Search Results (59,118)

“Power-to-ammonia” is widely regarded as a viable solution for large-scale consumption of wind and solar power, as well as for deep decarbonization in the energy and chemical sectors. However, the intermittent nature of renewable energy requires ammonia synthesis systems to operate across a wide and varying range of loads, posing challenges to their economic viability. To address this, we develop a simulation and optimization methodology for ammonia reactor operation under varying loads. Firstly, a high-fidelity reactor model is developed based on the reactor’s structural characteristics by incorporating reaction kinetics and thermodynamic mechanisms. This reactor model is then integrated with compression and separation units. To ensure computational efficiency, surrogate models are developed to approximate the ammonia synthesis and flash separation units. A case study of an ammonia plant with a nominal production rate of 100,000 tons/year is conducted to demonstrate the effectiveness of the proposed method. The results indicate that the feasible operation region of the reactor narrows significantly as the system production load decreases. System operation parameters, including reactor inlet temperature, reactor pressure, and ammonia separation temperature, are optimized for the ammonia synthesis loop over a wide operating window from 30% to 100% of nominal capacity. It is recommended to increase the system inlet temperature as the production load decreases, thereby compensating for the reduced heat release per unit product resulting from the decreased system pressure. Full article

Water scarcity in semi-arid regions threatens seed germination and early crop establishment, driving the development of biodegradable Nature-based Solutions to replace synthetic plastic mulches. Porous cellulose membranes were fabricated from rice husk (RH), banana pseudostem (BP), and sugarcane bagasse (SB) by thermo-chemical extraction and high-shear homogenization (n = 5 replicates per membrane type). Membranes were characterized by ATR-FTIR and scanning electron microscopy, confirming removal of non-cellulosic components and biogenic silica preservation in RH, and revealing biomass-dependent porous architectures linked to mechanical and transport behavior. RH produced the most compact fibrillar matrix (compressive strength: 8.16 ± 0.24 MPa; WVT: 170 ± 60 g m⁻² day⁻¹), BP an open interconnected network with superior deformability (9.83 ± 0.25% elongation) and moisture transport (WVT: 400 ± 100 g m⁻² day⁻¹), and SB the highest moisture-retention capacity (215.7 ± 15.8%). Germination assays with Brassica oleracea var. botrytis under water stress showed SB achieved the highest germination rate (90.5 ± 0.99%), confirming that sustained moisture availability governs germination more decisively than transport rate alone. Soil burial tests confirmed biodegradable behavior across all membranes (R² ≥ 0.995; k = 0.043–0.046 day⁻¹). These findings establish a hydrogen-bond-mediated structure–property–function framework for designing biomass-specific cellulose membranes as biodegradable solutions for water-limited agricultural systems. Full article

Ceramic additive manufacturing offers strong potential for fabricating geometrically complex and application-specific components, yet achieving reliable dimensional fidelity remains challenging because dimensional deviation is governed by highly coupled material, process, thermal, and environmental factors. To address this problem, this study proposes an uncertainty-aware computational framework for dimensional error prediction in ceramic 3D printing under variable material and process conditions. The contribution is positioned as a system-level integration of established learning, uncertainty estimation, calibration, and reliability-interpretation components within a ceramic additive manufacturing dimensional-error prediction workflow, rather than as a fundamental methodological breakthrough. The validation is conducted using the publicly available Ceramic 3D Printing Process Control Dataset, a 1000-sample tabular dataset, and the resulting findings are therefore interpreted as dataset-specific computational evidence rather than direct proof of industrial deployment readiness. The methodology begins with a structured data-driven preprocessing pipeline that transforms the Ceramic 3D Printing Process Control Dataset into a multi-condition feature space through data cleaning, one-hot material encoding, min–max normalization, and engineered descriptors capturing extrusion–speed balance, thermal gradients, cooling intensity, deposition density, and material-conditioned interactions. A multi-branch deep computational architecture is then developed to encode material, process, thermal-environmental, and engineered-feature streams separately, followed by adaptive cross-condition fusion to learn nonlinear dependencies across ceramic printing regimes. To improve reliability beyond deterministic regression, the framework jointly models aleatoric and epistemic uncertainty and incorporates calibration refinement to align predictive confidence with observed error behavior, thereby enabling preliminary reliability-oriented interpretation of stable and high-risk operating conditions. Experimental results demonstrate that the full model achieves the best overall within-dataset performance, with a test MAE of 0.0118, RMSE of 0.0172, $R^2=0.999$, MAPE of 1.74%, calibration error of 0.003, PICP of 0.996, reliability score of 0.992, and a stable prediction rate of 98.7%. Although these values indicate strong predictive behavior under the current structured dataset, the exceptionally high $R^2$ should be interpreted cautiously because external experimental validation, larger measured datasets, and cross-machine ceramic printing trials are still required. These findings show that the proposed framework provides an effective system-level computational strategy for dataset-specific reliability-aware dimensional quality prediction in ceramic additive manufacturing and offers a preliminary data-driven foundation for uncertainty-aware intelligent process optimization. Full article

Programmable gas flowmeters with remote reading and control are increasingly needed in the era of automation and AI. However, commercially available options remain expensive, hindering their adoption. We therefore developed a cost-effective programmable flowmeter as a compact device based on a low-cost glass rotameter. This system consists of a reading unit (webcam + rotameter), a control unit (development board + stepper motor-actuated valve) using an open-loop pre-calibrated step-to-flow matrix, and a terminal interface. The side-view imaging of the glass rotameter avoids occlusion of the rotor by the scale, enabling reliable rotor identification using well-established algorithms. The flow rate is derived by mapping the normalized rotor position to the scale instead of recognizing the scale markings or numerals. Two terminal configurations are offered: USB-connected (PC + MATLAB) and embedded (Raspberry Pi + Python). The USB-connected configuration uses direct serial communication between MATLAB and Arduino for stepper motor step control, ensuring fast response and good compatibility. The prototype retains manual control and direct eye reading. Experiments demonstrated strong programmability, fast response (0.2 s), high accuracy (mean error < 3%), and stable reading (fluctuation < 0.5%). This cost-effective yet programmable gas flowmeter is expected to benefit gas sensor development and accelerate automation in fluid-related fields. Full article

The carbon emission allowance (CEA) market is a complex socio-technical and environmental-management system in which regulatory design, trading activity, liquidity conditions, and price volatility interact dynamically. Accurate systems-level tail-risk forecasting and early warning remain challenging because carbon-market losses are affected by nonlinear dependence, episodic liquidity stress, and time-varying volatility. This study proposes an Interpretable Quantile Temporal Network (IQTN) as a systems-oriented risk-monitoring framework for China’s national CEA market. By integrating a feature-gating mechanism, a causal temporal convolutional encoder, and a non-crossing quantile output layer, IQTN directly models the conditional tail distribution of future carbon-market losses. The framework produces multi-horizon Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) forecasts for 1-day, 5-day, and 10-day horizons and converts predicted tail risk into operational early-warning signals. Compared with historical simulation, EWMA, GARCH-type models, machine-learning quantile models, and deep temporal benchmarks, IQTN achieved the lowest 95% VaR pinball loss across all horizons, with values of 0.1765, 0.3958, and 0.5732. VaR backtesting showed empirical exceedance rates of 5.23%, 6.04%, and 6.94%, closest to the nominal 5% level. Interpretability analysis identified rolling volatility, maximum loss, intraday range, trading value, and illiquidity as key risk drivers. The temporal importance results also show that recent observations dominated the risk forecasts, suggesting that the risk state of the CEA market is highly sensitive to short-term market information. This supports the use of a short-horizon temporal network as a systems-oriented tool for carbon-market tail-risk monitoring and early warning. Full article

Background/Objectives: Neoadjuvant cisplatin-based chemotherapy (NAC) followed by radical cystectomy (RC) is a standard treatment for cisplatin-eligible patients with muscle-invasive bladder cancer (MIBC), yet baseline tools to refine prognostic stratification remain limited. We aimed to develop and internally validate a clinicopathological nomogram integrating the neutrophil-to-lymphocyte ratio (NLR) to estimate event-free survival (EFS) in patients with MIBC treated with NAC. Methods: We retrospectively analyzed 210 patients with cT2–T4aN0–1M0 MIBC treated with cisplatin-based NAC at two Spanish institutions between 2010 and 2021. Candidate predictors included demographic, clinicopathological, and routine laboratory variables. A multivariable Cox model with backward selection based on the Akaike information criterion (AIC) was used to derive the final model, and internal validation was performed using 1000 bootstrap resamples. Results: Sex, age, prior non–muscle-invasive bladder cancer (NMIBC), and NLR were retained in the final nomogram. The model showed moderate discrimination, with a Harrell’s c-index of 0.60 and an optimism-corrected c-index of 0.58. The nomogram stratified patients into low-, intermediate-, and high-risk groups, with median EFS not reached, 47.5 months, and 18.0 months, respectively. High-risk patients also showed lower pathological complete response (pCR) rates. Conclusions: This exploratory nomogram integrates an accessible systemic inflammatory marker with baseline clinical variables to identify patients with poorer outcomes despite NAC. External validation in contemporary cohorts is warranted before clinical implementation. Full article

Background/Objectives: Oral cancer is one of the most common types of cancer, with high mortality rates if not detected early. Traditional diagnostic methods based on clinical examination rely on experience, leading to delays in early and reliable diagnosis. In recent years, medical imaging and AI-based computer-aided diagnostic systems have shown promising results in the automated identification of oral cancer. In particular, the efficient management of high-dimensional feature spaces in machine learning and deep learning approaches directly impacts classification performance. In this context, metaheuristic-based feature selection technics is a critical component because of eliminating redundant and irrelevant features. To address these challenges, this study proposes a metaheuristic-based feature selection method to reduce feature dimensionality and enhance the classification performance of oral cancer detection. Methods: This study proposes an improved Honey Badger Algorithm-based feature selection approach for the automated detection of oral cancer. In the proposed method, the distance vector used in the HBA method has been redefined to improve the balance between exploration and exploitation. Additionally, a new Cauchy mutation-based migration strategy was integrated into the proposed method to increase diversity in the search space and avoid getting stuck in local minima. The continuous-valued iHBA method was discretized with a modified sin–cos transfer function for feature selection. Oral cancer images were filtered using the CLAHE method, and after extracting deep features with the ResNet50 architecture, the proposed metaheuristic-based method was used to select discriminative features. Results: The proposed method was first tested for reliability and limitations through repeated runs on problems with different characteristics, such as unimodal and multimodal classical test functions. Then, the method was applied to extract significant features for oral cancer detection using a Histopathological Imaging Database containing 1224 histopathological oral tissue images at 100× and 400× magnification levels from 230 patients. The proposed approach was assessed in terms of accuracy, precision, recall, F1-score, and convergence curves in comparison with various classical feature selection techniques, such as wrapper-based, filter-based, and embedded-based methods, as well as other metaheuristic-based methods. The experimental results demonstrated that the suggested strategy outperformed both traditional feature selection techniques and alternative metaheuristic approaches. Conclusions: The effectiveness of the proposed method in improving diagnostic accuracy was evaluated through comprehensive experimental analyses. The obtained findings show that the proposed iHBA-based feature selection approach can reduce feature dimensionality, eliminate redundant and irrelevant features, and improve the classification performance of oral cancer detection. Therefore, the proposed method provides an effective and competitive computer-aided diagnostic framework for the automated classification of histopathological oral cancer images. Full article

University orientation programmes are a primary mechanism through which new students become familiar with campus facilities, academic spaces, and institutional procedures. However, many orientation activities are delivered as single in-person sessions, limiting opportunities for students to revisit spatial and procedural information after the event. To help address this constraint, a digital twin-inspired metaverse orientation application, the Digital Twin Metaverse Orientation (DTMO), was designed in Unity and hosted on Spatial.io as a spatially faithful virtual replica of a faculty environment. An exploratory pilot evaluation was conducted with 30 university students from multiple faculties after a facilitator-guided orientation session. The System Usability Scale (SUS), Net Promoter Score (NPS), and two open-ended questions were used to examine perceived usability, recommendation intention, and the reasons underpinning recommendation decisions. The application obtained a mean SUS score of 86.83, corresponding to an excellent perceived-usability rating, and an NPS of 53.33, indicating positive immediate recommendation intention. Qualitative responses suggested that participants valued the DTMO for engagement, accessibility, ease of navigation, and support for spatial familiarisation, while some participants emphasised that it should complement rather than replace physical orientation. These pilot findings indicate promising user reception in a small, guided-session sample, but they do not establish orientation effectiveness, learning transfer, wayfinding performance, retention, belonging, institutional integration, or sustained use. Further research with broader samples and outcome-based measures is therefore needed. Full article

Conventional defect inspection for warp-knitted lace relies on manual work and negative-sample-based training, resulting in low efficiency, frequent false detections and poor adaptability. This study presents a novel AI visual inspection system centered on positive-sample learning, which is built upon a five-layer 5G + Industrial Internet distributed architecture. Supported by modified looms, high-precision imaging devices and an optimized YOLOv5s model, the system accomplishes intelligent defect detection. A positive-sample self-learning paradigm and dual-model collaboration mechanism are proposed to reduce the demand for negative samples and cut labeling expenses. The integration of CBAM, FPN + PAN structure, self-supervised learning and hybrid loss further strengthens the recognition performance for subtle defects under complex patterns. Industrial tests show that the system reaches a grid-level classification accuracy of 95% and a frame-level detection rate over 98%, with a detection speed of 30 m/min. It reduces labor costs and product reject rates by 40% and 30% correspondingly while running stably in real production. This method breaks the constraints of traditional training modes, provides a scalable intelligent solution for the digital upgrading of the warp-knitted lace industry, and promotes the high-quality development of textile manufacturing. Full article

Software quality assessment in agile projects remains fragmented. Technical, process-related, and team-related indicators are often evaluated through separate models, tools, and reports. This fragmentation limits cross-project comparability and weakens evidence-based decisions for software quality improvement. To address this problem, this study proposes the Overall Software Quality Index (OSQI), a multidimensional decision-support framework for software quality assessment in agile projects. OSQI integrates code quality, process quality, and team quality into a single project-level assessment model. The framework was initially grounded in ISO/IEC 25010:2011 and is discussed in relation to the ISO/IEC 25010:2023 revision, particularly its explicit inclusion of Safety as a product quality characteristic. Since the industrial datasets used in this study were not collected from safety-critical systems, Safety was not modeled as a separate OSQI dimension in the current version; instead, it is addressed as a scope limitation and future extension. The measurement structure was defined using the Goal–Question–Metric (GQM) approach. An initial set of 49 candidate metrics was reduced to 15 core indicators. This reduction was performed using dimension-specific strategies: Random Forest-based feature importance for code quality, Delphi and Analytic Hierarchy Process (AHP) for process quality, and thematic consolidation for team quality. The selected indicators were normalized and integrated through entropy-based weighting. This process generates an interpretable composite quality score. The main contribution of OSQI is not the isolated use of these methods, but their integration into a reproducible and tool-supported framework. The framework converts heterogeneous software engineering signals into a unified decision-support index. OSQI was evaluated using industrial agile project data. The data included static code analysis outputs, issue-tracking records, team assessment results, and product outcome indicators. In an exploratory validation across five industrial projects, OSQI showed a strong positive association with Net Promoter Score ($r=0.97, p=0.0076$) and a strong negative association with churn rate ($r=-0.97, p=0.0061$). A supporting software tool was also developed to automate data integration, score calculation, visualization, and project-level comparison. The findings suggest that OSQI can support quality monitoring, project benchmarking, and evidence-based improvement decisions in agile software engineering contexts. Full article

This study introduces a Caputo fractional-order version of the SEIRS-V model to investigate the spreading dynamics of worms within wireless sensor networks. Traditional integer-order worm propagation models describe the instantaneous evolution of network states; however, they do not adequately account for memory and hereditary characteristics that may influence the transmission dynamics. Consequently, their ability to represent realistic network behavior can be limited in systems where past states affect current propagation patterns. The framework divides sensor nodes into susceptible, exposed, infectious, recovered, and vaccinated classes, while explicitly incorporating worm transmission rates, temporary loss of immunity, and the impact of preventive security measures under limited resource conditions. A detailed theoretical examination is performed, covering the existence, boundedness, and uniqueness of solutions of the fractional-order system. The coupled nonlinear fractional system is solved semi-analytically by means of the Fractional Reduced Differential Transform (FRDT) technique. To confirm accuracy and robustness, the identical system is also discretized and solved using the finite difference scheme (FDS). Unlike previous studies on worm propagation models in wireless sensor networks, which are mainly limited to equilibrium point analysis and qualitative investigations without deriving explicit solutions, the present work develops an approximate semi-analytical solution for the fractional-order SEIRS-V system using the FRDTM. Comparisons between the two solution sets demonstrate excellent agreement and high precision. Numerical outcomes are presented through a series of 2D graphical profiles that illustrate the time-dependent behavior of each compartment and reveal the sensitivity of worm propagation and suppression to variations in the fractional order and key model parameters. The integrated theoretical and computational findings underscore the strong protective role of vaccination in mitigating worm outbreaks and offer valuable guidelines for strengthening cybersecurity measures in wireless sensor networks. Full article

Background: Dedicated artificial intelligence (AI) systems for fracture detection already exist, yet general-purpose multimodal models are increasingly accessible to clinicians despite not being developed or formally validated as medical devices. Their behavior in focused orthopedic imaging tasks remains insufficiently characterized. Purpose: To characterize how two accessible general-purpose multimodal models interpret AP pelvis radiographs with hip fractures, focusing on context dependence, overconfidence, and complementary error patterns within a surgically confirmed positive-only cohort. This was a behavioral characterization study of a fracture-positive cohort, not a diagnostic accuracy evaluation. Methods: In April 2026, we retrospectively studied 214 surgically confirmed hip fractures on AP pelvis radiographs using two general-purpose multimodal models under six prompting conditions. In runs A–D, the models were explicitly told that a hip fracture was present and were asked to classify it; in runs E–F, they were not told whether a hip fracture was present. Each image was rerun de novo in a separate chat session through vendor APIs using a fixed base prompt and no image preprocessing. We recorded hip-fracture detection, correct laterality, coarse fracture pattern, intracapsular displacement, AO/OTA grading, subtrochanteric identification, and self-reported confidence. Because the cohort contained hip fractures only, we report fracture-detection rates and classification performance within a positive-only cohort rather than full diagnostic-accuracy metrics. Results: Using the more conservative endpoint of hip-fracture detection with correct laterality, GPT-5.4 was correct in 79.0% and 86.4% of cases in runs E and F, whereas Gemini was correct in 80.4% and 93.5%, respectively. When outputs from both models were combined, this endpoint reached 89.7% in run E and 96.7% in run F, indicating complementary rather than redundant error patterns. Incorrect laterality cues markedly degraded performance, from 90.7% to 66.4% in GPT-5.4 and from 97.7% to 57.0% in Gemini. Performance remained limited for treatment-relevant subtyping, particularly AO/OTA grading and subtrochanteric identification. Both models frequently remained highly confident when wrong, and self-reported confidence did not reliably distinguish correct from incorrect outputs. Conclusions: Accessible general-purpose multimodal models showed partial capability for coarse hip-fracture interpretation, but they remained context-sensitive, unreliable for treatment-relevant subtyping, and highly confident even when incorrect. Their complementary error patterns are hypothesis-generating rather than evidence of clinical readiness. On the basis of these findings, we do not support unvalidated or uncontrolled clinical use of such models. As access to these tools expands, explicit usage boundaries, minimum performance expectations, repeated local revalidation, and sustained human oversight become increasingly necessary. Full article

Emerging-market equity exchanges require regime forecasting systems that are continuous in time, robust to heavy-tailed distributions, and optimised against false alarms. No existing method addresses all three simultaneously, and no prior study has reported a crisis false-alarm rate on JSE equities. We propose S-NODE-ANF-RRC: a stochastic neural ODE within an Adaptive Neuro-Fuzzy Risk-Regime Clustering architecture, integrated by a Milstein scheme with Lyapunov-regularised dual-loss training. The system is evaluated as a one-step-ahead probabilistic forecaster ($h=1$ trading day) on 2696 daily observations across 17 JSE securities (March 2015–March 2026). Gaussian mixture clustering on raw features (kurtosis 54.8) inflates ARI by 1.3×; log-transformation corrects this artefact. Two operational profiles emerge: the N-ODE-ANF-RRC achieves the lowest cost (10,350 bp, 65.1% below GMM) and longest lead time (0.71 days); the S-NODE-ANF-RRC achieves the lowest false alarm rate among probabilistic architectures (FAR = 0.051), with a 42.0% cost reduction versus GMM (McNemar $p=0.027$, power $1-\beta =0.73$; bootstrap CI [5250, 19,600] bp excludes zero). Ablation confirms drift, diffusion, and dual-loss as the minimum viable daily-frequency configuration. Full article

Education systems face increasing pressure to adopt evidence-informed innovations that respond to learner diversity. In early childhood, understanding neuroplasticity is foundational for developing inclusive pedagogies, yet translating basic neuroscience into teacher professional development remains complex. This study presents a descriptive and exploratory bibliometric analysis characterizing the intersection of neuroplasticity, neuropedagogy, and early childhood to map how research evidence is organized. A corpus of 2937 documents spanning from 1975 to early 2026 was analyzed to identify publication trends, global collaboration networks, and thematic structures. Results indicate exponential growth in the field, with the United States leading in volume but European and South American nations demonstrating higher international collaboration rates. Thematic mapping reveals a structural separation between applied human studies and mechanistic basic science. This translational distance, combined with the documented prevalence of neuromyths among educators, presents a significant barrier to evidence-informed inclusive education. These findings provide researchers and policymakers with a diagnostic map of the field, outlining specific implications for the content, design, implementation, and evaluation of future teacher professional development to responsibly advance educational equity and inclusion. Full article

Accurate object detection remains a major challenge in autonomous systems and surveillance, particularly when objects are partially or fully obscured by occlusions. To address this issue, we revisit FuDensityNet as a multimodal detection framework that jointly leverages 2D RGB images and 3D LiDAR point clouds for robust feature representation. The model integrates spatial and depth cues through low-rank tensor fusion (LRTF) and incorporates an Occlusion Rate (OR) assessment module that estimates the degree of occlusion and dynamically selects the most suitable detection pathway to preserve performance. Experiments on the KITTI and NuScenes datasets indicate that this adaptive strategy improves robustness under high occlusion while maintaining competitive accuracy in less challenging conditions. In particular, FuDensityNet attains 76.6% AP for car detection under “Hard” conditions on KITTI and outperforms several RGB-only and RGB–LiDAR baselines. Owing to its adaptive and modular design, FuDensityNet remains compatible with both 2D and 3D detection pipelines, making it a practical option for real-world environments where visual obstructions are frequent. Full article