on the Borsa Istanbul Sustainability Index over a five-year period. The main objective is to develop and test a novel, data-driven analytical framework that reduces reliance on subjective expert judgments while providing actionable insights for sustainability-oriented decision-making. Within this framework, the entropy method from the Multi-Criteria Decision Making (MCDM) approach is first applied to calculate the objective weights of sustainability criteria, ensuring that the analysis is grounded in real performance data. Building on these weights, an innovative reverse Decision-Making Trial and Evaluation Laboratory (DEMATEL) model, implemented through a custom artificial neural network-based software, is introduced to estimate direct influence matrices and reveal the causal relationships among criteria. This methodological advance makes it possible to explore how environmental and financial factors interact with R&D expenditures and to simulate their systemic interdependencies. The findings demonstrate that R&D serves as a central driver of both environmental and financial sustainability, highlighting its dual role in fostering corporate innovation and long-term resilience. By positioning R&D as both an enabler and outcome of sustainability dynamics, the proposed framework contributes a novel tool for aligning innovation with strategic sustainability goals, offering broader implications for corporate managers, policymakers, and researchers. Full article

Background and Aim: Food hygiene is fundamental to public health, ensuring safe and nutritious food free from contaminants, and is vital for economic development and sustainability. The Hazard Analysis and Critical Control Points (HACCP) system is a crucial tool for managing risks in food production. Despite global recognition of food safety’s importance, significant disparities exist, especially in Southern Italy, where diverse food production, tourism, and economic factors pose challenges to enforcing hygiene standards. This study evaluates non-compliance with food hygiene regulations within a Local Health Authority (LHA) in Calabria, Southern Italy, to inform effective public health strategies. Materials and Methods Authorized by the Food Hygiene and Nutrition Service (FHNS) of the LHA, the study covers January 2022 to December 2024, analyzing 579 enterprises with 1469 production activities. Inspections followed EC Regulation No. 852/2004, verifying the correct application of procedures based on the Hazard Analysis and Critical Control Points (HACCP) principles, including the operator’s monitoring of Critical Control Points (CCPs), and adherence to Good Hygiene Practices (GHPs). Non-compliances were classified by severity, and corrective and punitive actions were applied. Data were analyzed annually and across the full period using descriptive statistics and chi-squared tests to assess trends. Results: Inspection coverage increased markedly from 29.8% of production activities in 2022 to 62.5% in 2023, sustaining 62.0% in early 2024, exceeding the growth of new activities. Inspections were mainly triggered by RASFF alerts (22.4%), routine controls (20.0%), and verification of previous prescriptions (14.3%). The most frequent corrective measures were long-term prescriptions (28.6%), violation reports (22.9%), and short-term prescriptions (20.0%). Enterprises averaged 4.61 production activities, highlighting operational complexity. Conclusions: This study provides a granular analysis of food hygiene non-compliance within a Local Health Authority (LHA) in Southern Italy, to inform effective public health strategies. While official control data may be publicly available in some contexts, our research offers a unique, in-depth view of inspection triggers, non-compliance patterns, and corrective measures, which is crucial for understanding specific regional challenges. The analysis reveals that the prevalence of long-term prescriptions and reliance on RASFF alerts indicate systemic challenges requiring sustained interventions. Full article

Although numerous studies have used ML to predict gear transmission error, few have provided a normalized, interpretable risk metric for early tolerance assessment. This work fills that gap by proposing the Tonal Risk Index (TRI). Kinematic Transmission Error (KTE) is a well-established primary excitation source of tonal gear noise in electric vehicle drivetrains. This study introduces the TRI, a novel, dimensionless indicator that quantifies relative tonal noise risk directly from predicted KTE values. We employ a large-scale dataset of 39,984 Monte Carlo simulations comprising 15 manufacturing tolerance and process-shift variables, with KTE values as the target. Baseline linear regression failed to capture the strongly non-linear relationships between tolerances and KTE (R² ≈ 0), whereas non-linear models—Random Forest and XGBoost—achieved high predictive accuracy (R² ≈ 0.82). Feature importance analysis revealed that pitch error, radial run-out, and misalignment are consistently the most influential parameters, with notable interaction effects such as pitch error × run-out and misalignment × form-defect shift. The TRI normalises predicted KTE values to a 0–1 scale, enabling rapid comparison of tolerance configurations in terms of tonal excitation risk. This approach supports early-stage design decision-making, reduces reliance on high-fidelity simulations and physical prototypes, and aligns with sustainability objectives by lowering material usage and energy consumption. The results demonstrate that data-driven surrogate models, combined with the TRI metric, can effectively bridge the gap between manufacturing tolerances and NVH performance assessment. Full article