Search Results (2,699)

Artificial intelligence (AI) is a new paradigm in software engineering that automates key phases of the development cycle. The methods of creating test cases and designing requirements are still mostly manual and prone to error. Unclear requirements can result in expensive rework and undiscovered defects in the development process. Scalability and dependability are crucial concerns in complex systems. These shortcomings highlight the need for improved methods to enhance accuracy and consistency throughout these critical phases. To generate well-organized system requirements, this article outlines a clear strategy that leverages Extended Finite State Machine models as formal inputs for large language models (LLMs). Five system models are used to assess the suggested framework. The comparison analysis evaluates the accuracy, completeness, test coverage, and runtime efficiency of the artifacts. Along with a comparison with a human-made reference standard, the study evaluates the performance of LLMs such as ChatGPT-5, Claude Sonnet 4.5, and DeepSeek V3.2. The findings demonstrate that AI models can achieve human-comparable accuracy by exceeding 90% with EFSM-based prompting. Claude Sonnet generated the most reliable findings, ChatGPT demonstrated exceptional flexibility, and DeepSeek demonstrated exceptional runtime economy. These findings show that human–AI workflows provide a new paradigm in scalable, traceable, and reproducible system engineering. Full article

The development of high-performance copper alloys requires balancing mechanical strength and electrical conductivity, properties that are often inversely correlated due to competing strengthening mechanisms. This study presents a comparative machine learning analysis of Cu-Ni-Si and Cu low alloys using a curated dataset of 1690 entries derived from the Gorsse et al. database, comprising 1507 samples with hardness measurements and 1685 samples with electrical conductivity data. Three ensemble-based regression algorithms, Random Forest, XGBoost, and Gradient Boosting, were trained to predict Vickers hardness (HV) and electrical conductivity (%IACS) from an augmented feature set encompassing alloy composition, thermomechanical processing parameters, missingness indicators, and physics-informed descriptors (valence electron concentration, atomic size mismatch, electronegativity difference, and Ni:Si atomic ratio). XGBoost achieved optimal performance for hardness prediction (R² = 0.8554, RMSE = 29.90 HV), while Gradient Boosting performed best for electrical conductivity (R² = 0.8400, RMSE = 5.96%IACS). Averaged tree-based feature-importance analysis identified valence electron concentration as the most influential predictor for hardness (39.9%), followed by aging temperature (11.2%), while Cu content dominated conductivity prediction (37.7%), followed by aging time (8.9%). Complementary SHAP analysis confirmed these trends while revealing directional relationships and nonlinear feature interaction effects. Composition-grouped cross-validation by unique alloy formula (K = 10) yielded substantially lower performance, with grouped CV R² = 0.438 for hardness and 0.293 for conductivity, indicating that generalization to unseen alloy formulations remains limited. The models were further applied for practical tasks, including property prediction for new alloy compositions, processing parameter optimization via differential evolution with metallurgical constraints (achieving hardness up to 293.9 HV or conductivity up to 45.7%IACS for the same base composition, with prediction intervals reported), and inverse design to identify alloy formulations meeting specified target properties. This work demonstrates the potential of interpretable machine learning to support copper alloy development by enabling rapid computational screening of the compositional and processing parameter space, subject to the generalization limitations identified herein. Full article

Cobalt–copper bimetallic oxides (CoCuO_x) show great potential for constructing high-performance micro-supercapacitors (MSCs) for micro-electronic applications. However, their poor conductivity and complex preparation procedures significantly hinder their broad applications. To address these challenges, oxygen-vacancy-modified CoCuO_x-based binder-free electrodes were fabricated using a one-step computer numerical control wire electrical discharge machining (CNCWEDM) strategy. This approach enabled the fabrication of CoCuO_x-based maze-like MSCs (CoCuMMSCs) with designable electrochemical performance, which could be simply controlled by their geometric shape and machining voltage. Subsequently, theoretical simulations were conducted for studying the effect of MSCs geometric shape on their capacitive behavior. Remarkably, the CoCuMMSCs fabricated by a machining voltage of 100 V achieved the maximum capacitance of 32.8 mF cm⁻² at 0.15 mA cm⁻². Furthermore, the CoCuMMSCs demonstrated outstanding performance at ultrahigh scan rates of up to 50,000 mV s⁻¹, exceeding by more than two orders of magnitude the values previously reported in the literature. The obtained results proved that the development of the CNCWEDM technique facilitated manufacturing CoCuMMSCs devices with excellent performance by the comprehensive utilization of oxygen-vacancy incorporation, synergistic effect of cobalt and copper oxides, binder-free electrode design, proper device construction and controllable machining voltage. The advanced CNCWEDM strategy creates a new pathway for the high-efficiency fabrication of high-performance bimetallic-oxide-based micro-electronic devices, such as MSCs, intelligent micro-sensors and micro-batteries. Full article

Unmanned Aerial Vehicle Ad Hoc Networks (UAVANETs) are characterized by highly dynamic topology changes and unstable link conditions, which necessitate deep collaboration between transport-layer congestion control and network-layer routing decisions to ensure service quality. However, the existing layered architecture of Software-Defined Networking (SDN) results in a significant separation between routing information and congestion control mechanisms, rendering traditional protocols ineffective in handling severe performance fluctuations caused by highly dynamic route switching. The significant disconnect between network-layer route planning and transport-layer congestion control strategies in Software-Defined Unmanned Aerial Vehicle Ad Hoc Networks (SD-UAVANETs) leads to degraded transmission performance of BBR (Bottleneck Bandwidth and Round-trip propagation time) under high-dynamic route switching scenarios. As such, this paper proposes an in-band network telemetry (INT)-based cross-layer optimization scheme for BBR, named SDN-BBR. Firstly, a lightweight real-time route switching detection mechanism based on INT is designed. Secondly, a QoS inequality model before and after path switching is established, deriving the critical bandwidth of the new path and integrating it into the BBR algorithm to accelerate convergence and avoid congestion. Finally, the BBR state machine is redesigned to achieve cross-layer information fusion and coordinated control, thereby optimizing transmission performance. Experimental results show that the proposed scheme reduces convergence time by 69.8% and increases throughput by 73.9% in low-bandwidth to high-bandwidth switching scenarios; decreases packet loss rate by 86.8% and reduces delay by 8.3% in high-bandwidth to low-bandwidth switching scenarios; and improves throughput by 12.3%, lowers packet loss rate by 21%, and reduces delay by 7.9% in multi-traffic flow concurrent scenarios. The scheme significantly enhances the transmission performance of BBR in highly dynamic routing environments of SD-UAVANET. Full article

With the increasing application of novel glass materials in the field of optics, traditional empirical and trial-and-error approaches to glass development are gradually becoming insufficient to meet escalating performance demands. In this study, we propose a neural network-based machine learning method for the design of advanced fluoride glass materials. Predictive models for density and refractive index were first developed based on online fluoride glass datasets. Moreover, SHapley Additive exPlanations (SHAP) analysis was adopted to uncover the quantitative composition-property relationship. Then, the well-trained model was employed for inverse design, identifying specific compositions that fulfill desired properties in terms of density and refractive index. Finally, several recommended compositions were experimentally validated and the measured density and refractive index matched well with the corresponding input values, thereby confirming the effectiveness of the proposed method in designing new fluoride glass materials. Full article

Cancer has become a major global health threat due to its high incidence and mortality. However, the development of anti-cancer drugs is limited by high costs, long cycles, and low success rates, slowing the progress of new treatments. As a method that simulates human cognitive functions, artificial intelligence (AI) has greatly improved the efficiency of drug development. Machine learning is a core part of AI and supports applications such as natural language processing and computer vision. This paper reviews recent advances in AI for optimizing anti-cancer drug discovery, development, and medication therapy management. First, we highlight the applications of AI in target identification, druggability assessment, drug screening, and repurposing. Second, we detail how AI optimizes drug combination therapy and clinical trial design. Finally, we describe the role of AI in treatment management, including nanoparticle delivery systems, personalized dosing, and adaptive therapy. AI greatly streamlines anti-cancer drug development and provides new directions for precision cancer therapy. Full article

Spiking neural networks (SNNs) support energy-efficient machine intelligence because event-driven computation and sparse activity map naturally to low-power digital hardware. In practical implementations, however, membrane states, synaptic weights, and thresholds are represented with finite-precision integer arithmetic. Quantization, clipping, and overflow can therefore alter network dynamics rather than merely approximate a higher-precision model. This paper adopts an integer-state dynamical perspective, modeling a quantized SNN with a hardware-relevant update rule as a deterministic map on a bounded integer lattice. Rather than claiming recurrence itself as a new property, we focus on how finite-precision representation and implementation semantics shape observed recurrent regimes and activity patterns. We introduce a shift-based update rule with integer-valued states and investigate its behaviour through simulation-based analysis with network sizes $N=30$–130, connection densities 0.1–0.9, and bit widths 1 to 16 over T = 1000 steps. The results show bounded and recurrent temporal structure with strong quantization sensitivity. The observed regimes depend heavily on the semantics of representation and the scaling choices. These findings suggest that numerical precision can act as a dynamical design variable and provide useful implications for hardware-oriented SNN design, while motivating future work on attractor analysis and FPGA/ASIC validation. Full article

Background/Objectives: Umami peptides enhance flavor and contribute to appetite regulation (satiety) and metabolic health. By signaling to the orbitofrontal cortex, umami has been shown to improve cognitive function in Alzheimer’s disease dementia. This taste boosts the immune system and induces saliva secretion. However, the molecular mechanisms linking umami peptides to systemic physiology remain poorly understood. This study provides the first integrated analysis of neurological, immunological, and endocrinological pathways activated by umami peptides. Methods: Novel umami peptides were identified using machine-learning and deep-learning analyses from a library of marine-derived bioactive peptides. T1R1-T1R3 heterodimer is the dominant receptor for umami taste transmission in humans, expressed on taste cells, intestinal cells, and hypothalamic tanycytes. Molecular docking confirmed the binding of novel ligands to the T1R1-T1R3 receptor complex. New candidates and experimentally validated umami peptides, identified by sensomics approaches from tauco, chicken soup, pufferfish, and dry-cured ham, were analyzed using gene ontology. Results: The functional enrichment analysis revealed crosstalk among key signaling processes, including glutamatergic and opioidergic pathways. In addition to the role of µ1 opioid receptor (OPRM1), hub gene intersections highlight cholecystokinin (CCK), glucagon-like peptide 1 (GLP-1), and the anorexigenic pro-opiomelanocortin (POMC) neurons as potential regulators of the gut–brain axis in satiety signaling. Chemokine-encoding genes, melanin-concentrating hormone (MCH), oxytocin (OXT), and neurotensin (NTS) were other key target genes. Conclusions: The identified targets reveal the coordinated crosstalk between peripheral and central umami signaling that may contribute to the regulation of feeding behavior, satiety, cognition, memory, learning, and immune function. These network-based insights generate hypotheses and guide the design of nutritional and drug-like effectors for metabolic and cognitive health. Full article

Recent advances in machine design, automation, and industrial digitalization have transformed Coordinate Measuring Machines (CMMs) from standalone inspection devices into fully integrated elements of automated manufacturing systems. In the automotive sector, CMMs increasingly operate in workshop, near-line, and in-line environments, interacting with production equipment and contributing directly to process control and zero-defect manufacturing strategies. This paper presents a structured methodology for the industrial deployment of automated CMMs in automotive mechanical manufacturing. The proposed approach is illustrated through an industrial use case involving the dimensional inspection of mechanically machined components under real production conditions. The methodology addresses machine design selection, sensor configuration, environmental constraints, and multi-axis architectures, as well as validation and acceptance procedures based on the ISO 10360 series. Particular attention is given to the integration of CMMs within automated manufacturing systems, including robustness against thermal variations, vibrations, and contamination, and the use of metrological data for feedback to machining processes. Rather than introducing new metrological principles, the proposed approach focuses on the structured integration of established engineering practices into a coherent lifecycle-based deployment framework. Based on industrial experience, the proposed methodology is illustrated through an industrial case study to support the reliable of automated dimensional inspection, reduce measurement-related risks, and support the integration of CMMs as active components of modern automated manufacturing systems. Full article

Titanium–silver (Ti–Ag) and titanium–copper (Ti–Cu) alloys have been developed to improve the mechanical properties and machinability of titanium (Ti) for dental applications while maintaining corrosion resistance comparable to that of pure Ti. Herein, cylindrical dental implants composed of experimental Ti–20Ag, Ti–30Ag, Ti–5Cu, and Ti–10Cu (mass%) alloys were fabricated and implanted into the jawbones of beagle dogs to evaluate bone compatibility. Pure Ti and Ti–6Al–4V alloy implants were used as controls. Because the implant surfaces were mechanically polished, the experimental alloys, which exhibited higher hardness than Ti, showed lower surface roughness than Ti. Radiographic observations revealed no remarkable bone resorption around any implants during the experimental period. Histological evaluation demonstrated new bone formation and partial bone contact around implants at 1 and 3 months post-implantation. Although the bone–implant contact ratio was relatively low owing to the cylindrical implant design and limited initial stability, no significant differences were observed between the experimental alloys and Ti. These results indicate that Ti–Ag and Ti–Cu alloys improve mechanical properties while maintaining bone compatibility comparable to that of Ti, suggesting their potential as candidate materials for dental implant applications, particularly for narrow dental implants. Full article

The increasing risk of air pollution in closed areas like passenger vehicles requires smart and real-time air quality reading solutions. Gases such as carbon monoxide (CO)—which is colorless and odorless and is produced by exhaust systems—air conditioners, and combustion sources are very dangerous to health because they can cause respiratory distress and poisoning at high levels. Traditional in-vehicle CO monitoring systems use a single-point sensor and a fixed threshold, which are insufficient in a dynamic cabin environment subject to factors such as vehicle size, ventilation rate, number of occupants, and incoming traffic. To address these drawbacks, this paper proposes a new E-Nose system with Virtual Sensor-Augmented Embedded Intelligence to estimate the CO concentration in vehicle cabins in real time. The system combines data from cheap gas sensors and improves it using virtual sensor machine learning models trained to predict or enhance sensor responses in real time. Embedded intelligence, deployed locally on edge hardware, supports low-latency processing, dynamic calibration, and noise filtering to respond to fluctuating environmental conditions adaptively. This architecture enables more accurate, robust, and context-aware estimation of CO levels compared to traditional threshold-based methods. Experimental validation across varied vehicular scenarios demonstrates superior precision and responsiveness, providing timely warnings even under complex dispersion patterns. Classifier Gradient Boosting, which builds an ensemble of weak learners sequentially, matched the Random Forest with 99.94% training and 98.59% model accuracy, confirming its strong predictive capability. The system is designed to be cost-effective, scalable, and easily integrable into modern automotive platforms. This study also contributes to the field of smart ecological recording and demonstrates the effectiveness of the virtual sensor-enhanced embedded system as an effective way to improve passenger safety by providing pre-emptive on-board air quality monitoring. Full article

The development of geopolymers as sustainable alternative binders has been accelerated by the environmental requirement to reduce the carbon footprint of cement. However, predicting their key properties, such as compressive strength, from their complex chemical composition remains a significant challenge. Although mixture ratios prepared on a macro-scale are widely used for quality control purposes, they do not account for the chemical structure, despite this having a direct impact on the materials’ structural properties. Predicting fundamental properties such as compressive strength from complex chemical compositions remains a significant challenge due to the nonlinear relationships between the elemental components. This research paper introduces a tailored hybrid machine learning framework that combines K-means clustering with classification algorithms. The method uses energy-dispersive X-ray spectroscopy (EDS) data to classify geopolymer samples into their specific mixture numbers, which allows scientists to predict material properties through compositional analysis. A new dataset featuring the elemental compositions of Si, Al, Na, Ca, O, and C, as well as the critical ratios of Si/Al and Ca/Si, was analyzed. The initial step involved clustering the data to discover natural compositional clusters, which served as the basis for training and testing five different classifiers, which included Random Forest (RF), Artificial Neural Networks (ANN), LightGBM, Naive Bayes (NB), and Linear Discriminant Analysis (LDA). The consequences proved that the hybrid method worked with outstanding efficiency. RF achieved the highest performance results through its 98% accuracy, 96% recall, 94% precision, and 95% F1-score results when it classified samples according to their clustered groups. SHAP (SHapley Additive exPlanations) and permutation feature importance analyses both showed that Si/Al proportion functioned as the most crucial predictive variable, while oxygen (O) content and silicon (Si) content followed in importance. The K-means cluster labels produced high accuracy results because they demonstrated that compositional data had strong natural groups, which matched the target property. The system delivers an efficient method which enables fast and dependable geopolymer property forecasts through direct analysis of chemical composition with chemical composition analysis, thus delivering essential information to enhance mix design processes and boost sustainable building material production. Full article

The integration of AI into journalism has intensified debates about the future of news production, yet existing scholarship has focused predominantly on AI’s capabilities rather than on irreplaceable human competencies. This study shifts analytical focus from replacement to complementarity, investigating the boundaries of AI through the perspectives of both journalists and AI developers. Ten participants—including field reporters, news anchors, broadcast journalists, and AI developers—were interviewed through in-depth, semi-structured interviews. Thematic analysis revealed three core dimensions of irreplaceable human competency: embodied presence and rapport-building, contextual judgment and meaning-making, and investigative initiative requiring moral agency. Practitioners and developers converged on AI’s persistent limitations in factual reliability, emotional authenticity, and ethical accountability. Based on these findings, a three-tier human–AI collaborative model is proposed, allocating computational tasks to AI while preserving human authority over editorial judgment, source relationships, and ethical decisions. These findings contribute to human–machine communication theory, extend algorithmic journalism literature beyond capability assessments, and offer practical implications for newsroom workflow design, journalism education, and AI governance. Findings are situated within the Korean media context and should be interpreted accordingly, with implications that may extend to other broadcasting-oriented journalism cultures. Full article

To mitigate the volatility and instability caused by large-scale wind power integration in new-type power systems, hybrid energy storage systems (HESSs) can offer effective frequency support to wind farms. This paper presents a coordinated wind storage frequency control strategy that incorporates optimal power allocation within an HESS. First, wind power output is decomposed and reconstructed into low- and high-frequency components via variational mode decomposition (VMD) optimized with the multi-verse optimization (MVO) algorithm, followed by the establishment of a PI-based HESS frequency response model. Second, an SOC-aware flexible frequency division strategy is designed by coordinating the participation sequence of the wind turbine and the HESS. The regulation process is divided into three stages, namely, wind turbine regulation, joint wind storage regulation, and HESS-dominant regulation, to suppress frequency fluctuations induced by wind power variations. Finally, primary frequency regulation performance indices are proposed and validated in a three-machine, nine-bus system. The simulation results demonstrate that the coordinated use of different storage types within the HESS enhances the grid-connected stability of the wind storage system, while the incorporation of hybrid storage improves wind power utilization. Full article

The increasing volume, temporal density, and diversity of satellite Earth observation (EO) data have fundamentally transformed quantitative vegetation remote sensing. Dense multi-sensor time series and computationally intensive modelling have rendered traditional download-and-process workflows increasingly impractical. Cloud-native computing—where data access, storage, and computation are co-located and analyses are executed in data-proximate environments—has therefore emerged as a key paradigm for scalable and reproducible vegetation EO analysis. This review provides a science-oriented synthesis of cloud-native EO for quantitative vegetation research. We examine architectural principles, data models, and compute patterns that shape how vegetation analyses are implemented, scaled, and scientifically interpreted. Particular attention is given to machine learning as a system component, including model lifecycle management, domain shift, and evaluation integrity in distributed environments. We analyse how cloud-native data abstractions influence algorithmic assumptions, validation design, and long-term product consistency, highlighting trade-offs between analytical complexity, computational cost, latency, and scientific robustness. We provide a forward-looking perspective on emerging imaging spectroscopy missions and the growing system-level requirements for reproducible, scalable, and uncertainty-aware vegetation analytics at continental-to-global scales. We also outline how cloud-native EO infrastructures are driving new scientific paradigms based on continuous monitoring, systematic reprocessing, and AI-driven modelling. Full article