Search Results (1,004)

Urban morphology plays a decisive role in regulating microclimate and outdoor thermal comfort in arid cities, where extreme heat and intense solar radiation amplify thermal stress. This study examines the influence of four contrasting urban fabrics in Béchar (Algerian Sahara): the vernacular Ksar, the regular-grid colonial fabric, a modern large-scale residential estate, and low-density detached housing, on local microclimatic conditions. An integrated methodological framework is adopted, combining qualitative morphological analysis, quantitative indicators including density, porosity, height-to-width ratio, and sky view factor, in situ microclimatic measurements, and high-resolution ENVI-met simulations performed for the hottest summer day. Results show that compact urban forms, characterized by low sky view factor values, markedly reduce radiative exposure and improve thermal performance. The vernacular Ksar, exhibiting the lowest SVF, records the lowest mean radiant temperature (approximately 45 °C) and the most favorable average comfort conditions (PMV = 3.77; UTCI = 38.37 °C), representing a reduction of about 3 °C, while its high-thermal-inertia earthen materials ensure effective nocturnal thermal recovery (PMV ≈ 1.06; UTCI = 27.8 °C at 06:00). In contrast, more open modern fabrics, including the colonial grid, large-scale estates, and low-density housing, experience higher thermal stress, reflecting vulnerability to solar exposure and limited thermal inertia. Validation against field measurements confirms model reliability. These findings highlight the continued relevance of vernacular bioclimatic principles for sustainable urban design in arid climates. Full article

In view of the problems of high weed regeneration rate in traditional mechanical weeding and environmental risk in chemical weeding, a synergetic strategy of “mechanical damage + wound spraying mechanism” was proposed, and an intelligent weeding device combining synchronous cutting and spraying was designed to enhance the efficacy of herbicides and reduce their use. Focusing on the physical characteristics of weeds and the cutting mechanism, the analysis of the weed-cutting system and the force characteristics of the cutting tool were conducted. Key factors affecting cutting quality were identified, and their respective value ranges were determined. A targeted spraying system was developed, featuring a conical nozzle, DC diaphragm pump, and electromagnetic control valve. The Delta parallel manipulator, equipped with both the cutting tool and nozzle, was designed, and a kinematic model was established for both its forward and inverse movements. Genetic algorithms were applied to optimize structural parameters, aiming to ensure effective coverage of typical weed distribution areas within the working space. A simulated environment measurement was built to verify the motion accuracy of the manipulator. Field experiments demonstrated that the equipment achieved an 81.5% wound weeding rate on malignant weeds in the seedling stage at an operating speed of 0.6 m/s, with a seedling injury rate below 5%. These results validate the high efficiency of the integrated mechanical cutting and targeted spraying system, offering a reliable technical solution for green and intelligent weed control in agriculture. This study fills the blank of only focusing on recognition accuracy or weeding rate under a single weeding method, but lacks a cooperative weeding operation. Full article

The high computational demand of dense voxel representations severely limits current vision-centric 3D semantic occupancy prediction methods, despite their capacity for granular scene understanding. This challenge is particularly acute in safety-critical applications like autonomous driving, where accurately perceiving dynamic instances often takes precedence over capturing the static background. This paper challenges the paradigm of dense prediction for such instance-focused tasks. We introduce the LSON-IP, a framework that strategically avoids the computational expense of dense 3D grids. LSON-IP operates on a sparse set of 3D instance queries, which are initialized directly from multi-view 2D images. These queries are then refined by our novel Sparse Instance Aggregator (SIA), an attention-based module. The SIA incorporates rich multi-view features while simultaneously modeling inter-query relationships to construct coherent object representations. Furthermore, to obviate the need for costly 3D annotations, we pioneer a Differentiable Sparse Rendering (DSR) technique. DSR innovatively defines a continuous field from the sparse voxel output, establishing a differentiable bridge between our sparse 3D representation and 2D supervision signals through volume rendering. Extensive experiments on major autonomous driving benchmarks, including SemanticKITTI and nuScenes, validate our approach. LSON-IP achieves strong performance on key dynamic instance categories and competitive overall semantic completion, all while reducing computational overhead by over 60% compared to dense baselines. Our work thus paves the way for efficient, high-fidelity instance-aware 3D perception. Full article

Quantum anomalies are traditionally understood as classical symmetries that fail to survive quantization, while experimental “anomalies” denote deviations between theoretical predictions and measured values. In this work, we develop a unified framework in which both phenomena can be interpreted through the lens of algebraic quantum field theory (AQFT). Building on the renormalization group viewed as an extension problem, we show that renormalization ambiguities correspond to nontrivial elements of Hochschild cohomology, giving rise to a deformation of the observable algebra $A\ast B=AB+\epsilon \omega (A,B)$, where $\omega$ is a Hochschild 2-cocycle. We interpret $\omega$ as an intrinsic algebraic curvature of the net of local algebras, namely the (local) Hochschild class that measures the obstruction to trivializing infinitesimal scheme changes by inner redefinitions under locality and covariance constraints. The transported product is associative; its first-order expansion is associative up to $\mathcal{O}\left({\epsilon }^2\right)$ while preserving the ∗-structure and Ward identities to the first order. We prove the existence of nontrivial cocycles in the perturbative AQFT setting, derive the conditions under which the deformed product respects positivity and locality, and establish the compatibility with current conservation. The construction provides a direct algebraic bridge to standard cohomological anomalies (chiral, trace, and gravitational) and yields correlated deformations of physical amplitudes. Fixing the small deformation parameter $\epsilon$ from the muon $(g-2)$ discrepancy, we propagate the framework to predictions for the electron $(g-2)$, charged lepton EDMs, and other low-energy observables. This approach reduces reliance on ad hoc form-factor parametrizations by organizing first-order scheme-induced deformations into correlation laws among low-energy observables. We argue that interpreting quantum anomalies as manifestations of algebraic curvature opens a pathway to a unified, testable account of renormalization ambiguities and their phenomenological consequences. We emphasize that the framework does not eliminate renormalization or quantum anomalies; rather, it repackages the finite renormalization freedom of pAQFT into cohomological data and relates it functorially to standard anomaly classes. Full article

The Daylight Glare Probability (DGP) includes the luminance of a glare source quadratically, but the solid angle only linearly. While this is in line with formulae of other glare metrics, it must be questioned for small glare sources, if the glare stimulus can no longer be distinguished from larger stimuli causing equal vertical illuminance at the eye, especially in the peripheral visual field. To account for this, the modified version Daylight Glare Metric (DGM) was previously developed. We conducted two studies to evaluate the effect of the modified DGM. First, in a laboratory study under an artificial sky with an LED sun, 35 test subjects evaluated different glare situations. Second, we performed a comprehensive simulation study for an office space, including three locations, three view directions, and 17 window systems (electrochromic glazing, fabric shades). The results from the perception study under the artificial sky provide evidence that the adapted DGM is better suited to predict glare from small, bright sources. The results from the simulation study for a realistic office setting show that, compared to the DGP, the DGM reduces glare ratings for many hours of the year, thus underscoring the practical relevance of improving the DGP formula. Full article

Addressing the issue of fruit recognition and localization failures in harvesting robots due to severe occlusion by branches and leaves in complex orchard environments, this paper proposes an occlusion avoidance method that combines a lightweight YOLOv8n model, developed by Ultralytics in the United States, with active perception. Firstly, to meet the stringent real-time requirements of the active perception system, a lightweight YOLOv8n model was developed. This model reduces computational redundancy by incorporating the C2f-FasterBlock module and enhances key feature representation by integrating the SE attention mechanism, significantly improving inference speed while maintaining high detection accuracy. Secondly, an end-to-end active perception model based on ResNet50 and multi-modal fusion was designed. This model can intelligently predict the optimal movement direction for the robotic arm based on the current observation image, actively avoiding occlusions to obtain a more complete field of view. The model was trained using a matrix dataset constructed through the robot’s dynamic exploration in real-world scenarios, achieving a direct mapping from visual perception to motion planning. Experimental results demonstrate that the proposed lightweight YOLOv8n model achieves a mAP of 0.885 in apple detection tasks, a frame rate of 83 FPS, a parameter count reduced to 1,983,068, and a model weight file size reduced to 4.3 MB, significantly outperforming the baseline model. In active perception experiments, the proposed method effectively guided the robotic arm to quickly find observation positions with minimal occlusion, substantially improving the success rate of target recognition and the overall operational efficiency of the system. The current research outcomes provide preliminary technical validation and a feasible exploratory pathway for developing agricultural harvesting robot systems suitable for real-world complex environments. It should be noted that the validation of this study was primarily conducted in controlled environments. Subsequent work still requires large-scale testing in diverse real-world orchard scenarios, as well as further system optimization and performance evaluation in more realistic application settings, which include natural lighting variations, complex weather conditions, and actual occlusion patterns. Full article

Positron Emission Tomography (PET) is undergoing a profound transformation. Driven by the convergence of highly sensitive long-axial field-of-view (LAFOV) total-body PET systems and an expanding portfolio of targeted radiopharmaceuticals, PET is progressively evolving beyond its traditional role in oncologic diagnosis and staging. Ultra-sensitive scanners enable whole-body imaging with markedly reduced radiotracer doses, rapid acquisition times, and true dynamic multiparametric imaging across all organs simultaneously. In parallel, molecularly targeted radioligands support tumour phenotyping, theranostic applications, and personalized dosimetry. Together, these advances position PET as a systemic imaging platform capable of interrogating whole-body tumour biology, guiding precision therapies, and potentially enabling early detection or surveillance strategies in selected high-risk populations. This narrative review summarizes the technological foundations of total-body PET, reviews current clinical and translational applications, discusses opportunities and limitations for early detection and surveillance, and outlines a research and implementation roadmap to responsibly translate this paradigm into clinical oncology. Full article

We present an inverse designed metalens for long-wave infrared (LWIR) imaging tailored to consumer and Internet of Things (IoT) platforms. Conventional LWIR optics either rely on costly specialty materials or suffer from low efficiency and narrow fields of view (FoV), limiting scalability. Our approach integrates adjoint-based inverse design with fabrication-aware constraints and a cone-shaped source model that efficiently captures oblique incidence during optimization. The resulting multi-level metalens achieves a wide FoV up to 100° while maintaining robust focusing efficiency and stable angle-to-position mapping on low-power $4\times 4$ microbolometer arrays representative of edge devices. We further demonstrate application-level imaging on $4\times 4$ microbolometer arrays, showing that the proposed metalens delivers a substantially wider FoV than a commercial narrow FoV lens while meeting low-resolution, low-cost, and low-power constraints for edge LWIR modules. By eliminating bulky multi-element stacks and reducing cost and form factor, the proposed design provides a practical pathway to compact, energy-efficient LWIR modules for consumer applications such as occupancy analytics, smart-building automation, mobile sensing, and outdoor fire surveillance. Full article

Cyber–physical systems (CPS) in healthcare represent a deep integration of computational intelligence, physical medical devices, and human-centric data, enabling continuous, adaptive, and personalized care. These systems combine real-time measurements, artificial intelligence (AI)-based analytics, and networked medical devices to monitor, predict, and optimize patient health outcomes. A key development in the field of CPS is the emergence of patient digital twins (DTs), virtual models of individual patients that simulate biological, behavioral, and social parameters. Using AI, DTs analyze complex medical and social data (genetics, lifestyle, environment, etc.) to support precise diagnosis and treatment planning. The implications of the bibliometric findings suggest that the field emerges from the conceptual phase, justifying the article’s emphasis on both the proposed architectures and their clinical validation. However, most research was conducted in computer science, engineering, and mathematics, rather than medicine and healthcare, suggesting an early stage of technological maturity. Leading countries were India, the United States, and China, but these countries did not have a high number of publications, nor did they record leading researchers or affiliations, suggesting significant research fragmentation. The most frequently observed Sustainable Development Goals indicate an industrial context. Reflecting insights from medical and social research, AI-based DT systems provide a holistic view of the patient, taking into account not only physiological states but also psychological and social well-being. These systems promote personalized therapy by dynamically adapting treatment based on real-time feedback from wearable sensors and electronic medical records. More broadly, CPS and DT systems increase healthcare system efficiency by reducing hospitalizations and supporting remote preventive care. Their implementation poses significant ethical and privacy challenges, particularly regarding data ownership, algorithm transparency, and patient autonomy. Full article

Amid growing “nature deficit” associated with urbanization and indoor living, flowering plants are increasingly used to support psychological restoration. Yet evidence on how floral color and structural morphology jointly shape restorative outcomes remains limited. This study employed a within-subjects, repeated-measures design, utilizing physiological instruments and psychological questionnaires to investigate the physiological and psychological restorative benefits of Hydrangea macrophylla and to quantify the differences in restorative effects across five colors (blue, pink, white, mauve, red), two inflorescence types (mophead, lacecap), and two petal structures (single, double). Twenty-eight healthy young adults viewed 15 live hydrangea stimuli under controlled laboratory conditions. Multimodal outcomes combined objective measures—eye-tracking and single-channel EEG—with subjective measures (SD; POMS). Hydrangea exposure significantly reduced negative mood, and color and structure exerted distinct and interactive effects on visual attention and arousal. Red and mauve elicited larger pupil diameters than white and pink, while lacecap inflorescences were associated with lower cognitive load and improved attentional recovery relative to mophead. Double-petaled forms showed greater attentional dispersion than single-petaled forms. Interactions indicated that morphology modulated color effects. The mauve lacecap double-flowered cultivar (M02) showed the strongest observed restorative potential within this sample. These findings highlight the importance of integrating color and structural cues when selecting flowering plants for restorative environments and horticultural therapy, and they motivate field-based replications with broader samples and higher-density physiology. Full article

This study investigates how digital lifestyles and cognitive fatigue influence sustainable behaviour and energy awareness among Generation Z. Drawing on environmental psychology and social science perspectives, it explores behavioural and cognitive mechanisms linking digital overexposure with pro-environmental engagement. A cross-sectional survey conducted among 683 Polish secondary-school students examined the relationships between digital activity, fatigue, self-regulation, and sustainability practices such as waste segregation, reuse, and consumption moderation. The results show that higher digital fatigue and problematic online use are negatively associated with sustainability engagement, supporting the view that cognitive overload reduces individuals’ capacity for mindful, sustainability-oriented action. Using k-means clustering and robust regression analyses based on ordinary least squares (OLS), this study identifies distinct sustainability behaviour profiles among Generation Z and examines how digital fatigue and problematic online use predict lower engagement in pro-environmental practices. Importantly, educational level moderated this effect, suggesting that energy and sustainability literacy can buffer the adverse consequences of digital exhaustion. The findings contribute to the growing field of digital sustainability and highlight the need to integrate digital well-being and environmental education into youth and social policy frameworks. Full article

Green roofs have emerged as a key nature-based solution for improving environmental quality, strengthening urban resilience, and enhancing human wellbeing. In the hospitality sector—where sustainable design and guest experience increasingly intersect—the incorporation of green roof gardens is particularly significant. Urban hotels face heightened challenges related to elevated temperatures, reduced green space, and the growing need for restorative environments within dense urban settings. This study aims to examine how green roof gardens function as integrated ecological, social, and psychological infrastructures in hotel environments. It evaluates the extent to which rooftop green spaces contribute to environmental sustainability, enhance guest experience, and foster community connections. The research adopts a qualitative design combining a comprehensive literature review conducted at selected five-star hotels in Greece. Data from secondary sources and field-based assessments were thematically analyzed to identify recurring patterns in environmental performance, social use, and psychological benefits. Findings indicate that hotel green roof gardens act as multifunctional systems that deliver significant ecological benefits—such as improved microclimate regulation, stormwater retention, and biodiversity support—while simultaneously enriching social interaction and guest experience through accessible, esthetically appealing spaces. Observations further highlight their contribution to psychological wellbeing by offering restorative environments characterized by greenery, natural light, and panoramic views. The study concludes that green roof gardens represent an effective design strategy that integrates sustainability, hospitality experience, and urban wellbeing. Their application in hotels provides both conceptual insight and practical guidance for the development of more resilient, livable, and guest-centered urban environments. These findings underscore the importance of incorporating green roofs into contemporary tourism and urban planning practices. Full article

Urban green space is essential for ecological functions, environmental quality, and human well-being, yet campus expansion can reduce vegetated areas. This study assessed UGS dynamics at Universitas Padjadjaran’s Jatinangor campus from 2015 to 2025 and evaluated an object-based machine learning approach for fine-scale land cover mapping. High-resolution WorldView-2, WorldView-3, and Legion-03 imagery were pan-sharpened, geometrically corrected, normalized, and used to compute NDVI and NDWI indices. Object-based image analysis segmented the imagery into homogeneous objects, followed by random forest classification into six land cover classes; UGS was derived from dense and sparse vegetation. Accuracy assessment included confusion matrices, overall accuracy 0.810–0.860, kappa coefficients 0.747–0.826, weighted F1 scores 0.807–0.860, and validation with 43 field points. The total UGS increased from 68.89% to 74.69%, bare land decreased from 13.49% to 5.81%, and building areas moderately increased from 10.36% to 11.52%. The maps captured vegetated and developed zones accurately, demonstrating the reliability of the classification approach. These findings indicate that campus expansion has been managed without compromising ecological integrity, providing spatially explicit, reliable data to inform sustainable campus planning and support green campus initiatives. Full article

Antimicrobial resistance (AMR) is an increasing concern in food animal production. In swine herds, viral infections often lead to secondary bacterial disease and higher antimicrobial use (AMU). This study describes how U.S. swine veterinarians view the role of viral vaccines in reducing this reliance on antimicrobials. We conducted a national survey of swine practitioners and follow-up semi-structured interviews with a subset of respondents. Across participants, porcine reproductive and respiratory syndrome (PRRS), swine influenza (SIV), and rotaviral enteritis were most often named as viral diseases in urgent need of improved vaccines. These diseases cause substantial economic losses and frequently trigger AMU in commercial herds. Veterinarians reported several recurring challenges with current vaccines, including limited cross-protection against field strains, interference from maternally derived antibodies, and short duration of protection. Despite these limitations, most respondents supported vaccination as a key tool to curb AMU and indicated they would accept higher prices for clearly improved products. These findings reveal both a clear need and specific opportunities for future vaccine development to provide broader and more reliable protection, reduce AMU, and help slow the development of AMR. Full article

Advancements in natural language processing (NLP), particularly Large Language Models (LLMs), have greatly improved how we access knowledge. However, in critical domains like biomedicine, challenges like hallucinations—where language models generate information not grounded in data—can lead to dangerous misinformation. This paper presents a hybrid approach that combines LLMs with Knowledge Graphs (KGs) to improve the accuracy and reliability of question-answering systems in the biomedical field. Our method, implemented using the LangChain framework, includes a query-checking algorithm that checks and, where possible, corrects LLM-generated Cypher queries, which are then executed on the Knowledge Graph, grounding answers in the KG and reducing hallucinations in the evaluated cases. We evaluated several LLMs, including several GPT models and Llama 3.3:70b, on a custom benchmark dataset of 50 biomedical questions. GPT-4 Turbo achieved 90% query accuracy, outperforming most other models. We also evaluated prompt engineering, but found little statistically significant improvement compared to the standard prompt, except for Llama 3:70b, which improved with few-shot prompting. To enhance usability, we developed a web-based interface that allows users to input natural language queries, view generated and corrected Cypher queries, and inspect results for accuracy. This framework improves reliability and accessibility by accepting natural language questions and returning verifiable answers directly from the knowledge graph, enabling inspection and reproducibility. The source code for generating the results of this paper and for the user-interface can be found in our Git repository: https://git.zib.de/lpusch/cyphergenkg-gui, accessed on 1 November 2025. Full article