Search Results (9,760)

The increasing demand for critical minerals is forcing the mineral exploration industry to search for deposits beneath deeper cover and over larger areas. MobileMT, an airborne passive, broadband, total-field AFMAG-class system, couples three-component measurements of airborne magnetic field variations with a remote electric-field base station to image electrical resistivity from the surface to depths of >1–2 km. We present a workflow that integrates MobileMT data with the parallelized, adaptive finite-element 2.5D open-source inversion code MARE2DEM, accompanied by automated mesh generation procedures, to create a rapid and scalable workflow for deep ore exploration. Using this software on two field trials, we demonstrate that (i) high-frequency (>2 kHz) data are essential for recovering not only shallow geology but also, when combined with low frequencies, for refining deep structures and targets and that (ii) base station effects modify the shape of the apparent conductivity curve but have negligible impact on the inverted sections. The proposed workflow is a reliable and effective approach for identifying mineralization-related features and refining geologic models based on data from extensive airborne geophysical surveys. Full article

Studying the effects of environmental factors on microbial community assemblies is crucial for understanding microbial biodiversity and ecosystem processes. Although numerous studies have explored the spatial patterns of microbial communities in surface soils, bacterial community distributions in subsurface layers remain poorly understood. We investigated multiple community metrics of soil bacteria in arid and semi-arid grasslands in China, and the V4 region of 16S rDNA was analyzed using soil property measurements, fluorescent PCR, and high-throughput sequencing techniques. Specifically, copiotrophic taxa dominate the topsoil, whereas oligotrophic taxa are prevalent in nutrient-limited subsoil. Bacterial diversity decreases from the topsoil to subsoil, and bacterial distribution and ecological community composition exhibit a strong dependence on environmental factors. Moreover, microbial interaction networks demonstrated a progressive simplification with increasing soil depth: topsoil communities displayed higher modularity and a greater prevalence of positive interactions, whereas subsoil networks were significantly less complex. Null model analyses evidenced assembly mechanisms: deterministic processes (particularly homogeneous selection) dominated the bacterial community assembly, but their influence weakened with depth, whereas stochastic processes (e.g., dispersal limitation) increased progressively from the topsoil to subsoil. The PLS-PM analysis demonstrated that the relative influence of abiotic factors (e.g., climatic conditions and nutrient availability), biotic factors (interspecific interactions), along with drift and dispersal limitations on fungal community assembly exhibited depth-dependent patterns. This study provides novel insights into the vertical stratification of bacterial community in arid and semi-arid grasslands, and advances our understanding of pedogenic process under climate change and microbial adaptive strategies in heterogeneous soil environments. Full article

The increasing prevalence of smartphone use has raised concerns about its impact on human psychological functioning. This literature review provides a comprehensive overview of the psychological dimensions influenced by smartphone use, spanning health psychology, individual differences, social psychology, and cognitive functioning. The review draws on findings from numerous studies, primarily conducted in highly developed Western and Asian countries, where cultural factors may influence usage patterns and psychological outcomes. Key limitations in the current body of research include geographical biases and methodological challenges such as sample homogeneity and reliance on self-report measures. Evidence suggests that excessive smartphone use can lead to addiction and is associated with negative psychological and health consequences. The review also highlights how individual differences—such as personality traits, age, and gender—affect smartphone usage. Social implications, both positive (e.g., increased connectivity) and negative (e.g., interpersonal conflict), are explored in depth. Cognitive effects are considered, particularly in relation to attention and memory, where findings suggest potential impairments in sustained focus and information retention. While the literature often emphasizes risks, this review also points to the need for further exploration of the potential benefits of smartphone use. In summary, the review offers valuable insights into the complex psychological effects of smartphones and underscores the importance of future research to better understand their nuanced impact on well-being. Full article

Aiming at the problems of intensified soil compaction under the conditions of no-tillage operations and machine blockage caused by large-scale straw returning to the field, an operation mode of “straw strip collecting-strip subsoiling” was proposed, and a Monomer Profiling Strip Tillage Machine (MPSTM) with Straw-Strip-Collecting and Subsoiling Functions was designed to achieve anti-blocking operation and three-dimensional soil compaction reduction. The principle and mechanism parameters of monomer profiling in strip tillage are analyzed, and the effective profiling conditions are clarified. It is determined that the deflection angle, inclination angle, and installation spacing have a key influence on the straw clearance effect. The theory of soil failure and soil compaction reduction under the operation of the subsoiling and strip tillage mechanism is studied, and a combination of a medium-sized Subsoiler shovel handle and a 150 mm double-wing shovel is adopted. Using the EDEM discrete element method, taking the spatial parameters of the stubble clean disc (SCD) as the test factors and the straw removal rate (SRR) as the test indicator, a quadratic orthogonal rotation test is conducted to clarify the influence of each parameter on the straw clearance. The optimal SCD spatial parameters were determined as a deflection angle of 16.5°, an inclination angle of 25°, and an installation spacing of 100 mm, achieving a maximum SRR of 95.34%. Field test results demonstrated stable machine operation. Post-operation measurements yielded the following results: the width of the straw-cleaning band (WSCB) in the sowing strip is 193.7 mm; the overall straw removal rate (OSRR) is 84.82%, which is basically consistent with the simulation results; the subsoiling depth (SD) is 271.7 mm; the subsoiling depth stability (SDS) is 91.85%; the soil fragmentation rate (SFR) is 81.19%; and the reduction of soil compaction in the 0–10, 10–20, and 20–30 cm soil layer is 50.08%, 21.78%, and 40.83%, respectively. These results confirm that the machine effectively cleaned straw within the seeding band and reduced soil compaction, meeting the agronomic and technical requirements for strip tillage. Full article

This study conducts numerical analysis of fatigue crack propagation in deck-rib welded joints of orthotropic steel decks (OSDs) using linear elastic fracture mechanics. The stress intensity factor for central surface cracks under constant range bending stress is calculated, and single and multi-crack propagation are simulated by a numerical integration method. The research results show that deck geometry critically influences crack propagation behavior. Wider decks accelerate propagation of cracks after the crack depth exceeds half the deck thickness, thicker decks exhibit linearly faster propagation rates yet retain larger residual section to bear loads, and increased weld penetration reduces fatigue life. Initial defects rapidly converge to a preferred propagation path, stabilizing near $a_f/c_f\approx 0.1$ ($a_f$ is the failure crack depth and $c_f$ is the half surface crack length) regardless of initial aspect ratio. For multi-crack scenarios, defect density dominates merging, doubling density increases final cracks by 45%. Merged cracks adhere closely to the single-crack path, while total section loss escalates with defect density and deck thickness but remains stress range independent. The identified convergence preferred propagation path enables depth estimation from surface-length measurements during real bridge inspections. Full article

Perfluoroisobutyronitrile (C₄F₇N) has emerged as a promising SF₆ alternative due to its superior dielectric properties and acceptable environmental impact. However, the gas–solid interfacial charge accumulation behavior in such gas mixtures requires in-depth and systematic investigation. This study investigated the surface charge accumulation behavior on scaled disc insulators in C₄F₇N/CO₂/O₂ mixtures under AC voltage. By constructing a high-precision surface charge measurement platform, the influence mechanisms of varying gas composition ratios of C₄F₇N (2–14%) with fixed O₂ content and O₂ (2–14%) with fixed C₄F₇N content on charge accumulation were analyzed. The results demonstrated that increasing C₄F₇N content significantly suppresses surface charge accumulation. When the C₄F₇N concentration rises from 2% to 14%, the maximum positive/negative charge densities decrease by 46.58% and 22.22% in the absence of metal particles. The suppression effect is more pronounced with the metal particle present, where the reductions in positive/negative charge densities reach 61.90% and 23.71% under the same conditions. In contrast, variations in O₂ content exhibit a weaker impact on charge accumulation, showing no consistent regulatory effect within the 2–14% range. By comparing charge distribution patterns under different gas compositions, it is revealed that C₄F₇N suppresses gas ionization primarily by enhancing electronegativity, while O₂ exhibits negligible influence on charge transport. This study provides critical experimental evidence for optimizing gas ratios and insulation design in AC GIS equipment. Full article

In the context of the “Dual Carbon” goals and ecological civilization development, enhancing forestry ecological total factor productivity (FETFP) has become vital for advancing green development and environmental governance. Confronted with tightening resource constraints and pressure to transform traditional growth models, whether digital intelligence integration can effectively empower improvements in FETFP requires in-depth empirical validation. Based on publicly available panel data from 30 Chinese provinces spanning 2012 to 2022, this study constructs an index system for measuring digital intelligence integration and FETFP. Using the Double Machine Learning (DML) framework, the study empirically identifies the impact of digital intelligence development on FETFP and explores its internal mechanisms. The key results show that (1) digital intelligence integration significantly enhances FETFP. For every unit increase in digital and intelligent integration, FETFP rises by an average of 19.97%; (2) mechanism analysis reveals that digital intelligence improves FETFP by optimizing the forestry industrial structure, promoting green technological innovation, and amplifying the synergistic effects of fiscal support; (3) and heterogeneity analysis suggests that the positive impact of digital intelligence integration is more pronounced in regions with higher environmental expenditures and stronger green finance support. Accordingly, this study proposes several policy recommendations, including accelerating digital infrastructure development, strengthening foundational digital intelligence capabilities, enhancing support for green innovation, leveraging the ecological multiplier effects of digital transformation, tailoring digital strategies to local conditions, and improving the precision of regional environmental governance. The findings provide robust empirical evidence for improving FETFP in developing and developed economies. Full article

This study examines the intersection of environmental, social, and governance (ESG) disclosure (operationalized through sustainability reporting), corporate financial performance, and the behavioral dynamics of herding in capital structure decisions among non-financial firms in five ASEAN countries. As ESG and sustainability finance gain prominence in addressing climate change and climate risk, understanding the behavioral factors that relate to ESG adoption is crucial. Employing a quantitative approach, this research utilizes a purposive sample of 125 non-financial firms from Indonesia, Malaysia, the Philippines, Singapore, and Thailand, gathered from the Bloomberg Terminal spanning 2018–2023. Managerial Herding Ratio (MHR) is used to assess herding behavior, while Sustainability Report Disclosure Index (SRDI) measures ESG disclosure. Partial Least Squares Structural Equation Modeling (PLS-SEM) and Multigroup Analysis (MGA) were applied for data analysis. This research finds that while sustainability reporting enhances return on assets (ROA) and Tobin’s Q, it does not significantly relate to net profit margin (NPM). The findings also confirm that herding behavior—where companies mimic the financial structures of peers—moderates the relationship between sustainability reporting and performance outcomes, with leader firms gaining more from transparency efforts. This highlights the double-edged nature of herding: while it can accelerate ESG adoption, it may dilute the strategic depth of climate action if firms merely follow rather than lead. The study provides actionable insights for regulators and corporate strategists seeking to strengthen ESG finance as a driver for climate resilience and long-term stakeholder value. Full article

Accurate soil moisture estimation is critical for precision agriculture and water resource management, yet traditional sampling methods are time-consuming, destructive, and provide limited spatial coverage. Ground Penetrating Radar (GPR) offers a promising non-destructive alternative, but optimal machine learning approaches for GPR-based soil moisture prediction remain unclear. This study presents a comparative analysis of regression tree and boosted tree algorithms for predicting soil moisture content from Ground Penetrating Radar (GPR) histogram features across 21 sites in Eastern Thailand. Soil moisture content was measured at multiple depths (0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 m) using samples collected during Standard Penetration Test procedures. Feature extraction was performed using 16-bin histograms from processed GPR radargrams. A single regression tree achieved a cross-validation RMSE of 5.082 and an R² of 0.761, demonstrating superior training accuracy and interpretability. In contrast, the boosted tree ensemble achieved significantly better generalization performance, with a cross-validation RMSE of 4.7915 and an R² of 0.708, representing a 5.7% improvement in predictive performance. Feature importance analysis revealed that specific histogram bins effectively captured moisture-related variations in GPR signal amplitude distributions. A comparative evaluation demonstrates that while single regression trees offer superior interpretability for research applications, boosted tree ensembles provide enhanced predictive performance that is essential for operational deployment in precision agriculture and hydrological monitoring systems. Full article

Measuring-while-drilling (MWD) techniques have great potential for use in geotechnical engineering research. This study first addresses the current use of MWD, which consists of recording data using sensors in a drilling machine operating on site. It then addresses the currently unsolved problems of quality control in drilled piles and assessments of their interaction with the soil under load. Next, an original method of drilling displacement piles using a special EGP auger (Electro-Geo-Probe) is presented. The innovation of this new drilling system lies in the placement of the sensors inside the EGP auger in the soil. These innovative sensors form an integrated measurement system, enabling improved real-time control during pile drilling. The most original idea is the use of a Cone Penetration Test (CPT) probe that can be periodically and remotely inserted at a specific depth below the pile base being drilled. This new MWD-EGP system with cutting-edge sensors to monitor the soil’s impact on piles during drilling revolutionizes pile drilling quality control. Furthermore, implementing this in-auger sensor system is a step towards the development of digital drilling rigs, which will provide better pile quality thanks to solutions based on the results of real-time, on-site soil testing. Finally, examples of measurements taken with the new sensor-equipped auger and a preliminary interpretation of the results in non-cohesive soils are presented. The obtained data confirm the usefulness of the new drilling system for improving the quality of piles and advancing research in geotechnical engineering. Full article

The recently proposed concept of “precursory fingerprint” is a logical consequence of the commonsense statement that seismic structures are unique and that their expected preshock behaviors, including precursory phenomena, are also unique. Our new prediction-related research strategy is conceptually based on the principles of (1) the uniqueness of seismogenic structures, (2) interconnected and interacting geospheres, and (3) non-equivalence of Earth’s surface spots in terms of precursory signal receptivity. The precursory fingerprint of a given seismic structure is a unique assemblage of precursory signals of various natures (seismic, physical, chemical, and biological), detectable in principle by using a system of proper monitoring equipment that consists of a matrix of n sensors placed on the ground at “sensitive” spots identified beforehand and on orbiting satellites. In principle, it is composed of a combination of signals that are emitted by the “responsive sensors”, in addition to the “non-responsive sensors”, coming from the sensor matrix, monitoring as many virtual precursory processes as possible by continuously measuring their relevant parameters. Each measured parameter has a pre-established (by experts) threshold value and an uncertainty interval, discriminating between background and anomalous values that are visualized similarly to traffic light signals (green, yellow, and red). The precursory fingerprint can thus be viewed as a particular configuration of “precursory signals” consisting of anomalous parameter values that are unique and characteristic to the targeted seismogenic structure. Presumably, it is a complex entity that consists of pattern, space, and time components. The “pattern component” is a particular arrangement of the responsive sensors on the master board of the monitoring system yielding anomalous parameter value signals, that can be re-arranged, after a series of experiments, in a spontaneously understandable new pattern. The “space component” is a map position configuration of the signal-detecting sensors, whereas the “time component” is a characteristic time sequence of the anomalous signals including the order, occurrence time before the event, transition time between yellow and red signals, etc. Artificial intelligence using pattern-recognition algorithms can be used to follow, evaluate, and validate the precursory signal assemblage and, finally, to judge, together with an expert board of human operators, its “precursory fingerprint” relevance. Signal interpretation limitations and uncertainties related to dependencies on sensor sensibility, focal depth, and magnitude can be established by completing all three phases (i.e., experimental, validation, and implementation) of the precursory fingerprint-based earthquake prediction research strategy. Full article

Cross-plate joints between panels are commonly used in diaphragm wall construction to ensure structural integrity. However, research on the mechanical behaviour of these joints remains limited, and they are often disregarded in numerical modelling due to their complexity. This paper fabricated two types of specimens with cross-plate joints, which were subsequently employed in bending and shear tests, respectively. The load–displacement curves and the joint openings were experimentally measured. It was found that the load–displacement curves exhibited approximately four linear stages in the bending tests and two in the shear tests. Based on the test results, a multi-linear spring model was proposed to simplify the mechanical behaviour of the joints, and the stiffness of each linear stage was determined through back-analysis of the tested data. The calculated load–displacement curves ultimately agreed well with those obtained from the tests, with average errors of 3.6% in the bending test and 2.6% in the shear test. The proposed model was then applied to a devised case study, thereby demonstrating its capacity to capture joint opening phenomena and revealing the spatial variability of joint opening within the excavation depth. Compared with conventional 2D and 3D models, the proposed model yields displacement results that better reflect the actual deformation of the diaphragm wall. Furthermore, the precise modelling calculation for joints, which is time-consuming, is also avoided, and the calculation time of the proposed model is only 1.52 times that of the conventional 3D model. Full article

This study investigates the deterioration of skid resistance and surface macrotexture following preventive maintenance using micro-milling techniques. Field data were collected from 31 asphalt pavement sections located across four climatic zones in Texas. The data encompasses a variety of surface types, milling depths, operational speeds, and drum configurations. A standardized data collection protocol was followed, with measurements taken before milling, immediately after treatment, and at 3, 6, 12, and 18 months post-treatment. Skid number and Mean Profile Depth (MPD) were used to evaluate surface friction and texture characteristics. The dataset was reformatted into a time-series structure with 930 observations, including contextual variables such as climatic zone, treatment parameters, and baseline surface condition. A comparative modeling framework was applied to predict the deterioration trends of both skid resistance and macrotexture over time. Eight regression models, including linear, tree-based, and ensemble methods, were evaluated alongside a time series Transformer model. The results show that the Transformer model achieved the highest prediction accuracy for skid resistance (R² = 0.981), while Random Forest performed best for macrotexture prediction (R² = 0.838). The findings indicate that the degradation of surface characteristics after preventive maintenance is non-linear and influenced by a combination of environmental and operational factors. This study demonstrates the effectiveness of data-driven modeling in supporting transportation agencies with pavement performance forecasting and maintenance planning. Full article

Tree diameter at breast height (DBH) is a fundamental metric in forest inventory and management. This paper presents a novel method for DBH estimation using the built-in light detection and ranging (LiDAR) and red, green and blue (RGB) sensors of an iPhone 13 Pro, aiming to improve measurement accuracy and field usability. A single snapshot of a tree, capturing both depth and RGB images, is used to reconstruct a 3D point cloud. The trunk orientation is estimated based on the point cloud to locate the breast height, enabling robust DBH estimation independent of the capture angle. The DBH is initially estimated by the geometrical relationship between trunk size on the image and the depth of the trunk. Finally, a pre-computed lookup table (LUT) is employed to improve the initial DBH estimates into accurate values. Experimental evaluation on 294 trees within a capture range of 0.25 m to 5 m demonstrates a mean absolute error of 0.53 cm and a root mean square error of 0.63 cm. Full article

This study provides a comprehensive six-year assessment (2018–2023) of heavy metal contamination in the Romanian Black Sea sector, integrating data from seawater, surface sediments, and benthic mollusks. Sampling was conducted across a broad spatial gradient, including transitional, coastal, shelf, and offshore waters beyond 200 m depth. Concentrations of six potentially toxic metals, including cadmium (Cd), lead (Pb), nickel (Ni), chromium (Cr), copper (Cu), and cobalt (Co), were measured to evaluate regional variability, potential sources, and ecological implications. Results indicate some exceedances of regulatory thresholds for Cd and Pb in transitional and coastal waters, associated with Danube River input and coastal pressures. Seabed substrate analysis revealed widespread enrichment in Ni, moderate levels of Cr, and sporadic Cd elevation in Danube-influenced areas, along with localized hotspots of Cu and Pb near port and industrial zones. Biological uptake patterns in mollusks (bivalves Mytilus galloprovincialis and Anadara inequivalvis and gastropod Rapana venosa) highlighted Cd among key metals of concern, with elevated Bioconcentration Factor (BCF) and Biota–Sediment Accumulation Factor (BAF). Offshore waters generally exhibited lower pollution levels. However, isolated exceedances, such as Cr outliers recorded in 2022, suggest that deep-sea inputs from atmospheric or maritime sources may be both episodic in nature and underrecognized due to limited monitoring coverage. The combined use of water, sediment, and biota data emphasize the strength of multi-matrix approaches in marine pollution evaluation, revealing persistent nearshore pressures and less predictable offshore anomalies. These findings contribute to a more complete understanding of heavy metal distribution in the northwestern Black Sea and provide a scientific basis for improving long-term environmental monitoring and risk management strategies in the region. Full article