Search Results (7,123)

In the context of the rapid development of smart horticulture, a deep remote sensing-based dual detection method for horticultural crop growth anomalies and safety risks was proposed to address the limitations of existing remote sensing monitoring approaches. These conventional methods, which predominantly focused on growth vigor assessment or single-task anomaly detection, had difficulty distinguishing anomalies from actual production risks and exhibited insufficient sensitivity to weak anomalies and complex temporal disturbances. Within a unified framework, a growth state modeling branch and an anomaly perception branch were constructed, enabling the joint modeling of normal growth trajectories and anomalous deviation features. By further introducing a risk joint discrimination mechanism, an integrated analysis pipeline from anomaly identification to risk assessment was achieved. Multi-temporal remote sensing features were used as inputs, through which normal crop growth patterns were characterized via trend perception, texture modeling, and temporal aggregation, while sensitivity to local disturbances and weak anomaly signals was enhanced by anomaly embeddings and energy representations. Systematic experiments conducted on multi-regional and multi-crop horticultural remote sensing datasets demonstrated that the proposed method significantly outperformed comparative approaches, including traditional threshold-based methods, support vector machines, random forests, autoencoders, ConvLSTM, and temporal transformer models. In the dual task of horticultural crop growth anomaly detection and safety risk identification, an accuracy of approximately 0.91 and an F1 score of 0.88 were achieved, indicating higher anomaly recognition accuracy and more stable risk discrimination capability. Further anomaly-type awareness experiments showed that consistent performance was maintained across diverse real-world production scenarios, including climate stress, disease-induced anomalies, and management errors. Full article

Effective port and coastal surveillance require persistent monitoring, flexible deployment, and reliable target detection in dynamic maritime environments. This paper presents a system- and deployment-oriented autonomous tethered drone architecture, integrated with AI-based perception, for persistent maritime surveillance in ports and coastal areas. Mounted on a moving maritime platform and powered through a tether, the drone provides a persistent elevated viewpoint without the endurance limitations of conventional battery-powered Unmanned Aerial Vehicles (UAVs). The system combines maritime platform integration, tethered flight operation, fail-safe and safety mechanisms, and a distributed Artificial Intelligence (AI) pipeline for real-time object detection and tracking. The perception module is based on YOLOv8m for vessel detection and BoT-SORT for multi-object tracking, enabling continuous monitoring of maritime targets in realistic operational scenarios. Field trials conducted from moving vessels in maritime environments demonstrate autonomous take-off and landing, stable surveillance operation under realistic wind and wave conditions, and effective vessel detection and tracking on real image sequences. The results show the potential of AI-enabled tethered drone surveillance as a persistent and operationally relevant tool for maritime monitoring and security. Full article

Reliable textile recycling requires automated unfolding to expose hidden hard components such as zippers, buttons, and metal fasteners, which otherwise risk damaging machinery and compromising downstream processes. This paper presents the design and implementation of an automated textile unfolding system based on a dual-arm robotic manipulation framework. The system uses two Interbotix WidowX 250s 6-DoF robotic arms and an Intel RealSense L515 LiDAR camera for visual perception. The unfolding process consists of three stages: initial dual-arm stretching to reduce major folds, refinement through a second stretch targeting the lower region, and a machine-learning stage that employs a YOLOv11 framework trained on depth-encoded textile images, followed by a depth-gradient-based estimator for fold direction. The system applies an extremity-based grasping strategy that selects leftmost and rightmost textile points from a custom error-corrected depth map, enabling robust grasp point selection, and a fold direction estimation method based on depth gradients around the detected fold. The most confident fold region is selected, an unfolding direction is determined using depth ranking, and the textile is manipulated until a flat state is confirmed through depth uniformity. Experiments show that depth correction significantly reduces spatial error in the robot frame, while segmentation and extremity detection achieve high accuracy across varied fold configurations, and the YOLOv11n-based model reaches 98.8% classification accuracy, while fold direction is estimated correctly in 87% of test cases. By enabling robust, largely autonomous textile unfolding, the system demonstrates a practical approach that could support safer and more efficient automated textile recycling workflows. Full article

Agricultural intelligent agent technology features autonomy in multimodal perception, scalability for cross-scenario collaboration and adaptability via closed-loop optimization, serving as a core technological pillar for industrial intelligent upgrading and refined production management. This paper systematically elucidates its technical essence and methodological framework, focusing on five key aspects: multimodal heterogeneous data perception and fusion, scenario-oriented knowledge modeling and dynamic memory, intelligent decision-making and planning, embodied artificial intelligence, and closed-loop feedback optimization. On this basis, the paper outlines its core agricultural applications in four domains: crop cultivation, efficient utilization of agricultural resources, intelligent upgrading of agricultural technologies and equipment, and collaborative governance of the entire agricultural industry chain. From an interdisciplinary “AI + Agriculture” perspective, the paper further analyzes its future development directions, aiming to provide insights for improving agricultural intelligent agent technologies and promoting their industrial application to accelerate agricultural intelligent transformation. This study constructs a three-dimensional integrated methodological framework encompassing technological analysis, application mapping and trend forecasting, systematically summarizes its agricultural application scenarios and technological evolution characteristics, enriches the theoretical system and methodological construction of agricultural intelligent agent research, and provides a reusable analytical paradigm for agricultural intelligent agent research and practice. Full article

The diversification of craft beer styles has stimulated interest in innovative yeast-driven strategies to enhance sensory complexity while maintaining process robustness and stylistic integrity. In this context, non-Saccharomyces yeasts represent promising biotechnological tools for modulating fermentation performance and flavor development in brewing systems. This study evaluated the application of Lachancea thermotolerans and Torulaspora delbrueckii in the production of a Porter-style beer using sequential inoculation with Saccharomyces cerevisiae. All fermentations were conducted in triplicate from a wort with an original gravity of 1042. The final alcohol content ranged from 4.82 to 4.99% (v/v), and apparent attenuation varied between 84.1 and 88.9%, with no significant differences among treatments (p > 0.05). Color (92–94 European Brewery Convention (EBC) and bitterness (~18 International Bitterness Units (IBU) remained within Porter-style parameters across all fermentations. Total acidity ranged from 0.19 to 0.21% (lactic acid equivalents), while volatile acidity was significantly higher in the L. thermotolerans treatment (0.55 g L⁻¹) compared with the control (0.22 g L⁻¹) (p < 0.05). Sequential inoculation influenced early fermentation kinetics and modulated selected sensory attributes. Quantitative Descriptive Analysis (n = 18 panelists) indicated higher aroma intensity and foam quantity in beers produced with L. thermotolerans, whereas T. delbrueckii was associated with moderate increases in foam persistence. The roasted character and overall stylistic perception remained stable across treatments. These findings indicate that sequential inoculation with selected non-Saccharomyces yeasts enables measurable sensory differentiation in dark beer matrices without compromising fermentative performance or stylistic integrity. The results support their controlled integration as technological tools for sensory innovation in Porter-style beers. Full article

Production greenhouse complexes increasingly require automation and digitalization to address rising labor costs, improve productivity, and support sustainable resource use. However, most existing solutions target isolated tasks and lack a unified framework for continuous monitoring and production-oriented accounting at facility scale. This paper proposes a system-level architecture that integrates robotic monitoring platforms, AI-based perception, and cloud-based data management into a coherent operational framework. The robotic monitoring platforms operate on rails and concrete surfaces and are capable of elevating cameras and sensors up to 5 m to support plant-health assessment, environmental monitoring, and production accounting. Aggregated data are incorporated into a digital twin that supports spatial traceability, historical analysis, and decision support. The proposed approach enables continuous inspection, improves early detection of crop stress, reduces repetitive manual scouting, and supports targeted interventions. The framework provides a scalable foundation for sustainable, data-driven greenhouse management and practical deployment of robotic monitoring systems in industrial production environments. Full article

Historical and cultural districts are the outcome of cultural sedimentation brought about by urban development, and they embody distinctive urban historical and cultural connotations. Ignoring the protection of the historical and cultural value contained in streetscapes will not only decrease the life quality of residents but will also diminish distinctive local urban features. This study focused on the Fangcheng historical and cultural district in Shenyang. The scenic beauty estimation method was employed to evaluate urban style and features’ visual quality, while the semantic differential method was used to obtain the subjective perceptual features of samples. The study also systematically explored the dynamic relationship between urban style and features’ quality and subjective perception in historical and cultural districts. The results show that color richness, coherence, iconic status, and continuum all exert significant positive predictive effects on visual preferences regarding urban style and features. Color richness was the primary determinant of urban style and features’ visual quality. Continuum interfaces, a unified spatial texture, and coordinated dimensions contributed significantly to improving urban style and features’ visual quality in historic and cultural districts. The distinctiveness and cultural iconic status of historical and cultural districts enhanced the residents’ identity and place memory. Moreover, the coherence and continuum of style between the old and new elements promoted an integrated aesthetic experience. The evaluation results revealed that the overall visual quality of urban style and features of most streets was medium. However, streets with a higher visual quality cluster among historical streets and commercial streets. The residential streets demonstrated a significantly lower visual quality. Establishing a comprehensive evaluation system that integrates urban style and features, subjective perception, and the style of historical and cultural districts can contribute to covering the shortage in the traditional urban style and features’ research and also provide a basis for urban regeneration at the micro scale. Full article

Multi-robot systems (MRS) have attracted a lot of attention from researchers due to their widespread application in various environments. However, in multi-robot collaborative search tasks, two problems often arise: sparse rewards for capturing targets and control oscillations. To address these issues, this paper proposes the hierarchical active perception multi-agent deep deterministic policy gradient (HAP-MADDPG) framework. This framework guides robots to efficiently explore maps and discover targets through global utility planning based on global exploration rate and local information aggregation based on local exploration rate. A stability control mechanism, which includes hysteresis logic and reward decay, is introduced to suppress control oscillations. Experimental results show that the HAP-MADDPG framework achieves a success rate of 96.25% and an average search time of 216.3 steps. The path trajectories are smooth, demonstrating the effectiveness of the proposed approach. Full article

Background: The resurgence of Mpox (formerly known as monkeypox) since the 2022 global outbreak has exposed weaknesses in surveillance, diagnosis, and public risk communication systems. Despite increased clinical understanding, limitations in knowledge, attitudes, and practices (KAP) among both healthcare workers (HCWs) and the general population continue to challenge prevention and control measures. Numerous systematic reviews have been published on KAP toward Mpox, yet their findings remain fragmented. This review aimed to consolidate the existing evidence from published systematic reviews to provide a unified understanding of global KAP levels related to Mpox. Methods: We followed the PRISMA guidelines for this systematic review of systematic reviews. The article search was conducted in PubMed, Embase, and the Cochrane Library for systematic reviews published between January 2010 and October 2025. Data was extracted on study design, population, and reported quantitative outcomes. Results: Five studies met the inclusion criteria: three focused on HCWs, while two focused on the general population. Among HCWs, knowledge ranged from 26.0% to 46.7%, and attitudes from 28.2% to 62.2%. In the general population, knowledge ranged from 33.0% to 46.6%, attitudes from 40.0% to 71.9%, and perceptions averaged around 40.0%. Across both groups, Mpox knowledge was limited, attitudes were moderately positive, and preventive behaviors remained consistently low, revealing a persistent gap between awareness and practice. Conclusions: This review highlights persistent gaps in knowledge, attitudes, and practices among HCWs and the general population. Although global attention increased substantially following the 2022 outbreak, important weaknesses remain in translating knowledge into consistent preventive behaviors. Addressing these gaps requires structured and context-specific interventions. Integrating Mpox-focused modules into mandatory Continuing Medical Education credits for HCWs could ensure sustained competency in diagnosis, infection prevention, and outbreak response beyond peak epidemic periods. For the general population, strategic risk communication campaigns should leverage trusted community leaders and social media influencers in high-risk regions to counter misinformation, reduce stigma, and promote evidence-based preventive behaviors. Embedding these targeted strategies within broader pandemic preparedness and global health security frameworks will be essential to strengthening early detection, public trust, and coordinated outbreak response in future Mpox or other emerging infectious disease events. Full article

The paper presents Nicholas of Cusa’s position in the debate on mystical theology, which had a place around the middle of the 15th century in monastic environments. His contribution to that debate was presented in the form of the treatise entitled On the Vision of God, complemented by a painted representation of the “All-seeing Face”. Both the treatise and the painting were designed to be aids in an experiment projected by Cusanus for his benedictine friends of Tegernsee Abbey, to help them in their progress towards mystical contemplation. The intention was to show them a way to lift their thought from the perception of the image, through meditation and prayer, to the contemplation of God. Thus, both the icon and his treatise were intended to fulfil an anagogical function for the users in inspiring them start on a journey of returning to God and teaching them how to effect that return. Besides giving an account of the experiment projected by Cusanus, the most important elements of his fascinating system are delineated, such as the way of mystical ascent, his use of paradox, his conception of God as the Infinity, and the conception of God’s seeing as the foundation of the existence of all things. Full article

Animal welfare in farmed animals is increasingly being identified as an integral part of ethical meat production; yet in most developing nations, including Ghana, little attention is being paid to this area of interest. The demand for chicken meat and egg products in Ghana has also increased because of rapid urbanisation and development; hence, public perception of poultry welfare is paramount in policy formulation and development in Ghana. This study investigates public perception of poultry welfare in Ghana, particularly laying hen farming. The study used a cross-sectional study and surveyed 1275 respondents aged 17 and older in Accra, Kumasi, and Tamale by collecting data in-person, and the questionnaire was administered using tablets or mobile devices. The study found that 69.1% of respondents poorly perceived farmed animal welfare, while 30.9% positively perceived farmed animal welfare in Ghana. There was a significant difference in perception levels among respondents in Accra and Kumasi, and those in Tamale, where respondents in Tamale indicated a slightly positive perception compared to those in Accra and Kumasi. Furthermore, 53.7% of respondents supported state intervention in farmed animal welfare, while 52.0% showed reluctance to pay a premium price for cage-free and free-range egg production in Ghana. Full article

Vehicular infrared object detection is a key technology supporting autonomous driving systems to achieve all-weather environmental perception. However, infrared images inherently lack texture, resulting in blurred object contours. Additionally, deep network propagation severely erodes and loses feature information of distant tiny objects. To address the above issues, this study proposes a Full-Scale Feature Synergistic Perception Architecture for vehicular infrared object detection. This architecture first designs a Gradient-Informed Attention module, which initializes convolution kernels through physical gradient operators to inject geometric prior information into the network, enhancing the model’s perception capability of blurred object boundaries. Secondly, it constructs a Full-Scale Feature Pyramid containing a $P_2$ high-resolution feature layer to effectively recover the geometric detail features of distant tiny objects. Finally, it proposes a Scale-Aware Shared Head, which relies on a cross-scale parameter sharing mechanism to achieve extreme parameter compression, and simultaneously introduces deep semantic information to form strong constraints, suppressing noise interference in shallow features. Experimental results on the FLIR v2 and M3FD datasets show that the proposed architecture exhibits excellent detection performance. On FLIR v2, it raises mAP@50 to 64.06% (6.51% relative gain vs. YOLOv11) while maintaining 547 FPS inference speed, achieving an optimal accuracy–efficiency balance. Full article

Water scarcity compels wastewater reuse, but lax discharge standards generate a regulatory mirage, misleading the public about safety. Here, “regulatory mirage” refers to situations where formal compliance with discharge standards creates a false perception of safety while ecological risks and degradation persist. Despite formal compliance, treated effluent severely harms Iran’s effluent-dependent Kashaf River, driving eutrophication, salinization, and the downstream transport of unregulated contaminants of emerging concern, including fluorinated substances (PFAS) and pharmaceuticals. These pressures extend beyond the river channel to adjacent natural wetlands, which act as de facto nature-based treatment systems yet are progressively transformed into sacrificial sinks for excess nutrients, salts, heavy metals, and micropollutants. By benchmarking the Iranian Wastewater Discharge Standards (IWDS) against international guidelines (WHO, EU, FAO), this study quantifies a “Permissibility Gap” frequently greater than 10 for key parameters such as BOD₅, nutrients, and trace metals, revealing how concentration-based limits ignore cumulative mass load and mixture toxicity at the basin scale. The Kashaf River case demonstrates that current end-of-pipe regulation undermines both natural wetlands and planned nature-based solutions, including constructed wetlands, in arid regions where effluent reuse is unavoidable. The study argues that aligning discharge standards with global benchmarks, adopting mass-based permits, and explicitly regulating contaminants of emerging concern are prerequisites for truly safe wastewater reuse and for protecting wetland ecosystems in effluent-dependent basins. This study shows that permissive, concentration-based discharge standards in effluent-dependent basins create a regulatory mirage that accelerates river and wetland degradation, and that stricter, mass-based limits are essential for safe wastewater reuse. Full article

Precision agriculture is a key technology for addressing challenges such as increasing food demand, labour shortages, and the environmental impact of intensive agrochemical use. In this context, selective weed management remains a critical issue due to its direct effect on crop productivity and sustainability. This article presents a simulation-based framework for the design and evaluation of an agricultural robotic module for the detection, classification, and selective intervention of weeds. The proposed system integrates convolutional neural networks and the kinematic model of a 2DOF robot manipulator with 5 links for weed classification and treatment. The system is evaluated in a virtual environment, where camera calibration, perception accuracy, and the performance of the kinematic model are analysed. Quantitative results include detection accuracy, localization error, and intervention success rate under simulated field conditions. The results demonstrate selective weed management and the feasibility of simulation for developing weed control systems, while also identifying the main challenges for real-world deployment. Full article