Search Results (23,587)

This work presents a time-domain analog-to-digital conversion method in which the amplitude of a sensor signal is encoded through its crossing instants with a periodic ramp. The proposed architecture departs from conventional ADC and PWM demodulation approaches by shifting quantization entirely to the time domain, enabling waveform reconstruction using only a ramp generator, an analog comparator, and a timer capture module. A theoretical framework is developed to formalize the voltage-to-time mapping, derive expressions for resolution and error, and identify the conditions ensuring monotonicity and single-crossing behavior. Simulation results demonstrate high-fidelity reconstruction for both periodic and non-periodic signals, including real photoplethysmographic (PPG) waveforms, with errors approaching the theoretical quantization limit. A hardware implementation on a PSoC 5LP microcontroller confirms the practicality of the method under realistic operating conditions. Despite ramp nonlinearity, comparator delay, and sensor noise, the system achieves effective resolutions above 12 bits using only native mixed-signal peripherals and no conventional ADC. These results show that accurate waveform reconstruction can be obtained from purely temporal information, positioning time-encoded sensing as a viable alternative to traditional amplitude-based conversion. The minimal analog front end, low power consumption, and scalability of timer-based processing highlight the potential of the proposed approach for embedded instrumentation, distributed sensor nodes, and biomedical monitoring applications. Full article

Regarding the issues arising from the addition of external curing agents in the application of epoxy resin in cement-based materials, this paper explores the feasibility of endogenous curing of epoxy resin in the alkaline environment of cement-based systems. It further analyzes and investigates the curing characteristics of epoxy resin without external curing agents and their impact on the performance of cement-based materials. Differential scanning calorimetry, mechanical property testing, microstructural observation, and electrochemical impedance spectroscopy were used to study the mechanism of sodium hydroxide (NaOH) catalyzing the process of bisphenol-A epoxy resin (E-51)-based curing, the influence of moisture and temperature on curing kinetics, and the performance of epoxy resins in mortar and self-healing concrete. The results showed that E-51 achieved self-curing under alkaline conditions in the absence of an external hardener. However, moisture significantly inhibited the reaction process. Elevating the temperature and reducing environmental humidity effectively promoted the curing reaction. In cement-based materials, E-51 exhibited endogenous curing by the inherent alkalinity of the system, remarkably enhancing the compressive strength of mortar. At 60 °C, mortar containing 10% E-51 (by cement mass) exhibited a 1.5-fold higher compressive strength than that of the control group without E-51 at 14 days of curing. It demonstrated higher healing efficiency in a microencapsulated self-healing concrete system than the traditional curing agent systems. Concrete specimens with damage induced by loading at 60% of their compressive strength exhibited 100% recovery of ultrasonic pulse velocity after storing indoors for 28 d. The findings of this study can provide theoretical basis and technical support for the application of epoxy resins in cement-based materials without the need for curing agents. Full article

Traditional scoring of makeup face templates in beauty skill assessments heavily relies on manual judgment, leading to inconsistencies and subjective bias. Hand-drawn templates often exhibit proportion distortions, asymmetry, and occlusions that reduce the accuracy of conventional facial landmark detection algorithms. This study proposes a novel approach that integrates Geometric Feature Enhancement (GFE) with Dlib’s 68-landmark detection to improve the robustness and precision of landmark localization. A comprehensive comparison among Haar Cascade, MTCNN-MobileNetV2, and Dlib was conducted using a curated dataset of 11,600 hand-drawn facial templates. The proposed GFE-enhanced Dlib achieved 60.5% accuracy—outperforming MTCNN (23.4%) and Haar (20.3%) by approximately 37 percentage points, with precision and F1-score improvements exceeding 20% and 25%, respectively. The results demonstrate that the proposed method significantly enhances detection accuracy and scoring consistency, providing a reliable framework for automated beauty skill evaluation, and laying a solid foundation for future applications such as digital archiving and style-guided synthesis. Full article

Monitoring muscle fatigue is essential to ensure safety and support activity in populations such as the elderly. This study introduces a novel deep learning framework for classifying muscle fatigue levels using data from wireless surface electromyographic sensors, with the long-term goal of supporting applications in Ambient Assisted Living. A new dataset was collected from healthy elderly and non-elderly adults performing dynamic tasks under controlled conditions, with muscle fatigue levels labelled through self-assessment. The proposed method employs a pipeline that transforms one-dimensional electromyographic signals into two-dimensional time–frequency images (scalograms) using the Continuous Wavelet Transform, which are then classified by a fine-tuned, pre-trained Convolutional Neural Network. These images are then classified by pretrained Convolutional Neural Networks on large-scale image datasets. The classification pipeline includes an initial binary discrimination between non-fatigued and fatigued conditions, followed by a refined three-level classification into No Fatigue, Moderate Fatigue, and Hard Fatigue. The system achieved an accuracy of 98.6% in the binary task and 95.6% in the multiclass setting. This integrated transfer learning pipeline outperformed traditional Machine Learning methods based on manually extracted features, which reached a maximum of 92% accuracy. These findings highlight the robustness and generalizability of the proposed approach, supporting its potential as a real-time, non-invasive muscle fatigue monitoring solution tailored to Ambient Assisted Living scenarios. Full article

This critical review explores the transformative impact of artificial intelligence (AI), particularly machine learning (ML) and computer vision (CV), on scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) for metallic microstructure analysis, spanning research from 2010 to 2025. It critically evaluates how AI techniques balance automation, accuracy, and scalability, analysing why certain methods (e.g., Vision Transformers for complex microstructures) excel in specific contexts and how trade-offs in data availability, computational resources, and interpretability shape their adoption. The review examines AI-driven techniques, including semantic segmentation, object detection, and instance segmentation, which automate the identification and characterisation of microstructural features, defects, and inclusions, achieving enhanced accuracy, efficiency, and reproducibility compared to traditional manual methods. It introduces the Microstructure Analysis Spectrum, a novel framework categorising techniques by task complexity and scalability, providing a new lens to understand AI’s role in materials science. The paper also evaluates AI’s role in chemical composition analysis and predictive modelling, facilitating rapid forecasts of mechanical properties such as hardness and fracture strain. Practical applications in steelmaking (e.g., automated inclusion characterisation) and case studies on high-entropy alloys and additively manufactured metals underscore AI’s benefits, including reduced analysis time and improved quality control. Extending prior reviews, this work incorporates recent advancements like Vision Transformers, 3D Convolutional Neural Networks (CNNs), and Generative Adversarial Networks (GANs). Key challenges—data scarcity, model interpretability, and computational demands—are critically analysed, with representative trade-offs from the literature highlighted (e.g., GANs can substantially augment effective dataset size through synthetic data generation, typically at the cost of significantly increased training time). Full article

The traditional cost estimation process in construction involves extracting information from diverse data sources and relying on human intuition and judgment, making it time-intensive and error-prone. While recent advancements in large language models offer opportunities to automate these processes, their effectiveness in cost estimation tasks remains underexplored. Prior studies have investigated LLM applications in construction, but there is a lack of studies that have systematically evaluated their performance in cost estimation or proposed a framework for systematic evaluations of their performance in cost estimation and ways to enhance their accuracy and reliability through prompt engineering. This study evaluates the performance of pre-trained LLMs (GPT-4o, LLaMA 3.2, Gemini 2.0, and Claude 3.5 Sonnet) for conceptual cost estimation, comparing zero-shot prompting with a modular chain-of-thought framework. The results indicate that zero-shot prompting produced incomplete responses with an average confidence score of 1.91 (64%), whereas the CoT framework improved accuracy to 2.52 (84%) and achieved significant gains across BLEU, ROUGE-L, METEOR, content overlap, and semantic similarity metrics. The proposed modular CoT framework enhances structured reasoning, contextual alignment, and reliability in estimation workflows. This study contributes by developing a conceptual cost estimation framework for LLMs, benchmarking baseline model performance, and demonstrating how structured prompting improves estimation accuracy. This offers a scalable foundation for integrating AI into construction cost estimation workflows. Full article

Deep reinforcement learning (DRL) has demonstrated considerable potential for autonomous collision avoidance in unmanned surface vessels (USVs). However, its application in complex multi-agent maritime environments is often limited by challenges such as convergence issues and high computational costs. To address these issues, this paper proposes an expert-guided DRL algorithm that integrates a Dual-Priority Experience Replay (DPER) mechanism with a Hybrid Reciprocal Velocity Obstacles (HRVO) expert module. Specifically, the DPER mechanism prioritizes high-value experiences by considering both temporal-difference (TD) error and collision avoidance quality. The TD error prioritization selects experiences with large TD errors, which typically correspond to critical state transitions with significant prediction discrepancies, thus accelerating value function updates and enhancing learning efficiency. At the same time, the collision avoidance quality prioritization reinforces successful evasive actions, preventing them from being overshadowed by a large volume of ordinary experiences. To further improve algorithm performance, this study integrates a COLREGs-compliant HRVO expert module, which guides early-stage policy exploration while ensuring compliance with regulatory constraints. The expert mechanism is incorporated into the Soft Actor-Critic (SAC) algorithm and validated in multi-vessel collision avoidance scenarios using maritime simulations. The experimental results demonstrate that, compared to traditional DRL baselines, the proposed algorithm reduces training time by 60.37% and, in comparison to rule-based algorithms, achieves shorter navigation times and lower rudder frequencies. Full article

Thrips are cosmopolitan agricultural pests and important vectors of plant viruses, and the increasing coexistence of multiple morphologically similar species has intensified the demand for species-specific molecular identification. However, traditional morphological identification and PCR assays using universal primers are often inadequate for mixed-species samples and field-adaptable application. In this study, we developed a species-specific molecular identification framework targeting a polymorphism-rich region of the mitochondrial cytochrome c oxidase subunit I (COI) gene, which is more time-efficient than sequencing-based COI DNA barcoding, for four economically important thrips species in southern China, including the globally invasive Frankliniella occidentalis. By aligning COI sequences, polymorphism-rich regions were identified and used to design four species-specific primer pairs, each containing a diagnostic 3′-terminal nucleotide. These primers were combined with a PBS-based DNA extraction workflow optimized for single-insect samples that minimizes dependence on column-based purification. The assay achieved a practical detection limit of 1 ng per reaction, demonstrated species-specific amplification, and maintained reproducible amplification at DNA inputs of ≥1 ng per reaction. Notably, PCR inhibition caused by crude extracts was effectively alleviated by fivefold dilution. Although the chemical identities of the inhibitors remain unknown, interspecific variation in inhibition strength was observed, with T. hawaiiensis exhibiting the strongest suppression, possibly due to differences in lysate composition. This integrated framework balances target specificity, operational simplicity, and dilution-mitigated inhibition, providing a field-adaptable tool for thrips species identification and invasive species monitoring. Moreover, it provides a species-specific molecular foundation for downstream integration with visual nucleic acid detection platforms, such as the CRISPR/Cas12a system, thereby facilitating the future development of portable molecular identification workflows for small agricultural pests. Full article

Fault diagnosis in the power systems of Hybrid Electric Vehicles (HEVs) is crucial for ensuring vehicle safety and energy efficiency. This study proposes an innovative CNN-LSTM fusion model for diagnosing common faults in HEV power systems, such as battery degradation, inverter anomalies, and motor failures. The model integrates the feature extraction capabilities of Convolutional Neural Networks (CNN) with the temporal dependency handling of Long Short-Term Memory (LSTM) networks. Through data preprocessing, model training, and validation, the approach achieves high-precision fault identification. Experimental results demonstrate an accuracy rate exceeding 95% on simulated datasets, outperforming traditional machine learning methods. This research provides a practical framework for HEV fault diagnosis and explores its potential in real-world applications. Full article

Physiological monitoring has become an inherently interdisciplinary field, merging advances in engineering, chemistry, biology, medicine, and data analytics to create sensors that continuously track the vital signals of the body. These developments are enabling more personalized and preventive healthcare, as wearable (bio)sensors and intelligent algorithms can detect subtle physiological changes in real-time. In the Special Issue ‘Advances in (Bio)Sensors for Physiological Monitoring’, researchers from diverse domains contributed 18 papers showcasing cutting-edge sensor technologies and applications for health and performance monitoring. In this review, we summarize these contributions by grouping them into logical themes based on their focus: (1) cardiovascular and autonomic monitoring, (2) glucose and metabolic monitoring, (3) wearable sensors for movement and musculoskeletal health, (4) neurophysiological monitoring and brain–computer interfaces, and (5) innovations in sensor technology and methods. This thematic organization highlights the breadth of the research, spanning from fundamental sensor hardware to data-driven analytics, and underscores how modern (bio)sensors are breaking traditional boundaries in healthcare. Full article

Sugarcane (Saccharum spp.) is a globally important crop for sugar and bioenergy production. However, genetic improvement through conventional breeding is constrained by long breeding cycles, low genetic gain, and considerable operational complexity arising from its highly allopolyploid and aneuploid genome. With the increasing global demand for sustainable food and renewable energy, sugarcane breeding programs must accelerate the development of high-yielding, stress-tolerant cultivars through the integration of advanced biotechnological tools with traditional breeding approaches. Recent advances in genetic engineering, genomic selection (GS), and high-throughput omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and phenomics, have substantially enhanced the efficiency of trait improvement related to growth, development, yield, and stress resilience. The integration of multi-omics data enables the dissection of regulatory networks linking genotype to phenotype, improves predictive accuracy, and provides deeper insights into the molecular mechanisms underlying complex traits. These integrative approaches support more informed selection decisions and accelerate genetic gain in sugarcane breeding programs. This review synthesizes recent technological developments and their practical applications in sugarcane improvement. It highlights the strategic implementation of transgenic and genome-editing technologies, genomic selection, and multi-omics integration to enhance yield potential and resistance to biotic and abiotic stresses, thereby contributing to sustainable sugarcane production and global food and bioenergy security. Full article

In recent years, with the rapid advancement of science and technology, generative artificial intelligence has increasingly entered the public eye. Primarily through intelligent algorithms that simulate human logic and integrate vast amounts of network data, it provides designers with solutions that transcend traditional thinking, enhancing both design efficiency and quality. Compared to traditional design methods reliant on human experience, generative design possesses robust data processing capabilities and the ability to refine design proposals, significantly reducing preliminary design time. This study employs the CiteSpace visualization tool to systematically organize and conduct knowledge map analysis of research literature related to generative AI in architectural design within the Web of Science database from 2005 to 2025. Findings reveal the following: (1) International research exhibits a trend toward interdisciplinary convergence. In recent years, research in this field has grown rapidly across nations, with continuously increasing academic influence; (2) Research primarily focuses on technological applications within architectural design, aiming to drive innovation and development by providing superior, more efficient technical support; (3) Generative AI in architectural design has emerged as a prominent international research focus, reflecting a shift from isolated design to industry-wide integration; (4) Generative AI has become a core global architectural design topic, with future research advancing toward full-process intelligent collaboration. High-quality knowledge graphs tailored for the architecture industry should be constructed to overcome data silos. Concurrently, a multidimensional evaluation system for generative quality must be established to deepen the symbiotic design paradigm of human–machine collaboration. This significantly enhances efficiency while reducing the iterative nature of traditional methods. This study aims to provide empirical support for theoretical and practical advancements, offering crucial references for practitioners to identify business opportunities and policymakers to optimize relevant strategies. Full article

Cnidium monnieri (L.) Cusson is a species of Umbelliferae plants, and it is one of China’s traditional medicinal herbs, widely distributed in China owing to its strong adaptability in fields. In this article, the research progress on the taxonomy, distribution, cultivation techniques, active components, analysis methods, antibacterial and insecticidal properties, and ecological applications of C. monnieri was reviewed. The main active components in C. monnieri are coumarins (mainly osthole) and volatile compounds, exhibiting multiple pharmacological effects, e.g., anti-inflammatory, antibacterial, antioxidant, anti-tumor, and immune-regulating effects. Some modern analytical techniques (e.g., HPLC, GC-MS, and UPLC-QTOF-MS) have enabled more precise detection and quality control of these chemical components in C. monnieri. The specific active constituents in C. monnieri (e.g., coumarins and volatile components) exhibit significant inhibitory effects against various pathogenic fungi and insect pests. Simultaneously, the resources provided during its flowering stage (e.g., pollen and nectar) and the specific volatiles released can repel herbivorous insect pests while attracting natural enemies, such as ladybugs, lacewings, and hoverflies, thereby enhancing ecological control of insect pests in farmland through a “push–pull” strategy. Additionally, C. monnieri has the ability to accumulate heavy metals, e.g., Zn and Cu, indicating its potential value for ecological restoration in agroecosystems. Overall, C. monnieri has medicinal, ecological, and economic value. Future research should focus on regulating active-component synthesis, improving our understanding of ecological mechanisms, and developing standardized cultivation systems to enhance the applications of C. monnieri in modernized traditional Chinese medicine and green agriculture production. Full article

The article focuses on the design and construction of a 3D printer capable of printing both traditional cement and alkali-activated cement (AAC). Research into alkali-activated cements, commonly known as geopolymers, has progressed beyond the basic research stage, with the current challenge being the implementation of practical applications. These include solving the shaping issues of AAC paste and forming the final shape of a given product. One of the most advanced methods for achieving this is through 3D printing. The printer was created by modifying the open-source RatRig V-Core 3D printer ecosystem design to fit this purpose. Based on these modifications, an appropriate material composition was determined, and printing tests were conducted, allowing development conclusions to be drawn. A three-dimensional model of the structure was first created using Autodesk Inventor 2024 CAD software, and critical load-bearing components were validated through simulation. Special attention was given to cost-effective manufacturability, with custom parts produced using 3D printing, while additional components (e.g., bearings, fasteners) were selected from commercial catalogs. Finally, test prints using the specified material composition were performed to examine potential construction improvements for the 3D printer and assess material properties. The core concept of the cement printer lies in the material deposition method, specifically, in achieving effective extrusion of the paste. Five different versions of this were tested, which will be discussed in detail. Full article

Organic pollution poses a serious threat to global water safety, while traditional treatment technologies suffer from low efficiency, high costs, and secondary pollution issues. This study successfully develops a highly efficient separation and photocatalytic degradation composite bismuth oxychloride@graphene oxide/polyimide (BiOCl@GO/PI) membrane by loading GO and BiOCl photocatalysts onto PI supporting membrane. The results show that this composite membrane achieves a rejection of 99.8% for methylene blue (MB) and 87.6% for tetracycline hydrochloride (TC). Under UV irradiation, the membrane exhibits a retention rate decline of only 6.8% after five cycles, with water flux stably maintaining at 605 L m⁻² h⁻¹ bar⁻¹. Compared to dark conditions, it demonstrates remarkable flux recovery. This is attributed to the membrane’s excellent photocatalytic degradation activity under UV irradiation. After five degradation cycles, the degradation efficiency is decreased from 97.5 to 88.3%. Studies on radical scavengers indicate that UV irradiation generates free radicals, thereby conferring excellent catalytic activity to the membrane. Its unique synergistic effect between separation and photocatalysis endows it with outstanding self-cleaning performance. This research provides an innovative integrated solution for antibiotic pollution control, demonstrating significant potential for environmental applications. Full article