Search Results (3,154)

Biochar is a porous, carbon-rich soil amendment that can enhance soil water retention capacity by modifying pore structure and physicochemical properties. Understanding the soil−water characteristic curve (SWCC) of biochar-amended soils is essential for evaluating their hydrological behavior and promoting the application of biochar in engineering practice. Given the demonstrated feasibility and accuracy of machine learning methods for predicting soil parameters, this study employed six machine learning models, namely, decision tree, random forest, XGBoost, LightGBM, CatBoost, and artificial neural network, to predict the SWCC of biochar-amended soils based on a constructed dataset. Feature importance analysis and partial dependence analysis were further conducted to reveal the influence patterns of key variables. The results indicate that all six models exhibit good predictive capability, with gradient boosting models (XGBoost, CatBoost, and LightGBM) performing best. Suction is the dominant factor controlling the volumetric water content variation, while soil particle-size distribution and dry density provide the physical basis for water retention. Biochar content, pyrolysis temperature, and feedstock type further modulate the water retention capacity of amended soils. Overall, the findings demonstrate that machine learning approaches can effectively predict the SWCC of biochar-amended soils and provide insights into the controlling mechanisms of soil water retention. Full article

Artificial intelligence is increasingly used to improve industrial quality control, but its practical value depends on whether models remain accurate under different operating conditions and fault classes. This study evaluates an artificial-intelligence-based workflow for gear quality control using vibration signals measured on a real two-stage reduction gearbox. Two orthogonal vibration channels were analyzed for six health states, three shaft speeds, and two load levels. Because the time-series data were only partly stationary, the dataset was divided chronologically into training and test segments. A 54-feature representation was built from rolling-window statistics and operating variables, and six classifiers were compared: Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting (XGBM), random forest, decision tree, multilayer perceptron (MLP), and logistic regression. LGBM achieved the best overall accuracy (0.9728) while maintaining substantially lower training time than several competing nonlinear models. Class-wise precision, recall, and F1-score ranged from 0.95 to 1.00, and the nominal response time for most operating-condition transitions was approximately 0.0998 s. The results show that vibration-based machine learning can support robust, near-real-time fault identification in mechanical manufacturing environments. The study also highlights the importance of chronological validation, feature engineering over multiple time windows, and the trade-off between predictive performance and deployment efficiency. Because the validation dataset originates from one gearbox platform, the results should be interpreted as promising internal evidence rather than as proof of universal industrial robustness. Full article

As exploration expands into deep and unconventional petroleum systems, light oil and condensate have become key targets for reserve growth and production enhancement. This study employs the gold tube pyrolysis of kerogens from Cretaceous Qingshankou shale to investigate the role of hydrocarbon (HC) expulsion in light oil and condensate generation during thermal maturation. The results show that HC expulsion significantly reduces overall HC yields and alters their chemical composition. Specifically, compared with immature kerogen, n-hexane-extracted mature kerogen (EasyR_o = 0.96%) exhibited reductions of 60%, 57%, and 50% in C₁₅₊ compounds, C_6–14 HCs, and C_1–5 gases, respectively. Moreover, the generation window of C_6–14 HCs (a proxy for light oil) is narrowed and shifted toward lower maturity. Kinetic parameters were further used to establish two separate evolutionary models for methane, wet gas, light oil, and heavy oil. Based on these models, the shale oil resource potential of the first member of the Qingshankou Formation, the Qijia–Gulong Sag, is estimated to be (6.95–8.80) × 10⁶ ton/km² for the no-HC-expulsion scenario and (3.63–3.85) × 10⁶ ton/km² for the significant-HC-expulsion scenario (HEE = 84.35%). These results provide a valuable reference for assessing the light oil and condensate potential of high-maturity Qingshankou shale in the Songliao Basin. Full article

Technological advances in optical imaging and artificial light technologies have substantially transformed modern neurosurgical practice by improving intraoperative visualization and surgical precision. This narrative review evaluates current applications of fluorescence-guided surgery, optical navigation systems, near-infrared imaging, augmented visualization platforms, and artificial intelligence-assisted intraoperative imaging in neurosurgery. Particular attention is given to clinically established fluorophores, including 5-aminolevulinic acid, fluorescein sodium, and indocyanine green, which are increasingly used for tumor delineation, vascular assessment, and real-time tissue perfusion analysis. Emerging technologies such as Raman spectroscopy, multispectral imaging, holographic navigation, and nerve-specific fluorescent probes are also discussed in the context of precision and minimally invasive neurosurgery. Current evidence demonstrates that advanced optical systems improve surgical orientation, maximize extent of resection, and support preservation of critical neurovascular structures, particularly in neuro-oncology, vascular neurosurgery, and skull base surgery. However, limitations related to fluorescence specificity, standardization, cost, and technological accessibility remain significant challenges. The continued integration of multimodal optical imaging, computational navigation, and AI-assisted visualization is expected to further enhance intraoperative decision-making, surgical safety, and postoperative outcomes, contributing to the future development of precision image-guided neurosurgery. Full article

This study presents a comprehensive analysis and predictive modeling framework for the axial compressive strength (f_cc) and ultimate axial strain (ε_cu) of concrete columns confined within fiber-reinforced polymer (FRP) systems. Large databases comprising 3312 samples for f_cc and 3319 for ε_cu were compiled from the literature, encompassing a wide range of key variables, including unconfined concrete strength from 7 MPa to 204 MPa and diverse FRP confinement configurations. The datasets were subjected to extensive statistical and multivariate analyses to identify the primary factors influencing axial behavior and guide feature selection for predictive modeling. Three groups of machine learning (ML) algorithms were subsequently considered: (i) artificial neural networks (including multilayer perceptrons with one and two hidden layers), (ii) kernel-based models (Gaussian process regression and support vector regression), and (iii) tree-based ensemble models (gradient boosting machine, eXtreme gradient boosting, and light gradient boosting machine). Hyperparameters were optimized using grid search cross-validation, while feature importance analyses were performed to quantify the contribution of each input variable. Among all ML models, eXtreme gradient boosting demonstrated superior predictive performance, effectively capturing the nonlinear and multivariate interactions governing confinement effectiveness. Comparative analysis with the top performing regression-based formulations further highlighted the accuracy, robustness, and generalization capability of the eXtreme gradient boosting model. The findings provide a data-driven and interpretable framework for the design and prediction of FRP-confined concrete columns. Full article

Business process reengineering (BPR) is usually remembered as one of the most visible management fashions of the 1990s. It rose rapidly, promised radical redesign and major performance gains, and later lost legitimacy as many implementations failed to match the rhetoric and became associated with disruption, downsizing, and managerial overreach. Yet the organizational problem to which BPR responded never disappeared: how should organizations redesign processes when new technologies alter what is possible? This paper revisits BPR in light of recent developments in generative and agentic artificial intelligence. This perspective article develops a conceptual interpretation rather than a systematic review or empirical test. Its purpose is to clarify an emerging pattern in management discourse and process-management research: the possible reactivation of BPR-style redesign logic under new technological and discursive conditions. Using management fashion theory as the main lens, it suggests that AI may be creating conditions under which elements of BPR’s underlying redesign logic become newly relevant. The argument is not that the BPR label has simply returned. Rather, aspects of its core ambition appear to be rearticulated through adjacent and more legitimate vocabularies such as business process management, AI-augmented business process management systems, Large Process Models, and agentic BPM. To capture this pattern, the paper introduces the concept of translated resurgence, referring to the renewed relevance of an older management idea through relabeling, reinterpretation, and mutation. The paper further argues that AI may alter the technical feasibility of radical process redesign while leaving many classic BPR risks intact. The result is best understood not as a simple revival, but as an emerging and still unsettled phase in the longer afterlife of a once-prominent management idea. Full article

Photodynamic therapy (PDT) is a minimally invasive therapeutic modality based on the interaction between a photosensitizer (PS), light, and molecular oxygen to generate reactive oxygen species (ROS) capable of inducing localized cytotoxicity. Natural products provide a chemically diverse source of photosensitizers, including curcumin, hypericin, hypocrellin, chlorin derivatives, alkaloids, flavonoids, anthraquinones, and other photoactive scaffolds. However, their translational development remains limited by poor solubility, aggregation, instability, variable purity, limited tissue penetration, suboptimal pharmacokinetics, and insufficient formulation readiness. In parallel, artificial intelligence (AI), including machine learning (ML), deep learning (DL), quantitative structure–activity relationship (QSAR) and quantitative structure–property relationship (QSPR) modeling, radiomics, and predictive analytics, is increasingly being applied to photosensitizer discovery, molecular property prediction, nanoformulation optimization, treatment planning, and precision PDT. This critical review evaluates the intersection between AI, natural photosensitizers, nanopharmaceutical development, and PDT, with emphasis on methodological strengths, current limitations, and translational priorities. A PRISMA 2020-inspired search strategy identified 27 studies for qualitative synthesis, comprising 11 review articles and 16 original investigations, while additional seminal references were used for historical and mechanistic contextualization. The analysis indicates that current AI applications in PDT are concentrated around molecular property prediction, QSAR/QSPR modeling, phototoxicity assessment, radiomics, image-guided therapy, and treatment-response prediction, whereas AI-guided exploration of natural photosensitizer chemical space and AI-assisted nanoformulation design remain comparatively underdeveloped. Key barriers include heterogeneous datasets, limited natural-product representation in predictive models, insufficient external validation, weak integration between formulation variables and photodynamic outcomes, and limited consideration of manufacturing and regulatory requirements. This review proposes an integrated AI-enabled translational framework connecting natural-product chemical diversity, photochemical prediction, nanocarrier optimization, precision PDT validation, and clinical implementation. Full article

Shading in agroforestry systems reduces light availability and increases the risk of lodging, limiting wheat (Triticum aestivum L.) yield. A two-year field experiment was conducted to assess the effects of chlormequat chloride (CCC) on wheat stem morphology, carbohydrate metabolism, lignin biosynthesis, lodging resistance, and yield under four shading intensities (S0: natural light; S1: 10% → 25% shading; S2: 20% → 50% shading; S3: 30% → 75% shading) from jointing to maturity stage. Shading resulted in taller plants with increased center of gravity, thinner stems, and reduced lignin, cellulose, and soluble sugar content, weakening stem-breaking strength and reducing yield. CCC application decreased internode length, increased stem solidity, and enhanced lignin and cellulose accumulation, as well as lignin biosynthesis enzyme activity. These changes improved stem-breaking strength and lodging resistance. Moreover, CCC significantly increased wheat yield by 2.7% to 23.3% (p < 0.05) depending on the shading intensity, and there was a significant interaction between shading intensity and CCC application on wheat grain yield (p < 0.05) The results demonstrate that CCC application can mitigate shading-induced yield loss by enhancing lodging resistance and improving stem integrity under the artificial-shading conditions evaluated in this study. Its effectiveness in actual agroforestry systems requires further field validation. Full article

Rapid growth in connected devices, artificial intelligence applications, and the Internet of Things (IoT) is driving demand for ultra-high data rates, low latency, and energy-efficient communication infrastructure. Wireless optical communication (WOC), including free-space optical (FSO), is recognized as a disruptive technology for 6G and future-generation networks. However, atmospheric visibility critically affects WOC/FSO link availability, capacity, and reliability. This study proposes a machine learning (ML)-based low-visibility classification model that integrates Meteorological Aerodrome Reports (METARs) with microwave-link received-signal (Rx) features. Visibility below 6000 m is predicted at the 1 h, 3 h, and 6 h horizons using 18 months of data from Suvarnabhumi Airport (VTBS) and Don Mueang Airport (VTBD) in Bangkok, Thailand. Four ML algorithms, namely logistic regression, random forest, extreme gradient boosting, and light gradient boosting machine (LGBM), are evaluated against persistence and Terminal Aerodrome Forecast (TAF) baselines. In a 100-round block-bootstrap evaluation, LGBM with METAR-Rx achieved the highest mean F1 scores at the 1 h and 3 h horizons, outperforming TAF by 28 and 20 percentage points at the 1 h horizon for VTBS and VTBD, respectively. SHAP and ablation analyses suggested that current visibility is the dominant predictor, while Rx features provide complementary information and improve F1 performance by approximately 1–4 percentage points. Seasonal analysis shows stronger cool-season performance, while rainy-season prediction remains challenging. Adding visibility-trend features further improves performance, with the best combined model achieving 1 h F1 scores of 0.7253 for VTBS and 0.6495 for VTBD. These findings indicate that integrating the METAR-Rx feature set can support short-term low-visibility classification. Full article

Gastrointestinal (GI) adverse drug reactions (ADRs) are common among glucagon-like peptide-1 receptor agonist (GLP-1 RA) users and frequently contribute to treatment discontinuation and reduced therapeutic benefit. This retrospective study aimed to develop and validate an explainable artificial intelligence (XAI) model to predict GI ADR risk among GLP-1 RA users using real-world clinical data from the NIH All of Us Research Program. Adults prescribed GLP-1 RAs were identified and classified according to the occurrence of GI ADRs following treatment initiation. Multiple supervised machine learning models, including logistic regression, random forest, extreme gradient boosting (XGBoost), support vector machine, neural network, LightGBM, and CatBoost, were evaluated using demographic, socioeconomic, clinical, medication, and laboratory variables. Model performance was assessed using area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1-score. A total of 8697 participants were included, of whom 59.1% experienced GI ADRs. All models demonstrated reasonable predictive performance, with AUC values ranging from 0.82 to 0.84. The XGBoost achieved discrimination of (AUC: 0.84 ± 0.01). SHapley Additive exPlanations (SHAP) identified gastroesophageal reflux disease, hemorrhoids, and elevated HbA1c as important predictors of GI ADR risk. These findings demonstrate the potential utility of explainable machine learning approaches for predicting the safety of GLP-1 RA therapy. Full article

Urban traffic congestion remains a major barrier to sustainable mobility, necessitating a transition from static signaling to Intelligent Traffic Light Systems (ITLS). This study presents a systematic review in computer vision, artificial intelligence, and adaptive control algorithms for urban intersection optimization. Following a methodological framework informed by PRISMA guidelines, the study examines state-of-the-art architectures, including deep learning-based perception models and reinforcement learning agents. The findings indicate that, although two-stage detectors provide benchmark accuracy for vehicle perception, single-stage models and Vision Transformers offer the high-speed processing required for real-time traffic management. In addition, deep reinforcement learning enables autonomous, lane-specific optimization that outperforms traditional actuated and fixed-time systems. The review also identifies a persistent research gap in the deployment of these computational frameworks in the resource-constrained and heterogeneous infrastructures of developing countries. For the urban context of Riobamba, Ecuador, a phased implementation strategy is proposed that balances computational demands with the city’s morphological and social characteristics. By bridging the gap between high-fidelity simulation and practical field deployment, this review provides a scalable framework for improving throughput, reducing emissions, and enhancing safety. Overall, these advances offer a promising pathway toward more resilient transportation systems in rapidly evolving Andean urban centers. Full article

Artificial intelligence (AI) is transforming oncologic imaging by enabling automated, accurate, and scalable diagnostic decision support. However, successful clinical translation requires not only strong predictive performance but also interpretability, transparency, and clinician confidence. We present an attention-guided deep learning framework for automated slice-level classification of parathyroid adenoma (PTA) from arterial-phase computed tomography (CT) images. The framework incorporates lightweight hierarchical attention mechanisms within residual neural networks to enhance feature representation and contextual understanding while maintaining computational efficiency for real-world deployment. Three novel architectures were developed: the Residual Block Light Attention Network (Res-BLANet), Residual Stage Light Attention Network (Res-SLANet), and Residual Layer Light Attention Network (Res-LLANet). Models were trained and evaluated on a rigorously curated, expert-annotated dataset of 63 patients, including 39 pathologically confirmed PTA cases, with 350–450 slices per patient. Training employed Hounsfield unit normalization, extensive data augmentation, and 10-fold cross-validation to ensure robust performance assessment. A key innovation is the integration of hierarchical attention modules that generate attention maps at multiple network levels, enabling qualitative visualization of diagnostically relevant regions and providing exploratory insight into the model’s decision-making process. Experimental evaluation showed strong diagnostic performance. Res-SLANet achieved the highest overall accuracy (87.37%), precision (92.56%), and F1-score (86.69%), while Res-LLANet attained the highest sensitivity (96.0%) on independent testing. These results should be interpreted as preliminary, given the limited size of the independent patient-level test cohort (n = 3). Res-BLANet delivered substantially faster inference with minimal computational overhead. These findings demonstrate that hierarchical attention-guided deep learning can achieve accurate and computationally efficient PTA detection from CT imaging while providing qualitative visual insights into the model’s decision-making process. The proposed framework represents a promising approach toward more interpretable AI-assisted diagnostic systems for oncologic imaging. Full article

The oxygen-evolving center (OEC) in photosynthesis is a unique biological Mn₄CaO₅-cluster that splits water into electrons, protons, and dioxygen. It is a great challenge for chemists to develop a robust and precise mimic of the OEC in the laboratory. Herein, we report the formation of a rare-earth-element-containing Mn₄YO₄-cluster that represents an excellent and robust model of the OEC. The key synthetic precursor, the Mn₃YO₂-cluster, is reported for the first time, which possesses an identical mixed-valence Mn^III₂Mn^IV metal core and a hydrogen-bonding network coordination sphere. This precursor is very reactive and can convert into various compounds in solution. Importantly, it has been found that the presence of organic bases significantly influences the distribution of intermediates and promotes the formation of the Mn₄YO₄-cluster. Meanwhile, two Mn₄YO₄-clusters are described, which closely mimic the main metal-oxide core and peripheral ligands, as well as the oxidation states of the four Mn ions in the OEC, revealing that both the terminal ligands and a bridging carboxylate are variable. This new Mn₄YO₄-cluster displays a remarkable stability in the presence of water in acetonitrile solution. These findings shed new light on the synthesis of rare-earth-element-containing clusters and the rational design of robust artificial water-splitting catalysts, and provide chemical insights into the dynamic structural changes of both biological and artificial clusters. Full article

Accurate prediction of photocatalytic degradation in TiO₂ reactors remains challenging due to the time-consuming, labor-intensive, and costly nature of experimental investigations, as well as the limited ability of conventional models. The degradation of benzoic acid was investigated in TiO₂-immobilized tubular plug-flow reactors under artificial UV radiation. The effects of reactor diameter and flow rate on the reaction rate constant were experimentally investigated. The results showed a constant reaction rate under artificial UV irradiation, which is attributed to higher electron photoactivation resulting in high photocatalytic activity. Three machine learning models, namely kernel extreme learning machine (KELM), Crested Porcupine Optimizer–Support Vector Regression (CPO-SVR), and Harris Hawks Optimizer (HHO)–SVR, were designed and compared for accurate prediction of reaction rates. The Pearson correlation coefficient (PCC), Willmott index (WI), Nash–Sutcliffe efficiency (NSE), and Legates–McCabe index (LM) were used to evaluate model performance. The highest predictive accuracy, resulting in the best PCC, WI, NSE, and LM values, was obtained from the HHO-SVR model, with PCC values of 0.990 and 0.990, WI values of 0.999 and 0.999, NSE values of 0.999 and 0.998, and LM values of 0.982 and 0.971 during training and testing, respectively. The CPO-SVR model also demonstrated good predictive performance and outperformed the KELM model on its own. A five-fold cross-validation ensured the stability and uncertainty of the HHO-SVR model. Shapley Additive Explanations (SHAP) analysis was used to determine the relative importance of operating parameters that affect the reaction rate over time, thereby enhancing model interpretability. The results showed the predominant factors controlling photocatalytic degradation and how these factors influenced the model predictions. Full article

Close visual inspection (CVI) forms a cornerstone of asset integrity. Advances in access technologies, including drones, have led to their increased use for remote visual inspection (RVI). However, comparative studies of RVI and CVI, in terms of defect detection, are currently limited. In this study, controlled trials were conducted with multiple industrial participants operating drones to inspect a concrete block wall containing representative defects. RVI performance was assessed in terms of defect detection, identification and sizing. RVI demonstrated moderate performance, with an overall defect detection rate of approximately 50% and no participant exceeding 0.6. Detection was strongly dependent on defect type, with larger defects such as spalling and chipping consistently identified, while finer defects such as cracking were frequently missed. Identification of defect type was less reliable and influenced by inspector experience, while sizing capability was limited, with only one participant providing approximate measurements for larger defects. An automated visual inspection device, termed ALICS (Adaptive Lighting for the Inspection of Concrete Structures), was deployed on two samples. Images were captured of the concrete surface under varying lighting conditions to enhance the visibility of any present defects. Images were then analysed using artificial intelligence (AI), with the device identifying all defects in the tested areas. These results highlight both the current limitations of RVI and the potential of illumination-enhanced automated approaches to improve inspection reliability. Full article