Search Results (6,859)

In the field of subgrade compaction quality assessment, accurate volume measurement of excavated pits is hindered by non-uniform point cloud distribution, environmental noise interference, and complex irregular boundary features. To address these challenges, this paper proposes a robust volume detection framework that integrates adaptive point cloud refinement and morphological discrimination. First, a pose normalization method employing RANSAC plane fitting and rigid body transformation corrects the spatial orientation of the raw point clouds. To balance data redundancy removal with feature preservation, a gradient adaptive simplification strategy based on local density feedback and K-nearest neighbor estimation is developed. Subsequently, a cross-sectional area calculation model utilizing piecewise-cubic polynomial fitting is proposed to mitigate boundary noise and accurately reconstruct irregular contours. Furthermore, a dynamic outlier removal mechanism based on the Median Absolute Deviation (MAD) and sliding windows is introduced to eliminate non-physical geometric fluctuations. Finally, the total volume is aggregated using a hybrid strategy of Simpson’s rule and a frustum compensation operator. Experimental results on simulated pits with typical topological defects demonstrate that the proposed algorithm outperforms traditional methods, achieving an average relative volume error of less than 0.8%. This approach significantly improves the robustness and precision of sensor-based automated subgrade compaction quality measurement. Full article

To achieve low-carbon and cost-effective operation of multi-park electricity–heat–hydrogen integrated energy systems (EHHSs), this paper proposes a low-carbon dispatch framework based on federated safe reinforcement learning. First, a multi-park EHHS dispatch model is established by considering heterogeneous park characteristics, electricity–heat–hydrogen coupling, stepped carbon trading, and peer-to-peer (P2P) energy trading. Then, to address the coupled challenges of privacy preservation, operational coupling, and safety constraints, the dispatch problem is formulated as a constrained Markov decision process (CMDP). On this basis, a federated safe proximal policy optimization algorithm (FedSafePPO) is developed by integrating PPO, Lagrangian-based safety constraint handling, and federated parameter aggregation. The proposed method enables each park to learn a local dispatch policy from private data while sharing global knowledge without exchanging raw operational data. In addition, an actor–dual-critic architecture is adopted to jointly evaluate economic returns and constraint costs, thereby improving convergence stability and dispatch feasibility. Case studies involving three heterogeneous parks—industrial, commercial, and residential—demonstrate that the proposed method effectively reduces total operating costs and carbon emissions while satisfying system constraints. Compared with PPO, FedPPO, and SafePPO, the proposed FedSafePPO achieves superior low-carbon economic performance, greater training stability, and better adaptability to heterogeneous operating conditions. The results verify the effectiveness and engineering applicability of the proposed method for the low-carbon dispatch of multi-park EHHSs. Full article

Traffic flow prediction is an important and fundamental task for the operation of Intelligent Transportation Systems. In recent years, most studies on traffic prediction have focused on two-dimensional network traffic flow prediction, while there is still no clear consensus on the study of one-dimensional highway traffic flow prediction, for instance, regarding which model is the most appropriate. To address this gap, we conducted a systematic comparative evaluation of 27 models across five classes, including Statistical models, Machine Learning, Artificial Neural Networks, Deep Neural Networks, and Graph Neural Networks, based on five representative highway traffic datasets. To ensure fairness, evaluations were performed on raw data without signal decomposition or auxiliary modules. Surprisingly, the experimental results reveal that complex deep learning models do not demonstrate advantages in terms of conventional metrics. Instead, simple models, particularly Historical Averaging and tree-based Machine Learning models, exhibit superior performance in most scenarios. And then, we study the underlying reasons for this phenomenon from various perspectives, including the complexity of prediction tasks, the tabular data characteristics, the spectral bias of Neural Networks, and theoretical error bounds. Furthermore, we also analyze why these findings were overlooked in the previous literature, attributing the oversight to the predominant focus on signal decomposition preprocessing, inconsistent prediction settings, and the lack of comprehensive benchmarking. Supported by rich data and extensive information, this work offers valuable references and practical implications for researchers in highway traffic flow prediction. It further advocates that in the era of pursuing sophisticated models, scenario-specific analysis and appropriate simple models still deserve more attention. Full article

This research paper examines the environmental impact of natural and synthetic fur coats, focusing exclusively on the processing and manufacturing stages. Using one coat weighing approximately 5 kg as the functional unit, a comparative Life Cycle Assessment (LCA) is conducted from raw material processing to final garment production, explicitly excluding animal farming. The analysis includes key processes such as cleaning, tanning, dyeing, and sewing for natural fur, and polymer production, fabric formation, dyeing, and finishing for synthetic fur. Data from international academic literature (Google Scholar and Scopus) are used to evaluate CO₂ emissions, energy and water consumption, chemical inputs, and waste generation. Results indicate that synthetic fur production is energy-intensive but requires relatively low water use, whereas natural fur processing involves high water consumption and chemical treatments, resulting in significantly higher emissions—often reaching hundreds to thousands of kg CO₂e per coat. The study further investigates the role of embedded IoT systems in improving efficiency within tanneries and textile manufacturing. Real-time monitoring and automated dosing systems can reduce emissions and chemical use by approximately 10–20%. Case studies of a smart tannery and an IoT-enabled synthetic fur production line illustrate potential implementation pathways. Although such optimizations can reduce environmental impacts, the findings clearly show that natural fur processing remains considerably more carbon-intensive than synthetic alternatives. This research highlights the importance of integrating digital technologies into industrial processes and suggests directions for future work based on real-world operational data. Full article

Background/Objectives: Diet is considered one of the most important modifiable risk factors for non-communicable diseases in modern society. While numerous studies have reported on the association between diet and mental health, including anxiety, research examining the relationship between dietary patterns and mental health is relatively scarce. Therefore, this study aimed to analyze the association between anxiety and suicidal ideation with macronutrient intake. Methods: This study was conducted on adults aged 19 years or older using raw data from the 2021–2023 Korea National Health and Nutrition Examination Survey. Excluding those with missing test items, 9002 subjects were included. The study subjects were divided into four groups based on macronutrient intake (normal diet group, high-carbohydrate diet group, high-fat diet group, and high-protein diet group; based on Korean Dietary Reference). Results: There was no significant association between dietary patterns and suicidal ideation. However, after adjusting for covariates for moderate or severe anxiety in the HP diet group, the odds ratio was reported to be 0.492 (95% CI 0.298–0.810). Subgroup analysis by gender revealed no significant difference between dietary types and anxiety in women, but in men, the HP diet significantly lowered the odds of moderate or severe anxiety (OR 0.230, 95% CI 0.089–0.599). Conclusions: This study found that higher protein intake was associated with lower levels of moderate to severe anxiety, and this trend was statistically significant, particularly in men. Further research is needed to confirm the causal relationship. Full article

Background/Objectives: Early identification of breast cancer patients who are likely or unlikely to benefit from neoadjuvant chemotherapy (NAC) remains clinically important because ineffective treatment may delay definitive surgery and expose patients to unnecessary toxicity. Quantitative ultrasound (QUS) radiomics offers a contrast-free and repeatable method for extracting tissue-sensitive imaging biomarkers from raw ultrasound data. This study aimed to evaluate whether baseline QUS radiomic features and early treatment-induced changes could predict a pathologic response to NAC in a real-world single-center cohort. Methods: We designed a prospective observational study including 96 consecutive women with biopsy-proven stage II–III breast cancer treated with NAC at Victor Babes University of Medicine and Pharmacy Timisoara. All patients underwent standardized QUS examinations before treatment and again at week 2. The response was defined pathologically at surgery as residual cancer burden class 0/I versus II/III. Clinical, histopathologic, and QUS variables were compared between responders and non-responders. Group comparisons used Student’s t test, Mann–Whitney U test, chi-square testing, and Fisher’s exact test where appropriate. Multivariable logistic regression was used to identify independent predictors of response. Model discrimination was summarized using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Results: Forty-three patients (44.8%) were classified as responders and 53 (55.2%) as non-responders. Responders had higher baseline Ki-67 values (47.8 ± 13.1% vs. 41.9 ± 13.0%, p = 0.033), lower baseline homogeneity (0.3 ± 0.1 vs. 0.4 ± 0.1, p = 0.010), and higher peritumoral heterogeneity (0.9 ± 0.1 vs. 0.8 ± 0.2, p = 0.027). At week 2, responders showed larger increases in mid-band fit (3.0 ± 0.8 vs. 1.2 ± 0.8 dB, p < 0.001), greater entropy change (0.7 ± 0.2 vs. 0.2 ± 0.2, p < 0.001), more pronounced spectral intercept reduction (−3.5 ± 1.4 vs. −1.2 ± 1.3, p < 0.001), and greater tumor shrinkage (−24.3 ± 7.0% vs. −11.1 ± 5.7%, p < 0.001). In multivariable analysis, Δ MBF and Δ entropy remained independent predictors of pathologic response. The combined clinical-plus-QUS model achieved an AUC of 0.89. Conclusions: Baseline microstructural heterogeneity and very early QUS-derived treatment changes were strongly associated with the pathologic response to NAC. These findings support the potential role of QUS radiomics as a low-cost, repeatable early-response biomarker in breast cancer. Full article

This study investigates how ongoing low-stakes quizzes and other learning management system (LMS)-based activities relate to performance on a summative course quiz in higher education. We analyzed course data from 37 first-year undergraduate students. Data were extracted from Moodle and covered weekly quiz scores across ten quizzes, number of attempts, attempt duration, latency between quiz release and first attempt, and student engagement with course materials. Descriptive statistics, Pearson correlations, and partial correlations were used to examine these relationships. The findings consistently point in the same direction: when and how often students engaged with quizzes mattered far more than how well they scored on them. Longer latency—that is, delaying the first quiz attempt after release—was strongly negatively associated with final quiz performance, while students who attempted quizzes more frequently and completed them more quickly tended to perform better. Among course materials, viewing the core lecture handouts showed the strongest positive association with final scores, while additional reading, Moodle lesson completion, and Padlet participation showed weaker but statistically significant positive associations. Topic materials were not significantly associated with final quiz performance. Partial correlation analyses confirmed that latency, number of attempts, and handout views each remained independently associated with final performance after controlling for average quiz score, suggesting these behavioral indicators capture something that raw accuracy alone does not. These results align with testing-effect and self-regulated learning research and point to a clear practical implication: course designs that encourage early, repeated engagement with structured core materials are likely to support better student outcomes than those that rely primarily on quiz scores as a proxy for learning. Full article

The knowledge base for many small vertebrate species remains limited, largely because traditional manual data collection methods often overlook less charismatic species, such as reptiles. To address this, our pilot study harnesses open-source deep learning and markerless pose estimation technologies to evaluate the technical feasibility of tracking the spatial use and activity profiles of captive ectotherms. Specifically, we tracked these patterns over two months in a dynamically modified environment for Australian barking geckos (Underwoodisaurus milii). Our findings reveal descriptive changes in spatial occupancy and proximity across varying structural layouts. The system achieved a high raw detection accuracy (96.4%) and spatial categorization accuracy (91.7%) when validated against manual ground-truth data, confirming its robust technical performance and precision. Additionally, we automatically evaluated spatial proxies such as activity time budget, velocity, acceleration, and height usage, standardizing the analysis of extensive video recordings for nocturnal species. This pilot test introduces a simple, cost-effective method for rapid data extraction, offering a reliable, scalable monitoring solution for the management of understudied species. Full article

This article discusses the practical implementation of a prototype academic transcript storage system based on blockchain technology and smart contracts. The digital transformation of higher education requires reliable mechanisms for ensuring the integrity and verifiability of academic documents. It presents the design and experimental validation of a blockchain-based system for storing and verifying academic transcripts within the higher education system of the Republic of Kazakhstan. The proposed solution is based on an Ethereum Virtual Machine-compatible smart contract implemented in Solidity and deployed on a test network. The testnet was used as the experimental environment, and transaction monitoring was performed using the BlockScout v11.0.3 explorer. The architecture of the TranscriptStorage smart contract is presented, including a role-based access model, a data indexing mechanism using keccak-256, and storage of transcripts in a mapping structure (bytes32 => Transcript[ ]). The experimental results confirm the successful recording of the Transcript in the distributed ledger, event recording (Logs), and the correctness of the ABI encoding of input parameters (Raw Input), as well as a change in state (State Changes) reflecting the fee payment. The use of events is shown to enable cost-effective third-party data verification without the need to store the entire text in the contract state. The comparative results showed that the proposed system reduced gas consumption by 804.5% compared to Blockcerts, 48.8% compared to ECertChain, 82.5% compared to ShikkhaChain, and 43.5% compared to zkEVM. These improvements were achieved while maintaining high scalability, robust privacy features, and security, making it a practical solution for Kazakhstan’s educational system. Full article

The Northern Sea Route (NSR) is becoming increasingly accessible as Arctic sea ice declines, motivating data-driven forecasts of seasonal navigability. We compiled a 13-year (2013–2025) monthly dataset of AMSR2 sea ice concentration (SIC) and ERA5 atmospheric reanalysis variables over the NSR corridor (68–80° N, 30–180° E) and benchmarked a hierarchy of forecasting models for 1-, 3-, and 6-month lead times. Baselines (climatology, persistence, anomaly persistence, SARIMA, ridge regression) were compared with compact deep learning architectures (LSTM, Transformer; 10,000–70,000 parameters) trained on 12-month sequences with anomaly targets and five-seed ensembles. Three findings emerge. First, the seasonal cycle explains 98.0% of the monthly SIC variance, so climatology alone yields RMSE = 4.56% and three-class navigability accuracy of 87.5%. Second, SARIMA, the compact LSTM ensemble, random forest, and MLP_small all yield small positive skill scores over climatology: SARIMA achieves the lowest 1-month RMSE (3.98%, skill score +0.239), while the compact LSTM ensemble shows positive skill at all horizons (mean skill score +0.038); however, the bootstrap confidence intervals overlap and these differences are not statistically distinguishable from climatology. Third, all skilful models converge to identical classification metrics (accuracy 0.875, macro-F1 0.78, κ = 0.76); McNemar tests and overlapping bootstrap confidence intervals show no statistically significant differences. Permutation importance confirms that AMSR2 ice-state features dominate, whereas the high raw correlations of ERA5 radiation variables collapse after detrending. These results indicate that compact statistical and deep learning models are equivalent for NSR seasonal navigability prediction and that honest baseline comparison is essential when seasonal cycles dominate. Full article

The German industrial and energy sectors accounted for over 52% of national greenhouse gas emissions in 2024. This is influenced both by an ongoing demand for fossil fuels and the usage of emission-intensive raw and processed materials. With the current European directive on corporate sustainability reporting, a push is being made for companies to publish annual emission reports. However, as per a study conducted by the authors, small and medium-sized companies have difficulties accurately calculating emissions across their supply chain without relying on external service providers. As a scientific institute with a real production facility for metal machining, the ETA (Energy Technologies and Applications) Factory bridges the gap between academia and manufacturing enterprises. The authors have used this disposition to calculate scope 1–3 emissions for the factory as per the Greenhouse Gas Protocol across three years, while progressively attempting to automate data collection for all scopes. CO₂e emissions for the years 2022–2024 were 86.3 tCO₂e, 146.9 tCO₂e, and 86.1 tCO₂e, respectively. Emission categories were assessed in terms of relevance to the institute and subsequently used to analyze the emission activities of the factory. The highest contributor to emissions was electricity purchasing for 2022 and 2024, along with business travel for 2023. Within scope 3, the emissions produced by business travel showed the highest impact across all years, followed by either energy-related activities or purchased goods. The sensitivity of CO₂e factors was also investigated, showing discrepancies between 25% and 130% for the utilized CO₂e factor for steel. Automation of data collection benefits largely from implemented manufacturing systems, such as manufacturing execution systems or enterprise resource planning systems. Full article

Reliable climate data are essential for sustainable water management systems, especially under the challenges posed by climate change. In data-scarce regions, reanalysis products such as ERA5 can support flood and drought risk assessment and water security analysis. However, raw reanalysis precipitation is systematically biased relative to local observations and can distort hydrological indicators; bias correction is therefore needed. This study tests five bias correction techniques (Linear Scaling, Empirical Quantile Mapping, Quantile Mapping Spline Bias Correction, Mean Bias Subtraction, and Simple Linear Regression) on ERA5 precipitation data for Georgia, using classical and sliding window approaches at daily and monthly scales. Results show the importance of selecting the most appropriate method according to data availability and study objectives. The sliding window approach improved performance, especially at the daily scale, and distribution-based methods proved most effective in data-scarce regions. Full article

The purpose of the article is to present the influences and their weights on the price formation of agricultural and food products in Slovenia. The influences are defined by the ratios of input and output prices and quantities of raw materials, semi-finished products, and products within the food systems of individual stakeholders in the theoretical design of price difference construction, the definition of individual stakeholders’ costs, and the assessment of the dynamics of price and quantity fluctuations from the annual average. The analysis is based on the specified econometric model bases on the Ridge formulation, which represent an analytical model of the price formation in the agri-food chains in Slovenia. The results determine and explain the weight of the impacts based on composite independent variables (based on the calculation of the relationships between individual variables with respect to the mutual responsiveness of changes–elasticity of behaviour) which were defined using available data collected in accordance with the Law on Agriculture in the Republic of Slovenia. Several new independent variables were developed to explain the effects of the independent variable representing the difference in the price of agricultural and food product between the beginning and the end in the analyzed food supply chain. The discussion connects practical actions that address three important future development components of agriculture: strengthening accessibility, competitiveness, and the stability of the position of Slovenian agriculture within the EU. Full article

Listeria monocytogenes is already a well-known foodborne bacterial pathogen, ubiquitously dispersed not only in the food production environment but also in the primary animal production environment as well. The present study performed whole-genome characterization of the multidrug-resistant (MDR) L. monocytogenes strain BF11, previously isolated from raw pet food and phenotypically described for antimicrobial resistance. To this end, the genomic analysis performed on the isolate confirmed the pathogen’s designation as a serotype 1/2b strain belonging to ST5 and CC5 (Lineage I), carrying multiple MDR genes, stress-related genes, and mobile genetic elements, despite the absence of plasmids. The strain is phylogenetically closely related to Lineage I epidemic strains (e.g., F2365), as it has a full-length inlA and a functional prfA, rendering it capable of invading human cells and marking its high virulence. Overall, this strain may represent a potentially novel genomic profile when core genome multilocus sequence typing (cgMLST) is used, although further data from additional isolates would be required to confirm its classification within a new Complex Type, while displaying a hybrid unique profile. It is an evolved ST5 L. monocytogenes strain that has acquired genetic material conferring a “clinical signature” (Lineage I-like) and an extensive resistance network. Therefore, presence of L. monocytogenes strain BF11 in pet food is alarming, since such hybrid strains often evade surveillance monitoring as they do not fit strictly into classical categories, posing a serious food safety and public health threat in the concept of One Health. Full article

Municipal solid waste management (MSWM) systems in Latin America are constrained by limited access to high-resolution operational data, compelling local authorities to depend on aggregated national statistics that are inadequate for behaviorally informed intervention design. This limitation is particularly evident in the State of Mexico, which generates about 16,187 tons of waste every day but only recycles only 11%. In this context, this study introduces a diagnostic data science framework to identify behaviorally grounded citizen segments and their defining attributes, supporting evidence-based decision-making in MSWM. Primary survey data from 560 households across three municipalities were used, and a three-stage analytical pipeline was implemented to account for contextual heterogeneity. First, k-means clustering was applied to identify behavioral segments. Second, random forest classifiers were used to validate cluster coherence and quantify feature importance. Third, the Apriori algorithm was used to extract association rules that capture recurrent material-mixing behaviors. The results revealed municipality-specific segmentation structures (Tequixquiac: K = 6; Tlalpujahua: K = 3; Xalatlaco: K = 2), with material-specific disposal behaviors emerging as stronger segmentation drivers. Random forest classifiers validated cluster coherence with 100% accuracy, confirming that segments represent behaviorally distinct archetypes. The proposed framework converts raw behavioral data into actionable municipal visions. This approach focuses on finding diagnostic patterns instead of making predictions by utilizing machine-learning-driven MSWM research. Full article