Search Results (748)

Precision Livestock Farming (PLF) requires real-time control systems that connect high-level Decision Support Systems with resource-constrained edge devices. This paper presents a hybrid Multi-Agent System (MAS) architecture for swine precision feeding designed to address the trade-off between semantic interoperability and real-time operational efficiency. The proposed Controlling Module uses a dual-layer communication strategy: a lightweight character-delimited TCP/IP protocol ensures deterministic performance for embedded controllers, while an XML-serialized format that maps to the FIPA Agent Communication Language preserves semantic interoperability. A custom serialization/deserialization algorithm was developed to process this XML structure within LabVIEW while avoiding the overhead typically associated with generic DOM/SAX parsers. The architecture was validated in a 120 h laboratory test that combined a Digital Twin simulation of 50 virtual feeders with Hardware-in-the-Loop testing of key sensing components. Under these test conditions, no communication failures were observed, all simulated network interruptions were recovered from, and the system operated with a modest resource footprint, including an average CPU use of 15% and a peak memory use of 350 MB. The platform also processed 2590 consumption events without reported data loss during the validation period. These results indicate that the proposed hybrid MAS architecture is a feasible solution for integrating interoperable decision support and deterministic edge control in PLF applications. Full article

This paper presents Resilient Edge-IVA (Intelligent Video Analytics), an integrated framework designed to ensure real-time inference stability and high-speed embedding-based similarity search in resource-constrained edge computing environments. Conventional systems often face Quality of Experience (QoE) degradation caused by computational overhead and hardware-level bottlenecks. To address these challenges, this study proposes a “Whole-cycle” methodology employing a perception-driven, three-tier adaptive control algorithm. This algorithm dynamically modulates encoding parameters, such as resolution and bitrate, by utilizing real-time inference latency and CPU utilization as feedback signals. Furthermore, the framework incorporates an event-density-based Data Diet mechanism. This mechanism selectively adjusts video quality based on object detection results, preserving high-fidelity imagery for critical events while significantly reducing data volume during static intervals. The backend implements a hybrid storage architecture combining the Milvus vector database for CLIP-based high-dimensional visual embeddings with a PostgreSQL relational database for structured metadata. These systems are linked via a deterministic hash key to ensure data atomicity and facilitate high-speed, multi-dimensional embedding-based retrieval. Experimental evaluations conducted on a Raspberry Pi 5 and Hailo-8 NPU demonstrate that the proposed framework maintains a frame drop rate below 0.3% even under extreme workloads, providing a 13-fold improvement in operational stability over static configurations. The results also confirm a 54.2% reduction in total storage occupancy and a Hash Mapping Consistency (HMC) score of 0.89. These findings validate the framework’s effectiveness in reconciling real-time processing stability with storage efficiency. Building upon this baseline, future research will extend the framework to multi-class environments, targeting applications such as Intelligent Transport Systems (ITS). Full article

Persistent organic contaminants and complex wastewater matrices challenge conventional treatment because parent-compound removal does not necessarily imply mineralization, detoxification, or improved environmental safety. Advanced oxidation processes can address these limitations, but practical effectiveness is often constrained by oxidant activation, gas–liquid mass transfer, reagent distribution, light penetration, catalyst contact, energy demand, and matrix scavenging. This work critically examines hydrodynamic cavitation-assisted advanced oxidation processes for water and wastewater treatment, including systems based on hydrogen peroxide, ozone, Fenton and Fenton-like reactions, persulfate, peroxydisulfate, peroxymonosulfate, UV irradiation, photocatalysis, cold plasma, multi-hybrid configurations, and emerging reduction-oriented approaches. The discussion covers reactor configurations, target contaminants, real matrices, and sustainability-related performance metrics. The central argument is that hydrodynamic cavitation is not automatically sustainable as a stand-alone treatment. It becomes relevant as a sustainable intensification module only when measurable improvements are demonstrated in oxidant activation, mass transfer, treatment depth, biodegradability, toxicity reduction, process integration, or scale-up at acceptable energy and chemical cost. A reporting framework is proposed based on mineralization, COD/TOC reduction, by-products, toxicity, biodegradability, normalized energy consumption, chemical efficiency, real-matrix validation, reproducibility, and cost-relevant indicators. Future progress should move from isolated degradation tests to integrated, controllable, and scalable treatment frameworks. Full article

Probiotics are widely studied as antibiotic alternatives in commercial aquaculture, yet their effects on fish maintained under long-term aquarium conditions remain poorly understood. This study addressed this gap by evaluating dietary Escherichia coli Nissle 1917 (EcN) supplementation on gut microbiota and inflammatory cytokine expression in hybrid grouper (Epinephelus fuscoguttatus♀ × E. lanceolatus♂) from a recirculating aquarium system. In this study, hybrid grouper were maintained in triplicate tanks under long-term aquarium environments, and fed a basal diet with 1 × 10⁸ CFU/g EcN (SS group) or a control diet (CS group) for 28 consecutive days. Based on 16S rRNA high-throughput sequencing and qPCR, the intestinal microbiota and expression levels of IL-4, TNF-α, and IL-1β were measured. At the phylum level, the relative abundance of Firmicutes increased from 15.63% (CS) to 66.70% (SS), while Proteobacteria decreased from 76.77% to 30.61%. At the genus level, Exiguobacterium became the dominant taxon in the SS group. Furthermore, EcN supplementation significantly upregulated IL-4 expression and downregulated TNF-α and IL-1β expression. EcN supplementation significantly altered gut microbiota composition, with marked changes in community structure and notable shifts in dominant taxa. Thus, this study provides one of the investigations into EcN-mediated restructuring of intestinal bacterial communities and modulation of host immune transcriptional responses in hybrid grouper maintained under controlled aquarium settings. These findings offer a foundation for designing microbiome-targeted interventions in captive marine fish systems. Full article

Distributed seismic sensor networks integrated into the Internet of Things (IoT) infrastructure enable continuous condition monitoring of large-scale engineering structures. During long-term operation, however, measurement channels are subject to sensitivity drift, increased noise, and pulse artifacts that statistically mimic real vibration events. Related deep-learning techniques for noisy and ill-posed inverse problems have demonstrated the value of combining principled physical priors with deep models. Although the application domain differs, the underlying methodological insight—that constrained, physics-aware feature mappings can stabilize learning under noisy and partially observed conditions—directly motivates the use of a parameterized quantum circuit as a nonlinear feature transformer in the present work, where Hilbert space mapping serves as an analogous structural prior for the latent representation. Three principal fault modes are considered in this work, corresponding to the dominant degradation mechanisms observed in long-term seismic instrumentation: sensor drift, increased noise, and sensor failure. Each fault mode produces a distinct signature in the windowed feature space; the proposed model is trained to discriminate between them based on the latent CNN-LSTM-VQC representation. We propose a hybrid quantum-inspired deep-learning model (QC-DL) for the detection and diagnosis of channel-degradation anomalies. The architecture combines a 1D-CNN+LSTM feature extractor with a parameterized variational quantum circuit (VQC) used as a nonlinear feature transformer. All quantum experiments were performed on the QPanda3 CPUQVM simulator. The data were split chronologically prior to windowing to avoid information leakage. On real-world labeled accelerometric data with four operating modes (normal/drift/high-noise/failure), the QC-DL model achieved a macro-averaged F1 score of approximately 0.69 and per-class AUC values in the range 0.88–0.99. The mean early-detection latency was 1.6 s versus 2.1 s for the CNN-LSTM baseline (~24% reduction). An ablation study against a parameter-matched classical MLP showed that the gain is modest and not solely attributable to additional nonlinearity. The reported p-values (p = 0.70, p = 0.29) do not establish statistical significance. The results support the feasibility of hybrid quantum-inspired deep learning for sensor-channel verification, while highlighting the need for evaluation on real NISQ hardware. This paper proposes a hybrid quantum-inspired approach for detecting and diagnosing such anomalies in the time series of distributed seismic networks. The architecture combines a classical temporal feature extraction module based on one-dimensional convolutional layers and a recurrent long short-term memory (LSTM) network, which generates a latent window representation of the signal, with a parameterized variational quantum circuit used as a nonlinear feature processor in a hybrid computational circuit. Experimental validation was performed on real-world labeled data with multiple sensor degradation modes. The evaluation was organized in a scoring framework aligned with autonomous operation through window ranking and threshold alarm generation. In the experiments, the proposed model provided a macro-averaged F1 score of approximately 0.69 and area under the receiver operating characteristic (AUC) curve values in the range of 0.88–0.99 across classes, outperforming baseline deep models. The average early detection latency was 1.6 s versus 2.1 s for the baseline recurrent model (a 24% reduction). An ablative comparison with a control model based on a classical multilayer perceptron of comparable dimension confirmed that the improvement is not limited to the addition of additional nonlinearity. The obtained results indicate the potential of quantum-supported deep learning for improving the reliability of long-term vibration monitoring and verifying the correctness of sensor channels in distributed seismic networks. Full article

Maintaining good indoor air quality (IAQ) is essential for student health, cognitive performance, and overall well-being. Traditional ventilation strategies, particularly constant air volume systems and manual window operation, often fail to maintain optimal IAQ while simultaneously increasing building energy consumption. In response, smart ventilation systems have emerged as a promising alternative capable of dynamically modulating airflow based on occupancy patterns and real-time pollutant levels. This study presents a systematic review of fourteen carefully selected peer-reviewed studies (2015–2025) that represent the most recent and methodologically robust research on smart ventilation applications in school environments across diverse climatic conditions. The selected studies encompass experimental, simulation-based, and hybrid methodologies, and classify control strategies into demand-controlled, temperature-adaptive, occupancy-based, AI-enhanced, and building management system (BMS)-integrated approaches. Collectively, the findings demonstrate measurable improvements in IAQ indicators (e.g., carbon dioxide (CO₂), particulate matter (PM_2.5), ozone (O₃), and volatile organic compounds (VOCs)) and significant energy savings, in some cases exceeding 60%, while also identifying system vulnerabilities such as fault sensitivity, short monitoring durations, and limited long-term validation. Importantly, the review reveals critical geographic and climatic research gaps, particularly in hot–arid regions where ventilation-related cooling demand is substantial, as well as limited long-term assessments in cold climates. Furthermore, although smart ventilation systems perform effectively under controlled conditions, insufficient real-world verification, user interaction analysis, and climate-specific optimization constrain broader implementation. Addressing these gaps through climate-dependent performance evaluation and long-term operational studies is essential to unlocking the full potential of smart ventilation systems in delivering healthier, energy-efficient classrooms. Full article

In hybrid-series-converter-based LCC-HVDC systems, controllable capacitor modules can provide additional voltage–time area during commutation, thereby improving inverter-side fault tolerance under AC faults. However, their switching behavior makes the commutation path impedance state-dependent, while most existing commutation-failure prediction methods still rely on fixed-reactance assumptions. To address this problem, this paper proposes a reactance-corrected predictive control and coordinated switching method. First, a capacitor switching coefficient is introduced to describe the insertion state of the controllable capacitor modules, and an equivalent commutation reactance of the HSC valve arm is derived. Then, the corrected reactance is incorporated into an extinction-angle margin index and an energy-margin index to quantify the influence of reactance variation on commutation capability. A segmented firing-angle controller with smooth compensation is further designed, and energy-margin feedback is coordinated with capacitor insertion control. PSCAD/EMTDC simulations verify that the proposed method reduces prediction error, provides a prediction lead time of 0.7–4.5 ms, and improves fault ride-through capability. Full article

Pd-decorated Janus ZrSSe monolayers provide a promising platform for adsorption-coupled electronic modulation in two-dimensional materials. Using first-principles density functional theory, we systematically investigate the structural stability, electronic properties, and adsorbate-induced electronic response of Pd-modified Janus ZrSSe. The results show that Pd is most stably anchored at the hollow site on the S-terminated surface, with a formation energy of $-1.45$ eV, while substitutional incorporation remains energetically unfavorable even after HSE06 refinement. Compared with pristine ZrSSe, Pd decoration markedly strengthens the interaction with adsorbates, leading to strong chemisorption for CO ($-1.026$ eV) and C₂H₂ ($-0.748$ eV), whereas H₂ remains comparatively weakly bound ($-0.258$ eV). Electronic-structure analysis reveals that CO induces the most pronounced perturbation because of strong orbital hybridization between Pd 4d states and C/O 2p states, resulting in the largest band-edge modulation among the three adsorbates. More importantly, biaxial strain provides an effective external degree of freedom for continuously tuning the electronic structure: tensile strain widens the band gap, whereas compressive strain systematically narrows it and ultimately drives a semiconductor-to-metal transition at sufficiently large compression. These findings establish Pd-decorated Janus ZrSSe as a strain-tunable electronic material in which adsorption, orbital hybridization, and band-edge evolution are intimately coupled, offering fundamental insights into controllable electronic modulation in polar two-dimensional systems. Full article

Complex agricultural question answering often requires multi-step reasoning over domain-specific knowledge and reliable retrieval of heterogeneous evidence. However, existing retrieval-augmented generation (RAG) methods usually rely on one-shot retrieval and provide limited control over whether the retrieved evidence is sufficient, accurate, and consistent for answering complex agricultural questions. To address this limitation, this paper proposes an agent-driven question-answering framework for the agricultural planting domain based on a Planning–Execution–Feedback (PEF) closed-loop mechanism. The framework decomposes complex queries into executable subtasks, performs knowledge acquisition through a knowledge-graph-guided hybrid retrieval module, and iteratively refines the reasoning process according to retrieval-quality feedback. Specifically, in the retrieval stage, a two-stage strategy is introduced to first localize candidate entities in the knowledge graph and then conduct context-enhanced dense retrieval with entity-consistency reranking, thereby reducing semantic drift and improving domain alignment. In the feedback stage, the agent evaluates the adequacy of the retrieved evidence and determines whether to continue execution, re-retrieve evidence, or replan the workflow. Experimental results on the AgroQA dataset show that the proposed method achieves 88.9%, 79.1%, and 92.6% on the Answer-C, Answer-R, and CR metrics, respectively, outperforming traditional retrieval-augmented and general large language model baselines. In addition, a three-dimensional digital human interface is implemented as an application prototype to demonstrate the feasibility of integrating the proposed framework into interactive agricultural knowledge services. Full article

Objective: Rapid vertical manoeuvres and intermittent vibration in autonomous electric vertical take-off and landing (eVTOL) aircraft can provoke pronounced psychological anxiety in passengers. To address this, we propose a closed-loop adaptive system that integrates an in-ear wearable sensor with dynamic regulation of the cabin microenvironment, enabling real-time monitoring of each passenger’s autonomic state and delivering individualised mitigation through a continuous sense–analyse–intervene–feedback loop. Methods: The system is built around a pair of custom in-ear modules that integrate dual-wavelength photoplethysmography (PPG; 525 nm green and 940 nm infrared), galvanic skin response (GSR), and a six-axis inertial measurement unit (IMU) sampled at 200 Hz. To suppress the 20–80 Hz vibration generated by the distributed electric propulsion system, a compliant silicone damping sleeve attenuates high-frequency components at the hardware level, while a Kalman filter fuses the IMU and PPG streams and an adaptive notch filter removes residual rotor harmonics. The pipeline raises the heart-rate-variability (HRV) signal-to-noise ratio (SNR) to 24.1 dB, with a Pearson correlation of 0.96 against a medical-grade chest strap. A hybrid CNN–LSTM network—two convolutional layers (32 filters each) followed by two LSTM layers (128 hidden units)—predicts impending anxiety from HRV time-domain features (RMSSD, pNN50) and frequency-domain features (LF/HF ratio), triggering intervention 8.2 s in advance on average. According to the predicted anxiety level (mild/moderate/severe), a fuzzy controller modulates transcutaneous auricular vagus nerve stimulation (1–5 mA), the binaural-beat frequency (4–8 Hz, theta band), and the cabin lighting colour temperature (2700–6500 K) in real time. The intervention parameters are continuously refined by SPSA-based stochastic optimisation of the HRV recovery rate (step size 0.01; updated every 30 s). Results: In a randomised controlled experiment conducted in a simulated flight environment (N = 50; aged 22–45 years; 1:1 sex ratio), the active group reached physiological recovery in 52.3 s on average, compared with 98.6 s for the sham-controlled group—a 47% reduction (Cohen’s d = 1.24, p < 0.001). User acceptance reached 94%. Conclusions: The proposed in-ear platform enables closed-loop adaptive regulation of anxiety in the eVTOL cabin and overcomes the limitations of conventional passive mitigation strategies. By combining vibration-tolerant physiological sensing with multimodal environmental control, the work offers a practical pathway for improving passenger experience in urban air mobility and provides a useful reference for human-factors standards governing autonomous aircraft. Full article

Direct cellular reprogramming, the conversion of one somatic cell type into another, represents a remarkable advancement in regenerative medicine. Its potential to transform fibrotic tissue into functional parenchyma underscores its therapeutic promise. However, several critical challenges remain unresolved, including limited reprogramming efficiency, the long-term functional stability of converted cells, their integration within pre-existing cellular circuits, and safety concerns related to transgene integration and immunological responses to reprogramming-based viral vectors. Approaches based on the exogenous administration of recombinant proteins and miRNAs have also emerged, though these rely on factors that are naturally prone to exhaustion and degradation, potentially restricting their efficacy. This review is divided into three main sections. The first part addresses direct cellular reprogramming in the context of other cell-based applications, outlining its main applications and current biological limitations. The second part examines how different biomaterials, ranging from hydrogel scaffolds to nanoparticles, can modulate direct cellular reprogramming by providing mechanical and topographical cues and by enabling tighter control over the concentration and spatiotemporal dynamics of reprogramming factors and viral vectors. The third part discusses key findings in biomaterial-assisted reprogramming strategies, highlighting emerging opportunities for clinically translatable approaches. The convergence of regenerative biology and biomaterials science may ultimately generate advanced gel-based and hybrid cellular reprogramming platforms for in vitro testing and, in situ applications, for promoting cell fate stabilization and facilitating the regeneration of damaged tissues and organs. Full article

Sign language understanding for automated vehicles sits at the intersection of accessibility, intelligent transportation, and safety-critical human–machine interaction. The existing sign-language recognition systems are largely confined to controlled environments, limiting their utility in mobility scenarios characterized by lighting variation, motion blur, and partial occlusion. This paper proposes STCM-HVNet, a safety-aware hybrid multimodal architecture integrating four components: a spatial visual encoder, a MediaPipe-based pose encoder, a bidirectional LSTM temporal encoder, and a context-aware fusion and safety decision module. The architecture is formulated as a multi-task system that jointly predicts sign category, interaction intent, and urgency level, and incorporates confidence-aware rejection and fail-safe action mapping. Experiments are conducted on two Arabic sign-language resources. On the RGBArS image benchmark (31 classes, 7856 images), the proposed pipeline achieves a Top-1 accuracy of 45.38%, Top-3 accuracy of 75.15%, and Macro-F1 of 0.4479, outperforming LinearECOC, kNN-5, and Bagged Trees baselines. On the Arabic sign-language video benchmark (12 classes, 479 clips), the BiLSTM temporal encoder achieves a Top-1 accuracy of 93.15% and Macro-F1 of 0.9383, outperforming frame-aggregation (87.67%) and CNN-LSTM (89.04%) baselines. Ablation results confirm complementary contributions from the visual and pose branches. A safety-threshold analysis and a Monte Carlo dropout comparison demonstrate that the proposed safety decision/gating layer provides a controllable trade-off between prediction coverage and reliability. Full article

We investigate chaos control and hybrid synchronization in a variable-order fractional Arneodo system by treating the differentiation order $\alpha \left(t\right)$ as a closed-loop control variable. A hybrid chaos indicator, combining a tracking error with a windowed estimate of the largest Lyapunov exponent, drives both static and dynamic order modulation laws. The presence and uniqueness of solutions are demonstrated through two distinct methodologies: a piecewise constant-order decomposition with an explicit convergence rate and a direct contraction-mapping argument on the variable-order Volterra operator. Local stability is analyzed via Matignon’s spectral criterion under a quasi-static (frozen-time) approximation. The modulation laws are designed to steer $\alpha \left(t\right)$ below the critical order ${\alpha }_c\approx 0.8632$, at which the nontrivial equilibria $E_{1,2}=(\pm \sqrt{5.5},0,0)$ become locally asymptotically stable. A second-order predictor–corrector scheme attains its expected convergence rate. A controlled ablation study over 200 Monte Carlo runs demonstrates that the proposed laws reduce the terminal tracking error by 81% relative to the best fixed-order baseline, while requiring approximately eight orders of magnitude less control effort than classical active control. Hybrid synchronization (complete in $(u,v)$ and anti-synchronization in w) is successfully achieved in the variable-order setting. Full article

To meet the requirements of precise temperature regulation in solar greenhouses, traditional machine learning algorithms often suffer from poor adaptability, high energy consumption, and difficulties in integrating agronomic expertise. This study developed an intelligent greenhouse temperature regulation framework based on Model Predictive Control (MPC). The core components of the framework include: (1) an expert-experience-based simulator using a Sparrow Search Algorithm-optimized Random Forest (SSA-RF) model to digitize the temperature management strategies of high-yield farmers into dynamic reference trajectories and (2) a hybrid prediction model (CNN-BiLSTM-Attention) combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Permutation Entropy (CEEMDAN-PE) denoising with a Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Attention mechanism to achieve high-precision multi-step temperature forecasting. Validation in a cucumber solar greenhouse demonstrated that the SSA-RF model achieved an R² of 0.976 on the test set, showing a significant improvement over the traditional RF model. Compared to the conventional LSTM model, the hybrid prediction model reduced the RMSE to 0.642 and 0.947 for 15 min and 30 min predictions, respectively, with a maximum R² of 0.994 and excellent generalization capabilities. Finally, these two components were theoretically integrated into an MPC-oriented decision framework. The framework describes how expert reference trajectories, multi-step predictions, actuator constraints, and control increments can be combined in a receding-horizon optimization problem. Since online actuator control data were not available, the MPC module was formulated as a theoretical decision framework rather than a fully validated closed-loop controller. This study provides a modelling basis and technical path for future real-time greenhouse temperature control. Full article

Pulmonary fibrosis (PF) is a progressive and often fatal interstitial lung disease for which the currently available pharmacological therapies remain largely limited to slowing disease progression rather than reversing established fibrosis. This limitation has stimulated increasing interest in innovative therapeutic platforms capable of modulating complex fibrotic pathways. In this context, exosomes—nanoscale extracellular vesicles—have emerged as promising cell-free nanocarriers due to their intrinsic biocompatibility, low immunogenicity, and ability to be engineered for targeted drug delivery. In this review, we provide a comprehensive overview of both natural and engineered exosome-based strategies for the diagnosis and treatment of pulmonary fibrosis. We summarize recent advances in exosome engineering, including ligand functionalization, glycoengineering, and therapeutic cargo loading, highlighting how these approaches may support the development of more targeted and potentially personalized nanotherapeutic strategies. We further discuss emerging hybrid delivery platforms, such as exosome–liposome chimeras and hydrogel-based depots, which may enhance pulmonary retention, improve therapeutic durability, and enable controlled drug release. Finally, we outline key challenges and opportunities for clinical translation, including large-scale manufacturing, regulatory considerations, and clinically relevant delivery routes such as inhalation-based administration. Collectively, this review provides a translational perspective on engineered exosomes as emerging nanotherapeutic platforms for pulmonary fibrosis. Full article