Search Results (43,465)

To identify chitinase genes from the genome of the mycoparasitic Cladosporium sp. strain SYC23, bioinformatical analyses and real-time quantitative PCR (RT-qPCR) were employed to screen mycoparasitism-associated genes at 12, 24, 48, and 72 h post-induction with Aecidium pourthiaea rust spores. A total of eight chitinase genes were identified from SYC23 via bioinformatics analysis and designated CaChi34, CaChi40, CaChi45, CaChi67, CaChi82, CaChi92, CaChi93, and CaChi286 based on sequence and phylogenetic analyses. Analysis of the chitinase protein sequence characteristics revealed molecular weights ranging from 33.86 to 286.03 kDa and theoretical isoelectric points from 4.48 to 7.7. All CaChi genes contained the conserved GH18 domain, and promoter analysis showed that each harbored MYB-binding sites and pathogen-responsive elements. Mycoparasitism-related sequence clustering analysis indicated that the chitinase sequences of SYC23 shared the closest phylogenetic relationship with those from Trichoderma sp. RT-qPCR results following rust spore induction showed that five CaChi genes reached their highest expression levels at 24 h post-induction, CaChi45 was most highly expressed at 72 h post-induction, CaChi93 was continuously upregulated, and CaChi82 was continuously downregulated throughout the induction period. His-tagged recombinant CaChi93 protein was purified from E. coli and characterized. The results demonstrate that the enzymatic activity of CaChi93 was 0.929 U/mg, with optimal reaction conditions at 65 °C and pH 7. Treatment of A. pourthiaea rust spores with the recombinant CaChi93 chitinase confirmed that CaChi93 could effectively dissolve rust spore walls. In conclusion, this study confirms that the mycoparasitic Cladosporium sp. strain SYC23 can secrete chitinase to degrade the rust spore wall and induce spore death, thereby providing novel gene resources and a theoretical basis for the biological control of A. pourthiaea. Full article

Digitalisation is transforming organisational practices, making digital readiness essential for strategic planning. However, customised digital maturity tools for the Irish Architecture, Engineering, Construction, and Operations (AECO) sector remain limited. This paper presents the development and validation of a Digital Maturity Gap Analysis Toolkit (DMGAT) for the Irish AECO sector. The toolkit assesses digital maturity across three dimensions—people, process and culture; technology; and policy and governance—covering 16 sub-dimensions and 69 assessment questions. Unlike existing tools such as the BIM Maturity Matrix, VDC BIM Scorecard, and Maturity Scan, the DMGAT uniquely integrates ISO 19650 maturity stages with a comprehensive maturity level matrix across three key dimensions, offering a customised, industry-specific assessment for the Irish AECO sector that combines structured benchmarking with actionable gap analysis. The toolkit supports gap analysis by comparing an organisation’s current maturity profile with the detailed descriptors of higher maturity levels (maturity level matrix), thereby enabling prioritised and context-specific improvement planning rather than pursuit of a uniform maximum level. The study uses a mixed-methods approach within a Design Science Research (DSR) framework, developing the tool across six phases: literature review, defining dimensions and key performance indicators (KPIs), prototype development, testing, refining and finalisation, and deployment for practical application and empirical evaluation within real organisational contexts in the Irish AECO sector, demonstrating its use as an operational diagnostic and learning tool. Alpha testing by the organisational research team refined structural enhancements including maturity stages, KPIs, and maturity matrix. Beta testing with 20 Irish AECO organisations confirmed the toolkit’s relevance, scope, and coverage. Participants highlighted its clarity and industry alignment, while suggesting minor improvements in wording, visuals, and support materials. This study concludes that DMGAT is a useful resource for informed decision-making and digital innovation in the Irish AECO sector. Full article

This work presents the design of a compact, wideband circular microstrip patch antenna for microwave imaging-based brain tumor detection. The main contribution is the development of a compact antenna structure incorporating enhanced ground-plane slot modifications, which significantly improves impedance bandwidth while maintaining a small electrical size, making it highly suitable for medical imaging systems. In addition, the study integrates antenna design, safety evaluation, and microwave imaging analysis within a unified framework to assess tumor localization feasibility using a realistic head model in CST Microwave Studio. The proposed antenna is fabricated on an FR-4 substrate with dimensions of 37 × 54.5 × 1.6 mm³, corresponding to an electrical size of 0.176λ × 0.260λ × 0.0076λ at the lowest operating frequency of 1.43 GHz. Ground-plane slot enhancements are introduced to achieve wideband performance, resulting in an impedance bandwidth from 1.43 to 4 GHz and a fractional bandwidth of 94.7%. The antenna exhibits a maximum realized gain of 3.7 dB. To evaluate its suitability for medical applications, specific absorption rate (SAR) analysis is performed using a realistic human head model at multiple antenna positions and at 1.5, 2.1, 2.5, 3.3, and 3.9 GHz frequencies. The computed SAR values range from 0.109 to 1.56 W/kg averaged over 10 g of tissue, satisfying the IEEE C95.1 safety guideline limit of 2 W/kg. For tumor detection assessment, time-domain simulations are conducted in CST Microwave Studio using a monostatic radar configuration, where the antenna operates as both transmitter and receiver at twelve angular positions around the head with 30° increments. The collected scattered signals are processed using the Delay-and-Sum (DAS) beamforming algorithm to reconstruct dielectric contrast maps and localize the tumor. It should be noted that the tumor-imaging demonstrations presented in this work are based on numerical simulations, while experimental validation is limited to the characterization of the fabricated antenna. Nevertheless, the findings indicate that the proposed antenna is a promising candidate for noninvasive, low-cost microwave brain tumor imaging applications. Full article

Open-plan workplaces are often associated with increased sensory exposure, which may present challenges for adults with Attention-Deficit/Hyperactivity Disorder (ADHD), a condition characterized by atypical arousal regulation and sensory sensitivity. Although the Prospect–Refuge Theory suggests that spatial configuration may influence perceived security and attentional states, objective neurophysiological evidence in workplace contexts remains limited. This exploratory pilot study employed a mixed design to examine whether desk orientation and office enclosure were associated with differences in neural activity among adults with ADHD (n = 6). Four desk configurations were tested within each office setting, while two office types (Open Office and Enclosed Private Office) were examined between participants. Neurophysiological data were collected using portable electroencephalography (EEG), and power spectral density (PSD) across canonical frequency bands was analyzed during standardized cognitive tasks. Results indicated context-dependent spatial effects. In the Open Office setting, configurations providing both outward visibility and visual backing were associated with lower beta and gamma power relative to orientations lacking these features. In the Enclosed Private Office, orientation-related differences were not statistically significant. These preliminary findings suggest that desk orientation may influence neural indicators of cognitive demand in open-plan environments. Given the small sample size, results should be interpreted cautiously but contribute initial physiological evidence to neurodiversity-informed workplace research. Full article

Ultra-High-Performance Concrete (UHPC) is increasingly used in slender and prestressed structural members due to its superior strength and durability. However, inaccurate or incomplete prediction of creep deformation may lead to excessive long-term deflection, prestress loss, cracking, and potential serviceability or safety risks in buildings and infrastructure. Therefore, reliable prediction methods for UHPC creep are essential for both structural design and long-term performance assessment. In this study, a database containing 60 literature-derived UHPC creep records was compiled to investigate the creep coefficient at approximately 100 days. Pearson correlation analysis revealed strong interdependence among predictors and weak single-variable linear relationships, indicating that creep behavior is governed by nonlinear interactions. A feedforward backpropagation neural network (BPNN) trained using the Levenberg–Marquardt algorithm was developed to predict the creep coefficient. To maintain engineering interpretability, the fib Model Code 2020 (MC2020) formulation was adopted as a code-based benchmark and further calibrated using ridge regression. Results show that the calibrated MC2020 model improves prediction consistency, while the BPNN model provides the highest predictive accuracy. The proposed framework integrates machine-learning prediction with interpretable code-based calibration, contributing to the development of creep modeling approaches for UHPC and providing practical support for the safe design of UHPC structures. Full article

This study developed an active packaging material by incorporating tea tree oil (TTO)-loaded lotus stalk biochar (BC@TTO) into a chitosan (CS) matrix. Biochar was prepared from lotus stalks via pyrolysis at 600 °C and characterized, revealing a mesoporous structure with a specific surface area of 35.9 m²/g. Adsorption studies demonstrated that BC exhibited high affinity for TTO, following pseudo-first-order kinetics and the Langmuir isotherm model, with a maximum adsorption capacity of 295.6 mg/g. Chitosan-based composite membranes with varying BC@TTO contents (1–7 wt%) were fabricated by solution casting. The incorporation of BC@TTO significantly enhanced the tensile strength, elongation at break, barrier properties (water vapor and oxygen), and antioxidant/antibacterial activities of the membranes, with optimal performance observed at 3 wt% loading. However, higher loadings led to filler aggregation, reduced transparency, and compromised mechanical properties. In vitro release studies indicated that TTO release followed the Avrami model, suggesting a diffusion-controlled mechanism. Preservation tests on blueberries showed that the CS-3BC@TTO membrane effectively reduced weight loss and maintained fruit quality during storage. This work presents a promising strategy for designing bioactive packaging materials with sustained release functionality for food preservation applications. Full article

Declining birth rates are reshaping higher education across East Asia, accelerating the large-scale underutilization and, in some contexts, partial abandonment of university campus assets. Although adaptive reuse has been widely discussed, campus transformation is often framed primarily as a programmatic or policy problem, with limited attention to the inherited spatial logic embedded in campus morphology. This study revisits Le Corbusier’s concept of unlimited growth as a generative framework for campus transformation. Rather than treating it as a museum-specific historical typology, the research reinterprets unlimited growth as a scalable spatial logic defined by modular continuity, circulation hierarchy, and open-ended sequencing. To enhance reproducibility and operational clarity, the study formalizes a typo-morphological decoding protocol—modules, circulation, and growth sequence—and applies it through plan-, section-, and diagram-based analysis. Through comparative examination of three museum precedents—Sanskar Kendra Museum, the National Museum of Western Art (Tokyo), and the Chandigarh Museum and Art Gallery—the study extracts a set of transferable spatial mechanisms: modular increment, circulation-centered ordering, directional displacement, and fifth-façade ecological continuity. These mechanisms are then translated into an operational right-sizing model and tested through a design-operational demonstrator on a single anonymized Taiwanese campus experiencing demographic contraction. The findings indicate that unlimited growth functions not merely as a formal principle but as a spatial governance logic that supports phased consolidation, adaptive recomposition, and system-level coherence under long-term uncertainty. Importantly, this framework contributes to sustainability by reducing land consumption through spatial consolidation, minimizing unnecessary new construction, enabling adaptive reuse of existing campus assets, and improving long-term resource-use efficiency through phased right-sizing and ecological continuity. This study further advances a reproducible, mechanism-based methodological framework for institutional spatial transformation, providing a transferable approach for large-scale campus restructuring under conditions of long-term demographic and environmental uncertainty. Full article

Background/Objectives: The Diet Quality Questionnaire (DQQ) is a brief, food group–based instrument designed for globally comparable population surveillance of diet quality. We culturally adapted the DQQ for Poland and evaluated its internal structure and reliability in an adult cohort. Methods: Following forward–backward translation and expert review, the Polish DQQ was administered online to adult females. Internal structure was explored and test–retest reliability was assessed for total DQQ scores. Diet quality indicators (Dietary Diversity Score [DDS], NCD-protect, NCD-risk, and Global Dietary Recommendations score [GDR]) were summarized descriptively. Results: The average age in the cohort was 29.4 ± 13.6 years. A total of 296 respondents completed the survey; 100 completed the retest. Item-level test–retest reliability was good to excellent (Cohen’s kappa 0.72–1.00). Agreement for total scores was high with minimal bias (Bland–Altman bias 0.2, >95% of observations within limits of agreement) and there was no heteroscedasticity; Passing–Bablok regression indicated equivalence between the test and retest. Median (IQR) diet quality indicators were: DDS 6.0 (5.0; 7.0), NCD-protect 2.5 (1.5; 4.0), NCD-risk 2.5 (1.0; 4.0), and GDR 9.0 (7.5; 10.5). Eighty percent met DDS ≥ 5, while one-third consumed all five recommended food groups. Conclusions: DQQ-PL demonstrates high item-level stability and strong agreement for total scores, with structural findings aligning with its design as a non-latent, food group checklist for population monitoring. The Polish adaptation is feasible and reliable in the studied population (young adult women), supporting its potential use for rapid dietary surveillance pending broader validation. Full article

Monkeypox virus, a zoonotic DNA virus belonging to the Orthopoxvirus genus, has emerged as a global health issue because of its fast spread to 104 nations over six continents. In the current study, an immunoinformatics pipeline was used to design a multiepitope-based prophylactic vaccine targeting the A35R glycoprotein and E8L membrane proteins of the monkeypox virus. Selected target proteins were surface-exposed, non-homologous to the human proteome, and essential for viral pathogenesis. B-cell and T-cell (MHC-I and MHC-II) epitopes with high antigenicity (>0.5), non-allergenicity, non-toxicity, and highly soluble in water with strong affinity towards innate and adaptive receptors, were prioritized. Shortlisted epitopes were combined to design the final vaccine utilizing an adjuvant (50S ribosomal L7/L12) and appropriate linkers for improved immunogenicity. Population coverage analysis showed wide HLA representation with 83.57% (MHC-I) and 88.8% (MHC-II) global coverage, including 89.6% for West Africa and 87.3% for Central Africa. Docking analysis of the vaccine construct with the TLR-4 receptor revealed stable interactions (−695.6 kcal/mol). Molecular dynamics simulations and binding free energies further confirmed structural stability. Immune simulations predicted strong activation of both humoral and cellular immune responses. These results indicate that the designed multiepitope vaccine construct is a viable option for additional experimental validation against the monkeypox virus. Full article

Speech-based depression detection is promising for objective mental health assessment. However, conventional methods relying on short-frame acoustic features often fail to capture long-term temporal and behavioral characteristics of speech essential for modeling depression-specific speaking patterns. Herein, four novel acoustic feature sets extracted from long-term speech are proposed: utterance interval feature set (UIFS), pause interval feature set (PIFS), response interval feature set (RIFS), and speech density (SD). These features explicitly characterize temporal structures and session-level speech behaviors beyond short-frame analysis. These features are combined with conventional acoustic features, including standard features extracted using openSMILE and voice level features, and evaluated using support vector machines under subject-independent conditions for the binary classification of depressed and nondepressed speakers. Incorporating the proposed features improves classification performance compared with baseline features (accuracy: 0.54 for openSMILE and 0.52 for openSMILE + voice level features). The configuration integrating all four proposed feature sets achieves an accuracy of 0.58, a precision of 0.56, a recall of 0.58, and a specificity of 0.58, indicating consistent performance gains under subject-independent and strictly controlled evaluation conditions. Thus, depression-related speech patterns can be captured by explicitly modeling temporal and behavioral speech characteristics across entire dialog sessions. This study contributes to advancing acoustic feature design for speech-based depression detection and developing clinically supportive screening and monitoring technologies. Full article

Compared to traditional offline collaboration, current online collaboration often lacks nonverbal social cues, resulting in lower efficiency and a reduced emotional connection between teammates. To address this issue, this study used a two-player collaborative puzzle game as the experimental setting to explore the impact of two nonverbal social cues, emotion and gaze, on collaborative experience and performance. Specifically, this study designed four collaborative modes: with and without teammates’ facial expressions, and with and without teammates’ gaze points. Sixty-two participants took part in the experiment, and each pair was required to complete these four patterns. Subsequently, we analyzed their collaborative experience through subjective questionnaires, objective facial expressions, and gaze overlap rates. The experimental results revealed that teammates’ gaze could effectively enhance collaborative efficiency, while facial expression is key to optimizing subjective experience. Combining both cues further acquires advantages in cognitive and emotional dimensions, leading to improved performance outcomes. The study also indicated that facial expressions could alleviate the social pressure triggered by shared gaze from teammates. Additionally, the study also examined how personality differences influenced collaborative experiences and performance. The results indicated that individuals with high agreeableness actively seek social cues, leading to more positive collaborative experiences. This study provides empirical evidence for understanding the interactive mechanisms of cognitive and emotional processes during online collaboration, and points the way toward designing adaptive, personalized intelligent collaborative systems. Full article

In this study, supervised machine learning (ML) regression models are employed to predict water uptake during the sorption process in a sorption reactor for thermal energy storage applications. Two main methods are used to study sorption storage systems: experimental studies and numerical simulations. Experimental studies involve physical testing and measurements but are often costly and time-consuming. Numerical simulations are more flexible and cost-effective, though they can require significant computational resources for large or complex systems. To address these challenges, researchers are increasingly employing various machine learning techniques, which offer strong potential for data analysis and predictive modeling. In this study, CFD-based sorption simulations are integrated with machine learning models to predict the spatiotemporal evolution of water uptake. Several ML techniques including support vector regression (SVR), Random Forest, XGBoost, CatBoost (gradient boosting decision trees), and multilayer perceptron neural networks (MLPs) are evaluated and compared. A fixed-bed reactor equipped with fins and tubes is considered within a closed adsorption thermal storage system. Numerical simulations are conducted for three different fin lengths (10 mm, 25 mm, and 35 mm) to generate a comprehensive dataset for training the ML models and capturing the complex temporal evolution of water uptake, thereby enabling predictions for unseen fin geometries. The results indicate that neural network-based models achieve superior predictive performance compared to the other methods. For water uptake training, the mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination $\left(R^2\right)$ are approximately 2.83, 4.37, and 0.91, respectively. The predicted water uptake shows close agreement with the numerical simulation results. For the prediction cases, the MAE, MSE, and $R^2$ values are approximately 1.13, 1.2, and 0.8, respectively. Overall, the study demonstrates that machine learning models can accurately predict water uptake beyond the training dataset, indicating strong generalization capability and significant potential for improving thermal management system design. Additionally, the proposed approach reduces simulation time and computational cost while providing an efficient and reliable framework for modeling complex sorption processes in thermal energy storage systems. Full article

Metal additive manufacturing (MAM) enables the production of complex, high-value components for sectors such as aerospace, energy, and biomedical engineering. However, its large-scale industrial adoption remains constrained by internal defects, residual stresses, distortions, microstructural variability, and the complexity of the coupled process-parameter space. This review examines how artificial intelligence (AI)—including machine learning, deep learning, and optimization algorithms—is being applied to address these challenges across the MAM workflow. A structured literature review was conducted covering studies published between 2015 and 2025, identified through searches in Scopus, Web of Science, and IEEE Xplore. The selected literature is analyzed according to key functional domains of metal additive manufacturing: design for additive manufacturing (DfAM), process modeling and simulation, in situ monitoring and control, and microstructure and property prediction. AI approaches are further categorized by learning paradigm, including supervised learning, deep learning, reinforcement learning, and hybrid physics–machine learning models. The review highlights recent advances in AI-assisted parameter optimization, defect detection, and digital-twin frameworks for process supervision. At the same time, it identifies persistent challenges, particularly the scarcity and heterogeneity of datasets, limited transferability across machines and materials, and the need for uncertainty-aware models capable of supporting validation and certification. Overall, the analysis indicates that the integration of multi-sensor monitoring with hybrid physics-informed AI models represents the most promising near-term pathway to improve process reliability, reduce trial-and-error experimentation, and accelerate industrial qualification in metal additive manufacturing. Full article

Potato is one of the most important food crops worldwide, playing a critical role in global food security and agricultural production. The broken and impurity rates are important indicators for evaluating the harvesting quality of potato combine harvesting operations. To address the difficulty of achieving continuous and online detection using traditional methods, this study investigates an online monitoring approach for potato combine harvesting based on machine vision. Considering the characteristics of large material volume, severe overlap, and similar appearance features under field operating conditions, an online monitoring device suitable for potato combine harvesters was designed, along with a corresponding image acquisition and processing workflow. For the online monitoring device, an improved You Only Look Once version 11 (YOLOv11) detection model, was proposed to meet the requirements of multi-object detection in complex operating scenarios. The model incorporates Multi-Scale Depthwise Convolution (MSDConv), C2PSA_DCA (with Directional Context Attention, DCA), and Directional Selective Attention (DSA) modules, and introduces the Efficient Intersection over Union (EIoU) loss function to enhance recognition capability for broken potatoes and multiple types of impurity targets. While maintaining lightweight characteristics, the improved model demonstrates favorable detection accuracy. Field experiment results show that when the combine harvester operates at a forward speed of 3 km/h, the relative errors for broken and impurity rates are measured as 3.78% and 3.67%, respectively. Under extreme operating conditions with a speed of 4 km/h, the corresponding average relative errors rise to 8.30% and 8.72%, respectively. Overall, the online detection results exhibit satisfactory consistency with manual measurements, providing effective technical support for real-time monitoring of harvesting quality in potato combine harvesting operations. Future research will focus on expanding multi-scenario datasets under diverse soil and illumination conditions, as well as integrating detection results with adaptive control strategies to further enhance intelligent harvesting performance. Full article

Active non-line-of-sight (NLOS) human detection aims to infer the presence of hidden individuals by analyzing indirectly reflected photons between a relay surface and occluded targets. In this study, a single-photon avalanche diode (SPAD) and time-correlated single-photon counting (TCSPC)-based acquisition system were used to measure time–photon waveforms in controlled NLOS environments designed to represent post-disaster rubble scenarios. Although the effective temporal resolution of the system is limited by the detector timing jitter and laser pulse width, the recorded transient signals retain distinguishable intensity and temporal delay patterns associated with the primary and secondary reflections. To construct a representative dataset, measurements were collected under varying subject poses, orientations, and surrounding object configurations. The recorded signals were processed using a unified preprocessing pipeline that included normalization, histogram shaping, and signal windowing. Three machine learning models, namely, Convolutional Neural Network, Gated Recurrent Unit, and Random Forest, were trained and evaluated for human presence classification. All models achieved full sensitivity in detecting human presence; however, notable differences emerged in the classification of human-absent scenarios. Among the tested approaches, random forest achieved the highest overall accuracy and specificity, demonstrating stronger robustness to statistical variations in time–photon histograms under limited photon conditions. These results suggest that tree-based classifiers capture amplitude distribution patterns and temporal dispersion characteristics more effectively than deep neural architectures under the present acquisition constraints. Overall, the findings indicate that low-cost SPAD-based NLOS sensing systems can provide reliable human detection in indirect-observation scenarios. Full article