Search Results (9,673)

Large-scale mapping of soil resources can be crucial and indispensable for several of the managerial applications and policy implications. With machine learning models being the most utilized modeling technique for digital soil mapping (DSM), the implementation of model-based deep learning methods for spatial soil predictions is still under scrutiny. In this study, soil continuous (pH and OC) and categorical variables (order and suborder) were predicted using deep learning–multi layer perceptron (DL-MLP) and one-dimensional convolutional neural networks (1D-CNN) for the entire state of Tamil Nadu, India. For training the deep learning models, 27,098 profile observations (0–30 cm) were extracted from the generated soil database, considering soil series as the distinctive stratum. A total of 43 SCORPAN-based environmental covariates were considered, of which 37 covariates were retained after the recursive feature elimination (RFE) process. The validation and test results obtained for each of the soil attributes for both the algorithms were most comparable with the DL-MLP algorithm depicting the attributes’ most intricate spatial organization details, compared to the 1D-CNN model. Irrespective of the algorithms and datasets, the R² and RMSE values of the pH attribute ranged from 0.15 to 0.30 and 0.97 to 1.15, respectively. Similarly, the R² and RMSE of the OC attribute ranged from 0.20 to 0.39 and 0.38 to 0.42, respectively. Further, the overall accuracy (OA) of the order and suborder classification ranged from 39% to 67% and 35% to 64%, respectively. The explicit quantification of the covariate importance derived from the permutation feature importance implied that both the models tried to incorporate the covariate importance with respect to the genesis of the soil attribute under study. Such approaches of the deep learning models integrating soil–environmental relationships under limited parameterization and computing costs can serve as a baseline study, emphasizing opportunities in increasing the transferability and generalizability of the model while accounting for the associated environmental dependencies. Full article

Ammonia (NH₃) gas is prevalent in industrial production as a health hazardous gas. Consequently, it is essential to develop a straightforward, reliable, and stable NH₃ sensor capable of operating at room temperature. This paper presents an innovative approach to modifying SnO₂ colloidal quantum dots (CQDs) on the surface of Ti₃C₂T_x MXene to form a heterojunction, which introduces a significant number of adsorption sites and enhances the response of the sensor. Zero-dimensional (0D) SnO₂ quantum dots and two-dimensional (2D) Ti₃C₂T_x MXene were prepared by solvothermal and in situ etching methods, respectively. The impact of the mass ratio between two materials on the performance was assessed. The sensor based on 12 wt% Ti₃C₂T_x MXene/SnO₂ composites demonstrates excellent performance in terms of sensitivity and response/recovery speed. Upon exposure to 50 ppm NH₃, the frequency shift in the sensor is −1140 Hz, which is 5.6 times larger than that of pure Ti₃C₂T_x MXene and 2.8 times higher than that of SnO₂ CQDs. The response/recovery time of the sensor for 10 ppm NH₃ was 36/54 s, respectively. The sensor exhibited a theoretical detection limit of 73 ppb and good repeatability. Furthermore, a stable sensing performance can be maintained after 30 days. The enhanced sensor performance can be attributed to the abundant active sites provided by the accumulation/depletion layer in the Ti₃C₂T_x/SnO₂ heterojunction, which facilitates the adsorption of oxygen molecules. This work promotes the gas sensing application of MXenes and provides a way to improve gas sensing performance. Full article

Individual physiotherapy is crucial in treating patients with various pain and health issues, and significantly impacts abdominal surgical outcomes and further medical problems. Recent technological and artificial intelligent advancements have equipped healthcare professionals with innovative tools, such as sensor systems and telemedicine equipment, offering groundbreaking opportunities to monitor and analyze patients’ physical activity. This paper investigates the potential applications of mobile accelerometers in evaluating the symmetry of specific rehabilitation exercises using a dataset of 1280 tests on 16 individuals in the age range between 8 and 75 years. A comprehensive computational methodology is introduced, incorporating traditional digital signal processing, feature extraction in both time and transform domains, and advanced classification techniques. The study employs a range of machine learning methods, including support vector machines, Bayesian analysis, and neural networks, to evaluate the balance of various physical activities. The proposed approach achieved a high classification accuracy of 90.6% in distinguishing between left- and right-side motion patterns by employing features from both the time and frequency domains using a two-layer neural network. These findings demonstrate promising applications of precise monitoring of rehabilitation exercises to increase the probability of successful surgical recovery, highlighting the potential to significantly enhance patient care and treatment outcomes. Full article

Turbomachinery shock wave patterns occur as a natural result of operating at off-design points and are accountable for some of the loss in performance. In some cases, shock wave–boundary layer (SW-BLIs) interactions may even lead to map restrictions. The current paper refers to experimental findings on a transonic linear cascade specifically designed to mitigate shock waves using porous walls on the blades. Schlieren visualization reveals two phenomena: Firstly, the shock waves were dissipated in all bladed passages, as predicted by the CFD studies. Secondly, a lower-pressure wave pattern was observed upstream of the blades. It is this phenomenon that the paper reports and attempts to describe. Attempts to replicate this pattern using Reynolds-averaged Navier–Stokes (RANS) calculations indicate that the numerical method may be too dissipative to accurately capture it. The experimental campaign demonstrated a 4% increase in flow rate, accompanied by minimal variations in pressure and temperature, highlighting the potential of this approach for enhancing turbomachinery performance. Full article

For feedback to be productive, it relies on the interactions of participants, design elements, and resources. Yet, complexities in clinical education pose challenges for feedback practices in students and teachers, and efforts to improve feedback often ignore the influence of culture and context. A recent sociocultural approach to feedback practices recognized three layers to understand the complexity of productive feedback: the encounter layer, the design layer, and the knowledge layer. This study explores the sociocultural factors that influence productive feedback practices in clinical settings from the clinical teacher–student dyad perspective. A cross-sectional qualitative study in a physiotherapy clerkship involved semi-structured interviews with ten students and eight clinical educators. Convenience sampling was used, and participation was voluntary. Employing thematic analysis from a sociocultural perspective, this study examined feedback practices across the three layers of feedback practices. The analysis yielded different elements along the three layers that enable productive feedback practices in the clinical workplace: (1) the feedback encounter layer: dyadic relationships, mutual trust, continuity of supervision, and dialogue; (2) the feedback design layer: enabled learning opportunities and feedback scaffolding; (3) the knowledge domain layer in the clinical culture: Growing clinical experience and accountability. In the context of undergraduate clinical education, productive feedback practices are shaped by social–cultural factors. Designing feedback practices should consciously integrate these components, such as cultivating relationships, fostering guidance, enhancing feedback agency, and enabling supervised autonomy to promote productive feedback. Full article

Next point-of-interest (POI) recommendation provides users with location suggestions that they may be interested in, allowing them to explore their surroundings. Existing sequence-based or graph-based POI recommendation methods have matured in capturing spatiotemporal information; however, POI recommendation methods based on large language models (LLMs) focus more on capturing sequential transition relationships. This raises an unexplored challenge: how to leverage LLMs to better capture geographic contextual information. To address this, we propose interpretable embeddings for next point-of-interest recommendation via large language model question–answering, named QA-POI, which transforms the POI recommendation task into obtaining interpretable embeddings via LLM prompts, followed by lightweight MLP fine-tuning. We introduce question–answer embeddings, which are generated by asking LLMs yes/no questions about the user’s trajectory sequence. By asking spatiotemporal questions about the trajectory sequence, we aim to extract as much spatiotemporal information from the LLM as possible. During training, QA-POI iteratively selects the most valuable subset of potential questions from a set of questions to prompt the LLM for the next POI recommendation. It is then fine-tuned for the next POI recommendation task using a lightweight Multi-Layer Perceptron (MLP). Extensive experiments on two datasets demonstrate the effectiveness of our approach. Full article

Raman spectroscopy (RS) is a promising tool for cancer diagnosis. In particular, in the last years several studies have demonstrated how the diagnostic performances of RS can be significantly improved by employing machine learning (ML) algorithms for the interpretation of Raman-based data. Recently, it has been demonstrated that RS can perform an accurate classification of chondrosarcoma tissues. Chondrosarcoma is a cancer of bones, that can occur in the soft tissues near the bones. It is normally characterized by three different malignant degrees and a benign counterpart, knows as enchondroma. In line with these findings, in this paper, we exploited ML algorithms to distinguish, as well as possible, between the three grades of chondrosarcoma and to distinguish between chondrosarcoma and enchondroma. We obtained a high level of accuracy of classification by analyzing a dataset composed of a relatively small number of Raman spectra, collected in a previous study by one of the authors of this paper. Such spectra were acquired from micrometric tissue sections with a confocal Raman microscope. We tested the classification performances of a support vector machine (SVM) and a random forest classifier (RFC), as representatives of ML algorithms, and two versions of the multi-layer perceptron (MLPC) as representatives of deep learning (DL). These models, especially RFC and MLPC, showed excellent classification performances, with accuracy reaching 99.7%. This outcome makes the aforementioned models a promising route for future improvements of diagnostic devices focused on detecting cancerous bone tissues. Alongside the diagnostic purpose, the aforementioned approach allowed us to identify characteristic molecules, i.e., amino acids, nucleic acids, and bioapatites, relevant for obtaining the final diagnostic response, through the use of a tool named by us Raman Band Identification (RBI). The method to evaluate RBI is the most important contribution of this paper, because RBI could represent a relevant parameter for the identification of biochemical processes on the basis of the tumor progression and associated malignant degree. In turn, the spectral bands highlighted by RBI could provide precious indicators in an attempt to restrict the spectral acquisition to specific Raman bands. This last objective could help to reduce the amount of experimental data needed to obtain an accurate final grading outcome, with a consequent reduction in the computational cost. Full article

Hydraulic fracturing is a widely employed technique for stimulating unconventional shale gas reservoirs. Supercritical CO₂ (SC-CO₂) has emerged as a promising fracturing fluid due to its unique physicochemical properties. Existing theoretical models for calculating breakdown pressure often fail to accurately predict the outcomes of SC-CO₂ fracturing due to the complex, nonlinear interactions among multiple influencing factors. In this study, we conducted fracturing experiments considering parameters such as fluid type, flow rate, temperature, and confining pressure. A fully connected neural network was then employed to predict breakdown pressure, integrating both our experimental data and published datasets. This approach facilitated the identification of key influencing factors and allowed us to quantify their relative importance. The results demonstrate that SC-CO₂ significantly reduces breakdown pressure compared to traditional water-based fluids. Additionally, breakdown pressure increases with higher confining pressures and elevated flow rates, while it decreases with increasing temperatures. The multi-layer neural network achieved high predictive accuracy, with R, RMSE, and MAE values of 0.9482 (0.9123), 3.424 (4.421), and 2.283 (3.188) for training (testing) sets, respectively. Sensitivity analysis identified fracturing fluid type and tensile strength as the most influential factors, contributing 28.31% and 21.39%, respectively, followed by flow rate at 12.34%. Our findings provide valuable insights into the optimization of fracturing parameters, offering a promising approach to better predict breakdown pressure in SC-CO₂ fracturing operations. Full article

Accurate and reliable bearing-fault diagnosis is important for ensuring the efficiency and safety of industrial machinery. This paper presents a novel method for bearing-fault diagnosis using Mel-transformed scalograms obtained from vibrational signals (VS). The signals are windowed and pass through a Mel filter bank, converting them into a Mel spectrum. These scalograms are subsequently fed into an autoencoder comprising convolutional and pooling layers to extract robust features. The classification is performed using an artificial neural network (ANN) optimized with the FOX optimizer, which replaces traditional backpropagation. The FOX optimizer enhances synaptic weight adjustments, leading to superior classification accuracy, minimal loss, improved generalization, and increased interpretability. The proposed model was validated on a laboratory dataset obtained from a bearing testbed with multiple fault conditions. Experimental results demonstrate that the model achieves perfect precision, recall, F1-scores, and an AUC of 1.00 across all fault categories, significantly outperforming comparison models. The t-SNE plots illustrate clear separability between different fault classes, confirming the model’s robustness and reliability. This approach offers an efficient and highly accurate solution for real-time predictive maintenance in industrial applications. Full article

We propose a novel solution to streamline the migration of existing Transport Layer Security (TLS) protocol implementations to a post-quantum Key Encapsulation Mechanism for Transport Layer Security (KEMTLS). By leveraging Identity-Based Encryption (IBE), our solution minimizes the necessary modifications to the surrounding infrastructure, enabling the reuse of existing keys and certificates. We provide a proof-of-concept implementation and performance analysis, demonstrating the practical feasibility and effectiveness of our proposed approach. Full article

Soft marine soil exhibits unique mechanical properties that can lead to significant deformation and instability in the surrounding rock of urban subway tunnels. This presents a critical challenge for tunnel engineering researchers and designers. This thesis investigates the stability characteristics of surrounding rock in marine soft soil tunnels under cyclic loading conditions. Focusing on the shield tunnel segment between Left Fortress Station and Taiziwan Station of Shenzhen Urban Rail Transit Line 12, a discrete–continuous coupled numerical analysis method is employed to examine the deformation characteristics of the surrounding rock. This analysis takes into account the effects of dynamic loads resulting from train operations on the arch bottom’s surrounding rock. The findings indicate that damage to the surrounding rock occurs gradually, with the marine soft soil layer, particularly at higher water content, being prone to substantial plastic deformation. Additionally, under the influence of train vibration loads, the degree of vertical fluctuation in the internal marine soft soil diminishes with increasing depth from the bottom of the tunnel arch. This coupled numerical analysis approach offers valuable insights and methodologies for assessing the structural safety of tunnel projects throughout their operational periods. Full article

In recent years, ablation of the buffer layer in high-voltage cables has become a prevalent issue compromising the reliability of power transmission systems. Given the internal location of these faults, direct monitoring and assessment are challenging, resulting in numerous undetected ablation hazards. Previous practice has demonstrated that detecting buffer layer ablation through surface temperature distribution changes is feasible, offering a convenient, efficient, and non-destructive approach. However, the variability in heat generation and the subtle temperature differences in thermal infrared images, compounded by noise interference, can impair the accuracy and timeliness of fault detection. To overcome these challenges, this paper introduces an adaptive enhancement method for the thermal infrared imaging of high-voltage cable buffer layer ablation. The method involves an Average Gradient Weighted Guided Filtering (AGWGF) technique to decompose the image into background and detail layers, preventing noise amplification during enhancement. The background layer, containing the primary information, is enhanced using an improved Contrast Limited Adaptive Histogram Equalization (CLAHE) to accentuate temperature differences. The detail layer, rich in high-frequency content, undergoes improved Adaptive Bilateral Filtering (ABF) for noise reduction. The enhanced background and detail layers are then fused and stretched to produce the final enhanced thermal image. To vividly depict temperature variations in the buffer layer, pseudo-color processing is applied to generate color-infrared thermal images. The results indicate that the proposed method’s enhanced images and pseudo-colored infrared thermal images provide a clearer and more intuitive representation of temperature differences compared to the original images, with an average increase of 2.17 in information entropy and 8.38 in average gradient. This enhancement facilitates the detection and assessment of buffer layer ablation faults, enabling the prompt identification of faults. Full article

We present TOLGAN that generates traditional oriental landscape (TOL) image from a map that specifies the locations and shapes of the elements composing TOL. Users can create a TOL map by using a user interface or a segmentation scheme from a photograph. We design the generator of TOLGAN as a series of decoding layers where the map is applied between the layers. The generated TOL image is further enhanced through an AdaIN architecture. The discriminator of TOLGAN processes a generated image and its groundtruth TOL artwork image. TOLGAN is trained through a dataset composed of paired TOL artwork images and their TOL maps. We present a tool through which users can produce a TOL map by specifying and organizing the elements of TOL artworks. TOLGAN successfully generates a series of TOL images from the TOL map. We evaluate our approach using a quantitative way by estimating FID and ArtFID scores and a qualitative way by executing two user studies. Through these studies, we prove the excellence of our approach by comparing our results with those from several important existing works. Full article

This paper addresses the challenge of preserving user privacy in location-based services (LBSs) by proposing a novel, complementary approach to existing privacy-preserving techniques such as k-anonymity and l-diversity. Our approach implements collaborative caching strategies within a mobile ad hoc network (MANET), exploiting the geographic of location-based queries (LBQs) to reduce data exposure to untrusted LBS servers. Unlike existing approaches that rely on centralized servers or stationary infrastructure, our solution facilitates direct data exchange between users’ devices, providing an additional layer of privacy protection. We introduce a new privacy entropy-based metric called accumulated privacy loss (APL) to quantify the privacy loss incurred when accessing either the LBS or our proposed system. Our approach implements a two-tier caching strategy: local caching maintained by each user and neighbor caching based on proximity. This strategy not only reduces the number of queries to the LBS server but also significantly enhances user privacy by minimizing the exposure of location data to centralized entities. Empirical results demonstrate that while our collaborative caching system incurs some communication costs, it significantly mitigates redundant data among user caches and reduces the need to access potentially privacy-compromising LBS servers. Our findings show a 40% reduction in LBS queries, a 64% decrease in data redundancy within cells, and a 31% reduction in accumulated privacy loss compared to baseline methods. In addition, we analyze the impact of data obsolescence on cache performance and privacy loss, proposing mechanisms for maintaining the relevance and accuracy of cached data. This work contributes to the field of privacy-preserving LBSs by providing a decentralized, user-centric approach that improves both cache redundancy and privacy protection, particularly in scenarios where central infrastructure is unreachable or untrusted. Full article

Yttrium oxyfluoride (YOF) coatings with different oxygen content were prepared using atmospheric plasma spraying (APS) technology. The etching resistance of the coatings in HBr/O₂ plasma was investigated. Shifts in diffraction peaks of the X-ray diffraction, along with XPS analysis conducted before and after etching, demonstrated that Br ions could replace O and F ions and fill the oxygen vacancies after exposure to HBr/O₂ plasma, which is supported by the first-principles calculations. Br ions formed a protective layer on the surface of the YOF coating, slowing down further etching by Br ions. By adjusting the oxygen mass fraction in YOF powder, the oxygen vacancy concentration and Br ion filling were regulated to enhance etching resistance. YOF coatings with 6% oxygen content exhibited improved etching resistance compared to YOF coatings with 3% and 9% oxygen content. This improvement was primarily due to the increased Br ion concentration. These findings provide a new approach for developing coatings with enhanced etching resistance. Full article