Search Results (54,540)

Additive manufacturing, and particularly the vat photopolymerization process, enables the fabrication of complex geometries at high resolution and small length scales, making it well-suited for fabricating cellular structures (e.g., foams and lattices). Among these, elastomeric cellular structures are of growing interest due to their tunable compliance and energy dissipation. However, comprehensive data on the compressive behavior of these structures remains limited, especially for investigating the structure-property effects from changing the density and distribution of material within the cellular structure. This study explores how the mechanical response of polyurethane-based simple cubic structures changes when varying volume fraction, unit cell length, and unit cell patterning, which have not been systematically investigated previously in additively manufactured elastomers. Increasing volume fraction from 10% to 50% yielded significant changes in compressive stress–strain performance (decreasing strain at 0.5 MPa by 41.6% and increasing energy absorption density by 3962.5%). Although changing the unit cell length between 2.5 and 7 mm in ~30 mm parts did not result in statistically different stress–strain responses, modifying the configuration of struts of different thicknesses across designs with 30% volume fraction altered the stress–strain behavior (differences of 12.5% in strain at 0.5 MPa and 109.4% for energy absorption density). Power law relationships were developed to understand the interactions between volume fraction, unit cell length, and elastic modulus, and experimental data showed strong fits (R² > 0.91). These findings enhance the understanding of how multiple structural design aspects influence the performance of elastomeric cellular materials, providing a foundation for informing strategic design of tailorable materials for diverse mechanical applications. Full article

Efficient retrieval of mathematical and structural similarities in System Dynamics models remains a significant challenge for traditional lexical systems, which often fail to capture the contextual dependencies of simulation processes. This paper presents an architectural approach and implementation of a semantic search module integrated into an existing cloud-based modeling and simulation system. The proposed method employs a strategy for serializing graph structures into textual descriptions, followed by the generation of vector embeddings via local ONNX inference and indexing within a vector database (Qdrant). Experimental validation performed on a diverse corpus of complex dynamic models, compares the proposed approach against traditional information retrieval methods (Full-Text Search, Keyword Search in PostgreSQL, and Apache Lucene with Standard and BM25 scoring). The results demonstrate the distinct advantage of semantic search, achieving high precision (over 90%) within the scope of the evaluated corpus and effectively eliminating information noise. In comparison, keyword search exhibited only 24.8% precision with a significant rate of false positives, while standard full-text analysis failed to identify relevant models for complex conceptual queries (0 results). Despite a recorded increase in latency (~2 s), the study proves that the vector-based approach is a significantly more robust solution for detecting hidden semantic connections in mathematical model databases, providing a foundation for future developments toward multi-vector indexing strategies. Full article

The present paper compares and analyzes the design of AES-128 encryption and decryption using Finite State Machine (FSM) architecture on FPGA and MATLAB platforms. This study aims to evaluate performance disparities in terms of execution time, throughput, and hardware efficiency under identical input data and key conditions. The FSM-based AES algorithm was modeled in MATLAB for functional validation and synthesized on an Artix-7 FPGA using VHDL. The experimental results confirmed that both platforms produced identical ciphertext and plaintext outputs, verifying the correctness of the processes employed. However, the FPGA demonstrated significantly better performance in terms of execution speed. Encryption and decryption times were measured in microseconds on the FPGA, while similar operations on the MATLAB platform required hundreds of milliseconds. The FPGA implementation achieved throughput of 872.53 Mbps for encryption and 858.49 Mbps for decryption with area usage of 1263 and 1428 slices, respectively. This yields an efficiency of 0.691 and 0.601 Mbps/slice, which is considered efficient according to established benchmarks. Compared to previous MATLAB-only and FPGA pipelined implementations, the current design strikes a balance between resource usage and performance, making it ideal for lightweight cryptographic applications in embedded systems. These results provide practical insights into selecting platforms for secure, real-time data processing. Full article

(1) Background: Menstruation is subject to stigma worldwide, which has led to restrictive cultural norms and taboos rooted in religion, customs, and patriarchal systems. The resulting ‘cultural stigma’ associated with menstruation exacerbates health inequities, restricts access to sexual and reproductive health rights (SRHRs), and undermines girls’ and women’s participation in educational, economic, social, and spiritual activities. This scoping review examines interventions to address menstrual stigma experienced by girls and women in India (2) Methods: We used the Joanna Briggs Institute (JBI) methodology for scoping reviews. After systematic searches on 14 March 2024 across six databases (Academic Search complete, APA PsycInfo, Womens Studies International, Web of Science Core collection, MEDLINE, and Index Medicus-SEAR), we screened 1323 records. (3) Results: Findings from 13 unique study reports reveal diverse approaches to addressing menstrual stigma, including income generation initiatives, sexual education, peer training, technological tools, and arts-based approaches. While the interventions initiated dialogue among girls and women in India, they often lacked broader community engagement, leaving structurally embedded patriarchal norms unchallenged. Additionally, most programs targeted adolescent schoolgirls, with limited attention to waged girls and adult women. (4) Conclusions: Addressing menstrual stigma is critical to advancing gender equality and health equity in India. More research is needed to understand effective ways to galvanize community-wide support in dismantling the deeply rooted patriarchal structures that shape interconnected stigma processes leading to health inequities among girls and women in India. Full article

The paper introduces a novel intelligent modeling system of a railway cargo delivery which combines queuing theory and station-level technological activities to model the manner in which re-handling and waiting processes produce delivery delays. The proposed model is in contrast to the available literature, which focuses more on routing or time management; it clearly connects processing of the stations, queue behavior, and reliability of the delivery in one decision system. When applied to a real-life railway route, the optimization of technological sequences is demonstrated to decrease delivery time and congestion rates significantly, as well as decrease the possibility of punishment in case of late deliveries. These findings show that the study is original in terms of the presentation of a data-driven and operationally based approach on the enhancement of railway freight performance. This study introduces a shipment-type-specific intelligent delivery model that integrates queuing theory with real station technological processes. Unlike existing approaches focused mainly on routing or average travel time, the proposed framework explicitly accounts for wagon processing sequences, re-handling operations, and delay-risk assessment. Validation on the Khamza–Bukhara corridor demonstrates a reduction in intermediate re-handlings from four to two and total delivery time from 68 h to 54 h, confirming the operational and economic effectiveness of the model. Full article

This study reviews the application of wire arc additive manufacturing (WAAM) technology in maritime engineering and investigates an experimentally driven analytical approach for prediction of thermal distributions based on the Rosenthal solution. Two ER70S-6 low-carbon steel WAAM cylinders were fabricated using gas metal arc welding (GMAW) and plasma arc welding (PAW) processes, with interlayer temperatures of 453 °C and 250 °C, respectively. Accurately measuring the temperature field to tailor the microstructure has long been a challenge. The results indicated a significant deviation between the analytical predictions and the experimental data. To address this discrepancy, a hybrid approach combining analytical and experimental results was implemented. Time intervals between layers, extracted from the experimental data, were incorporated into the Rosenthal equation to improve the accuracy of temperature field predictions. The microstructure at the bottom, middle, and top regions of the WAAM components was examined using optical microscopy. Tensile testing and Vickers microhardness measurements were conducted to evaluate mechanical properties. Scanning electron microscopy (SEM) was used to analyze fracture surfaces and identify fracture modes. The results were consistent with those reported for other ER70S-6 cylindrical WAAM components. This work highlights limitations of the Rosenthal solution and emphasizes the need for thermal models in WAAM applications. Full article

Phytoplankton blooms represent a typical ecological process in marine systems. Climate change drives shifts in its phenology, both directly via impacts on physiology and indirectly by modifying stratification intensity, nutrients, light availability, and grazing pressure. Using satellite remote sensing and reanalysis data from 2000 to 2022, this study partitions the Yellow Sea based on interannual variability in the Yellow Sea Cold Water Mass (YSCWM). Clear spatial differences in autumn bloom phenology are observed within the YSCWM. Earlier initiation dominates the Southern YSCWM (SYSCWM), while delayed later initiation concentrates in the Northern YSCWM (NYSCWM) and along the SYSCWM’s eastern margins. This pattern can be explained by the differences in regional hydrodynamics, i.e., the Yellow Sea Warm Current (YSWC) enhances upwelling and convergence in some YSCWM areas, boosting nutrient supply and earlier blooms, whereas weaker circulation-driven nutrient supply causes the bloom delay. Interannual variation analysis further reveals that the bloom timing is regulated by seasonal YSCWM dissipation since intensified autumn northerly winds accelerate dissipation and nutrient supply, thereby advancing blooms, while weaker northerly winds and stable circulation delay bloom progress by maintaining strong thermocline stability. These findings provide further insights into the underlying mechanisms driving autumn bloom dynamics and support ecosystem monitoring efforts in shelf seas. Full article

Tunisian traditional fermented foods represent a valuable cultural heritage transmitted across generations and are highly appreciated by consumers for their distinctive flavours, textures, and nutraceutical value. This review provides the first comprehensive and exclusive overview of lactic acid bacteria (LAB) associated with Tunisian traditional fermented foods, both plant- and animal-based. The overview integrates data across dairy, meat, fish, vegetable, and cereal matrices, highlighting the central role that LAB play in the processing of these foods, driving fermentation and shaping the quality and safety of final products. During fermentation, LAB produce a variety of bioactive metabolites, including organic acids, antimicrobial compounds, exopolysaccharides, enzymes, and vitamins, which enhance food safety, shelf life, nutritional quality, and health-promoting potential. The studies include evidence of LAB’s long history of safe use by humans, including the characterisation of autochthonous strains with protechnological, bioprotective, and probiotic properties, providing candidates for the design of starter, protective and probiotic cultures. By consolidating evidence on the relevance of microbial diversity, this review positions Tunisian LAB as valuable resources for both traditional food valorisation and innovative food system development. Importantly, key knowledge gaps are identified, including the limited application of omics-based tools, insufficient genomic safety assessments, and the lack of systematic analysis linking LAB diversity with the desired attributes to promote innovations. Overall, this review provides a structured framework for the valorisation of Tunisian agrofood heritage, bridging artisanal knowledge with modern food microbiology and offering strategic directions for future research, industrial translation, and sustainable innovation in fermented foods. Full article

Firm-level financial statement data form multivariate annual time series with strong cross-variable dependencies and temporal dynamics, yet publicly available panels are often short and incomplete, limiting the generalization of predictive models. We present Latent Financial Time-Series Diffusion (LFTD), a structure-aware augmentation framework that synthesizes realistic firm-level financial time series in a compact latent space. LFTD first learns information-preserving representations with a dual encoder: an FT-Transformer that captures within-year interactions across financial variables and a Time Series Transformer (TST) that models long-horizon evolution across years. On this latent sequence, we train a Transformer-based denoising diffusion model whose reverse process is FiLM-conditioned on the diffusion step as well as year, firm identity, and firm age, enabling controllable generation aligned with firm- and time-specific context. A TST-based Cross-Decoder then reconstructs continuous and binary financial variables for each year. Empirical evaluation on Korean listed-firm data from 2011 to 2023 shows that augmenting training sets with LFTD-generated samples consistently improves firm-value prediction for market-to-book and Tobin’s Q under both static (same-year) and dynamic (τ → τ + 1) forecasting settings and outperforms conventional generative augmentation baselines and ablated variants. These results suggest that domain-conditioned latent diffusion is a practical route to reliable augmentation for firm-level financial time series. Full article

Open-source software is transforming visualization-oriented digital documentation and conceptual BIM by lowering financial and technical barriers, enabling broader participation in the digitalization of the AEC sector. This study develops and validates a cost-effective Scan-to-BIM workflow that combines low-cost hardware with freely available software for 3D data acquisition, processing, and modeling. Photogrammetry and SLAM-based techniques generate accurate point clouds, which, once verified against terrestrial laser scanning data, can be integrated into open-source BIM environments. The workflow leverages COLMAP for 3D reconstruction and BlenderBIM for parametric modeling, combining geometric and semantic information to produce fully interoperable models. While open-source tools offer accessibility and transparency, they require supplementary validation in precision-critical applications and may involve trade-offs in accuracy, stability, and automation compared to commercial solutions. Application to a case study shows how efficient and rapid the process is, representing the trend for the scientific community. Full article

This study, based on stable hydrogen and oxygen isotope observations of multiple water bodies (precipitation, river water, soil water, and groundwater) in the Ami Dongsou alpine arid watershed on the southern slope of the Qilian Mountains during 2023–2024, reveals significant seasonal fluctuations in water isotope characteristics and water source renewal mechanisms. The results show that precipitation and soil water exhibit notable enrichment during the dry season, primarily due to enhanced evaporation causing light isotopes to evaporate and heavy isotopes to accumulate. River water, influenced by both precipitation recharge and evaporation, shows smaller seasonal fluctuations. Groundwater isotopes remain stable, reflecting a slower water source renewal process with minimal seasonal influence. Through quantitative comparisons of the evaporation line’s slope and intercept, this study finds that precipitation is most significantly affected by evaporation, while groundwater is least influenced, showing more stable isotope characteristics. Climate and topography in high-altitude areas significantly regulate water isotope characteristics, especially during the dry season, where evaporation plays a dominant role in the enrichment of precipitation and river water isotopes. This study innovatively establishes an evidence framework for the linkage of multiple water body isotopes, revealing the “seasonal strong fluctuations + differential water body responses + high-altitude regulation” mechanism of water isotopes in alpine arid regions. It provides new data support for water resource management, particularly in aspects such as water source allocation during the dry season, groundwater protection, and evaporation enrichment effect prediction. Future research could expand the sample size and integrate multi-source data and hydrological models to further improve the accuracy of hydrological process predictions, offering more precise support for watershed water resource management and ecological protection. Full article

In logistics systems, the organization of information retrieval plays a key role in human interaction with technical systems to ensure decision-making speed, route optimization, planning, and resource allocation. At the same time, the efficiency of the logistics system when simultaneously processing large volumes of data and constantly updating it is determined by the speed of processing user requests and the accuracy of the responses provided by the system. Within the retrieval-augmented generation architecture, a hybrid information retrieval method has been proposed, based on the combined use of a vector-graph data representation structure and large language model. Experiments showed that the hybrid method achieved best accuracy rates of 0.24–0.25 (among all considered methods) with enhanced scalability capabilities (when the number of nodes increases fourfold, the time increases only twofold—from 0.09 s to 0.20 s) due to the limitation of the graph traversal area when implementing the graph component of the hybrid search. An optimal range of 30–50 nodes to be traversed was also identified, balancing precision and query processing speed. The findings are of practical value to logistics system developers and supply chain managers aiming to implement high-precision, natural language-based information retrieval in dynamic operational environments. Full article

Pastures represent one of the most significant ecological components of Mediterranean landscapes, occupying large surfaces and guaranteeing ecosystem functions of primary importance. In Mediterranean silvo-pastoral systems, the coexistence of trees, shrubs, and herbaceous layers creates a complex ecological mosaic in which grazing activity plays a decisive role. In this framework, understanding the ongoing transformations affecting Mediterranean pastures becomes essential for identifying the main degradation processes and their ecological implications. Remote sensing (RS) technologies are robust and cost-effective tools for quantifying vegetation dynamics, identifying degradation patterns, and supporting sustainable management decisions. This review aims to summarize the most recent scientific evidence on the role of Mediterranean pastures as elements of ecological regulation and fire risk mitigation, while highlighting the potential of RS as a monitoring and decision-support tool. The analysis was performed considering papers from January 2000 to October 2025, by querying the Scopus and Web of Science databases. The analysis allowed the selection of 83 pertinent papers. The selected papers were analyzed, allowing exploration of the literature on RS applied to Mediterranean pastures from multiple angles, highlighting the historical progression of publications, the main geographical locations of study areas, and the evolution and intertwining of recurring themes. Full article

The emerging scope of the Industrial Internet of Services (IIoS) requires a robust intrusion detection system to detect malicious attacks. The increasing frequency of sophisticated and high-impact cyber attacks has resulted in financial losses and catastrophes in IIoS-based manufacturing industries. However, existing solutions often struggle to adapt and generalize to new cyber attacks. This study proposes a unique approach designed for known and zero-day network attack detection in IIoS environments, called Denoising Adaptive Multi-Branch Architecture (DA-MBA). The proposed approach is a smart, conformal, and self-adjusting cyber attack detection framework featuring denoising representation learning, hybrid neural inference, and open-set uncertainty calibration. The model merges a denoising autoencoder (DAE) to generate noise-tolerant latent representations, which are processed using a hybrid multi-branch classifier combining dense and bidirectional recurrent layers to capture both static and temporal attack signatures. Moreover, it addresses challenges such as adaptability and generalizability by hybridizing a Multilayer Perceptron (MLP) and bidirectional LSTM (BiLSTM). The proposed hybrid model was designed to fuse feed-forward transformations with sequence-aware modeling, which can capture direct feature interactions and any underlying temporal and order-dependent patterns. Multiple approaches have been applied to strengthen the dual-branch architecture, such as class weighting and comprehensive hyperparameter optimization via Optuna, which collectively address imbalanced data, overfitting, and dynamically shifting threat vectors. The proposed DA-MBA is evaluated on two widely recognized IIoT-based datasets, Edge-IIoT set and WUSTL-IIoT-2021 and achieves over 99% accuracy and a near 0.02 loss, underscoring its effectiveness in detecting the most sophisticated attacks and outperforming recent deep learning IDS baselines. The solution offers a scalable and flexible architecture for enhancing cybersecurity within evolving IIoS environments by coupling feature denoising, multi-branch classification, and automated hyperparameter tuning. The results confirm that coupling robust feature denoising with sequence-aware classification can provide a scalable and flexible framework for improving cybersecurity within the IIoS. The proposed architecture offers a scalable, interpretable, and risk sensitive defense mechanism for IIoS, advancing secure, adaptive, and trustworthy industrial cyber-resilience. Full article

As organizations strive to balance environmental stewardship with economic competitiveness, understanding the performance implications of Green Innovation (GI) has become increasingly important. Although the nexus between Green Product Innovation (GPI), Green Process Innovation (GPrI), and organizational outcomes has attracted sustained scholarly attention, empirical evidence remains inconclusive. To reconcile these inconsistencies and delineate boundary conditions, this study synthesizes data from 48 empirical investigations (2012–2025) via a random-effects meta-analysis with the Hartung–Knapp adjustment and trim-and-fill procedures to strengthen statistical inference. Results reveal significant small-to-moderate positive associations between GI and environmental (r = 0.172), financial (r = 0.191), and innovation performance (r = 0.143). Notably, moderator analyses demonstrate a synergy premium, where Integrated GI measures significantly outperform isolated GPI or GPrI approaches (r = 0.353). Substantial heterogeneity exists (I² = 91.2%), which is significantly moderated by innovation type, industry pollution intensity, geographic region, and research design. Our findings reinforce the Natural-Resource-Based View (NRBV) and the Dynamic Capabilities framework, highlighting that strategic returns depend on asset orchestration and contextual factors. We conclude that firms should adopt a holistic approach, integrating both product and process innovations to enhance competitive advantage in an incremental and context-contingent manner, while interpreting innovation-performance results cautiously given the limited evidence base. Full article