Search Results (9,596)

Highly deviated wells commonly exhibit large errors in horizon calibration because the logging path follows an inclined borehole trajectory, whereas post-stack seismic processing effectively treats wave propagation as vertical. This mismatch has received limited attention. Here, we performed horizon calibration and velocity-model building for highly deviated wells drilled in the Mahu Sag, Junggar Basin, and obtained three key findings. First, the assumed vertical travel path in post-stack data is the primary cause of the initial mis-tie for highly deviated wells. Second, calibration in the deviated interval requires a strategy distinct from that of vertical wells and may involve substantial stretching or squeezing of the original logs to achieve a consistent time-depth relationship. Third, the map-view projection of a highly deviated well is essentially linear; relative to vertical wells, it provides denser in situ velocity constraints and, with pseudo-well control, supplies 2D velocity information along the well-trajectory plane, thereby improving velocity-field modeling. Validation against drilling data showed that this workflow improved well ties and refined the velocity model, providing practical guidance for geological well planning and reducing drilling risk. Full article

WSN node energy forecasting contributes to improving network efficiency, extending network lifespan, and providing energy management strategies. In this study, a deep-learning-based wireless sensor network (WSN) node energy forecasting model based on Long Short-Term Memory (LSTM) and stacked-LSTM was developed across different wireless communication technologies in both static and dynamic WSN setups. The performance of the deep-learning-based models was compared with traditional forecasting techniques such as Exponential Smoothing and Prophet. The results showed the superiority of LSTM and stacked-LSTM in terms of root mean square error and mean absolute error, with consistently lower values compared with the traditional forecasting techniques. The results also show that the models perform best with Long Range technology. The deep learning-based model also demonstrates its ability to perform better in both static and dynamic WSN scenarios. These results demonstrate the potential of deep-learning-based models in WSN node energy management, which can result in an optimal energy efficiency and prolong the network lifetime. Future research is needed to explore hybrid approaches to further improve the prediction performance of deep learning-based models by combining their strengths with statistical or traditional forecasting techniques. Full article

A bidirectional infrared absorber leveraging the Fabry–Perot resonance within a cascaded metal-dielectric nano-film structure is proposed. The absorber integrates a top Ag–VO₂–SiO₂ film stack, an intermediate thin Ag metal layer, and a bottom Al₂O₃–Ti–Al₂O₃ layer, enabling switchable narrowband and broadband absorption under forward and backward illumination, respectively. Under front illumination, the structure exhibits a high narrowband absorption peak of 98% at a wavelength of 1110 nm when VO₂ is in its metallic state. In contrast, when VO₂ transitions to its insulating state, the absorption peak shifts to 1165 nm. Additionally, under back illumination, ultra-broadband absorption is achieved, covering a wavelength range of 1000–2760 nm with an average absorption of 98%. The proposed absorber demonstrates excellent absorption performance with structural simplicity and low manufacturing cost, offering great potential for applications in solar photovoltaic devices, photodetectors, and related fields. Full article

Background/Objectives: Poly(ADP-ribose) polymerase 1 (PARP1) is an important therapeutic target in DNA repair-deficient cancers, but discovery of new inhibitors remains constrained by scaffold convergence, tolerability limits, and acquired resistance. This study aimed to develop an interpretable, reliability-stratified cheminformatics workflow for PARP1 potency prioritization and structure-based follow-up. Methods: A curated ChEMBL dataset of 3339 PARP1 inhibitors was encoded using RDKit 2D descriptors and Avalon fingerprints (1143 initial features), then reduced to 132 informative variables by Random Forest-based feature selection. Five regression models were optimized, including a stacked ensemble. Model interpretation was performed using permutation feature importance and SHAP. External near-domain corroboration was assessed using a stringent PubChem similarity expansion (Tanimoto > 0.90) around sub-10 nM seed compounds, followed by comparison with retrievable experimental PARP1 activity values. Top scaffold-diverse candidates were further evaluated by complementary docking against PARP1 (PDB: 4R6E) using AutoDock Vina and cavity-guided docking through the SwissDock platform. Results: The stacked ensemble achieved the best held-out performance (test R² = 0.723; RMSE = 0.610 pIC₅₀ units), with 83.7% of test predictions within ≤0.75 pIC₅₀ units and only 2.7% exceeding 1.5 pIC₅₀ units. PubChem similarity expansion retrieved approximately 32,450 analogs, of which 3349 were predicted to have IC₅₀ ≤ 10 nM. Among 366 compounds with retrievable experimental PARP1 activity values, predicted versus experimental pIC₅₀ showed a positive association (R² = 0.124; Pearson r = 0.479), with RMSE = 0.491 and MAE = 0.330 pIC₅₀ units. Three ligands—CID 168873053, CID 175154210, and CID 172894737—showed the strongest complementary docking support and pocket-consistent poses relative to niraparib. Conclusions: This workflow provides a transparent and practically useful framework for near-domain PARP1 inhibitor prioritization. The combined QSAR, explainability, external corroboration, and docking strategy supports shortlist generation for experimental follow-up. Full article

With accelerating global urbanization and increasingly diverse demands for public spaces, promoting urban low-carbon transitions and enhancing residents’ quality of life have become central missions of modern urban development. As one of the city’s primary arteries, streets—through their green landscapes, slow-moving transportation systems, and public facilities—play an indispensable role in reducing carbon emissions, promoting healthy living, and improving residents’ well-being. In this study, the Yubei District of Chongqing was selected as the research area, and an automated evaluation framework was proposed for street visual quality, based on multi-source street view data and ensemble learning. PSP-Net semantic segmentation model was employed to extract eight key visual indicators from street view images, including green view index, Visual Entropy (Entropy), sky view factor (SVF), drivable space, sidewalk, safety facilities, buildings, and enclosure. Based on these features, a Stacking-based ensemble learning model was constructed, integrating multiple base models such as Random Forest, XGBoost, and LightGBM, with Linear Regression as the meta-learner, to predict street visual quality. The results demonstrate that the ensemble model significantly outperforms any single model, achieving a correlation coefficient (r) of 0.77 and effectively capturing the complex perceptual features of street environments. This study provides a reliable, intelligent, and quantitative method for large-scale evaluation of urban street visual quality, while supplying data support and decision-making references for street renewal and spatial optimization. Full article

Proton exchange membrane water electrolyzers (PEMWEs) are an emerging hydrogen production technology with significant advantages. However, their structural design remains incompletely matured. During assembly, the clamping force is transmitted through the endplate to internal components. Improper clamping force causes uneven stress distribution across electrolysis cells, compromising sealing integrity and hydrogen production efficiency. To address uneven force transmission in conventional rectangular endplates, this study proposes a curved stack-facing endplate structure. A multi-objective optimization methodology is employed to identify the optimal curvature radius, which provides pre-deformation compensation during operation. This enables the surface to flatten under clamping force and to ensure tight contact with underlying cells. After optimization, the standard deviation of deformation along each path on the single electrolysis cell decreased by over 10% and the standard deviation of equivalent stress along each path on the endplate dropped by more than 5%. Subsequently, an orthogonal experimental design considering curvature radius and bolt arrangement is conducted to find the optimal combination in stack assembly. The optimal combination is identified and compared with the stack equipped with the original rectangular endplate. The maximum deformation at the four corners of the optimized endplate decreases from 0.28399 mm to 0.27452 mm. Additionally, the stress concentration area in the optimized endplate is reduced by more than half. Results demonstrate significantly reduced stress concentration and substantially more uniform stress distribution in the optimized endplate. Full article

Composite materials have traditionally been employed in the aerospace sector due to their ability to withstand highly demanding service conditions. In recent years, their application has expanded significantly into other engineering domains, including wind energy, shipbuilding, and the automotive industry. The design of composite structures often involves geometric discontinuities, such as cut-outs for access or fastener holes for mechanical joining, which typically become critical regions under load. Consequently, the stress concentrations induced by notches represent a major concern, as they can lead to substantial reductions in strength compared with unnotched laminates. A comprehensive understanding of the behaviour of notched specimens is therefore essential for the design of complex composite assemblies, where components are commonly joined using bolts and rivets. The objective of this study is to examine the tensile response and notch sensitivity of carbon fibre-reinforced polymer (CFRP) laminates with different stacking sequences, through a comparative analysis of unnotched and open-hole specimens. A central circular hole was introduced to reproduce the geometric discontinuities frequently encountered in structural applications, enabling a detailed assessment of stress concentration effects. The experimental results indicate that unidirectional laminates exhibit the highest sensitivity to notches, whereas quasi-isotropic configurations among the multidirectional laminates display the most pronounced reduction in strength, approaching 50%. Moreover, the Point Stress Criterion (PSC) and the Average Stress Criterion (ASC) were employed to determine the characteristic lengths of the specimens, revealing significant differences among the values obtained for each lay-up configuration. Overall, the findings highlight the strong influence of stacking sequence on the mechanical response of notched CFRP laminates and underscore the need to further refine existing failure criteria to accommodate novel laminate architectures, including Bouligand-type helicoidal bioinspired stacking sequences. Full article

Digital transformation is reshaping construction asset compliance, but fragmented information and weak evidence trails still constrain effective management. This systematic grey literature review (2014–2025) identifies technologies supporting asset management and compliance assurance and compares adoption maturity across the United Kingdom (UK), the United States (US), Singapore, and the Gulf Cooperation Council (GCC). Using multi-channel search strategies and the AACODS appraisal (Authority, Accuracy, Coverage, Objectivity, Date, Significance), 131 records were identified; 92 full texts reviewed; 82 eligible; and 43 sources retained. Coding identified a recurring five-technology “core digital stack”: Building Information Modelling (BIM), Digital Twins (DT), Internet of Things (IoT), Artificial Intelligence/Machine Learning (AI/ML), and Blockchain (BC). Within the retained corpus, BIM and AI/ML were the most frequently referenced technologies, whereas BC was referenced more selectively and discussed mainly for tamper-evident traceability. DT and IoT were typically discussed alongside BIM, while IoT also frequently co-occurred with AI/ML in analytics-led compliance workflows. A (Region × Technology) maturity matrix suggests higher, policy-led maturity where mandates and audit-ready information align with national frameworks (UK, Singapore), and more uneven, project-led adoption in decentralised contexts (US, GCC). Overall, the findings emphasise that effective compliance relies on integrated, evidence-focused digital stacks supported by standardised information governance rather than isolated tools. Full article

Integrating agricultural remote sensing and phenomics for full-growth-period rice quality prediction is vital for early non-destructive screening and breeding; however, studies integrating genomic and multi-source phenotypic data across multiple environments remain limited. This study addressed this gap by integrating genomic SNP data, UAV-based spectral data, and individual multidimensional phenotypic data of 61 indica rice varieties (field and greenhouse environments). As a proof-of-concept study, feature selection methods (LASSO, MI, RFE, SPA) were used to mitigate overfitting and the “p >> n” problem, with further validation needed in larger populations. The results showed that amylose content is genetically dominated, protein content is genetically determined and influenced by gene-environment interactions, and chalkiness traits are determined by three combined factors. For amylose content, SNP data under the Random Forest model at the population level (phenomics data from field UAV remote sensing of variety populations) achieved optimal performance (R² = 0.92; MAE = 1.1; RMSE = 1.5), while the Stacking Ensemble method enhanced accuracy at the individual level (phenomics data from greenhouse single-plant phenotyping per variety). Chalky grain rate and chalkiness degree showed SNP-comparable prediction accuracy, with Stacking significantly improving performance at the population level (R² = 0.89 and 0.85, respectively). Protein content prediction remained relatively low (optimal R² = 0.56) due to strong environmental sensitivity and complex interactions. This framework extends traditional single-environment/single-data-source approaches, providing an effective strategy for early, high-throughput, non-destructive rice quality screening. Further validation with larger datasets, more growing seasons, or independent populations is required for reliable application in breeding-related practices. Full article

The increasing deployment of collaborative robots in industrial manufacturing environments has enabled close human–robot collaboration, making rapid and reliable collision detection essential for worker safety. This paper presents a learning-based framework for real-time detection and classification of hard and soft collisions using stacked Gated Recurrent Unit (GRU) networks. A two-stage pipeline is introduced, in which collision detection and collision type classification are performed sequentially using separate models, and its performance is validated through extensive experiments on a collision dataset collected from a six-joint collaborative robot executing random point-to-point motions. Without requiring joint torque sensors, unmodeled joint friction is implicitly compensated through learning for both detection and classification. Compared to our previous work, the proposed method achieves improved detection performance, and its robustness is further demonstrated through systematic generalization experiments under simulated dynamic model uncertainties. In addition, the classification model accurately distinguishes between hard and soft collisions, providing a basis for differentiated post-collision reaction strategies. Overall, the proposed sensorless collision detection and classification framework provides a practical and cost-effective solution for real-world industrial human–robot collaboration. Full article

High-power jamming may drive the radio-frequency (RF) front end of a satellite receiver into a nonlinear regime, thereby invalidating the linear superposition assumption underlying conventional excision and blanking methods. We formulate dual-receiver direct-sequence spread-spectrum (DSSS) anti-jamming as a nonlinear source-separation problem in complex baseband using stacked I/Q observations. We then propose a time-domain separator that jointly estimates the desired DSSS signal and the jammer on a designated reference receiver. The separator combines a multi-scale convolutional front end with a Transformer encoder and is pretrained on synthetic nonlinear mixtures that include multi-tone or burst jamming as well as typical satellite impairments, including Doppler/carrier-frequency offset (CFO), phase noise, multipath, and additive white Gaussian noise (AWGN). Robustness under high-jammer-to-signal-ratio (JSR) conditions is improved through high-JSR oversampling and JSR-aware loss reweighting. After Stage I supervised pretraining on labeled synthetic mixtures, an optional Stage II mixture-only adaptation step further refines the separator using nonlinear reconstruction consistency and lightweight communication-motivated priors. Across 1000 test mixtures with JSRs from −5 to 15 dB, SNRs from 15 to 25 dB, and cubic coefficients $a\in \left[\mathrm{0,0.5}\right]$, the proposed method improves the desired-signal scale-invariant signal-to-noise ratio (SI-SNR) from −4.79 dB for the mixture baseline to 13.32 dB after supervised pretraining and to 17.73 dB after mixture-only blind fine-tuning. Over the same test set, the failure rate (SI-SNR < 0 dB) decreases from 60.7% to 2.3%. Full article

Proton exchange membrane fuel cells (PEMFCs) have considerable potential for frequency support in grid-forming applications. However, their transient dispatchable power is nonlinearly influenced by operating conditions, such as the oxygen excess ratio and stack temperature, thereby weakening frequency support performance by delaying power compensation during disturbances. To address this issue, a coordinated control strategy for inertia support and active power compensation is proposed that explicitly accounts for operating-state effects. Based on a dynamic PEMFC model, the effects of the oxygen excess ratio and stack temperature on transient output capability are analyzed, and a jointly corrected inertia coefficient is introduced into the virtual synchronous generator (VSG) rotor motion equation to achieve adaptive adjustment of virtual inertia under varying operating conditions. In addition, model predictive control (MPC) is incorporated into the VSG control framework, and a performance index is formulated using weighted quadratic terms of frequency variation and input power, thereby enabling the compensation power to be determined online and the PEMFC power reference to be updated accordingly. Simulation results show that the proposed strategy can effectively suppress frequency fluctuations under disturbance conditions. Compared with Conventional PI-VSG, the maximum frequency deviation and the peak rate of change of frequency (ROCOF) are reduced by 49.1% and 62.1%, respectively. Full article

In this paper, a drive–vibration integrated piezoelectric actuator (DVIPA) is proposed for vibration-assisted implantation of flexible electrodes. Conventional implantation systems typically rely on separate actuation and vibration modules, which increase system complexity and limit integration. To address this limitation, the proposed DVIPA integrates driving and vibration functions within a single compact structure by employing two piezoelectric bimorphs for clamping and a piezoelectric stack for combined actuation. A composite excitation waveform, consisting of high-frequency sinusoidal signals superimposed on the rising stage of a low-frequency trapezoidal wave, is applied to simultaneously generate forward motion and vibration. This configuration enables a coupled motion mode that facilitates insertion while reducing the risk of buckling. A prototype of the DVIPA was developed and experimentally evaluated. The results show that vibration-assisted implantation can be achieved under various operating conditions, with independently adjustable driving and vibration parameters. A maximum speed of 328 μm/s is obtained, meeting the requirements for flexible electrode implantation. Agarose gel experiments further demonstrate that vibration frequencies above 40 Hz and voltages between 20 and 40 V can effectively assist implantation of polydimethylsiloxane (PDMS) without buckling failure. Overall, the proposed actuator provides a compact and integrated solution for vibration-assisted implantation, offering potential advantages in applications with limited space. Full article

Perylene bisimide derivatives are typical n-type semiconductors as well as redox-active materials. However, it has been difficult to produce thin films by solution processes because of their low solubilities in organic solvents. Perylene bisimide derivatives bearing oligosiloxane moieties exhibit columnar phases over wide temperature ranges, including room temperature and high solubilities in organic solvents. The columnar phases are stabilized by nanosegregation between crystal-like one-dimensional π-stacks and liquid-like mantle consisting of oligosiloxane moieties. The electron mobility at room temperature exceeded 0.1 cm²V⁻¹s⁻¹ in the ordered columnar phases of perylene bisimide derivatives bearing four disiloxane chains. Uniaxially aligned thin films of the perylene bisimide derivatives bearing oligosiloxane moieties could be produced by a spin-coating method. The spin-coated films of the perylene bisimide derivatives bearing cyclotetrasiloxane rings could be insolubilized via in situ ring-opening polymerization by the exposure of the thin films to trifluoromethanesulfonic acid vapors. Uniaxially aligned thin films of perylene bisimide derivatives bearing an ethylene oxide chain as well as cyclotetrasiloxane rings could be doped in an aqueous solution of sodium dithionate, resulting in an anisotropic electrical conductivity. Polymerized thin films of perylene bisimide derivatives bearing a crown ether ring exhibited electrochromism in electrolyte solutions. These compounds formed 1:1 complexes with lithium triflate, exhibiting columnar phases at room temperature. The nanostructures of the complexes were stabilized by the electrostatic interaction between cationic crown-metal units and triflate anions. Full article

Cold-mercury gilding uses mercury as an adhesive to bond gold foil onto the surface of copper and silver artifacts. This technique and mercury gilding (fire gilding) both belong to the Au-Hg system and are closely related in technology. Clarifying the technical differences between them is of great significance for revealing the developmental sequence of ancient gilding technologies. On the basis of reconstructing traditional fire gilding, simulated cold-mercury-gilded samples were successfully prepared using experimental archeological methods, and multi-scale characterization was performed using SEM-EDS, XRD, and XPS. The results show that the surface of cold-mercury-gilded samples displays a micromorphology of folded and overlapped gold foil accompanied by locally dense particle aggregation. The cross-section of the gold layer exhibits a multilayer stacked structure, in which mercury is enriched at the gold layer/substrate interface and forms an AuHgCu/Ag diffusion layer. Room-temperature-stable Au-Hg and Ag-Hg phases such as Au₂Hg and AgHg are present in the gold layer, reflecting complex phase transformation behavior of the Au-Hg/Ag-Hg system at room temperature. During cold-mercury gilding, liquid mercury first adheres to the gold foil, and then interdiffusion and phase reactions occur between mercury, gold, and copper/silver atoms at room temperature. Intermetallic compounds and diffusion layers formed at the interface achieve firm bonding between the gold layer and the substrate. Both cold-mercury gilding and mercury gilding achieve metallurgical bonding through atomic interdiffusion. However, affected by differences in the initial state of mercury and operating temperature, the phase transformation and atomic diffusion behaviors of the system differ significantly, which are ultimately reflected in the cross-sectional structure of the gold layer, the composition of the interfacial diffusion layer, and the types of phases. Therefore, mercury-gilded artifacts show superior gold layer durability and bonding strength with the substrate compared with cold-mercury-gilded artifacts. Both techniques pioneered the application of mercury in metallic gilding and represent important innovations in ancient surface decoration technology. Full article