Search Results (789)

The connected operating room captures, transmits, and displays data, but it does not learn. This perspective presents Chirurgie 4.0 (C40), a French-led initiative born within the Commission Innovation of the Académie nationale de chirurgie, which proposes not only a concept but a method for the safe adoption of artificial intelligence (AI) in surgery. At its core is the C40 Maturity Model of the Operating Room, a human-governed “surgical world model” describing the transition from the Smart OR to the Learning OR across six levels, from the conventional operating room to a sovereign, federated network of surgical world models. We situate surgical autonomy on an explicit six-level scale, show that autonomous devices are already an accepted clinical reality in fields such as interventional cardiology, ophthalmology, neuro- and orthopedic surgery, and argue that governance must be native rather than retrofitted, through a Cognitive Governance Layer resting on human oversight, explainability, auditability and agent governance. We describe the economic and sovereignty stakes specific to intelligent surgical technologies and set out the design of the 2026 C40 field survey, whose results will feed a Livre Blanc for public decision-makers. C40 offers five steps that can genuinely be climbed, and a method for climbing them safely, with the surgeon retaining final clinical authority at every step. Full article

Renewable-powered agricultural water systems are often assessed as solar-pumping devices, but their sustainability depends on a service chain linking crop-water demand, hydraulic duty point, power electronics, storage, water quality, governance, operation and end-of-life management. This structured integrative review synthesizes peer-reviewed and practice-oriented evidence on photovoltaic pumping, hybrid renewable irrigation, grid-interactive pumps, micro-hydro assistance and renewable-powered brackish-water reverse osmosis (PV-RO). Evidence was screened across four source families and coded by service function, energy architecture, hydraulic duty and dominant sustainability pathway; recurring combinations were consolidated using explicit separation and merge rules. It develops an archetype-based screening framework for ex ante appraisal of irrigation, desalination and circularity risks. Seven technology-service archetypes are identified: direct PV pumping, PV-to-tank pumping, PV with electrical buffering, grid-interactive PV pumping, PV–wind hybrid irrigation, micro-hydro-assisted irrigation and PV-RO water making. The framework links each archetype to its operating envelope, evidence maturity, enabling subsystems, sustainability pathways, minimum indicators and ordinal triggers for deeper due diligence. Hydraulic storage is usually the lowest-regret reliability buffer for open-field irrigation, whereas batteries are justified mainly when pressure stability, fertigation timing or night-time operation has high agronomic value. PV-RO is a distinct water-making archetype and is environmentally defensible only where feed-water characterization, energy recovery, pretreatment, product-water agronomy, membrane management and permitted concentrate disposal are embedded in design. Two synthetic applications demonstrate archetype selection and due-diligence escalation. Responsible deployment requires service-oriented screening that integrates hydraulic design, groundwater governance, procurement quality assurance, circularity obligations and social inclusion before field implementation. Full article

This paper presents the design and development of a laboratory-scale 3D printing platform intended for research on sustainable construction materials. The growing interest in low-carbon and locally available materials, including clay, geopolymers, recycled aggregates, and bio-based composites, has increased the need for flexible experimental printing systems. However, most existing construction 3D printers are designed for industrial applications and remain costly, bulky, or limited to specific material categories. The proposed platform was developed using a Model-Based Systems Engineering approach in order to structure the design process and establish links between user needs, system requirements, functions, and physical components. The platform is based on modular Cartesian architecture and includes interchangeable extrusion systems. A syringe extruder is used for relatively fluid materials such as clay slurries, ceramic pastes, gypsum-based mixtures, and fluid geopolymers, while a screw extruder is designed for more viscous materials such as mortars, cement-based mixtures, and dense geopolymer pastes. The system also integrates motion-control components, material feeding devices, monitoring elements, and safety functions to ensure stable and repeatable printing conditions. The platform is intended to support the evaluation of printability, material flow, layer deposition, dimensional stability, and interlayer bonding. By combining a flexible hardware configuration with an MBSE-based design methodology, the proposed system provides a practical research tool for the development and validation of sustainable construction materials. It also creates opportunities for future work on multi-material printing, automated process control, and digital manufacturing applications. Full article

Electroantennography (EAG) is a valuable approach for monitoring insect antennal responses to insecticidal and test compounds and may provide a preliminary screening method for identifying compounds with potential activity against medically important insect vectors. The objective of this study was to evaluate the EAG responses of Rhodnius prolixus to different xenobiotics and to compare these electrophysiological responses with host approach and protection against biting. Antennae from adult triatomines subjected to prolonged fasting (≥30 days) were exposed to the tested compounds. In parallel, live-bait behavioral bioassays were conducted using Gallus gallus and a newly designed laboratory device. Exposure to IR3535 and carvone produced a greater than 60% reduction in ammonia-induced EAG amplitude, consistent with the protection times observed in the behavioral assays (135.6 ± 43.29 min and 108.0 ± 26.33 min, respectively). These findings indicate that IR3535 and carvone alter antennal responses to ammonia and provide prolonged protection against R. prolixus bites. Because neither compound significantly delayed host approach, the observed behavioral effect was more consistent with biting deterrence than with demonstrated spatial repellency. EAG may therefore serve as a preliminary complementary screening approach, although its ability to identify compounds that interfere with hematophagous feeding requires further validation using larger and chemically diverse compound libraries. Full article

Uneven straw conveying and unstable throwing can reduce the operational performance of rice combine harvesters. To address these problems, an integrated straw conveying and throwing device combining guided conveying with pneumatic throwing was developed. The brachistochrone principle was introduced into the curved-surface design of the diversion plate as a geometry-guided approach to provide a continuous transition between the straw-falling region and the conveying inlet. Based on the motion characteristics of straw in the diversion and throwing regions, a coordinated feeding–acceleration–throwing process was established. The effects of diversion plate angle, blade rotational speed, and blade installation angle on throwing distance and distribution stability were investigated. A computational fluid dynamics model based on the mixture multiphase approach was used to characterize the macroscopic gas–solid flow field and compare airflow organization under different blade installation angles. A Box–Behnken response surface design was subsequently employed to establish regression models for throwing distance and the coefficient of variation in straw distribution, followed by multi-response numerical optimization. The optimal parameter combination consisted of a blade rotational speed of 2500 r/min, a diversion plate angle of 1.25 rad, and a backward blade installation angle of 15°. Under these conditions, the predicted throwing distance and coefficient of variation were 7.89 m and 14.6%, respectively. Validation tests produced throwing distances of 6.94–8.21 m and coefficients of variation of approximately 13%, showing good agreement with the predicted performance. The developed device and optimization results provide a basis for improving the conveying continuity and throwing uniformity of straw-handling systems in combine harvesters. Full article

This paper presents a hardware–software co-design framework for real-time super-resolution (SR) of low-quality video on resource-constrained edge platforms. At its core is a compact residual network obtained by once-for-all (OFA) neural architecture search over the Residual Channel Attention Network (RCAN) design space, trained conventionally and then optimized with quantization-aware training (QAT) for deployment on an integer-only deep-learning processing unit (DPU). Loop tiling and data-flow scheduling are applied within a custom high-level synthesis (HLS) pre-processing pipeline that feeds the DPU, and a per-directive ablation isolates the contribution of each optimization to post-route resource usage and timing. Deployed on a Kria KV260 board with a $128\times 128$ network input, the INT8 network sustains $96.37$ FPS at the ×2 scale at a measured board power of $5.38$ W, corresponding to $6.32$ Mpixel/s of reconstructed output at $1.17$ Mpixel/J, within 63.2% of the device LUT budget and with timing closed at 275 MHz. Relative to the FP32 model, INT8 quantization costs $0.274$ dB of peak signal-to-noise ratio (PSNR) on Set5, $0.172$ dB on Set14, $0.116$ dB on B100, and $0.146$ dB on Urban100, a loss dominated (81–90%) by activation rather than weight quantization. On a held-out UAV subset drawn from VisDrone2019, which is the operating domain the system targets, the network reconstructs at $25.94$ dB and $0.748$ SSIM. These results show that a twenty-three-layer residual SR network can be deployed within a 5.38 W envelope on a low-cost integer-only edge FPGA, making the approach suitable for autonomous systems, robotics, and airborne surveillance. Full article

Protonic ceramic electrochemical cells (PCECs), including protonic ceramic fuel cells, electrolysis cells and reversible cells, have attracted increasing attention as efficient solid-state devices for electricity generation, hydrogen production and chemical conversion at intermediate temperatures. Recent advances in electrolyte thinning, electrode nanostructuring and interface engineering have enabled remarkable device performance, including reversible operation at 500–650 °C, operation below 450 °C, and expanded fuel flexibility toward hydrogen, ammonia and methane-containing feeds. However, the working-state mechanisms governing their performance and durability remain insufficiently understood. In particular, proton incorporation, surface hydration, proton exchange, proton-coupled oxygen reduction/evolution, electrode reconstruction and buried interface degradation are highly dynamic processes that cannot be fully resolved by ex situ or post-mortem characterization. Operando characterization provides a powerful route to bridge this knowledge gap by directly correlating structural, chemical and electrochemical evolution under realistic temperature, steam, gas atmosphere and electrochemical bias. In this review, we summarize recent progress in operando and in situ characterization of PCECs, with emphasis on vibrational spectroscopy, X-ray-based techniques, neutron methods, electron microscopy and electrochemical diagnostics. We discuss how operando DRIFTS and H/D isotope exchange reveal voltage-dependent proton exchange kinetics, how operando Raman captures oxygen-electrode surface reconstruction, how X-ray and neutron methods probe redox chemistry and proton dynamics, and how EIS/DRT analysis links structural changes to reaction resistance. We further highlight current challenges, including limited access to buried interfaces, difficulty in quantifying protonic defects, insufficient multimodal correlation and the lack of standardized operando cell configurations. Finally, we propose future directions based on isotope-resolved spectroscopy, multimodal operando platforms, AI-assisted spectral/impedance analysis and theory-guided interpretation. This review aims to establish a working-state mechanistic framework for rationally designing durable, high-performance PCECs. Full article

The Social Internet of Things (SIoT) connects smart devices into social networks in which they share, forward, and consume visual content on behalf of their owners. As generative models become more capable, the images that circulate among these connected devices are increasingly easy to fake or manipulate, which threatens the trust relationships that hold an SIoT network together. Most existing forgery detectors return only a real or fake label, which gives a connected device no basis on which to decide whether to trust a neighbor or relay a piece of content. We propose an explainable image authentication framework for SIoT that classifies an image as real or fake and also provides a human-readable explanation and localized visual evidence for its decision. Our architecture, the Domain-Tag Guided Explainable Forgery Detection Module (DTE-FDM), uses a domain tag generator to predict the manipulation type (Photoshop, DeepFake, or AI-generated inpainting) and feeds it as a prompt to a multimodal large language model, which improves cross-domain generalization and produces a textual rationale. A Multimodal Forgery Localization Module (MFLM) and then grounds the explanation in the image by highlighting manipulated regions using a Tamper Comprehension Module combined with the Segment-Anything Model. We train the two modules in two stages on a multimodal tampered image dataset (MMTD) with triplet annotations. On MMTD, the method reaches 87.17% accuracy and 0.8696 F1, outperforming recent baselines, generates more relevant explanations (0.8566 CSS, 0.4348 ROUGE-L), and localizes manipulated regions with a mean IoU of 0.3438. The results show that large multimodal models can support accurate, transparent, and trust-aware content authentication for SIoT. Full article

This study aimed to analyze the differences in serum metabolomes at different stages of estrus synchronization in Kazakh horses, to characterize dynamic changes in serum metabolites during estrus synchronization, and to screen potential metabolic markers and key regulatory pathways. Four sampling stages (M1: intravaginal device insertion; M3: device removal, labeled consistently with the pre-experiment timeline; P: 24 h post-PG injection; L: tertiary follicle emergence) were selected. The research results showed that 37 significantly different metabolites (DAMs) were identified in the M1-vs.-M3 positive ion mode, with 16 metabolites significantly upregulated and 21 significantly downregulated. In the M1-vs.-M3 negative ion mode, 47 significantly different metabolites were identified, including 17 upregulated and 30 downregulated. In the M3-vs.-P positive ion mode, 69 significantly different metabolites were identified, with 53 upregulated and 16 downregulated. In the M3-vs.-P negative ion mode, 63 significantly different metabolites were identified, including 39 upregulated and 24 downregulated. In the Pvs-L positive ion mode, 65 significantly different metabolites were identified, with 16 upregulated and 49 downregulated. In the P-vs.-L negative ion mode, 61 significantly different metabolites were identified, including 13 upregulated and 48 downregulated. The significantly different metabolites mainly included lipids and lipid-like molecules, organic heterocyclic compounds, and organic acids and their derivatives. KEGG enrichment analysis showed that the significantly different metabolites in Kazakh horses at different stages were mainly enriched in nicotinic acid and nicotinamide metabolism, amino acid synthesis, and related pathways. This study revealed significant differences in the serum metabolome, and nicotinic acid and nicotinamide metabolism, by generating NAD⁺ and NADPH, simultaneously supporting energy supply, steroid hormone synthesis, and antioxidant protection, which are the basic metabolic guarantees for the development of follicles from quiescence to growth; amino acid synthesis not only provides protein raw materials for follicular cell proliferation but also participates in the regulation of the follicular microenvironment by synthesizing signaling molecules and antioxidant substances. This study enriched the research on the metabolic regulation of estrus synchronization in equine species, filled the gap in the metabolomics research of estrus synchronization in Kazakh horses, and provided an important theoretical basis and practical reference for optimizing the estrus synchronization treatment plan for Kazakh horses, adjusting the focus of feeding and management at each stage and improving the conception rate. Full article

This study aims to develop a lightweight, edge-deployable artificial intelligence (AI) system for real-time, non-contact recognition of cattle eating, standing, and lying behaviors in farm environments. Automated monitoring of these behaviors in cattle provides fundamental behavioral data for the future development of systems that analyze feeding duration, lying duration, and behavioral rhythms. Nevertheless, practical deployment on farms is hindered by data imbalance, dense animal groupings, scale variation, occlusion, and the need for low-cost edge computing. To address these challenges, we propose BoviFusionNet, a lightweight, edge-deployable AI system. A box balanced augmentation strategy rebalances training instances at the object level without altering the validation or test sets. Built upon YOLO11n, the model integrates three targeted enhancements: information-preserving downsampling (ADown), adaptive bidirectional feature fusion (BiFPN), and local window attention (C2CGA) to improve multi-scale representation and fine-grained behavior discrimination. Experimental results show that BoviFusionNet achieves 0.7851 recall, 0.7763 F1-score, 0.7976 mAP@0.50, and 0.6305 mAP@0.50:0.95, with only 5.4 GFLOPs and a 3.4 MB model size. Compared with the YOLO11n baseline, it improves mAP@0.50:0.95 by 9.92% and reduces the parameter count by 39.8%. After INT8 quantization and deployment on an RK3588S edge device, real-time inference reaches 28.08 frames per second (FPS). Therefore, BoviFusionNet offers an effective accuracy-complexity trade-off for on-farm edge AI applications. By enabling continuous, non-invasive monitoring of health-relevant behaviors, it provides fundamental behavioral data for the future development of veterinary health assessment tools without relying on cloud services or wearable sensors. Full article

Heat stress is a growing concern in cattle production systems due to the increasing frequency and intensity of extreme weather events driven by climate change. This review synthesizes current knowledge on the multifaceted impacts of heat stress and focuses on nutritional strategies to mitigate its effects on ruminating cattle. A comprehensive literature search was conducted using PubMed, Scopus, Web of Science, and Google Scholar. Heat stress adversely affects cattle physiology, behavior, rumen function, and overall productivity, particularly in dairy animals with high metabolic activity. During heat stress episodes, changes in the microbial population have been reported; however, there is no clear consensus, as findings vary widely among studies depending on diet, feed intake, animal type and experimental design. This variability limits the ability to draw general conclusions regarding changes in the rumen microbiome driven by heat stress. In this context, dietary nutritional intervention strategies offer a practical and scalable approach to enhance thermotolerance and maintain performance under heat stress conditions. Key nutritional strategies include modifications in diet composition to reduce metabolic heat production, with some approaches carrying potential risks to animal health, e.g., increasing dietary energy density through concentrates while minimizing forage content. Supplementation with rumen-protected nutrients like amino acids, vitamins, and minerals can be used to support immune function, antioxidant capacity, and metabolic stability. Polyphenols and betaine contribute to oxidative stress reduction and gut integrity, while probiotics may be used to improve rumen fermentation and nutrient utilization. Sensor technologies, including rumen boluses and wearable devices, offer the potential to monitor physiological responses to heat stress in real time and offer opportunities for precision feeding and early intervention. Most published studies only cover short periods of heat stress, and there is a lack of in vitro models simulating rumen hyperthermia. In parallel, future research should therefore prioritize longitudinal, in vivo trials that integrate physiological, metabolic, and microbial responses to understand the long term and systemic effect of heat stress. In addition, controlled trials in commercial settings are necessary to prove the transferability of results to commercial herds. A multidisciplinary approach combining nutritional, environmental, and technological strategies is likely to play an important role in safeguarding cattle welfare and productivity in a warming climate. Full article

In this study, a sustainable advanced manufacturing technology was developed using a holistic approach for finishing heat-treated 42CrMo4 steel cylindrical parts. The proposed technology is based on a hybrid combined process (HCP) involving cool-assisted dry hard turning and subsequent cool-assisted dry diamond burnishing (DB). A cold-air cooling (without lubrication) condition was achieved using a special device with a cold-air nozzle based on the principle of vortex tubes. The study was conducted in two stages. In the first stage, only the hard turning process was investigated using variance analysis to determine the significant governing factors (feed rate and cutting insert radius). The second stage involved studying and optimising the HCP. This approach incorporated the two significant turning process factors, along with three additional DB process factors: the radius of the diamond insert, burnishing force and feed rate. The selected objective functions were the average roughness, skewness, kurtosis, surface microhardness, residual surface axial stress and fatigue limit. The fatigue limit was determined using the accelerated Locati method. Mathematical models of the objective functions were obtained using experiments and regression analyses. Using multi-objective optimisation, the HCP was optimised based on two criteria: (1) maximum wear resistance under boundary lubrication conditions and (2) maximum fatigue limit. The optimisation tasks were solved by searching for the Pareto optimal solution approach using QStatLab and the NSGA II algorithm. The compromise optimal values of the governing factors, maximising the fatigue limit (690 MPa), are as follows: feed rate in turning and DB of 0.05 mm/rev, radius of the cutting insert of 0.8 mm, diamond insert radius of 2 mm, and burnishing force of 50 N. Experimental verification showed a good agreement with the optimised solutions for surface integrity and fatigue limit characteristics. Full article

A hypothesis was proposed that continuous dual-circuit mixing can be achieved by equipping a feed mixer-distributor with two leveling–mixing finger shafts, which, after lifting the feed mass to a certain height, collect it in the central part of the hopper and divide it into two flows directed toward the end walls of the hopper. In this case, continuous dual-circuit mixing is performed during each rotation of the leveling–mixing shaft. A structural and technological scheme, engineering documentation, and an experimental prototype of the feed mixer-distributor were developed. The machine consists of a 3.0 m³ hopper, two horizontal augers, two leveling–mixing finger shafts, a loading conveyor, and a drive mechanism. Theoretical investigations were carried out, and analytical expressions were obtained to determine the circumferential velocity of the fingers of the leveling–mixing device. This velocity must ensure the movement of the feed mixture without scattering and guarantee the release of the feed mass from the finger surface when the finger rotation angle exceeds 20°. Calculations based on the obtained analytical expressions showed that the critical circumferential velocity of the fingers is 0.866 m/s, while the calculated minimum rotational speed of the finger shaft is 20.7 min⁻¹. Therefore, a rotational speed of approximately 20 min⁻¹ was adopted for the experimental investigations. Experimental studies conducted at different rotational speeds of the leveling–mixing device showed that the optimal rotational speed of the finger shaft is 20 min⁻¹. At this rotational speed, the mixture uniformity exceeded 90%. An analytical expression was also derived to determine the velocity of feed mixture movement along the finger surface. Calculations showed that the optimal velocity ranged from 0.5 to 0.94 m/s. This value corresponds to the rational velocity of feed mixture transportation toward the end walls of the hopper. Laboratory experiments were carried out using the feed mixer-distributor at a leveling–mixing finger shaft rotational speed of n = 20 min⁻¹. The optimal mixing time required to achieve the target mixture uniformity was 5.5 min under the tested operating conditions. Comparative experiments also showed that operation of the feed mixer-distributor without the leveling–mixing device resulted in a 34% higher power consumption than operation with the leveling–mixing device. Full article

Nuclei segmentation is a key task in digital histopathology, highlighting important aspects of nuclear morphology and topology in many cancer-related evaluations and studies. Variability in nuclear appearance both within and across different organs, stain heterogeneity, and inconsistencies in acquisition procedures contribute to the complexity of the task. The existing nuclei segmentation methods apply deep learning to address these challenges, using models with millions of parameters, thereby significantly increasing computational complexity. They also face limitations in generalizing to unseen organs and slide preparations. In this paper, we propose a transparent and lightweight Green U-Shaped Learning model for nuclei segmentation (NS-GUSL). NS-GUSL features a multi-scale architecture for coarse-to-fine refinement of probability maps, which are subsequently binarized using a novel low-confidence sample binarization (LCSB) technique. The model features a modular, feed-forward feature learning scheme with unsupervised representation learning and supervised feature selection and generation. A final morphological post-processing step refines the segmentation maps to improve instance separation while preserving nuclei convexity. The model was trained and tested on the MoNuSeg dataset and compared against other deep learning baselines for segmentation performance. In addition, external validation experiments were conducted to evaluate the proposed model’s generalizability to unseen organs and staining procedures. NS-GUSL exhibits the best panoptic segmentation performance and competitive detection quality across all datasets. Moreover, our model is shown to be compact, low in computational complexity, and to have a minimal carbon footprint, compared to other deep learning models, making it a suitable choice for deployment on edge devices. Full article

IoT networks now handle traffic from billions of devices, and edge nodes are under constant pressure to classify that traffic and dispatch tasks within tight latency deadlines. Most existing systems treat classification and scheduling as two separate steps that run one after the other. This sequence adds unnecessary delay and breaks the feedback between the two tasks: the scheduler never sees the traffic type, and the classifier never sees the queue state. We propose ParallelEdge-AI, a system built around a shared flow encoder that feeds two task-specific heads in parallel, one for multi-class traffic classification and one for task-urgency scoring. Both heads are trained end-to-end using a joint loss that combines cross-entropy and pairwise ranking. A load-balance controller then reads the urgency scores alongside live queue lengths to decide, every 200 ms, whether a task stays local or moves to a less-loaded edge node. No global synchronisation is needed. We test the system on three real IoT datasets: RT-IoT2022, N-BaIoT, and CICIoT2023. ParallelEdge-AI reaches 97.63% accuracy and an F1-score of 97.34%, which is 3.16 percentage points above the best baseline. Inference latency is 19.62 ms per batch, the deadline-miss rate is 2.34%, and the load-imbalance index is 0.083, all three are the best results in our comparison. These numbers show that running classification and scheduling together on a shared representation is both faster and more accurate than treating them as separate problems. Full article