Search Results (461)

Viticulture is facing growing economic and environmental pressures that demand a transition toward intelligent and autonomous crop management systems. Phytopathologies remain one of the most critical threats, causing substantial yield losses and reducing grape quality, while regulatory restrictions on agrochemicals and sustainability goals are driving the development of precision agriculture solutions. In this context, early disease detection is crucial; however, current visual inspection methods are hindered by subjectivity, cost, and delayed symptom recognition. This study presents a fully autonomous robotic platform developed within the Agrimet project, enabling continuous, high-frequency monitoring in vineyard environments. The system integrates a tracked mobility base, multimodal sensing using RGB-D and thermal cameras, an AI-based perception framework for leaf localisation, and a compliant six-axis manipulator for biological sampling. A custom control architecture bridges standard autopilot PWM signals with industrial CANopen motor drivers, achieving seamless coordination among all subsystems. Field validation in a Sicilian vineyard demonstrated the platform’s capability to navigate autonomously, acquire multimodal data, and perform precise georeferenced sampling under unstructured conditions. The results confirm the feasibility of holistic robotic systems as a key enabler for sustainable, data-driven viticulture and early disease management. The YOLOv10s detection model achieved good precision and F1-score for leaf detection, while the integrated Kalman filtering visual servoing system demonstrated low spatial tolerance under field conditions despite foliage sway and vibrations. Full article

In the context of Industry 4.0 and intelligent manufacturing, conventional serial manipulators face limitations in dynamic task environments due to fixed structural parameters and the traditional decoupling of mechanism design from motion planning. To address this issue, this study proposes SAC-SC (Soft Actor–Critic-based Structure–Control Co-Design), a reinforcement learning framework for the co-design of manipulator link lengths and motion planning policies. The approach is implemented on a custom four-degree-of-freedom PRRR manipulator with manually adjustable link lengths, where a hybrid action space integrates configuration selection at the beginning of each episode with subsequent continuous joint-level control, guided by a multi-objective reward function that balances task accuracy, execution efficiency, and obstacle avoidance. Evaluated in both a simplified kinematic simulator and the high-fidelity MuJoCo physics engine, SAC-SC achieves 100% task success rate in obstacle-free scenarios and 85% in cluttered environments, with a planning time of only 0.145 s per task, over 15 times faster than the two-stage baseline. The learned policy also demonstrates zero-shot transfer between simulation environments. These results indicate that integrating structural parameter optimization and motion planning within a unified reinforcement learning framework enables more adaptive and efficient robotic operation in unstructured environments, offering a promising alternative to conventional decoupled design paradigms. Full article

The study investigated the developmental trajectory of the speech act of request among L1 Greek-speaking children spanning the preschool and primary school years (ages 4–11), aiming to address the scarcity of pragmatic research within this age range in Greek. Seventy-three children participated in an experimental task that elicited oral requests based on scenarios systematically manipulating addressee status/familiarity and the cost of the requested action. Responses were analysed via a bottom-up coding method, which showed that three quarters of all utterances adhered to four highly conventionalised, interrogative request constructions: (i) Can-you V-_SUBJUNCTIVE?, (ii) Will-you V?, (iii) Can-I V-_SUBJUNCTIVE?, and (iv) V-_PRESENT-YOU?. Notably, the direct Imperative mood was marginal even among the youngest participants. Results indicate a statistically significant variation in the distribution of these dominant patterns across age groups. Increasing age correlates with greater sensitivity to sociocultural parameters of communication, specifically the imposition/cost and the addressee’s face needs. This is further evidenced by a more elaborated repertoire of modifiers and supportive moves. We conclude that requestive behaviour progresses developmentally from largely underspecified directive forms toward a repertoire of more complex and contextually specified constructions, thereby providing empirical support for usage-based accounts of language acquisition. Full article

Workers with upper-limb disabilities face difficulties in performing manufacturing tasks requiring fine manipulation, stable handling, and multistep procedural understanding. To address these limitations, this paper presents an integrated collaborative workcell designed to support disability-inclusive manufacturing. The system comprises four core modules: a JSON-based collaboration database that structures manufacturing processes into robot–human cooperative units; a projection-based augmented reality (AR) interface that provides spatially aligned task guidance and virtual interaction elements; a multimodal interaction channel combining gesture tracking with speech and language-based communication; and a personalization mechanism that enables users to adjust robot behaviors—such as delivery poses and user-driven task role switching—which are then stored for future operations. The system is implemented using ROS-style modular nodes with an external WPF-based projection module and evaluated through scenario-based experiments involving workers with upper-limb impairments. The experimental scenarios illustrate that the proposed workcell is capable of supporting step transitions, part handover, contextual feedback, and user-preference adaptation within a unified system framework, suggesting its feasibility as an integrated foundation for disability-inclusive human–robot collaboration in manufacturing environments. Full article

Ruminant livestock production faces increasing pressure to reduce environmental impacts while maintaining productivity and food security. This comprehensive review examines current strategies and emerging technologies for enhancing environmental sustainability in ruminant systems. The review synthesizes recent advances across four interconnected domains: genetic and genomic approaches for breeding environmentally efficient animals, rumen microbiome manipulation, nutritional strategies for emission reduction, and precision management practices. Specifically, genetic and genomic strategies demonstrate significant potential for long-term sustainability improvements through selective breeding for feed efficiency, methane reduction, and enhanced longevity. Understanding host–microbe interactions and developing targeted interventions have also shown promising effects on optimizing fermentation efficiency and reducing methane production. Key nutritional interventions include dietary optimization strategies that improve feed efficiency, feed additives, and precision feeding systems that minimize nutrient waste. Furthermore, management approaches encompass precision livestock farming technologies including sensor-based monitoring systems, automated feeding platforms, and real-time emission measurement tools that enable data-driven decision making. Integration of these approaches through system-based frameworks offers the greatest potential for achieving substantial environmental improvements while maintaining economic viability. In addition, this review identifies key research gaps including the need for standardized measurement protocols, long-term sustainability assessments, and economic evaluation frameworks. Future directions emphasize the importance of interdisciplinary collaboration, policy support, and technology transfer to accelerate adoption of sustainable practices across diverse production systems. Full article

The literature on derivative pricing in illiquid markets has mostly focused on computing optimal hedging controls, but empirical microstructure studies show that large order flow generates persistent and predictable price effects. Therefore, these controls can themselves induce endogenous market manipulation because traders can internalize the impact of their own trades. We identify the key shortcoming as the absence of a formal separation between a large trader’s informational advantage and the mechanical price impact and temporary cost-of-hedging. To address this gap, we introduce a counterfactual informed observer—an agent who knows the large trader’s strategy but does not face trading frictions—and use this device to isolate informational order-flow effects from mechanical price impact, a distinction explicitly observed in microstructure data. We prove the existence of information-neutral probability measures under which the discounted asset is a martingale for this observer and derive a hedging framework that jointly accounts for transaction costs and permanent market impact. Numerical experiments show that because price pressure and order-flow effects create non-linear execution costs, the optimal hedge for an out-of-the-money call can deviate substantially from the Black–Scholes hedge, with implications for risk management and regulatory monitoring. Full article

Background/Objectives: The poultry industry faces severe heat-stress challenges that threaten both economic sustainability and animal welfare. Embryonic thermal manipulation (ETM) has been proposed as a thermal programming strategy to enhance chick heat tolerance, yet its efficacy in layers requires verification, and its effects on growth performance and neurodevelopment remain unclear. Methods: White Leghorn embryos at embryonic days 13 to 18 (ED 13–18) were exposed to 39.5 °C (ETM). Hatch traits and thermotolerance were recorded, and morphometric and histopathological analyses were performed on brain sections. Transcriptome profiling of the whole brains and hypothalami was conducted to identify differentially expressed genes (DEGs). Representative pathway genes responsive to ETM were validated by RT-qPCR. Results: ETM reduced hatchability, increased deformity rate, and decreased hatch weight and daily weight gain. During a 37.5 °C challenge, ETM chicks exhibited delayed panting and lower cloacal temperature. Histopathology revealed impaired neuronal development and myelination. Transcriptomic analysis of ED18 whole brains showed DEGs enriched in neurodevelopment, stimulus response, and homeostasis pathways. RT-qPCR confirmed hypothalamic sensitivity to ETM: up-regulation of heat-shock gene HSP70, antioxidant gene GPX1, the inflammatory marker IL-6, and apoptotic genes CASP3, CASP6, CASP9; elevated neurodevelopmental marker DCX, indicative of a stress-responsive neuronal state; and reduced orexigenic neuropeptide AGRP. Conclusions: ETM improves heat tolerance in layers but compromises hatching performance and brain development, with widespread perturbation of hypothalamic stress responses and neurodevelopmental gene networks. These findings elucidate the mechanisms underlying ETM and provide a reference for enhancing thermotolerance in poultry. Full article

Major depressive disorder (MDD) was long framed as a single clinical entity arising from a linear stress–monoamine–hypothalamic–pituitary–adrenal (HPA) axis cascade. This view was shaped by forced swim and learned helplessness tests in animals and by short-term symptom-based trials using scales such as the Hamilton Depression Rating Scale (HAM-D) and the Montgomery–Åsberg Depression Rating Scale (MADRS). This “unitary cascade” view has been dismantled by advances in neuroimaging, immune–metabolic profiling, sleep phenotyping, and plasticity markers, which reveal divergent circuit-level, inflammatory, and chronobiological patterns across anxiety-linked, pain-burdened, and cognitively weighted depressive presentations, all characterized by high rates of non-response and relapse. Translationally, face-valid rodent assays that equated immobility with despair have yielded limited bedside benefit, whereas cross-species bridges—electroencephalography (EEG) motifs, rapid eye movement (REM) architecture, effort-based reward tasks, and inflammatory/metabolic panels—are beginning to provide mechanistically grounded, clinically actionable readouts. In current practice, depression care is shifting toward systems psychiatry: inflammation-high and metabolic-high archetypes, anhedonia- and circadian-dominant subgroups, formal treatment-resistant depression (TRD) staging, connectivity-guided neuromodulation, esketamine, selected pharmacogenomic panels, and early digital phenotyping, as endpoints broaden to functioning and durability. A central gap is that heterogeneity is acknowledged but rarely built into trial design or implementation. This perspective advances a plasticity-centered systems psychiatry in which a testable prediction is that manipulating defined prefrontal–striatal and prefrontal–limbic circuits in sex-balanced, chronic-stress models will reproduce human network-defined biotypes and treatment response, and proposes hybrid effectiveness–implementation platforms that embed immune–metabolic and sleep panels, circuit-sensitive tasks, and digital monitoring under a shared, preregistered data standard. Full article

Intensive Care Units (ICUs) face unprecedented challenges in resource allocation, particularly during health crises in which algorithmic systems may be exposed to adversarial manipulation. A transformer-based expert system, ICU-Transformer, is presented to optimize resource allocation across 200 ICUs in Physionet while maintaining robustness against data poisoning attacks. The framework incorporates a Robust Multi-Head Attention mechanism that achieves an AUC-ROC of 0.891 in mortality prediction under 20% data contamination, outperforming conventional baselines. The system is trained and evaluated using data from the MIMIC-IV and eICU Collaborative Research Database and is deployed to manage more than 50,000 ICU admissions annually. A Resource Optimization Engine (ROE) is introduced to dynamically allocate ventilators, Extracorporeal Membrane Oxygenation (ECMO) machines, and specialized clinical staff based on predicted deterioration risk, resulting in an 18% reduction in preventable deaths. A Surge Capacity Planner (SCP) is further employed to simulate disaster scenarios and optimize cross-hospital resource distribution. Deployment across the Physionet ICU Network demonstrates improvements, including a 2.1-day reduction in average ICU bed turnover time, a 31% decrease in unnecessary admissions, and an estimated USD 142 million in annual operational savings. During the observation period, 234 algorithmic manipulation attempts were detected, with targeted disparities identified and mitigated through enhanced auditing protocols. Full article

Soil microbial communities face the combined pressures of climate change and biodiversity loss, yet how these stressors interact to shape ecosystem function remains a critical uncertainty. To investigate this, we established a constructed grassland plant community and conducted a fully factorial experiment manipulating plant diversity (1, 3, and 6 species), temperature (ambient, +2 °C), and precipitation (ambient, +50%). High-throughput 16S rRNA gene sequencing revealed that plant diversity exerted a stronger influence on soil bacterial community structure than did warming or precipitation changes. Beta diversity analysis revealed a distinct clustering of bacterial communities corresponding to the plant diversity gradient. This shift was characterized by a consistent enrichment of the metabolically versatile genus Sphingomonas in medium-diversity plots that experienced elevated precipitation, suggesting a predicted potential for enhanced organic matter decomposition. Despite overall stability in alpha diversity, the interaction between plant diversity and warming significantly modulated bacterial diversity and dominance patterns. Our findings highlight that plant diversity plays a key role in mediating soil bacterial responses to simulated climate factors in the short term. Incorporating these plant–soil feedback mechanisms into ecological models appears crucial for advancing predictions of ecosystem dynamics under future climate conditions. Full article

Robust control over the coupling and propagation of surface plasmon polaritons (SPPs) is essential for advancing various plasmonic applications. Traditional planar structures, commonly used to design SPP directional couplers, face limitations such as low extinction ratios and design complexities. These issues frequently hinder the dense integration and miniaturisation of photonic systems. Recently, exceptional points (EPs)—unique degeneracies within the parameter space of non-Hermitian systems—have garnered significant attention for enabling a range of counterintuitive phenomena in non-conservative photonic systems, including the non-trivial control of light propagation. In this work, we develop a rigorous temporal coupled-mode theory (TCMT) description of a non-Hermitian metagrating composed of alternating silicon–germanium nanostrips and use it to explore the unidirectional excitation of SPPs at EPs in the visible spectrum. Within this framework, EPs, typically associated with the coalescence of eigenvalues and eigenstates, are leveraged to manipulate light propagation in nonconservative photonic systems, facilitating the refined control of SPPs. By spatially modulating the permittivity profile at a dielectric–metal interface, we induce a passive parity–time ($\mathcal{PT}$)-symmetry, which allows for refined tuning of the SPPs’ directional propagation by optimising the structure to operate at EPs. At these EPs, a unidirectional excitation of SPPs with a directional intensity extinction ratio as high as 40 dB between the left and right excited SPP modes can be reached, with potential applications in integrated optical circuits, visible communication technologies, and optical routing, where robust and flexible control of light at the nanoscale is crucial. Full article

Vibration band gap structures are advanced materials for vibration wave mitigation from metamaterials to phononic crystals from simple geometrical manipulations. Here, we present geometrical structures, made from platonic solids, that are capable of providing multi-passband frequency ranges with face symmetry in each unit cell. We fabricated the metamaterial structures using stereolithography, after which we experimentally characterized band gaps through impulse vibration testing. Experimental results have shown that the band gaps can be changed for different types of platonic structures along with the loading direction. This provided a comparison between axial and two bending direction band gaps, revealing ranges where the structures behave in either a “fluid-like” or an “optical-like” manner. Dodecahedron unit cells have exhibited the most promising results, when compared with reduced relative densities and a number of stacking unit cells. We utilized the coherence function during signal processing analysis, which provided strong predictions for the band gap frequency ranges. Full article

Blockchain networks face a critical but understudied threat: wave attacks that exploit difficulty adjustment algorithms through strategic mining participation. Adversaries cyclically withdraw and re-enter mining to create oscillations that degrade network liveness and destabilize honest miners’ revenue. We present the first production-ready framework that maintains network responsiveness while enabling robust, post hoc threat detection. The framework employs a statistically rigorous pipeline featuring controller-aligned anomaly detection, transitive collusion grouping via union-find, and Benjamini–Hochberg False Discovery Rate control. We formally prove the economic viability of this architecture: when penalties on unvested rewards are enabled by governance, wave attacks become asymptotically unprofitable for rational adversaries. Evaluated on a 128-node distributed testbed simulating Bitcoin, Ethereum Classic, and Monacoin networks over 30 independent runs, our framework achieves 92.7% F1-score in detecting attacks, significantly outperforming baseline methods (74.7%). This work provides a complete, theoretically-grounded solution for securing proof-of-work blockchains against difficulty manipulation, forming the foundation for the adaptive AI-driven enhancements presented in our companion paper (Part II). Full article

Glaucoma is the leading cause of irreversible blindness, primarily characterized by retinal ganglion cell (RGC) loss and optic nerve damage due to abnormal alterations in intraocular pressure (IOP). While in vivo models provide valuable insights into its pathophysiology, they face limitations in controlling biomechanical parameters and long-term IOP monitoring. In vitro models offer greater experimental control but often lack the complexity of the ocular microenvironment, limiting their physiological relevance. To better understand RGC degeneration from a biomechanical perspective, advancements are needed to improve these models, including precise pressure manipulation and more realistic cell culture conditions. This review summarizes current in vitro approaches for studying pressure-induced RGC degeneration and explores the potential of microfluidic technologies to enhance model fidelity. Incorporating microfluidic technologies holds promise for creating more physiologically relevant models, potentially advancing our understanding of IOP-related RGC degeneration from biomechanical perspectives. Full article

Graph-based Recommender Systems (GRSs) model complex user–item relationships. They offer improved accuracy and personalization in recommendations compared to traditional models. However, GRSs also face severe challenges from novel poisoning attacks. Attackers often manipulate GRS graph structures by injecting attack users and their interaction data. This leads to misleading recommendations. Existing detection methods lack the ability to identify such attacks targeting graph-based systems. To address this, we propose AutoDAP, a novel autoencoder-based detection method for poisoning attacks in GRSs. AutoDAP first extracts key statistical features from user interaction data. It fuses them with original interaction information. Then, an autoencoder architecture processes this data. The encoder extracts deep features and connects to an output layer for classification prediction probabilities. The decoder reconstructs graph structure features. By jointly optimizing classification and reconstruction losses, AutoDAP effectively integrates supervised and unsupervised signals. This enhances the detection of attack users. Evaluations on the MovieLens-10M dataset against various poisoning attacks, and on the Amazon dataset with real attack data, demonstrate AutoDAP’s superiority. It outperforms several representative baseline methods in both simulated (MovieLens) and real-world (Amazon) attack scenarios, demonstrating its effectiveness and robustness. Full article