Search Results (2,650)

Accurate estimation of downhole weight on bit (DWOB) is important for drilling parameter optimization and safe drilling in extended-reach horizontal wells. However, DWOB is difficult to obtain continuously because surface weight on bit (SWOB) is attenuated by drill-string friction, wellbore trajectory, and borehole-wall contact. To address this problem, a physics-informed convolutional neural network combined with bidirectional gated recurrent unit (CNN-BiGRU) prediction model optimized by the whale optimization algorithm (WOA) is proposed using surface sensor data and downhole measurement-while-drilling (MWD) measurements. The model first uses a one-dimensional CNN to extract local features from drilling parameters along the measured-depth direction and then employs a BiGRU to capture depth-series dependencies. Meanwhile, a drill-string frictional attenuation relationship is embedded into the loss function as a physical prior, and WOA is used to optimize network hyperparameters and the physics-constrained weight. Field data from a horizontal well were used for validation. The proposed model achieved a coefficient of determination (R²)of 0.9245 on the test set, with root mean square error (RMSE), mean absolute error (MAE), and mean relative error (MRE) values of 0.1856, 0.1135, and 0.0124, respectively. The results demonstrate that the proposed data–physics hybrid framework improves the accuracy and physical consistency of DWOB prediction. Full article

This paper presents a flexible and efficient design method for optimizing the mooring systems of floating structures. Mooring system optimization is challenging because of the strong nonlinearity of mooring system behavior and the many technical constraints that must be satisfied. Furthermore, different mooring configurations can have very different design spaces. While some successful examples of mooring design optimization exist in the literature, developing an optimization approach that can work across various mooring design problems is a larger challenge. We present such a method based on a flexible parameterization that allows a wide variety of mooring designs to be described by a list of variables, a quasi-static mooring model that provides efficient evaluation of a mooring design without directly considering mooring system dynamics, and an optimization framework that generates, evaluates, and adjusts the mooring design while considering user-specified constraints such as offset limits, strength safety factors, and seabed contact limits. We demonstrate the design optimization framework on four mooring design problems, each for a different type of mooring system. We compare the use of different design modes to simplify the optimization problem, showing that they can reduce the computation time by up to 75%. We also compare different optimization algorithms and find that the resulting computational speed can vary by up to 51 times. We perform a sensitivity study on one design and find that the local sensitivity of anchoring radius to water depth has a positive correlation of 0.29, but the global sensitivity shows large nonlinearities. Lastly, we perform a coupled dynamic analysis on one of the optimized designs and find that the predicted mean platform motions and mooring line tensions are within 1% of dynamic results and the extreme motions and tensions are within 14%. Lastly, we show that a DEA-Chain-Polyester mooring configuration is cost-optimal for the given design problem of the demonstrations, which aligns with general industry practice. Full article

Green low-carbon data centers operate as coupled cyber-energy systems whose dispatch must coordinate renewable generation, grid exchange, battery storage, cooling load, flexible computing workload, carbon-intensity signals, and reliability constraints. This study develops and evaluates a safety-constrained deep reinforcement learning framework for source–load–storage coordinated operation of a grid-connected green data center. The operating problem is formulated as a constrained Markov decision process with state variables describing the IT load, deferrable workload backlog, renewable availability, electricity price, marginal carbon intensity, battery state of charge, server-room temperature, reserve margin, and calendar context. The action space covers grid import and export, renewable utilization, storage charge and discharge, workload shifting, and cooling control. The learning architecture combines a constrained actor–critic policy, adaptive Lagrangian safety critics, and a control barrier function (CBF)-based action shield that projects unsafe actions onto an explicitly defined operating set before plant execution. The shield is specified as a low-dimensional quadratic projection over state-dependent SOC, thermal, reserve, SLA, and grid-interface constraints, while cumulative risks are priced through Lagrangian safety budgets during policy training. The evaluation uses a controlled and auditable benchmark simulation with normalized public-data-compatible profiles, declared scenarios, random seeds, neural-network settings, and mechanism-matched baselines; it is not a telemetry-based verification or hardware certification of a deployed data center. Within this declared benchmark, the proposed safe DRL controller produces a simulated 13.1% emission reduction relative to the Rule-based controller, 95.8% renewable utilization, a normalized annual cost of 0.91, and fewer boundary contacts than the tested unconstrained, Lagrangian-only, and shield-only PPO variants. These percentages are simulator outputs relative to the stated benchmark and must not be interpreted as measured field savings. The results show how separating reward learning, cumulative safety pricing, and one-step engineering projection changes low-carbon dispatch within the specified model. Full article

During operation, several parts of the thermal reactor burners sustain heavy damage and need to be replaced. Different solutions for parts manufacturing are investigated: thermal spraying, Selective Laser Melting (SLM), and hardfacing by Directed Energy Deposition plasma arc (DED-arc). Based on the comparison to original material and the duration of usage, application of those three methods for replacement is studied in order to determine the most suitable one, with Additive Manufacturing (AM) being proposed for targeting the problem. Thermal-sprayed items have a zirconium-oxide-based outer layer. SLM produces a monolithic item, while with the help of DED-arc, a composite structure with a sound metallurgical bond between the base and the added material is produced. The microstructures with the interface zones are observed. Samples are machined and ground, and their friction characteristics are taken with the help of acoustic emission (AE) and Electrical Contact Resistances (ECR) sensors during scratching. As a result, overlaying of the base stainless steel by DED-arc is proposed due to the better metallurgical stability of the added mixture in a hot environment above 800 °C and its hardness characteristics. Full article

An experimental morphology-based information extraction methodology is presented for locating reinforcing bars and estimating their effective ultrasonic scattering diameters in reinforced concrete (RC) members using ultrasonic pitch–catch (UPC) non-destructive testing combined with synthetic aperture focusing technique (SAFT) reconstruction. Reinforced concrete slab specimens with known reinforcement layouts were constructed and tested using a commercial ultrasonic device equipped with dry point contact shear wave transducers in a pitch–catch configuration. The collected ultrasonic data were post-processed using an in-house software package to reconstruct two-dimensional (2D) SAFT images of the specimens. In the reconstructed images, embedded rebars consistently produced characteristic bipolar scattering responses composed of dominant trough–crest waveform pairs. Based on these repeatable scattering response features, an empirical morphology-based procedure was developed in which rebar depth was estimated from the midpoint of the dominant trough–crest pair, while the effective ultrasonic scattering diameter was characterized using their vertical separation. The experimental results obtained from twelve reinforced concrete slab specimens demonstrated reliable depth estimation and a strong monotonic relationship between reconstructed scattering response width and nominal rebar diameter under the investigated testing conditions. Regression analysis indicated that larger rebars generally produced broader reconstructed bipolar scattering responses due to cylindrical wave interaction, diffraction, interference, finite transducer aperture effects, and SAFT reconstruction characteristics. The proposed methodology does not attempt to reconstruct the exact physical boundary of the reinforcement or solve the full elastodynamic inverse problem. Instead, the proposed methodology provides a practical and computationally efficient morphology-based information extraction framework that transforms reconstructed ultrasonic scattering responses into quantitative morphological descriptors for embedded reinforcement characterization. By extracting repeatable scattering response features from SAFT-reconstructed images and establishing an empirical mapping between these descriptors and reinforcement characteristics, the proposed approach enables the quantitative interpretation of ultrasonic images without requiring computationally intensive full waveform inversion. Full article

Customer complaints are considered one of the key indicators of customer discontentment with a service. In organizations, such as telecommunications companies, not all customers raise their complaints, which raises concerns about their potential churn or retention. As firms usually rely on the complaints raised to customer services, there exists an important portion of customers who claim their complaints through other platforms, such as social media, even though another portion does not complain at all. This places the company’s image in jeopardy and might affect its productivity and profits. To address this challenge, it is important to address possible customer problems before they turn into effective complaints. To do so, the current study aims to predict the complaints of customers in a telecommunication company and their potential churn through the usage of supervised machine learning models to test the correlation between churn and complaints. Through a thorough data analysis, it becomes evident that a good portion of clients who encounter service issues decide not to present any complaints to the company. In addition, among complainers, some do not complain directly to the company, while others who contact the company have their problems postponed. Among those, there is a proportion, considered as having unresolved concerns, turned into churn. Using a dataset of 1000 clients, recruited over a period of six months, the results showed that a considerable portion of customers using the services during the day were non-churners and continued using it over the overall period of 6 months. Whereas, day churn and evening churn both showed much lower frequencies compared to non-churn customers, with fewer calls across all durations. Additionally, the findings showed that there exists a correlation between customer complaints and customer churn, where churn events frequently coincide with complaints, indicating that customers without churn are generally content with their service, while those having complaints are more likely to quit. This study presents important insights into telecommunications companies to improve their service offerings, enhance customer satisfaction, and reduce churn rates, leading to a more stable and profitable customer base. Full article

Catalyst activity loss reduces both the performance and operating flexibility of industrial sulfur dioxide converters, yet its consequences are rarely assessed beyond conversion declines. This work develops an activity-loss vulnerability framework for a four-bed double-contact SO₂ converter model evaluated against an industrial fresh-catalyst benchmark and applies it to four prescribed activity scenarios ($a=1.0$, 0.8, 0.6, 0.4). At the reference inlet-temperature policy, reducing activity from $a=1.0$ to $a=0.4$ lowered conversion from 99.758% to 96.812%, increased outlet SO₂ slip from 230 to 2960 ppmv, and raised the hotspot from 613.7 to 660.3 °C, exceeding the adopted illustrative limit of 650 °C. Sensitivity, vulnerability, hotspot risk, and feasible-region maps show that the prescribed activity loss progressively shrinks the permissible operating envelope and creates a coupled productivity–emissions–thermal-safety tradeoff. A non-uniform activity profile at the same mean activity as uniform $a=0.6$ produced a hotspot that was 9.3 °C higher, demonstrating that average activity alone is insufficient for thermal-risk assessment. Finally, a scenario-relative Operating Efficiency Reduction Index (OERI) integrates conversion loss, SO₂-slip increase, and thermal-margin loss into an illustrative scenario-screening score. The results show that catalyst activity loss should be assessed as a coupled performance, emissions, and operational-vulnerability problem rather than conversion decline alone. Full article

Cut blasting beneath operating tank farms poses a coupled design problem: the charge must create a cavity while keeping vibration contours outside protected structures. Existing checks evaluate peak particle velocity (PPV) at monitoring points or excavation quality separately, leaving the feasible charge range unclear. This study proposes a site-calibrated dual-threshold framework for a construction adit in a water-sealed cavern. A refined cut-hole damage model defines the lower charge threshold from damage connectivity, damage volume ratio and maximum block volume, while a field-scale vibration model defines the upper threshold from the 3.5 cm/s PPV contour relative to surface structures. Field fragmentation recognition and five vibration records support the comparison, with PPV errors of 0.24–3.37% and waveform correlations of r = 0.62–0.69. At the studied site, charges below 67.2 kg failed to satisfy cut-cavity criteria, whereas 79.8 and 84.0 kg caused the exceedance contour to contact or cross the structural boundary. A 75.6 kg grouped cut charge satisfied both thresholds and reduced cut explosive consumption by 10.0%. These charge values are site-specific; transfer to other excavations requires recalibration against local geology, structural layout and vibration limits. The framework converts charge selection into a bounded safety-and-effectiveness window for constrained underground excavations. Full article

Electrical slip rings are key components for power and signal transmission in rotating equipment, and degradation of sliding electrical contact is a major factor limiting their reliability. To analyze the sliding electrical contact behavior of slip rings, an elastoplastic contact framework incorporating thermal–mechanical–electrical coupling is developed. The semi-analytical method combined with discrete convolution-fast Fourier transform is employed to efficiently solve the coupled contact problem, while J₂ flow theory and radial return algorithm are adopted to determine plastic deformation. Based on the proposed model, the elastoplastic sliding electrical contact behaviors of smooth and sinusoidal surfaces are systematically investigated. The results show that plastic deformation increases the contact area, thereby reducing the electrical contact resistance, current density at the contact edge, and maximum temperature rise, although it may induce the residual stress. Reducing the asperity height of sinusoidal surfaces while maintaining multiple discrete micro contact spots can effectively lower the electrical contact resistance and interfacial temperature rise. The proposed model provides a useful theoretical tool for evaluating the thermal–mechanical–electrical performance of sliding electrical contact in slip rings. Full article

Reverse engineering of printed circuit boards is still largely manual when original computer-aided design documentation is unavailable. This paper presents a semi-automatic vision-based pipeline that prepares an editable KiCad schematic draft for use in an Electronic Design Automation (EDA) workflow from paired TOP and BOTTOM board images. The method combines color-profile estimation, pad and through-hole detection, trace segmentation, optical character recognition, component inference, an explicit evidence graph and schematic export with drawn wires. A separate readability step aligns symbols to a grid and reroutes the reconstructed nets with orthogonal wires; it does not change the reconstructed netlist. The primary quantitative evaluation used twelve synthetic KiCad fixtures and three solver configurations: the default sequential pipeline, an opt-in global component solver and an opt-in probabilistic contact solver. These fixtures provide controlled regression cases and are complemented by a small exploratory acquisition trial on real photographed boards. All configurations completed all runs and passed the export round-trip validation without falling back to label-only connectivity. This round-trip check confirms consistency between the internal reconstruction and the exported schematic, but it is reported separately from electrical correctness against the KiCad reference design. The stricter reconstruction-quality criterion still failed on four stress cases involving repeated component chains, long meandering variable-width traces, circular distractors near pads and two-sided transistor layouts. The probabilistic contact solver was therefore kept as an opt-in diagnostic mode rather than enabled by default; it reduced the global pin-to-pin netlist edit distance from 642 to 525 while preserving schematic export checks. The real-board trial indicates that pad and hole detection can transfer to simple photographs, with trace extraction remaining sensitive to uncontrolled illumination and weak copper contrast. The results support the use of the system as a human-in-the-loop reconstruction assistant and identify component grouping, trace-contact reasoning, real-photograph benchmarking and safe missing-edge activation as the main remaining research problems. Full article

The groove curvature coefficient plays a critical role in determining the thermo-mechanical performance of angular-contact ball bearings. However, its optimization remains challenging due to strong nonlinear coupling among stress, stiffness, heat generation, and fatigue capacity. To address this issue, this study proposes a hybrid optimization framework integrating a corrected two-dimensional axisymmetric finite element method (2D-AxFEM) model, a deep learning surrogate model, and a genetic algorithm. Firstly, an efficient 2D-AxFEM model calibrated by a bearing dynamic model is developed to accurately predict key performance metrics, including contact stress, stiffness, heat generation, and dynamic load rating, with significantly reduced computational cost compared to the conventional 3D FEM model. Based on the generated data, a multi-layer perceptron deep learning surrogate model is trained to establish a fast nonlinear mapping between groove curvature coefficients and performance indicators. The model achieves high accuracy, with R² values of 0.9745, 0.9414, and 0.9756 at three different rotational speeds, as well as significantly improved computational efficiency. Building upon this, single- and multi-constraint optimization problems are solved using a genetic algorithm. The results reveal clear trade-offs among stiffness, heat generation, and load capacity. Under stiffness constraints, optimal solutions consistently converge to the constraint boundary, indicating its dominant role in thermal optimization. Under multiple constraints, the framework effectively identifies feasible design regions, enabling reduced heat generation while maintaining acceptable stress and load capacity. Overall, the proposed framework enables efficient exploration of multi-physics design spaces and provides a scalable solution for high-speed bearing optimization. Full article

Die forging is a widely used production method for manufacturing high-strength components with high accuracy and good mechanical properties. Since the die forging process involves many complicated thermo-mechanical coupled field physical problems, such as large plastic deformation, high temperature, friction, and heat transfer, etc., experimental studies are difficult and expensive to perform. The numerical simulation method has become the main method of study and optimal design for the die forging process. This paper reviews the published papers on numerical simulation of the die forging process from 2016 to 2026, in a structured literature review of computational simulations dealing with die forging processes. The literature search was conducted using the Scopus and Web of Science databases. After screening and full-text assessment, 24 relevant journal articles were selected for this paper. The articles studied were analyzed in terms of finite element modelling strategies, constitutive and material models used, friction and thermal boundary conditions considered, and process parameters. Typical results obtained from the studies discussed in the paper include stress, strain, temperature, forging load, and metal flow. The current state-of-the-art research has evolved from simple metal-flow predictions to more complex thermo-mechanical models, and even optimization-based models. Most of the current studies are based on experimentally derived constitutive equations, as well as more complex friction and heat-transfer models. Furthermore, studies applying optimization methods (Taguchi methods, design of experiments, machine learning, artificial intelligence) are increasingly common. The growing interest in the digital twin concept and real-time process control is observed. However, experimental validation, thermal contact modelling, and simulation of stress, strain, temperature, and microstructure in one simulation remain key challenges. Full article

Background/Objectives: Single-retainer Maryland adhesive restorations provide a minimally invasive fixed option for replacing missing anterior teeth in young patients. When congenital absence of a maxillary lateral incisor is associated with a contralateral conoid lateral incisor, the clinical problem extends beyond tooth replacement to bilateral symmetry management. This case report describes a digital-restorative workflow combining a single-retainer lithium disilicate Maryland adhesive restoration with a pontic-derived stamp technique for contralateral symmetry correction. Methods: A medically healthy 13-year-old patient presented with congenital absence of the maxillary left lateral incisor (FDI 22; Universal 10) after orthodontic treatment and had been wearing a removable appliance to replace the single missing tooth. The contralateral maxillary right lateral incisor (FDI 12; Universal 7) presented with conoid morphology, further compromising anterior symmetry. Clinical assessment confirmed ideal mesiodistal space at FDI 22, a vital and unrestored FDI 21 with intact enamel and healthy periodontal support, and the absence of abnormal overjet, overbite, or excursive contact pattern. Based on the findings, a single-retainer Maryland adhesive restoration was fabricated from lithium disilicate (IPS e.max CAD) and bonded to the maxillary left central incisor (FDI 21; Universal 9) using an enamel-only preparation and adhesive cementation protocol. The fixed restoration was combined with a pontic-derived stamp workflow to guide direct composite reshaping of the contralateral conoid lateral incisor. Results: At 1-year follow-up, no debonding, sensitivity, marginal discoloration, or soft-tissue inflammation was observed. The contralateral composite reshaping remained clinically stable, and the patient and guardian reported improved comfort and esthetic satisfaction compared with the previous removable appliance. Conclusions: This case suggests the short-term clinical feasibility of combining a single-retainer lithium disilicate Maryland adhesive restoration with a pontic-derived stamp workflow to achieve minimally invasive tooth replacement and contralateral symmetry correction. Longer follow-up and broader case series are required to confirm the long-term predictability and reproducibility of this approach. Full article

The Philippines achieved World Health Organization (WHO) certification for the elimination of leprosy as a public health problem in 1998. Despite this milestone, new cases continue to be reported each year, highlighting the need for sustained surveillance and interventions to achieve zero transmission. This paper provides an update on the country’s progress toward interruption of leprosy transmission using national surveillance data and programmatic reports from 2020–2024. Quantitative data were obtained from the Department of Health (DOH) Field Health Services Information System (FHSIS), while policy and programmatic information were drawn from national reports, WHO guidance, and implementation reviews. Descriptive analyses were conducted to examine trends in prevalence, case detection rates (CDRs), and age-sex distribution patterns to identify high-risk groups. Leprosy prevalence declined from 0.41 per 10,000 population in 2020 to 0.11 in 2024, remaining below the WHO elimination threshold. The CDR increased from 0.45 in 2022 to 1.17 per 100,000 in 2024, indicating recovery of active surveillance after COVID-19-related disruptions. Most newly detected cases occurred among adults aged 20–59 years (72%), although continued detection among children aged 0–14 years (6–7%) suggests ongoing transmission in selected endemic areas. Key program strengths include policy integration and WHO-supported surveillance initiatives, while major barriers include stigma, uneven local implementation, and limited access to rehabilitation. The Philippines has maintained low national prevalence while strengthening efforts toward transmission interruption. Continued investment in surveillance, contact tracing, stigma reduction, and integrated neglected tropical disease (NTD) services will be essential to achieving zero transmission, zero disability, and zero discrimination by 2030. Full article

Background/Objectives: The aim of this study was to show a standardized four-step technique that can offer individually personalized full-mouth therapy for each complex dental patient with erosive tooth wear resulting from bulimia nervosa, focusing on the individualized vertical dimension of occlusion (VDO), functional and aesthetic stability, and patient-reported outcomes, including dental symptoms, nutrition, self-perception, and quality of life. Methods: The following steps are proposed for structured full-mouth rehabilitation. Step 1: Intraoral diagnosis via a single computer-aided impression. Step 2: Determination of a new adequate vertical dimension of occlusion and soft tissue prediction. Step 3: Removable sample dentures—prototypes. Step 4: Non-prep/minimal-prep crowns as the long-term provisional/definitive treatment. Results: Nine adults (11% male) with dental erosion caused by bulimia nervosa (78%), gastro-esophageal reflux (11%), and soft drinks (11%) were part of this cohort. The novel digital workflow enabled restoration of an individualized vertical dimension of occlusion, stable occlusion, appropriate centric and eccentric contacts, biomimetic dental anatomy, harmonious tooth proportions, and optimized red–white aesthetics. Dental problems (hypersensitivity, dental pain), nutritional behavior, body perception, and quality of life improved after the full-mouth rehabilitation. Conclusions: The presented digital workflow offers a promising approach for full-mouth rehabilitation in patients with severe dental erosion, particularly associated with bulimia nervosa, enabling structured restoration planning and stepwise evaluation of the vertical dimension of occlusion and functional adaptation. Prospective studies with larger cohorts are needed to confirm long-term clinical outcomes and patient-reported benefits. Full article