Search Results (4,078)

The open sharing of data as a factor of production is an important institutional mechanism for promoting sustainable innovation in the digital economy. Using Chinese A-share listed firms as the research sample and exploiting the staggered rollout of government open data (GOD) platforms across prefecture-level cities as a quasi-natural experiment, this paper constructs a staggered difference-in-differences (DID) model to examine the effect of GOD on green collaborative innovation (GCI) and its underlying mechanisms. The results show that GOD significantly promotes GCI, indicating that open government data can help firms strengthen collaboration in green innovation and contribute to more sustainable development. Mechanism analysis shows that GOD promotes GCI through four channels: increasing government subsidies, reducing information asymmetry, raising public environmental awareness, and advancing corporate digital transformation. Heterogeneity analysis reveals that the innovation-promoting effect of GOD is more pronounced in large cities, non-resource-based cities, and southern cities, and is more salient among state-owned enterprises, capital-intensive firms, and mature firms. This paper provides empirical evidence on the microeconomic effects of market-oriented data allocation and highlights the role of GOD in supporting GCI, corporate sustainable transformation, and the sustainable development of the digital economy. Full article

Rotating machinery is a key component in modern industry, and its operating condition directly affects equipment safety and production reliability. However, discrepancies among different machines cause source–target distribution shifts, while fault annotation for target machines is costly, limiting the performance of deep learning-based diagnosis under cross-machine scenarios with limited labels. To address these issues, this paper proposes a multi-scale time–frequency semantic consistency model based on self-supervised transferable representation learning, termed MTFSC. First, augmented waveform views and multi-scale frequency-domain views are constructed from unlabeled source-domain vibration signals for self-supervised pre-training without source labels. Then, a time-domain impulse-aware feature extractor and a time–frequency decoupled spectral feature extractor are designed to enhance local impulsive responses and emphasize fault-sensitive time–frequency patterns. Furthermore, a semantic-aware soft contrastive loss is developed to mine potential semantic neighbors from multi-scale frequency-domain structural similarity, reducing false-negative effects in conventional hard-label contrastive learning. Finally, the pre-trained time-domain extractor is transferred to the target machine and fine-tuned with limited labeled samples. Experimental results show that MTFSC outperforms comparison methods under different labeled sample ratios and achieves an average accuracy of 97.5% across four cross-machine diagnostic tasks. Full article

Bioenergy crops are widely regarded as a promising approach to support renewable energy production, diversify farm income, and enhance land-use efficiency. Despite these potential benefits, adoption rates remain low, and empirical understanding of landowners’ decision-making processes is still emerging. This study examines landowners’ interest in and likelihood of adopting bioenergy crops, explicitly differentiating between early-stage interest and near-term adoption intentions. Survey data from 207 landowners are analyzed using a bivariate probit model to identify key factors influencing both outcomes. The results reveal a marked disparity between expressed interest and adoption likelihood, with a significantly greater proportion of landowners indicating interest than those willing to adopt in the near term. Economic orientation increases adoption interest by 9.5 percentage points, while identity orientation increases adoption likelihood by 6.6 percentage points. Determinants such as increased awareness, land size, experience, and participation in conservation programs exert varying influences across different decision stages. These findings suggest that stated interest and stated near-term adoption likelihood represent related but distinct dimensions of adoption readiness, shaped by different economic, identity-based, and institutional factors. Effective promotion of bioenergy crops requires more than general awareness campaigns. Policies should combine financial incentives, technical assistance, market development support, and outreach strategies that present bioenergy crops as compatible with landowners’ economic goals, stewardship values, recreational uses, and long-term attachment to their land. Full article

Manufacturing equipment logs are an important source of information for quality monitoring, but building reliable defect prediction models from such logs is still difficult in practice. Defective samples are rare, and many process variables are missing because measurements are recorded only under certain sensing or process conditions. These properties make defect prediction difficult and limit the usefulness of accuracy-based evaluation. This paper evaluates defect prediction using the Bosch Production Line Performance dataset, with a supplementary validation experiment on the semiconductor manufacturing process (SECOM) dataset. Two feature configurations are compared: a baseline representation using imputed numerical variables and a missingness-aware representation that adds feature-wise missing indicators and a sample-level missing ratio. Logistic Regression, Random Forest, and LightGBM are evaluated using validation-based threshold selection. To examine the effect of imputation choice, zero, median, and KNN imputation are also compared in the SECOM experiment. In the Bosch experiment, explicitly representing missingness improves PR-AUC for all tested model configurations. The supplementary SECOM experiment shows a more mixed pattern, suggesting that the usefulness of missingness-aware features depends on the dataset, imputation strategy, and model family. The latency analysis further shows a practical trade-off: Random Forest with missingness-aware features gives the highest PR-AUC on Bosch but has the highest inference latency, while LightGBM provides a more balanced choice when prediction performance and response time are considered together. Full article

Industrial systems increasingly rely on MQTT-based message streaming to enable automated, data-driven production processes at the network edge. While semantic models such as the SSN/SOSA ontology enable machine-interpretable descriptions of observations and actuations, an explicit model of message transport is rarely considered. Consequently, MQTT-based communication remains opaque, particularly regarding information processing, hindering the semantic analysis of application-specific topic structures and the behavior of transport protocols. To close this gap, this work introduces the revised MQTT4SSN ontology as a key contribution, extending existing semantic models with protocol-aware representations of MQTT entities, control packets, and transport-level interactions. MQTT4SSN enables end-to-end semantic traceability, from sensor observations and actuator controls to the underlying message transmission within distributed systems. Building on this contribution, the MQTT2RDF integration framework incorporates MQTT4SSN as its core to capture live MQTT traffic and represent both payload meaning and transport-level provenance within an RDF knowledge graph. This work presents a novel approach for representing edge computing and information processing over MQTT, addressing two key challenges. First, the framework supports semantic interpretation of topic hierarchies and provides configurable mappings between MQTT topics, payload structures, and observation or actuation semantics. This approach facilitates the setup of edge computing systems and enables context-aware subscription management and structured data formatting, thereby improving interoperability between heterogeneous deployments. Second, transport-level provenance analytics provide a semantic basis for query-based detection, classification support, and diagnostic analysis of malformed or incomplete MQTT communication. The approach provides explainable, traceable information processing through transport provenance, which is essential for safety-critical industrial environments. The contributions are validated through an industrial use case from a production environment, demonstrating its applicability for system monitoring, troubleshooting, and semantic analytics of MQTT-based infrastructures. Full article

Port operators must now reduce emissions without weakening the reliability of cargo-handling and logistics services. Two load groups are especially important in this setting: vessels connected to shore-side facilities during berthing and heavy-duty vehicles working inside the terminal area. Their energy-use patterns shape both dispatch stability and the carbon intensity of the port energy system. This paper therefore proposes an integrated port energy management model that jointly schedules wind power, photovoltaic generation, hydrogen production and storage, shore power, conventional purchases, berthed-vessel demand, and low-carbon heavy-duty transport demand. The model combines price-based demand response with a tiered carbon-trading penalty so that flexible electricity consumption and emission costs are reflected in the dispatch decision. Numerical simulations show that the joint use of demand response and the carbon-penalty mechanism lowers total economic dispatch cost by about $11.05\%$ and reduces carbon emissions by $24.52\%$. The results indicate that coordinated renewable-energy and logistics-aware scheduling can improve the economic and environmental performance of port operations. Full article

Accurate pig counting during herd transfers is fundamental to effective livestock management in large-scale swine production, yet existing methods struggle with bidirectional passages, boundary oscillations, and occlusion in real corridor environments. This study proposes an integrated system combining an improved YOLO-based detection model with a Hysteresis-based Multi-frame Temporal Confirmation Counting Strategy (HMTC). The YOLO11s baseline was enhanced using lightweight RepViT blocks, dynamic upsampling (DySample), and shape-aware bounding box regression (Shape-IoU). The resulting model achieves a mAP₅₀ of 0.982 with a compact architecture of 8.28M parameters, representing a 12.3% reduction relative to the baseline while improving detection accuracy. To address bidirectional counting challenges, the HMTC strategy utilizes hysteresis-based region classification, temporal confirmation, and trajectory verification to suppress boundary jitter and ensure directional correctness. Evaluated on nine videos from a single transfer corridor, the proposed system achieves an overall counting accuracy of 99.21% on this test set and runs in real time on an embedded edge device at over 30 FPS without loss of counting accuracy. Together, the improved detection model and HMTC counting strategy provide a cohesive approach to pig passage counting, validated here under a single transfer-corridor condition; these results offer a promising basis for automated animal inventory management, pending further validation across more diverse farm environments. Full article

Indoor air quality has a significant impact on occupant health, comfort, and productivity in residential and commercial indoor environments. This paper proposes an IoT-edge enabled deep–fuzzy hybrid framework for real-time IAQ prediction and adaptive control. The proposed system integrates IoT-based environmental sensing, Temporal Fusion Transformer-based multivariate forecasting, knowledge distillation, edge-deployed Bi-LSTM inference, and Mamdani fuzzy logic control within a unified IAQ management architecture. A composite Comfort Risk Index is introduced to combine environmental parameters and occupant discomfort feedback into a single adaptive control indicator. Experimental evaluation under varying indoor conditions demonstrated strong forecasting performance, with prediction accuracies reaching 96.3% for CO₂ and 95.7% for PM_2.5 prediction, while reducing inference latency from 575 ms to 295 ms. Comparative analysis against baseline threshold-based control strategies further indicated improved comfort stability, smoother actuator behavior, and reduced estimated actuator operating intensity during deployment. The proposed framework also demonstrated resilient operation under simulated sensor-failure conditions while maintaining low computational overhead suitable for resource-constrained IoT-edge environments. Overall, the results indicate that combining lightweight deep learning models with interpretable fuzzy control can provide an effective, scalable, and energy-aware solution for intelligent real-time IAQ optimization in smart indoor environments. Full article

Strawberry greenhouse cultivation is increasingly supported by sensing technologies, artificial intelligence (AI), and decision-support infrastructure, but their horticultural value depends on whether heterogeneous measurements can be translated into biologically meaningful crop states and practical management decisions. This review synthesizes strawberry phenotyping, multimodal sensing, AI-based crop-state interpretation, and supervised agentic coordination as a phenotyping-to-action framework for greenhouse strawberry cultivation. The reviewed studies show substantial progress in measuring and interpreting vegetative, reproductive, fruit-quality, stress-related, and environmental crop states through imaging, spectral, environmental, root-zone, and modeling approaches. However, much of the literature still emphasizes measurement accuracy, model performance, or infrastructure capability, whereas fewer studies validate whether AI-derived outputs improve crop response, management decisions, workflow, resource use, or production outcomes. The review therefore distinguishes sensing technologies for data acquisition and measurement from AI-based methods for interpretation and prediction, and examines how crop-state information can be connected to practical greenhouse decision making. It also compares established decision technologies, including expert systems, model predictive control, digital twins, and closed-loop coordination, with supervised agentic coordination as bounded decision-support concepts rather than as evidence of unrestricted autonomous control. Future work should emphasize phenotype-to-action validation, domain-aware benchmarking, and supervised deployment studies that connect model outputs with decision rules, crop outcomes, operational constraints, and grower oversight. By grounding sensing technologies and AI-based interpretation methods in crop-response validation, strawberry greenhouse systems can progress toward supervised, crop-state-driven decision support. Full article

Introduction: Biological invasions constitute one of the main threats to freshwater ecosystems, causing significant ecological changes through the introduction of exotic species that compete with or prey upon native species. In Portugal, the introduction and spread of exotic species in lotic and lentic ecosystems, such as pike (Esox lucius), European catfish (Silurus glanis), and largemouth bass (Micropterus salmoides), all top predators, may have a big impact on autochthonous species. In contrast, bleak (Alburnus alburnus), European perch (Perca fluviatilis), and common carp (Cyprinus carpio) compete aggressively for food resources. In the Sousa River basin, gudgeon (Gobio lozanoi) is considered an exotic species with potential ecological impact, with the minnow (Phoxinus phoxinus) stand having been recently identified in Portugal and, so far, recorded only in this river basin, and not yet being classified as an invasive species in Portugal. Public knowledge regarding invasive aquatic biodiversity remains a significant bottleneck for conservation. Because recreational angling is a prominent dispersal vector, initiatives that directly target this community are relevant. Objective: The aim is to carry out a bibliographic review on the exotic ichthyofauna species present in the Sousa River, with special focus on the invasion potential of the minnow (P. phoxinus). Methodology: The literature review was conducted based on the ScienceDirect, Springer Nature Link, and Fauna Norvegica databases, selecting publications between 2006 and 2025 concerning relevant studies on the potentially invasive characteristics of the minnow (P. phoxinus). The methodology is based on the analysis of studies regarding the impacts caused on riparian ecosystems. Results: The species P. phoxinus presents a generalist diet and high adaptive capacity, allowing it to colonise new habitats and compete aggressively with native species for trophic resources. Its presence is associated with negative impacts on brown trout populations (Salmo trutta), reducing growth and productivity, especially in mountain ecosystems. Increased species density also causes a significant decrease in benthic macroinvertebrate biodiversity. Studies conducted in the Douro basin indicate that the arrival of minnow in Portugal resulted from human action, probably associated with its use as live bait in recreational fishing. Conservation programmes use diverse tactics to bridge the awareness gap. Recent initiatives feature electrofishing demonstrations to visually differentiate species, theatrical performances, and even culinary show-cooking events using invasive predators like the European catfish to promote harvesting. Conclusions: The potential transition of P. phoxinus into an exotic and invasive species may be associated with the ecological pressure exerted on native communities, particularly through competition for trophic resources, highlighting the need to assess its dispersion in the Sousa basin and its impacts on fish fauna and benthic macroinvertebrates. It is important to do more sampling to understand its real distribution in the Sousa Basin. Additionally is important to explain to recreational anglers and the general population the impacts of fish transfer and the adverse effects of invasive species on freshwater Portuguese ecosystems. Full article

The rapid scaling of large language model (LLM) training has intensified demand for Graphics Processing Unit (GPU) clusters balancing throughput with energy efficiency. While NVIDIA’s H100 and B200 architectures are increasingly deployed in production datacenters, their comparative behavior under distributed training remains insufficiently characterized beyond vendor specifications, leaving datacenter operators without empirical guidance on metrics such as TFLOPs/kW and tokens-per-kilojoule. This work presents a system-level evaluation of single-node 8× H100 and 8× B200 configurations using Distributed Data Parallel (DDP) training across LLMs and vision–language models (VLMs) ranging from 7B to 32B parameters, spanning various real AI workload scenarios. We benchmark end-to-end throughput, utilization, power, energy, TFLOPs/kW, and tokens-per-kilojoule, complemented by architectural analysis explaining observed behavioral differences. Across LLM workloads, B200 achieves higher utilization (1–6%), faster training (up to 15%), and greater compute efficiency (up to 32% higher TFLOPs/GPU), attributable to higher memory bandwidth and large streaming multiprocessor (SM) count. However, B200 exhibits lower TFLOPs/kW and tokens-per-kilojoule, revealing a fundamental trade-off: throughput gains come at a measurable energy cost per useful token. VLM results further expose model-dependent asymmetries, with B200 consuming disproportionately more energy for lighter compute kernels due to elevated baseline power draw. These findings provide an empirical framework distinguishing compute efficiency from energy efficiency across next-generation GPU nodes, offering practical guidance for energy-aware AI datacenter design. Full article

Introduction: The global ornamental fish trade is a rapidly expanding sector and a major pathway for the introduction of non-native species, particularly in freshwater ecosystems in developed countries. The introduction of non-native species can result in a range of ecological impacts, including predation, competition, hybridization, and disease transmission, often leading to ecosystem degradation and biotic homogenization. Therefore, it represents a clear ecological risk, especially serious in freshwater systems with a high endemism rate, such as the Iberian Peninsula. The occurrence of ornamental non-native species in the Iberian Peninsula has been common, yet little has been done to describe the overall ornamental fish trade as a first step to evaluate invasion risk. Objective: This study characterizes the import dynamics of ornamental freshwater fish in Portugal between 2020 and 2024 and evaluates its potential role as a pathway for species introductions. Methodology: Data were obtained from the Institute for Nature Conservation and Forests database, including information on species composition, quantities, sizes, prices, and countries of origin. A total of 431 records were analyzed, resulting in 27,689 validated entries of imported freshwater fish, which were taxonomically verified and filtered to retain only freshwater species. Results: A total of 666 species from 88 families were identified, with an average of 380 species imported annually, reflecting high taxonomic diversity. Import volumes increased from approximately 1.25 million individuals in 2020 to 1.75 million in 2024, while total import value nearly doubled from €300,000 to €600,000. Imports were predominantly from five Southeast Asian countries, particularly Indonesia and Vietnam, and largely supported by aquaculture production (88%). A stable core of highly traded species, including Carassius auratus, Poecilia reticulata, and Paracheirodon innesi, suggests a sustained and very high propagule pressure, while some species variability was observed on yearly basis, suggesting the importance of monitoring programs on actual imports. Conclusions: Overall, the ornamental fish trade represents a significant and growing pathway for biological invasions in Portugal. The combination of increasing trade volume, high species diversity, and persistent dominance of key taxa highlights the need for improved monitoring, regulatory frameworks, and public awareness to mitigate ecological risks. Full article

Consumption trends have shifted towards added-value, natural, less-processed, and more nutritious foods. Key factors shaping these trends include animal welfare, sustainability, globalization, cultural influences, socio-demographics, food safety, health, and nutrition. This structured and narrative review, following a systematic approach, analyzes future trends in food consumption, considers preclinical and clinical studies, and examines related industrial challenges. A comprehensive search across Scopus, Web of Science, and Google Scholar was conducted, including original articles and reviews on food consumption trends or industrial processes, using Boolean operators. Potential gaps and biases of the analyzed articles were also included. Of 8742 articles, 58 studies were included. It was found that animal welfare has led consumers to adopt plant-based alternatives, protein, and more sustainable food consumption. Rising health awareness has led to the development of personalized nutrition, functional, and nanoparticle-encapsulated nutrient-based foods. Physiologically, trends indicate improvements in body weight, glycemic control, and lipid profiles, whereas emerging formulations show promise in enhancing cognitive function and nutrient bioavailability. Industrial challenges include refining and scaling up new technologies, encouraging sustainable production practices, ensuring food safety, fulfilling consumer demands, and developing safe, nutritious, and functional foods. Compliance with global health regulations should be prioritized. Continued multidisciplinary research is essential to understand the impact of emerging food trends on consumer health. Full article

Aqueous precipitation and crystallization are central to impurity removal, product formation, and resource recovery in mineral and chemical processing, but robust inline monitoring remains challenging because supersaturation is not measured directly and conductivity signals are affected by temperature, composition drift, bubbles, solids, polarization, and fouling. Electrical conductivity (EC) is attractive as a low-cost, rugged process analytical tool, yet its usefulness depends on mechanistic interpretation: EC reflects charge-carrier concentration and mobility rather than supersaturation itself. This review organizes the literature into a layered framework covering (i) measurement integrity and deployment, (ii) bulk-signal extraction in multiphase media, (iii) estimation of latent variables such as dissolved concentration or supersaturation proxies, and (iv) control readiness based on conductivity-derived targets. Frequency-aware conductivity extraction, event-anchored verification, and observer-based estimation are treated as optional, complementary modules. A Ca-carbonate/CaCO₃ system is used as an illustrative case because its coupling among conductivity, pH/speciation, supersaturation, and precipitation is especially transparent, although the framework is intended for broader processing systems, including complex liquors and slurries. Opportunities are also highlighted for nanomaterials to improve both precipitation control and EC information content. Full article

Consolidated bioprocessing (CBP) is difficult to monitor because enzyme production, lignocellulose degradation, sugar release, and fermentation occur simultaneously under sparse measurement, feedstock variability, and plant–model mismatch conditions. This study proposes a computational sensor-set design framework for digital-twin-assisted CBP monitoring. A five-state virtual plant, consisting of active biomass, cellulolytic enzyme activity, residual insoluble substrate, soluble sugar, and ethanol, was used to evaluate all 16 ethanol-mandatory measurement packages formed from ethanol, sugar, biomass, enzyme, and residual-substrate proxy channels. Candidate sensor sets were assessed using finite-difference output sensitivities, Fisher-information-based state-observability and parameter-identifiability analyses, eigenvalue and parameter-correlation diagnostics, and paired Monte Carlo unscented Kalman filter soft-sensing reconstruction. Within the tested five-state virtual-plant benchmark and with the specified excitation schedule, noise assumptions, burden indices, and scoring objective, ethanol-only sensing provided the weakest support for state-aware CBP digital-twin reconstruction. At a $6\mathrm{h}$ sampling interval, the state-observability log-pseudodeterminant increased from 4.18 with ethanol-only sensing to 8.56 after adding soluble sugar and to 16.42 with full-proxy monitoring. The ethanol–sugar–biomass–substrate package also gave strong reduced state-observability performance, with log-pseudodeterminants of 15.12, 13.76, and 12.51 at 6, 12, and $24\mathrm{h}$, respectively. Biomass and enzyme proxies contributed strongly to parameter learning, and the ethanol–sugar–biomass–enzyme package gave the strongest active parameter-identifiability performance, with log-pseudodeterminants of 10.82, 9.06, and 6.67 at 6, 12, and $24\mathrm{h}$, respectively. In the paired soft-sensing analysis, full-proxy monitoring reduced the mean latent-state RMSE from 1.1899 to 0.3756, followed by ethanol–biomass–enzyme–substrate with 0.3843 and ethanol–sugar–biomass–substrate with 0.4121. The primary aggregate ranking identified ethanol–sugar–biomass–substrate as the best overall package, with a sensor-value score of 0.8432 and a burden index of 7.0, followed by full-proxy monitoring with a score of 0.8173 and a burden index of 10.0. Robustness tests showed that ethanol–sugar–biomass–substrate remained top-ranked under uniform noise scaling, full UKF missingness, delay and bias stress test conditions, most scoring-weight scenarios, and all tested sensor-specific burden workflows. Full-proxy monitoring remained a close competitor under independent sensor-specific noise variation conditions and became top-ranked for some alternative operating trajectories. The proposed framework provides a simulation-based method for prioritizing informative measurement packages before implementing CBP digital twins in laboratory and pilot-plant settings. Full article