Search Results (9,110)

Reducing building energy consumption is central to EU climate-neutrality targets and to sustainable development goals: buildings account for around 40% of EU final energy consumption, placing Building Energy Management Systems (BEMS) at the intersection of the European Green Deal and the EU Artificial Intelligence Act. A scoping review following PRISMA-ScR guidelines charted 61 Machine Learning (ML) for BEMS papers (2020–2026) across three sub-domains (load forecasting and energy monitoring, HVAC control, and demand response), using a nine-point Technology Readiness Level (TRL) rubric and three Trustworthy AI (TAI) dimensions (Privacy & Data Governance, Robustness, and Transparency). The review finds that 90.2% of papers remain at the development stage (TRL 4–6), with no multi-site production deployment documented. TAI coverage is heterogeneous at publication level: transparency is addressed in only 3 of 61 papers (4.9%), and privacy provisions (the best-covered ALTAI dimension) are concentrated in demand-response papers (9 of 17, 52.9%), largely via Federated Learning (6 of 9 privacy-tagged papers). A three-level EU AI Act risk classification identifies 23 borderline-candidacy papers (37.7%), predominantly Reinforcement Learning-based HVAC control systems, whose high-risk proximity cannot be resolved at abstract level; explicit compliance engagement is absent from all 61 mapped sources, including the 22 papers published after the Act entered into force in August 2024. The findings document adouble readiness gap: a TRL ceiling co-located with limited documented engagement with TAI obligations and EU AI Act compliance at publication level. Closing this gap is necessary before AI-driven building energy management can be deployed at scale under EU governance requirements. Full article

Probiotic lactic acid bacteria are widely recognized for their health-promoting effects. However, the use of live microorganisms may pose safety concerns and stability limitations. Consequently, postbiotics, defined as inactivated microbial cells and/or their components, have emerged as a promising alternative. This study integrates genome-guided evaluation of probiotic potential, experimental validation of in silico predictions and process optimization for the production of inactivated Lactiplantibacillus plantarum DM1 and KK1 cells as postbiotics. Genome mining identified genes and gene clusters associated with metabolic versatility, antimicrobial activity, gastrointestinal stress tolerance, adhesion and prebiotic substrate utilization. Building on these findings, to generate postbiotics, the efficiency of thermal, enzymatic, mechanical and radiation-based inactivation methods was evaluated in bacterial suspensions prepared in three dairy by-product matrices: milk permeate, sweet whey and sour whey. Complete inactivation of both strain cells was achieved by thermal treatment (3 min pasteurization), γ-irradiation (3 kGy), and combined lysozyme–pasteurization treatment, whereas other treatments showed partial and matrix-dependent effects. Matrix composition significantly influenced treatment efficacy, suggesting a protective role of food components used. These findings highlight the importance of combining genome mining for potential probiotic strain characterization with robust, matrix-adapted inactivation strategies for the development of stable postbiotic formulations. Full article

Electronic textiles (e-textiles) have gained significant importance due to their potential to enable wearable electronic systems. Conductive pathways in textiles can be fabricated using various approaches; among these, printing technologies stand out for their cost-effectiveness and suitability for rapid design customization. In this study, conductive patterns were produced on 100% cotton woven fabrics using rotary screen printing with different conductive paste formulations and printing layer configurations. The electrical resistance, fabric thickness, microscopic surface morphology, tensile strength, elongation, and tearing strength of the printed e-textiles were evaluated. Results indicated that resistance decreased with increasing printed track width and number of printed layers, with samples A4 and A5 exhibiting the highest conductivity. Thickness measurements and microscopic surface images showed that repeated printing increased layer build-up and surface coverage, particularly for A3 and A4. Mechanical performance tests revealed reductions in tensile strength, elongation, and tear strength after printing, attributed to restricted fiber mobility caused by the conductive paste and curing process. Despite these reductions, the mechanical property losses remained within acceptable limits for wearable applications. To determine the most suitable conductive textile for use in electronic textile product design, the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method, a multi-criteria decision-making (MCDM) approach, was applied using mechanical performance criteria. Electrical resistance was evaluated separately as a functional performance indicator and interpreted together with the MOORA-based mechanical ranking. Considering both mechanical and electrical performance, sample A5 was identified as the optimal alternative. Overall, this study demonstrates that printed conductive textiles can be systematically evaluated and ranked using a multi-criteria decision-making approach for material selection in wearable electronics. Full article

Heat stress (HS) is at the top of the challenges facing modern dairy production, with annual losses according to global projections, under high-emission scenarios, reaching US$14.7–40.0 billion by the end of the century. This review emphasizes three interconnected topics that account for most of the proportion of the productive and reproductive losses during HS. First, the physiological consequences of HS are reviewed, with emphasis on the pair-fed thermal neutral (PFTN) paradigm, which established that reduced dry matter intake (DMI) accounts for only 35–50% of the observed milk yield decline, with the remainder arising from tissue-level effects of hyperthermia on mammary function, metabolism, and reproductive performance. Second, HS-induced microbiome disruption is examined as an active pathophysiological amplifier, whereby rumen dysbiosis compromises intestinal barrier integrity and drives systemic endotoxaemia, chronically amplifying the immune suppression already imposed by the thermal insult. Third, we focus on the integration of multi-omics platforms as a management approach, since single-omics analyses capture only a fraction of the biological complexity underlying the HS response. As the available datasets expand in coverage and scale, their integration through AI-driven analytical frameworks has the potential to substantially advance beyond the current fragmented picture, progressively building toward a systems-level model of thermal stress. Evidence-based mitigation strategies spanning environmental cooling, targeted nutritional supplementation, and genomic selection are critically evaluated within this framework, with emphasis on equity of access to evidence-based solutions across global dairy production systems. Full article

Garlic, a significant global specialty economic crop, is currently facing severe challenges from labor shortages and escalating production costs. Achieving full-process mechanized production is the core approach to ensuring sustainable industrial development and enhancing international competitiveness. This paper systematically reviews the research progress and application status of mechanized equipment throughout the entire crop cycle of garlic production, including seeding, field management, harvesting, and post-harvest processing and sorting. The study reveals that garlic equipment is undergoing a profound transformation from traditional mechanization to “opto-electro-mechanical integration” and intelligence. In the seeding phase, breakthroughs have been made in pneumatic precision seed-metering and machine vision-based clove bud orientation technologies, significantly improving the quality of upright planting. In field management, precise variable-rate application and targeted weeding have been preliminary realized through plant protection Unmanned Aerial Vehicle (UAV) downwash airflow field simulation (CFD) and deep learning-based image segmentation. In the harvesting phase, relying on 3D Discrete Element Method (3D-DEM) soil-cutting simulation and adaptive profile root-trimming technology, the industry is accelerating the transition from inefficient segmented harvesting to low-damage combined harvesting. In the post-harvest phase, hyperspectral imaging (HSI) and multi-label convolutional neural networks (CNNs) have been utilized to achieve high-speed non-destructive detection of internal and external quality. However, industry still faces critical bottlenecks such as the insufficient integration of machinery and agronomy, poor robustness of intelligent perception algorithms in complex environments, and high damage rates of core soil-engaging components. Future research should focus on lightweight algorithm deployment, digital twin-driven virtual prototyping, and the construction of regional standardized machinery–agronomy systems, aiming to build an efficient and universal intelligent production closed-loop for garlic. Full article

This study aims to investigate the effects of cysteamine zinc supplementation on milk production, composition, amino acid profile, and metabolites in mares. Building on prior experimental findings, a dose of 7 mg/kg body weight of CS-Zn was selected for the experimental group, which was compared with a control group. Milk samples were collected at various time points, and milk yield was recorded each time. Routine analysis of milk components, as well as the determination of milk metabolites and amino acids, were performed. The results indicated that, compared to the control group, the experimental group exhibited increases in milk yield and the content of milk fat, lactose, and non-fat solids (p < 0.05), with an extremely significant increase in milk protein (p < 0.01). Conversely, the levels of L-glutamine and L-proline in milk were significantly reduced (p < 0.05). Metabolomic analysis revealed that differentially expressed metabolites were enriched in pathways such as ABC transporters, D-aminoadipate metabolism, aminoacyl-tRNA biosynthesis, and protein digestion and absorption. Notably, milk metabolites including cAMp, biotin, and taurine showed a tendency to be upregulated, while oxoglutaric acid, methionine, and diacetyloxyxanthone were downregulated. Based on evidence from the literature other species, it is speculated that CS-Zn supplementation may be associated with alterations in endocrine and amino acid metabolism pathways, potentially influencing lactation performance in mares. However, because no hormones were directly measured in this study, such a mechanism remains speculative and requires direct experimental validation. Full article

The European Union (EU) and national governments have set clear targets to reduce agricultural emissions, including ammonia from manure spreading practice, with regulations such as the Ambient Air Quality (AQ) and Clean Air Directives, the Common Agricultural Policy (CAP), and the Green Deal, with implication for ecosystem services and landscape planning, reflecting broader environmental sustainability objectives including those addressed by the Sustainable Development Goals (SDGs). Informative Inventory Reports (IIRs) are critical tools within the EMEP/EEA framework for monitoring long-range transboundary air pollution. They utilize three distinct methodological tiers (Tiers 1, 2, and 3) to estimate emission data across Europe. Despite the availability of Earth Observation (EO) data and products from the Copernicus Programme current estimation methods still rarely integrate EO information to produce spatially explicit estimates. This paper reviews current methodologies for estimating ammonia in IIRs and in scientific literature, including advanced methods not yet implemented in official inventories but potentially capable of supporting more spatially explicit and process-oriented estimation. A Medium Effort Methodology (MEM) is identified among those reviewed as a representative methodological pathway for integrating EO information with Tier 3 approaches. Building on this, the paper explores the association between specific EO data and Copernicus products, and input variables required by MEM, identifying opportunities and barriers for environmental monitoring with potential relevance to sustainable agriculture. Full article

Industry 4.0 is speeding up the move to connected, data-driven, and automated production, where the Internet of Things (IoT) enables sensing, communication, and real-time support for decisions. At the same time, rapid growth in industrial IoT studies has led to scattered technologies, architectures, and results. This paper fills this gap through a systematic literature review on IoT for Industry 4.0. It also helps readers compare methods and choose suitable building blocks for real deployments today. We focus on key technologies, integration architectures, application areas, challenges, trends, and reported benefits. Using PRISMA 2020, we searched five major databases (Scopus, MDPI, IEEE Xplore, ScienceDirect, and Web of Science) for 2020–2025 and found 584 records. After removing duplicates and screening, we kept 96 peer-reviewed studies for detailed analysis. Results show that most studies use a layered stack that combines sensing/actuation, industrial networking, data collection pipelines, and analytics across edge, fog, and cloud resources. MQTT, OPC UA, CoAP, LPWAN, and 5G connectivity are often used for communication, while RAMI 4.0, IIRA, and similar layered models guide system design. Many architectures follow an edge–cloud pattern, with growing focus on digital twin/CPS links and security-by-design. Applications are mainly smart manufacturing, predictive maintenance, and logistics, with added work in energy management, Construction 4.0, and agri-food monitoring. The key barriers remain interoperability, data quality and evaluation gaps, cybersecurity risks, legacy integration, and deployment limits. The review points to future work on edge AI/TinyML, deterministic connectivity, scalable digital twins, trusted data sharing, and sustainable industrial IoT. Full article

Female fashion models face intense bodily surveillance within a competitive and largely unregulated industry. While many young recruits endure the industry’s initial demands, less is known about how these formative experiences are later interpreted. Grounded in feminist and phenomenological perspectives, this article presents an inductive analysis of 18 semi-structured interviews with female models aged 25+, exploring entry into modelling, socialization into industry norms, and longer-term effects on body image, eating attitudes, and embodied practices. Five interconnected themes were identified: growing up “superfast”; learning to eat less; being treated as a product; boundary violations as a darker side of modelling; and building resilience within a callous industry. Early immersion in modelling culture shaped psychological development, with self-worth becoming closely tied to conformity with a narrowly defined thin ideal. Aesthetic labour extended beyond paid work to continuous bodily regulation and self-surveillance, producing a persistent tension between the lived, sensing body (Leib) and the body as an object for evaluation (Körper). Disordered eating emerged as a normalised employment strategy. While maturity could soften this process, participants described ongoing attempts to reconcile bodily needs with external judgements. The fashion industry thus emerges as a concentrated site of objectification, with enduring consequences for embodied wellbeing. Full article

The current need for thermal insulation building materials has led to the development of new materials and technologies, which are necessary to reduce carbon emissions. Lignocellulose materials are promising options for thermal insulation materials in construction, offering appropriate mechanical and environmental properties. While recent reviews focus primarily on material properties, a critical gap remains in the technical analysis of processing parameters and the comparative evaluation of alternative fabrication methods. This study provides a semi-systematic overview of manufacturing processes for lignocellulose-based thermal insulation, highlighting key production methods at the development stage: the most common hot pressing and compression molding, as well as less used hot drying, air-laid, wet-laid, needle-punching, and biological fabrication (mycelium-based). The results show that there is no single ideal method due to a fundamental trade-off: hot pressing provides superior mechanical strength, mycelium and needle-punching provide optimal thermal insulation, while room-temperature drying and blow-molding methods are the most environmentally friendly due to their minimal energy consumption. The key factors determining material performance are the material density, size, and type of raw material, which are strictly regulated by processing parameters. Full article

Knowledge of river flood risk in semiarid rural areas is often based on outdated, low-resolution geoinformation. Consequently, identification of exposed settlements, assets and risk-reduction measures remains challenging. This dataset provides up-to-date, fine-grained information for a rural area spanning 931 km² that is exposed to flooding from the Niger River and the Karma Wadi. The dataset includes information on (i) areas exposed to the two flood types that characterise the river’s hydrological regime and flash floods from the wadi, (ii) flood-prone crops, buildings and (iii) measures for risk treatment. Discharge data, a 4 m horizontal-resolution digital elevation model, and two-dimensional hydraulic modelling with BASEMENT were used to identify flood-prone areas. Visual interpretation of high-resolution satellite imagery in Google Earth, together with field inspections, enabled the identification of exposed assets. The Information System on Rural Markets of Niger and house compensation values recognised during resettlement-related works enabled asset valuation. Risk was expressed in monetary terms as the product of flood probability and expected damage. Risk-reduction measures were identified with stakeholders through a SWOT analysis and prioritised using eight criteria. The dataset can support emergency plans, flood early warning systems, rescue and recovery operations and flood risk management. Full article

Construction materials like steel and concrete have been used for thousands of years; however, their industrial-scale production began relatively recently in the 19th century. These materials are still being improved as the drive to build taller buildings, longer bridges, larger dams, and similar engineering marvels keeps pushing boundaries and requirements to previously unimaginable values. Yet, testing and characterization of construction materials that make all that progress possible are overshadowed in scientific literature by more trendy materials such as graphene, composites, nanomaterials, smart materials, and biomaterials. The objective of this review was to identify, collect, and systematically analyze recent papers in which the researchers performed experimental testing on construction materials to document how state-of-the-art experimental practice extends beyond what standardized protocols prescribe. This paper covers Uniaxial Tensile Testing (UT), Compact Tension C(T), Uniaxial Compression (UC), and Single Edge Notched Bending SEN(B), as they are the most commonly used and best-suited techniques for construction material analysis. State-of-the-art papers featuring these techniques were systematically gathered using AI-assisted literature discovery tools, and their contributions beyond ISO and ASTM standards were identified and summarized. Using this review, material scientists and engineers can quickly discover the most influential and relevant papers with the actual experimental data and can apply the testing procedures described in these papers in their laboratories so they can compare their results with the previously published measurements and make an engineering decision based on appropriate comparisons. Full article

This study aims to explore the preparation process and properties of unburned lightweight aggregate using red mud synergistically with fly ash, granulated blast-furnace slag, and other multi-source solid wastes. Curing regimes and alkali-activated systems were controlled. Their effects on physical properties and environmental safety of lightweight aggregate were systematically evaluated. Results show that curing temperature and alkali activator exert significant synergistic effects on physical properties of lightweight aggregates. Steam curing performs better than standard curing. Performance improves with increasing steam temperature. Sodium silicate solution with a modulus of 1.0 is determined as the optimal activator. Under 90 °C steam curing, Sample D2 achieves the best overall performance. Its cylinder compressive strength reaches 6.92 MPa. 1 h water absorption is 14.8%. Softening coefficient is 0.93. Porosity is as low as 31.07%. Microscopic analysis reveals that higher curing temperature significantly accelerates the hydration reaction of the RMLWA system. It promotes the formation of abundant cementitious products such as C-S-H gel. These products fully fill internal pores and microcracks of the aggregate. A dense three-dimensional network skeleton structure is finally formed. For environmental safety, heavy metal leaching concentrations of steam-cured samples are generally lower than those of standard-cured samples. This study realizes high-value resource utilization of industrial solid wastes. It also provides a new technical route for the development of green building lightweight aggregate. Full article

Natural gas hydrate is an important emerging strategic resource, but low permeability makes the horizontal well length a key factor limiting productivity. A prediction model for friction torque of deepwater riserless drilling strings was established, and the segmented friction coefficient of hydrate horizontal wells was inverted and applied to the Shenhu hydrate reservoir. The results show that the main limiting factor for the extreme extension length of natural gas hydrate horizontal wells is the mechanical extreme extension length. The main affecting factor of the mechanical extreme extension length is the running limit of the screen pipe. The friction coefficient is the most significant factor affecting the mechanical extreme extension of horizontal wells, with the friction coefficient inside the casing in the high-build-rate section being the largest. The research identifies the primary factors governing the limit extension during horizontal well construction. The findings provide theoretical guidance for reservoir selection, well site determination, and wellbore configuration optimization in hydrate development. Ultimately, this contributes to maximizing single-well productivity and advancing the commercialization of hydrate resources. Full article

Background/Objectives: Pediatric group 3 (G3) medulloblastomas (MB) are therapy resistant and have a significantly worse prognosis than the other MB subtypes. Aggressive radiation/chemotherapy improves survival, but potential long-term comorbidities include neurocognitive deficits. In previous work, we demonstrated that low-dose X-ray radiation (LDXR) acts as an immunological adjuvant. Recent studies have demonstrated that galectin-3 (Gal-3) expression in MB tumors accelerates M2 macrophage infiltration and restricts T cell receptor (TCR)-mediated signaling. Immunotherapy with an agonistic anti-4-1BB monoclonal antibody (mAb) activates CD8+ T cells, promoting their survival and acquisition of potent cytolytic properties. Building on these findings, we hypothesized that immune priming via sublethal LDXR, combined with a Gal-3 inhibitor and an anti-4-1BB mAb, would boost anti-tumor effects, resulting in survival benefits. Methods: We tested this hypothesis in vitro in co-cultures of human MB cells and in vivo, in an immunocompetent G3MB mouse model (MP1). Treatment effects were assessed using Western blot, flow cytometry, hematoxylin and eosin (H&E) staining, immunofluorescence imaging, and analysis of cytokine and chemokine expression. Results: Our data demonstrated higher Gal-3 expression in MB patient-derived tumor tissue than in non-tumor tissue. LDXR modulated major histocompatibility complex molecules, and, combined with a Gal-3 inhibitor and an anti-4-1BB mAb, altered T-cell/tumor-cell interactions, enhanced T-cell-mediated MB cell death, and shifted cytokine production to drive microglial polarization toward the M1 subtype. Furthermore, H&E-stained tumor sections showed a ~70% reduction in tumor size compared with untreated controls. Conclusions: These preclinical findings suggest that combining immune priming with sublethal LDXR, Gal-3 inhibition, and 4-1BB activation may be an effective treatment strategy for G3MB. Full article