Search Results (72)

Hydrogeochemistry is a practical and low-impact tool for mineral exploration that relies primarily on groundwater as sampling media. It is particularly valuable for blind or deeply buried deposits where surface geochemical methods are ineffective, as groundwater acts as a natural integrator of geochemical signals from depth. This study presents a PRISMA 2020-compliant systematic review of hydrogeochemical exploration practices published between 1946 and 2025, synthesizing 118 empirically screened case studies from diverse geological and climatic settings. The review evaluates the geochemical processes governing aqueous dispersion halos, including sulphide oxidation, water–rock interaction, redox controls, and physicochemical speciation, and assesses how these processes influence pathfinder behaviour and anomaly expression. Quantitative synthesis highlights consistent patterns in hydrogeochemical footprints across major mineral systems and demonstrates the effectiveness of thermodynamically informed and multivariate interpretation strategies over simple concentration-based approaches. Emerging trends identified include the growing application of non-traditional stable isotope fractionation, nanoparticle geochemistry using single-particle ICP-MS, and integration of hydrogeochemical datasets with GIS, geophysics, and machine learning-based prospectivity modelling. Unlike recent narrative reviews, this study provides a fully reproducible, structured evaluation of the global evidence base and formalizes a standardized end-to-end workflow. Full article

Abrasive air jet (AAJ) machining is a non-traditional technology used to pattern microstructures on a wide variety of engineering materials. Understanding the material removal mechanisms and the formation of micro-channels produced by AAJ is essential for optimizing process parameters and enhancing machining quality. Therefore, this study develops and validates a discrete element model to simulate abrasive air jet machining of micro-channels on quartz crystals. It shows that the crack network, which consists of opening mode cracks and shearing mode cracks, contributes to the removal of target particles. Opening mode cracks dominate the material removal process. The histories of the number of newly generated cracks and newly removed target particles can be divided into three stages: an incubation stage, a transitional stage, and a stable stage. Both the number of newly generated cracks and the number of newly removed target particles first increase and then decrease as the machining process progresses. An indicator called the “contribution rate” is proposed, showing that damage accumulates in the target substrate during abrasive air jet machining. During the machining of micro-channels, erosion impression appears at first. As the width and depth of the erosion impressions increase, micro-channels gradually form. Full article

Abrasive water jet machining (AWJM) is a non-traditional machining process that is increasingly employed for shaping hard-to-machine materials, particularly titanium (Ti)-based alloys such as Ti-6Al-4V. Owing to its non-thermal nature, AWJM enables effective material removal while minimising metallurgical damage and preserving subsurface integrity. The process performance is governed by several interacting parameters, including jet pressure, abrasive type and flow rate, nozzle traverse speed, stand-off distance, jet incident angle, and nozzle design. These parameters collectively influence key output responses such as the material removal rate (MRR), surface roughness, kerf geometry, and subsurface quality. The existing studies consistently report that the jet pressure and abrasive flow rate are directly proportional to MRR, whereas the nozzle traverse speed and stand-off distance exhibit inverse relationships. Nozzle geometry plays a critical role in jet acceleration and abrasive entrainment through the Venturi effect, thereby affecting the cutting efficiency and surface finish. Optimisation studies based on the design of the experiments identify jet pressure and traverse speed as the most significant parameters controlling the surface quality in the AWJM of titanium alloys. Recent research demonstrates the effectiveness of artificial neural networks (ANNs) for process modelling and optimisation of AWJM of Ti-6Al-4V, achieving high predictive accuracy with limited experimental data. This review highlights research gaps in artificial intelligence-based fatigue behaviour prediction, computational fluid dynamics analysis of nozzle wear mechanisms and jet behaviour, and the development of hybrid AWJM systems for enhanced machining performance. Full article

Labor market forecasting relies heavily on economic time-series data, often overlooking the “health–wealth” gradient that links population health to workforce participation. This study develops a machine learning framework integrating non-traditional health and social metrics to predict state-level employment density. Methods: We constructed a multi-source longitudinal dataset (2014–2024) by aggregating county-level Quarterly Census of Employment and Wages (QCEW) data with County Health Rankings to the state level. Using a time-aware split to evaluate performance across the COVID-19 structural break, we compared LASSO, Random Forest, and regularized XGBoost models, employing SHAP values for interpretability. Results: The tuned, regularized XGBoost model achieved strong out-of-sample performance (Test $R^2$ = 0.800). A leakage-safe stacked Ridge ensemble yielded comparable performance (Test $R^2$ = 0.827), while preserving the interpretability of the underlying tree model used for SHAP analysis. Full article

Chagas disease (CD), caused by the parasite Trypanosoma cruzi, remains one of the most important neglected tropical diseases. Historically confined to rural areas of Latin America, the disease has now become a global health challenge due to increased migration, urbanization, and ecological changes. This review explores how patterns of transmission and endemicity have evolved, emphasizing the emergence of new geographic hotspots and non-traditional routes of transmission, such as congenital and oral infections. We integrate evidence from ecological studies showing how deforestation, urban sprawl, and climate change are reshaping vector habitats and influencing the spread of triatomine insects. Furthermore, we highlight advances made between 2020 and 2025 in key research areas, including vector genomics and climate-based predictive mapping, as well as digital surveillance strategies that leverage machine learning and citizen science. These innovations provide valuable insights for predicting future risks and improving disease control. By linking global epidemiological trends, ecological drivers, and cutting-edge scientific advances, this review underscores the urgent need for integrated, collaborative strategies to prevent further spread and to protect vulnerable populations worldwide. Full article

In recent years, ecosystems in Saudi Arabia have experienced severe degradation due to factors such as hyperaridity, overgrazing, climate change, urban expansion, and an increase in uncontrolled wildfires. Among these, wildfires have emerged as the second most significant threat to forests after urban expansion. This study aims to map wildfire susceptibility in southwestern Saudi Arabia by identifying key driving factors and evaluating the performance of several machine learning models under conditions of limited and imbalanced data. The models tested include Maxent, logistic regression, random forest, XGBoost, and support vector machine. In addition, an NDVI-based phenological approach was applied to assess seasonal vegetation dynamics and to compare its effectiveness with conventional machine learning-based susceptibility mapping. All methods generated effective wildfire risk maps, with Maxent achieving the highest predictive accuracy (AUC = 0.974). The results indicate that human activities and dense vegetation cover are the primary contributors to wildfire occurrence. This research provides valuable insights for wildfire risk assessment in data-scarce regions and supports proactive fire management strategies in non-traditional fire-prone environments. Full article

Despite the critical importance of biomedical databases like MEDLINE, users are often hampered by search tools with stagnant designs that fail to support complex exploratory tasks. To address this limitation, we synthesized research from visual analytics and related fields to propose a new design framework for non-traditional search interfaces. This framework was built upon seven core principle: visualization, interaction, machine learning, ontology, triaging, progressive disclosure, and evolutionary design. For each principle, we detail its rationale and demonstrate how its integration can transcend the limitations of conventional search tools. We contend that by leveraging this framework, designers can create more powerful and effective search tools that empower users to navigate complex information landscapes. Full article

Abrasive air jet machining is a burgeoning non-traditional machining technology particularly suitable for machining brittle non-metallic materials and metals with high hardness. It is very challenging to select the optimal process parameters to achieve desirable machining performance metrics, such as maximizing material removal rate and minimizing machining width while controlling machining depth. In this study, we aimed to achieve multi-objective process parameter optimization for abrasive air jet machining of silicon based on the artificial bee colony algorithm. A series of experiments was carried out to investigate the effect of process parameters, including air pressure, standoff distance, and nozzle traverse speed, on material removal rate, machining width, and machining depth. Mathematical models for machining performance metrics were developed by regression analysis, and a multi-objective optimization model was further formulated. The artificial bee colony algorithm was proposed to solve the optimization problem, and a set of Pareto-optimal solutions was found. The results indicate that the artificial bee colony algorithm is an effective method for multi-objective process parameter optimization in abrasive air jet machining. Full article

Background: Passive brain–computer interface (pBCI) systems use a combination of electroencephalography (EEG) and machine learning (ML) to evaluate a user’s cognitive and physiological state, with increasing applications in both clinical and non-clinical scenarios. pBCI systems have been limited by their traditional reliance on sensor technologies that cannot easily be integrated into non-laboratory settings where pBCIs are most needed. Advances in textile-electrode-based EEG show promise in overcoming the operational limitations; however, no study has demonstrated their use in pBCIs. This study presents the first application of fully textile-based EEG for pBCIs in differentiating cognitive states. Methods: Cognitive state comparisons between eyes-open (EO) and eyes-closed (EC) conditions were conducted using publicly available data for both novel textile and traditional dry-electrode EEG. EO vs. EC differences across both EEG sensor technologies were assessed in delta, theta, alpha, and beta EEG power bands, followed by the application of a Support Vector Machine (SVM) classifier. The SVM was applied to each EEG system separately and in a combined setting, where the classifier was trained on dry EEG data and tested on textile EEG data. Results: The textile EEG system accurately captured the characteristic increase in alpha power from EO to EC (p < 0.01), but power values were lower than those of dry EEG across all frequency bands. Classification accuracies for the standalone dry and textile systems were 96% and 92%, respectively. The cross-sensor generalizability assessment resulted in a 91% classification accuracy. Conclusions: This study presents the first use of textile-based EEG for pBCI applications. Our results indicate that textile-based EEG can reliably capture changes in EEG power bands between EO and EC, and that a pBCI system utilizing non-traditional textile electrodes is both accurate and generalizable. Full article

Background and Objectives: Despite significant advances in surgical techniques and perioperative care, major adverse cardiovascular events (MACE) remain a leading cause of postoperative morbidity and mortality in patients undergoing coronary artery bypass grafting and/or aortic valve replacement. Accurate preoperative risk stratification is essential yet often limited by models that overlook atrial mechanics and underutilized biomarkers. Materials and Methods: This study aimed to develop an interpretable machine learning model for predicting perioperative MACE by integrating clinical, biochemical, and echocardiographic features, with a particular focus on novel physiological markers. A retrospective cohort of 131 patients was analyzed. An Extreme Gradient Boosting (XGBoost) classifier was trained on a comprehensive feature set, and SHapley Additive exPlanations (SHAPs) were used to quantify each variable’s contribution to model predictions. Results: In a stratified 80:20 train–test split, the model initially achieved an AUC of 1.00. Acknowledging the potential for overfitting in small datasets, additional validation was performed using 10 independent random splits and 5-fold cross-validation. These analyses yielded an average AUC of 0.846 ± 0.092 and an F1-score of 0.807 ± 0.096, supporting the model’s stability and generalizability. The most influential predictors included total atrial conduction time, mitral and tricuspid annular orifice areas, and high-density lipoprotein (HDL) cholesterol. These variables, spanning electrophysiological, structural, and metabolic domains, significantly enhanced discriminative performance, even in patients with preserved left ventricular function. The model’s transparency provides clinically intuitive insights into individual risk profiles, emphasizing the significance of non-traditional parameters in perioperative assessments. Conclusions: This study demonstrates the feasibility and potential clinical value of combining advanced echocardiographic, biochemical, and machine learning tools for individualized cardiovascular risk prediction. While promising, these findings require prospective validation in larger, multicenter cohorts before being integrated into routine clinical decision-making. Full article

The enduring manufacturing goals are increasingly shifting toward ultra-precision manufacturing and micro-nano fabrication, driven by the demand for sophisticated products. Unconventional machining processes such as electrochemical jet machining (ECJM), electrical discharge machining (EDM), electrochemical machining (ECM), abrasive water jet machining (AWJM), and laser beam machining (LBM) have been widely adopted as feasible alternatives to traditional methods, enabling the production of high-quality engineering components with specific characteristics. ECJM, a non-contact machining technology, employs electrodes on the nozzle and workpiece to establish an electrical circuit via the jet. As a prominent special machining technology, ECJM has demonstrated significant advantages, such as rapid, non-thermal, and stress-free machining capabilities, in past research. This review is dedicated to outline the research progress of ECJM, focusing on its fundamental concepts, material processing capabilities, technological advancements, and its variants (e.g., ultrasonic-, laser-, abrasive-, and magnetism-assisted ECJM) along with their applications. Special attention is given to the application of ECJM in the semiconductor and biomedical fields, where the demand for ultra-precision components is most pronounced. Furthermore, this review explores recent innovations in process optimization, significantly boosting machining efficiency and quality. This review not only provides a snapshot of the current status of ECJM technology, but also discusses the current challenges and possible future improvements of the technology. Full article

Wire electrical discharge machining (WEDM), as a significant branch of non-traditional machining technologies, is widely applied in fields such as mold manufacturing and aerospace due to its high-precision machining capabilities for hard and complex materials. This paper systematically reviews the research progress in WEDM process optimization from two main perspectives: traditional optimization methods and artificial intelligence (AI) techniques. Firstly, it discusses in detail the applications and limitations of traditional optimization methods—such as statistical approaches (Taguchi method and response surface methodology), Adaptive Neuro-Fuzzy Inference Systems, and regression analysis—in parameter control, surface quality improvement, and material removal-rate optimization for cutting metal materials in WEDM. Subsequently, this paper reviews AI-based approaches, traditional machine-learning methods (e.g., neural networks, support vector machines, and random forests), and deep-learning models (e.g., convolutional neural networks and deep neural networks) in aspects such as state recognition, process prediction, multi-objective optimization, and intelligent control. The review systematically compares the advantages and disadvantages of traditional methods and AI models in terms of nonlinear modeling capabilities, adaptability, and generalization. It highlights that the integration of AI by optimization algorithms (such as Genetic Algorithms, particle swarm optimization, and manta ray foraging optimization) offers an effective path toward the intelligent evolution of WEDM processes. Finally, this investigation looks ahead to the key application scenarios and development trends of AI techniques in the WEDM field for cutting metal materials. Full article

Silicon is the most commonly used material in the electronic industries due to its unique properties, which also make it very difficult to machine using conventional machining. Electrical discharge machining (EDM) is a non-traditional process that is gaining popularity for machining silicon, although a slower machining rate is one of its limitations. This study investigates two electrode design strategies to enhance the efficiency of EDM by improving the material removal rates, reducing tool wear, and refining the quality of machined features. The first approach involves using graphite electrodes in various array configurations (1 × 4 to 6 × 4) and leg heights (0.2″ and 0.3″). The second approach employs hollow electrodes with differing wall thicknesses (0.04″, 0.08″, and 0.12″). The effects of these variables on performance were evaluated by maintaining constant EDM parameters. The results indicate that increasing the number of electrode legs improves the flushing conditions, resulting in shorter machining times. Meanwhile, the shorter electrode height outperforms the taller electrode, providing a higher machining speed. The thinnest wall thickness for hollow electrodes yielded the best performance due to the increased energy distribution. Both electrode design methodologies can be used for the mass fabrication of features with targeted profiles on silicon using the die-sinking EDM process. Full article

Background: The quality of a provisional restoration, especially its color and marginal integrity, can play a critical role in its survival and overall patient satisfaction. This study aims to evaluate the color stability and marginal fit differences between provisional restorations fabricated by non-traditional methods compared to manual fabrication. Methods: A total of 80 extracted teeth were prepared for ceramic crowns and randomly divided into four groups: acrylic, 3D printing, computer-aided design/computer-aided manufacturing (CAD/CAM), and bis-acryl. The examined teeth were subjected to artificial aging using a thermocycling machine dwelling for 5000 cycles (simulating 6 clinical months). Color stability and marginal integrity were measured before and after thermal aging using a VITA Easyshade V spectrophotometer and 3D surface non-contact profilometer. ANOVA was used to determine whether the mean value difference was significantly different. Results: The 3D-printed and bis-acryl provisional crowns displayed the lowest change in marginal integrity, while the acrylic provisional crowns showed the greatest change in marginal integrity (p = 0.0001). Additionally, the acrylic provisional material revealed a significantly greater color change. Conclusions: The 3D-printed provisional crowns demonstrated the best marginal integrity and color stability. Full article

Inconel 718 is one of the most used alloys within the aerospace gas turbine industry. The acceptance of Inconel 718 within the aerospace gas turbine industry has largely been due to its high strength and fatigue capabilities up to 677 °C (1250 °F). This alloy is traditionally produced through conventional manufacturing methods, such as casting, wrought, and sheet forming. The various traditional manufacturing methods of this alloy have been well understood and characterized for use in critical components. However, Inconel 718 can also be produced with non-traditional manufacturing methods, such as by additive manufacturing. Producing Inconel 718 by additive manufacturing has the opportunity to design more complex components that provide distinct advantages over conventionally produced components. However, prior to implementing additively manufactured Inconel 718 within the aerospace gas turbine industry, there needs to be a complete understanding of the material’s performance. In an effort to completely characterize additively manufactured Inconel 718, this study focuses on the characterization of the alloy’s low-cycle fatigue performance. Specimens were produced via the laser powder bed fusion process in a vertical orientation. Both as-printed surfaces and fully machined surface specimens were evaluated at 24 °C (75 °F) and 538 °C (1000 °F). Fractography analysis was then completed on the specimens to understand differences in the crack initiation and propagation with respect to test temperatures and surface conditions. Based on these tests, it was shown that the fatigue life knockdown due to the as-printed surface conditions was 62.8% at 538 °C (1000 °F) versus only 8.5% at 24 °C (75 °F). These findings are discussed in detail within this article, and future work is proposed. Full article