Search Results (1,792)

Grain quality in bread wheat is a complex trait determined by multiple genetic factors and their interaction with environmental conditions. This study investigated the genetic architecture of key grain quality traits in the Avalon × Cadenza double haploid (DH) population under contrasting climatic conditions in Kazakhstan. A set of 101 spring-type DH lines was evaluated over three years in three major wheat-growing regions of Kazakhstan, representing northern, central, and southern environments. Grain yield and nine grain quality traits were assessed, including amylose content (Amc, %), test weight per liter (TWL, g/L), grain protein content (GPC, %), gliadin content (Gli, %), glutenin content (Glu, %), grain hardness (GH, %), grain vitreousness (GV, %), falling number (FN, s), and sedimentation value determined in a 2% acetic acid solution (SV, mL). The objectives were to characterize phenotypic variation, examine trait relationships, and identify major and environmentally stable quantitative trait loci (QTLs) controlling grain quality. QTL mapping identified 89 QTLs associated with the nine studied traits, including 82 major QTLs explaining more than 10% of phenotypic variation and 16 stable QTLs detected in two or more environments. The largest numbers of QTLs were found for GPC, SV, and TWL. Stable QTLs were distributed across all three wheat genomes, with important regions detected on chromosomes 1A, 1B, 2D, 4A, 4D, 5A, 6A, and 7D. Several stable QTLs co-localized with genomic regions previously associated with grain quality and developmental regulation, including loci near Wx-B1, Rht-D1, and Ppd-D1, suggesting biologically meaningful links among gluten composition, starch biosynthesis, plant development, and grain physical properties. These results improve understanding of the genetic control of wheat grain quality across diverse environments in Kazakhstan and provide promising targets for marker-assisted selection to combine improved end-use quality with wide environmental adaptation. Full article

Single-cell RNA-sequencing (scRNA-seq) studies over the past several years have provided unprecedented resolution into the transcriptomic landscape of both major and minor salivary glands. This technology enables the identification of diverse and functionally specialized cell populations that underlie glandular architecture and physiology. Increasingly, scRNA-seq has become an integral component of experimental design, used not only to validate prior observations but also to uncover novel cell types, pathways, and molecular regulatory mechanisms. As a result, a growing number of publicly available datasets now encompass a wide spectrum of biological contexts including homeostasis, disease, and regeneration. However, inconsistencies in data processing and incomplete reporting of experimental methods pose challenges for reproducibility and limit the ability to distinguish high-quality datasets. As single-cell technologies continue to evolve and become more accessible, their application in salivary gland research is expected to expand, offering deeper insight into both basic biology and clinical translation. This review compiles and summarizes findings from a growing body of scRNA-seq studies of the salivary glands, highlights current limitations, provides methodological considerations, and expounds on key cellular and genomic discoveries to help guide future investigations. Full article

Cloud-based telephony platforms face growing fraud risks including voice phishing (vishing), subscription abuse, and organizational impersonation, with detection being especially challenging in low-resource languages such as Arabic. We present an Artificial Intelligence (AI)-based security architecture for fraud detection in Arabic cloud call centers, combining onboarding verification, behavioral monitoring, domain-adapted Automatic Speech Recognition (ASR), semantic transcript search, and Large Language Model (LLM)-based entity verification. The domain-adapted Langa ASR model achieves a Word Error Rate (WER) of 41.0% and Character Error Rate (CER) of 18.2%, outperforming all evaluated commercial baselines. LLM-based entity extraction with multi-call consensus achieves 97.3% company-name accuracy (Generative Pre-trained Transformer 4, GPT-4) and 92.0% in the cost-effective deployed configuration (GPT-3.5 with log-probability filtering). Evaluated on production data from a Middle East and North Africa (MENA)-region provider spanning more than 1000 accounts, the pipeline flagged 47 accounts of which 41 were confirmed fraudulent (directly observed precision 87.2%, 95% confidence interval (CI): 74.3–95.2%; estimated recall 51–82% under conservative base-rate assumptions—not directly measured), providing evidence for the viability of a unified, threat-model-driven architecture for low-resource telephony fraud detection. Full article

Contemporary emergency systems operate at the intersection of climate volatility, digital interdependence, and cascading institutional disruptions. Despite growing research on resilience, adaptive governance, and digital transformation, these fields remain largely disconnected, leaving a theoretical gap in explaining how emergency systems perform under compound uncertainty. This integrative review synthesizes 32 peer-reviewed articles (post-2020) using structured narrative methodology and VOSviewer bibliometric analysis to map the field’s intellectual architecture and identify its structural gaps. The analysis reveals six thematic clusters organized around resilience as the central construct, yet characterized by three recurring disconnections: the weak integration between digital transformation and governance theory, the operational underdevelopment of polycentric governance frameworks, and the temporal separation between emergency response and climate adaptation. Drawing on this structural diagnosis, the study advances the Complex Adaptive Governance (CAG) model—a three-layer framework encompassing systemic architecture, adaptive mechanisms, and operational resilience—in which digital interoperability functions as a cross-cutting accelerator. The CAG model reconceptualizes resilience as a relational property of governance ecosystems, enhanced by digital interoperability, and offers design principles for climate-resilient emergency systems aligned with SDG 9, SDG 11, SDG 13, and SDG 16. Full article

The growing need for sustainable aviation propulsion has increased interest in hydrogen fuel cell systems as alternatives to combustion engines. This study presents the modeling, simulation, and optimization of a hybrid hydrogen–electric powertrain for the MIMIQ unmanned aerial vehicle (UAV). A 2 kW proton exchange membrane fuel cell is integrated with a 12S lithium-polymer battery via a DC–DC converter, enabling parallel power sharing and in-flight battery recharging. A MATLAB-based dynamic model was developed using mission power profiles derived from flight data and refined using momentum theory. The developed model was benchmarked through a comparative simulation of a combustion-based hybrid-electric powertrain variant of the same platform, demonstrating consistency in electrical and energetic behavior. Multi-objective optimization using NSGA-II was performed to maximize hover endurance and to minimize energy consumption while maximizing payload over a full mission. Results from this computational framework show that endurance is primarily constrained by hydrogen availability rather than battery capacity, with the fuel cell operating near its optimal efficiency region. The findings indicate that hydrogen–electric architectures offer improved endurance, reduced emissions and better scalability compared to combustion-based systems, supporting their suitability for long-endurance UAV applications. Full article

As social robots are increasingly deployed in domains such as healthcare, education, and entertainment, there is growing demand for affective agents that can interpret users’ affective states and respond in contextually appropriate ways. Existing work has established strong foundations for emotion generation and appraisal, but the step that connects generated emotion to behavioral execution still relies heavily on model-specific rules or implicit links. We frame this issue as a Mechanism Gap and propose an Emotional BDI framework that introduces Frijda’s action tendency as an intermediate representation layer between the Affective Core and the Belief–Desire–Intention (BDI) Executor. Rather than mapping emotion directly to concrete behavior, the framework first transforms affective state into a directional action tendency and then lets BDI reasoning realize that tendency according to role and context. This creates an explicit emotion-to-behavior mediation structure through which the same emotion can be expressed differently across situations and roles. In an exploratory user evaluation with 26 participants, the proposed model received more favorable ratings than an Emotion-Driven Agent in satisfaction ($p=0.010$) and appropriateness ($p=0.002$). Compared with a Cooperative Agent, the proposed model showed a significant advantage only in satisfaction ($p=0.030$). These findings suggest that the proposed framework offers a useful architectural direction for affective decision making beyond direct mapping or unconditional compliance. Full article

Biochemical detection is fundamental to various scientific disciplines, yet conventional methods still face inherent bottlenecks in achieving rapid, ultrasensitive, and simultaneous multi-target analysis. Terahertz (THz) waves, characterized by their unique spectral fingerprinting capabilities and non-destructive properties, have emerged as a compelling platform for advanced biochemical sensing. This review outlines the evolution of THz biochemical sensing over the past two decades, tracing its progression from passive identification toward intelligent perception. We structure this technological trajectory around four core themes: sensitivity enhancement, specific recognition, multi-target visualization, and system intelligence. We first evaluate the fundamental limitations of direct detection techniques, such as THz time-domain spectroscopy (THz-TDS). Building on this, we examine how metamaterial-assisted architectures utilize high-quality-factor resonances to achieve trace-level detection, pushing the limits of detection (LOD) down to the ng/mL or even pg/mL scale, and how surface chemical functionalization provides a molecular lock mechanism for selective targeting in complex samples. Furthermore, we highlight the paradigm shift from single-point spectral measurements to spatially resolved multi-target imaging using pixelated metasurfaces. Finally, the review addresses emerging directions, including dynamically tunable intelligent metasurfaces, multimodal on-chip integration platforms, and the growing integration of artificial intelligence (AI) in inverse design and data interpretation, which achieves classification accuracies exceeding 95% even in complex matrices. By synthesizing these developments, this review provides a comprehensive perspective on the future trajectory of THz sensing technologies. Full article

The NP-hard coordination of heterogeneous robots for time-windowed warehouse tasks remains challenging: metaheuristics are precise but slow, whereas neural methods cannot handle heterogeneous constraints, leading to infeasible allocations. This paper presents the Constraint-Aware Heterogeneous Transformer (CAHT), a lightweight encoder–decoder architecture that performs end-to-end task assignment and sequencing in a single forward pass. The central innovation is a dynamic feasibility masking mechanism that enforces capacity and energy constraints directly within the softmax computation, eliminating infeasible allocations at the architectural level. This is complemented by a spatial-bias Transformer encoder and a two-stage supervised–reinforcement learning training paradigm using ALNS-generated labels. Experiments across four problem scales (5–20 robots, 50–200 tasks) demonstrate that CAHT achieves objective values within 7–13% of the ALNS reference while being 29–91× faster (23–104 ms vs. 2–3 s). Constraint violation rates remain below 6%, with time-window satisfaction above 94%. Ablation analysis identifies dynamic masking as the dominant contribution (+213% degradation upon removal), and cross-scale generalization reveals that the optimality gap decreases from 13.0% to 10.7% as the problem scale grows. With only 0.91 M parameters, CAHT occupies a new trade-off point on the Pareto frontier, offering a practical path toward real-time autonomous warehouse coordination. Full article

Peptide-based materials represent a rapidly growing field in nanotechnology, bridging bottom-up self-assembly and top-down approaches for the development of functional nanostructures. Among these systems, peptide-based nanogels (NGs), namely nanogels in which peptides assume a structural role, have emerged as a promising class of injectable formulations. Typically characterized by a core–shell architecture, these systems are closely related to peptide hydrogels in terms of structural organization. This review provides a state-of-the-art overview of peptides used as core structural elements for NG formulation, focusing on the peptide building blocks employed, the main formulation methodologies, and their current applications, with particular emphasis on pharmaceutical ones. Their potential as drug delivery systems and stimuli-responsive platforms for controlled and targeted release is also reported. For clarity, the reported formulations are classified according to the chemical nature of the core-structuration peptide, distinguishing systems based on Fmoc-FF from those derived from other primary sequences, including Boc-protected tripeptides, dehydropeptides, and chemically crosslinked peptide assemblies. Full article

Morocco, facing a growing prevalence of chronic diseases such as diabetes, hypertension, and cardiovascular diseases, must overcome significant challenges to modernize its healthcare system. In this context, the integration of digital technologies, including telemedicine, the Internet of Medical Things (IoMT), Artificial Intelligence (AI), and healthcare system interoperability, represents a promising solution to improve the management of chronic diseases. This article examines how these technologies can be utilized to transform the Moroccan healthcare system into a more accessible, efficient, and patient-focused model of care. The paper reviews recent pilot projects and initiatives, focusing on infrastructure development, remote monitoring, AI and IoMT integration, public health campaigns, and national health programs aimed at improving access to treatment. Building on these observations, the paper explores the potential of an integrated digital health system for managing chronic diseases and proposes a national integrated care architecture that connects Morocco’s public and private healthcare providers. These insights highlight the significance of digital health in Morocco and provide a framework for improved, more patient-centered, and more efficient advanced healthcare. Future perspectives focus on developing an adapted digital transformation approach to further enhance chronic disease management. Full article

To address the challenges of high labor intensity, low efficiency, and growing labor shortages in Morchella harvesting, this paper proposes the YOLO-CAS algorithm and develops a corresponding maturity detection system. Built upon the YOLOv8s architecture, the proposed model integrates the Convolutional Block Attention Module (CBAM) and Alterable Kernel Convolution (AKConv) while replacing the CIoU loss function with SIoU. These enhancements prioritize the refinement of feature extraction and selection for key Morchella characteristics, providing a robust algorithmic core for the detection system. The specific technical improvements are threefold: (1) CBAM modules are integrated into the three C2f-to-Detect coupling interfaces and the multi-scale output paths of the neck; (2) AKConv is embedded into the C2f structure at the end of the backbone prior to the SPPF module; and (3) the original CIoU is substituted with the SIoU loss function. Experimental results demonstrate that the refined model’s precision increased from 0.847 to 0.939, recall rose from 0.761 to 0.867, mAP50 improved from 0.853 to 0.927, and mAP50:95 advanced from 0.531 to 0.599, alongside a boost in inference speed. Furthermore, a deployment system with a streamlined and intuitive interface was developed using PyQt5. This system offers high flexibility and scalability, effectively meeting the practical demands of diverse agricultural environments. Full article

Carbon fiber-reinforced polymers (CFRP) are widely employed in advanced manufacturing sectors such as aerospace, wind energy, and new energy vehicles owing to their high specific strength and stiffness. The growing demand for lightweight, high-performance, and multifunctional materials has accelerated the development of structurally and functionally integrated CFRP. Introducing functional interlayers between composite laminates is an effective strategy to impart additional functionalities; however, such interlayers are often multi-component and structurally complex. A critical challenge remains to integrate functionality without compromising, and preferably enhancing, the load-bearing capability of CFRP, particularly their resistance to interlaminar delamination. In this study, electrically heated CFRP incorporating a sandwich-structured interlayer composed of glass fiber mesh fabric/CNT veils doped with carbon nanotubes/glass fiber mesh fabric (GF/CNTs-CNTv/GF) was investigated. The effects of interlayer architecture and CNT loading on the Mode II interlaminar fracture toughness were systematically examined. Delamination failure modes and interlaminar toughening mechanisms were analyzed using scanning electron microscopy and ultra-depth-of-field three-dimensional microscopy. The results demonstrate that an optimal CNT pre-impregnation concentration of 1.0 wt% yielded a maximum G_IIC of 1644.8 J/m², corresponding to a 103.06% increase relative to the reference laminate. The enhanced performance is attributed to simultaneous optimization of interfacial “nano-engineering” effects, including matrix toughening and a pronounced “nano-anchoring” mechanism induced by CNT. These effects promote a transition in failure mode from weak interfacial debonding to a mesh-block composite delamination pattern, thereby activating multiple energy-dissipation mechanisms such as crack deflection, fiber pull-out, rupture, and bridging. This work highlights the effectiveness of CNT-modified sandwich interlayers in improving delamination resistance and provides both theoretical insight and experimental validation for the design of multifunctional CFRP with superior interlaminar fracture toughness. Full article

In recent years, deep learning (DL) has emerged as a transformative technology with significant potential to advance the Architecture, Engineering, and Construction (AEC) industry. DL enables automation, intelligent decision-making, and predictive analytics across various phases of construction, including design, site monitoring, safety management, and facility operations. Despite its growing adoption, research on the comprehensive methods, practical challenges and emerging opportunities of DL in the AEC industry remains limited. This study presents a state-of-the-art review of DL applications in the AEC industry by focusing on key methods, challenges, emerging opportunities and future research directions. A systematic literature review (SLR) was conducted in this study. Three major DL methods applied in the AEC industry were examined: (i) data-driven computer vision, (ii) natural language processing (NLP), and (iii) generative and simulation-based methods. Key challenges were identified: (i) data scarcity issues, (ii) high computational requirements, (iii) limited generalization across projects, (iv) human factors and resistance to adoption, and (v) lack of standardization and interoperability. Additionally, emerging opportunities and future research directions are highlighted: (i) advanced construction site monitoring and safety management, (ii) automated design and generative modeling, (iii) predictive maintenance and facility management, (iv) integration with robotics and autonomous construction systems, and (v) smart project management and decision support systems. This study advances a holistic understanding of DL in the AEC industry by systematically synthesizing current methods, challenges, and emerging trends. It establishes a structured foundation for future research to overcome technical, practical, and organizational challenges, thereby supporting the scalable, intelligent, and sustainable transformation of construction practices. Full article

X-ray techniques provide powerful, non-destructive tools for structural characterization in semiconductor manufacturing and advanced packaging. Their strong penetration capability and sensitivity to multiple contrast mechanisms enable the investigation of lattice structure, strain, defects, interfaces, and elemental distribution across a wide range of length scales. As semiconductor devices evolve toward three-dimensional architectures and heterogeneous integration, there is an increasing demand for characterization approaches capable of probing complex, buried, and multi-scale structures in a consistent manner. In this review, we present a systematic overview of X-ray characterization techniques for advanced semiconductor systems, including diffraction-based methods, small-angle scattering, computed tomography, X-ray fluorescence, and spectroscopic approaches. These techniques are discussed in terms of the type of structural, morphological, and compositional information they provide, their applicable length scales, and their strengths and limitations in addressing key challenges such as thin films, high-aspect-ratio structures, buried interfaces, and full wafers. Particular attention is given to the complementary nature of different X-ray modalities and their roles in addressing practical metrology problems. The limitations associated with resolution, model dependence, and data interpretation are also outlined. Finally, emerging opportunities in laboratory X-ray sources, synchrotron-based methods, and integrated characterization strategies are briefly discussed. This review aims to provide a unified perspective for understanding and integrating X-ray techniques, offering insights into their roles in addressing the growing complexity of next-generation semiconductor devices. Full article

The rising demand for cooling in hot semi-arid cities like Jaipur is putting increasing pressure on energy infrastructure and urban resilience. This study investigates the potential of Jaali, a traditional perforated screen used in Indian architecture, as a passive strategy to reduce energy demand in a contemporary office building through data-driven optimisation and computational analysis. Using detailed energy simulations in DesignBuilder, this research explores how variations in orientation, cavity depth, perforation ratio and screen thickness affect cooling performance during the summer months through a systematic parametric study generating 84 simulation configurations. The model is based on a 12-storey office building designed according to local energy codes. The results show that the optimal configuration differs by orientation. On the south façade, the optimal combination is a 100 mm Jaali with 20% perforation and a 1.5 m cavity, which delivers the best performance. The west façade performs best with a thicker 150 mm screen, the same 20% perforation ratio, and a 1.0 m cavity depth. On the east façade, the strongest performance is achieved with a 150 mm Jaali, 50% perforation, and a 1.5 m cavity, with cooling demand reduction of up to 8.71%. These findings demonstrate that traditional design elements, when optimised for modern use, can offer measurable energy savings through predictive modelling frameworks. More importantly, their widespread adoption could support urban cooling strategies, reduce peak electricity loads and contribute to sustainable development across rapidly growing cities in hot climates. The comprehensive dataset generated provides a foundation for future AI-enhanced building energy optimisation applications. Full article