Search Results (42)

This paper presents a stochastic analysis of a two-unit cold standby system incorporating imperfect switching mechanisms. Each unit operates in one of three states: normal, partial failure, or total failure. Employing Markov processes, the study evaluates system reliability by examining the mean time to failure (MTTF) and steady-state availability metrics. Failure and repair times are assumed to follow exponential distributions, while the switching mechanism is modeled as either perfect or imperfect. The results highlight the significant influence of switching reliability on both MTTF and system availability. This analysis is crucial for optimizing the performance of complex systems, such as thermal power plants, where continuous and reliable operation is imperative. The study also aligns with recent research trends emphasizing the integration of preventive maintenance and advanced reliability modeling approaches to enhance overall system resilience. Full article

Industrial maintenance has shifted from reactive repairs and calendar-based servicing toward data-driven predictive strategies. This paper presents a non-intrusive, low-cost IoT hardware platform for sustainable predictive maintenance of rotating machinery. The system integrates an ESP32-S3 sensor node that captures vibration (100 kHz) and temperature data, performs local logging, and communicates wirelessly. An automated spectral band segmentation framework is introduced, comparing equal-energy, linear-width, nonlinear, clustering, and peak–valley partitioning methods, followed by a weighted feature scheme that emphasizes high-value bands. Three unsupervised one-class classifiers—transformer autoencoders, GANomaly, and Isolation Forest—are evaluated on these weighted spectral features. Experiments conducted on a custom pump test bench with controlled anomaly severities demonstrate strong anomaly classification performance across multiple configurations, supported by detailed threshold-characterization metrics. Among 150 model–segmentation configurations, 25 achieved perfect classification (100% precision, recall, and F1 score) with ROC-AUC = 1.0, 43 configurations achieved ≥90% accuracy, and the lowest-performing setup maintained 81.8% accuracy. The proposed end-to-end solution reduces the downtime, lowers maintenance costs, and extends the asset life, offering a scalable, predictive maintenance approach for diverse industrial settings. Full article

Background and Objectives: Penile tissue loss, which can be an acquired condition due to trauma or infection, but is also seen in congenital anomalies, is a rare condition in children. A standard surgical approach is often not possible due to the different degrees and etiologies of penile tissue loss. The continuing growth and the presence of various congenital anomalies in children require a different penile reconstruction approach than in adults. We aimed to share our experience and surgical results with children in whom we performed penile reconstruction with different techniques due to penile tissue loss. Materials and Methods: Ten cases that underwent penile reconstruction between 2018 and 2023 were evaluated retrospectively. Age at initial operation, associated anomalies, surgical technique, and other related surgical attempts, as well as functional and cosmetic results, were recorded. Results: Ten boys aged between 6 months and 17 years underwent phalloplasty due to penile tissue absence. In six cases, penile tissue loss was due to acquired causes, and in four cases, congenital anomalies were the reason. The most common cause of penile tissue loss was circumcision complications. In four cases, penile reconstruction was achieved by mobilization of the remaining corpus cavernosum tissues, in two cases, the cavernous tissue was adequate and repaired with glansplasty and penile skin graft. Phalloplasty was performed by tubularization of a skin and subcutaneous fat flap, removed from the pubic region and scrotal region, in two cases. A microvascular radial forearm flap was performed in a 17-year-old patient with penile tissue loss because of trauma, and a free skin flap taken from the forearm was used for penile reconstruction. Thirty percent of patients required a second surgery. Urinary continence was present in eight of the cases. Although four cases were classified as cosmetically unsatisfactory in our evaluation, all patients and their families reported being satisfied with the cosmetic results. Conclusions: Penile reconstruction for penile tissue loss in children should be performed in clinics where different scenarios can be applied. With maximum preservation and mobilization of existing cavernous tissues, temporary penile reconstruction with local flaps should be performed in young children at an early stage to minimize the psychological effects of penile absence. Although an esthetically perfect result cannot be guaranteed, patients and families are generally satisfied with the outcome. Full article

This paper explores how generative AI can enhance the modelling and optimization of maintenance policies by incorporating real-time problem-solving techniques into structured maintenance frameworks. Maintenance policies, evolving from simple calendar-dependent or age-dependent preventive maintenance strategies to more complex approaches involving partial system replacement, minimal repairs, or imperfect maintenance, have traditionally been optimized based on minimizing costs, maximizing reliability, and ensuring operational continuity. In this work, we leverage AI models to simulate and analyze the implementation and overlap of different maintenance strategies to an industrial asset, including the combined use of different preventive (total and partial replacement) and corrective actions (minimal repair and normal repairs), with perfect or imperfect maintenance results. Integrating generative AI with well-established maintenance policies and optimization criteria, this paper tries to demonstrate how AI-assisted tools can model maintenance scenarios dynamically, learning from predefined strategies and improving decision-making in real-time. Python-based simulations are employed to validate the approach, showcasing the benefits of using AI to enhance the flexibility and efficiency of maintenance policies. The results highlight the potential for AI to revolutionize maintenance optimization, particularly in single-unit systems, and lay the groundwork for future studies in multi-unit systems. Full article

Background/Objectives: Colorectal cancer (CRC) is the third leading cause of cancer death globally, with rising incidence. The immunohistochemistry (IHC) for mismatch repair (MMR) proteins is the first technique used in routine practice to evaluate an MMR status. Microsatellite instability (MSI) may be tested in case of doubt during IHC staining. This study introduces a novel high-resolution melt (HRM) protocol for MSI detection and compares it with traditional fragment length analysis (FLA) via capillary electrophoresis. Methods: A total of 100 formalin-fixed and paraffin-embedded CRC specimens were analyzed using two distinct protocols: one based on FLA (TrueMark MSI Assay kit) and another one based on HRM (AmoyDx^® Microsatellite Instability Detection Kit). Results: Overall, 68 (68.0%) of the cases were MSS, and 32 (32.0%) were MSI-H. HRM analysis was first successfully carried out in all the cases. A perfect concordance in MSI evaluation between HRM and FLA was observed. HRM showed slightly shorter hands-on time and turnaround time. Conclusions: We provided evidence of the validity of this new HRM approach in determining the MSI status of colorectal carcinomas. Full article

Background/Objectives: The accurate quantification of peripheral nerve axonal regeneration after injury is critically important. Current strategies are limited to detecting early reinnervation. DTI is an MRI modality permitting the assessment of fractional anisotropy, which increases with axonal regeneration. The aim of this pilot study is to evaluate DTI as a potential predictive factor of clinical outcome after median nerve section and microsurgical repair. Methods: We included 10 patients with a complete section of the median nerve, who underwent microsurgical repair up to 7 days after injury. The follow-up period was 1 year, including the current strategy with clinical visits, the Rosén–Lundborg score and electroneuromyography. Additionally, DTI MRI of the injured wrist was planned 1, 3 and 12 months post-operatively and once for the contralateral wrist. Results: The interobserver reliability of DTI measures was almost perfect (ICC 0.802). We report an early statistically significant increase in the fractional anisotropy value after median nerve repair, especially in the region located distal to the suture. Meanwhile, Rosén–Lundborg score gradually increased between the third and sixth month, and continued to increase between the sixth and twelfth month. Conclusions: DTI outcomes three months post-operation could offer greater predictability compared to current strategies. This would enable faster decision-making regarding the need for a potential re-operation in cases of inadequate early reinnervation. Full article

Background: The use of telerehabilitation after sports medicine procedures such as an arthroscopic rotator cuff repair (ARCR) and anterior cruciate ligament reconstruction (ACLR) has rapidly increased in recent years; however, the functional outcomes and patient satisfaction with telerehabilitation compared to in-person rehabilitation remain unclear. The purpose of this study was to compare the functional outcomes and patient satisfaction with telerehabilitation to in-person rehabilitation in a randomized controlled trial after two common sports procedures, ARCR and ACLR. Methods: Two randomized controlled trials were conducted involving patients scheduled to undergo ARCR or ACLR by one of six fellowship-trained sports medicine surgeons between October 2020 and November 2021. Each trial had an enrollment goal of 60 patients. Subjects were randomized 1:1 to receive telerehabilitation or in-person rehabilitation postoperatively. Functional outcome and satisfaction metrics were collected at baseline and at post-operative visits and compared between groups. Results: In total, 16 ACLR patients were enrolled, of whom 10 (62.5%) were assigned to in-person rehabilitation and 6 (37.5%) to telerehabilitation. Additionally, 32 ARCR patients were enrolled, of whom 20 (62.5%) were assigned in-person rehabilitation and 12 (37.5%) were assigned telerehabilitation. In total, of the 30 patients assigned to in-person rehabilitation, none reported a crossover to telerehabilitation. Of the 18 patients initially assigned to telerehabilitation, 12 (67%) completed the final follow-up survey, of which 11 (92%) reported a crossover; 9 patients completed in-person rehabilitation and 2 patients completed hybrid in-person and telerehabilitation. Conclusions: Patients preferred in-person rehabilitation compared to telerehabilitation after ACLR and ARCR, as evidenced by the nearly ubiquitous crossover from telerehabilitation to in-person rehabilitation in both studies. Our findings suggest that telerehabilitation protocols still need to be perfected, and that there may be a role for a hybrid in-person and tele-rehab model. Full article

In current software applications, numerous vulnerabilities may be present. Attackers attempt to exploit these vulnerabilities, leading to security breaches, unauthorized entry, data theft, or the incapacitation of computer systems. Instead of addressing software or hardware vulnerabilities at a later stage, it is better to address them immediately or during the development phase. Tools such as AIBugHunter provide solutions designed to tackle software issues by predicting, categorizing, and fixing coding vulnerabilities. Essentially, developers can see where their code is susceptible to attacks and obtain details about the nature and severity of these vulnerabilities. AIBugHunter incorporates VulRepair to detect and repair vulnerabilities. VulRepair currently predicts patches for vulnerable functions at 44%. To be truly effective, this number needs to be increased. This study examines VulRepair to see whether the 44% perfect prediction can be increased. VulRepair is based on T5 and uses both natural language and programming languages during its pretraining phase, along with byte pair encoding. T5 is a text-to-text transfer transformer model with an encoder and decoder as part of its neural network. It outperforms other models such as VRepair and CodeBERT. However, the hyperparameters may not be optimized due to the development of new optimizers. We reviewed a deep neural network (DNN) optimizer developed by Google in 2023. This optimizer, the Evolved Sign Momentum (LION), is available in PyTorch. We applied LION to VulRepair and tested its influence on the hyperparameters. After adjusting the hyperparameters, we obtained a 56% perfect prediction, which exceeds the value of the VulRepair report of 44%. This means that VulRepair can repair more vulnerabilities and avoid more attacks. As far as we know, our approach utilizing an alternative to AdamW, the standard optimizer, has not been previously applied to enhance VulRepair and similar models. Full article

In practical scenarios, such as in electronics, where barcodes on electronic component carriers often wear out, and in logistics, where package labels frequently get damaged, this type of damage makes the recognition of two-dimensional (2D) barcodes challenging. In this study, a new repair method was introduced for quick response (QR) and PDF417 codes. In addition, a structural fusion attention (SFA) mechanism with a recurrent feature reasoning network was integrated to enhance structural integrity and recognition rates. The proposed method significantly outperforms existing inpainting models in terms of accuracy and robustness, which is demonstrated by the custom dataset provided by the authors. Notably, the approach ensures near-perfect recognition rates despite extensive structural impairments. It achieves an accuracy of 98% for large-area PDF417 occlusions and maintains a recognition rate of 100% for QR codes with 75–90% structural damage. These findings highlight the exceptional ability of the proposed method to restore 2D barcodes impaired by diverse levels of structural occlusion. Full article

In recent years, the incidence of bone defects has been increasing year by year. Bone transplantation has become the most needed surgery after a blood transfusion and shows a rising trend. Three-dimensional-printed implants can be arbitrarily shaped according to the defects of tissues and organs to achieve perfect morphological repair, opening a new way for non-traumatic repair and functional reconstruction. In this paper, strontium-doped mineralized collagen was first prepared by an in vitro biomimetic mineralization method and then polylactic acid was homogeneously blended with the mineralized collagen to produce a comprehensive bone repair scaffold by a gas extrusion 3D printing method. Characterization through scanning electron microscopy, X-ray diffraction, and mechanical testing revealed that the strontium-functionalized composite scaffold exhibits an inorganic composition and nanostructure akin to those of human bone tissue. The scaffold possesses uniformly distributed and interconnected pores, with a compressive strength reaching 21.04 MPa. The strontium doping in the mineralized collagen improved the biocompatibility of the scaffold and inhibited the differentiation of osteoclasts to promote bone regeneration. This innovative composite scaffold holds significant promise in the field of bone tissue engineering, providing a forward-thinking solution for prospective bone injury repair. Full article

Additive manufacturing is used to produce parts with complex near-net shape geometries. It can also be used to repair parts that have worn out in service using specific processes such as Wire Arc Additive Manufacturing (WAAM) and Directed Energy Deposition (DED). Wire additive manufacturing processes allow for relatively high deposition rates compared to powder technologies but necessitate a stable welding process and a controlled heat input. As the perfect transfer mode for low welding energy and low spatter, Cold Metal Transfer (CMT) is the process of choice for WAAM. In this project, parts made from aluminium 4943 and 6061 were additively manufactured using CMT technology. The deposition rate, porosity level, and mechanical properties are discussed herein. For the 6061 alloy, after heat treatment, it is possible to attain properties that are close to those obtained for T6 for wrought products or even higher when samples are hot-isostatically pressed. Full article

The antimicrobial and antibiofilm properties of arginine-based surfactants have been evaluated. These two biological properties depend on both the alkyl chain length and the spacer chain nature. These gemini surfactants exhibit good activity against a wide range of bacteria, including some problematic resistant microorganisms such us methicillin-resistant Staphylococcus aureus (MRSA) and Pseudomonas aeruginosa. Moreover, surfactants with a C₁₀ alkyl chain and C₃ spacer inhibit the (MRSA) and Pseudomonas aeruginosa biofilm formation at concentrations as low as 8 µg/mL and are able to eradicate established biofilms of these two bacteria at 32 µg/mL. The inhibitory activities of the surfactants over key enzymes enrolled in the skin repairing processes (collagenase, elastase and hyaluronidase) were evaluated. They exhibited moderate anti-collagenase activity while the activity of hyaluronidase was boosted by the presence of these surfactants. These biological properties render these gemini arginine-based surfactants as perfect promising candidates for pharmaceutical and biological properties. Full article

The new biological interaction cross-section-based repairable–homologically repairable (RHR) damage formulation for radiation-induced cellular inactivation, repair, misrepair, and apoptosis was applied to optimize radiation therapy. This new formulation implies renewed thinking about biologically optimized radiation therapy, suggesting that most TP53 intact normal tissues are low-dose hypersensitive (LDHS) and low-dose apoptotic (LDA). This generates a fractionation window in LDHS normal tissues, indicating that the maximum dose to organs at risk should be ≤2.3 Gy/Fr, preferably of low LET. This calls for biologically optimized treatments using a few high tumor dose-intensity-modulated light ion beams, thereby avoiding secondary cancer risks and generating a real tumor cure without a caspase-3-induced accelerated tumor cell repopulation. Light ions with the lowest possible LET in normal tissues and high LET only in the tumor imply the use of the lightest ions, from lithium to boron. The high microscopic heterogeneity in the tumor will cause local microscopic cold spots; thus, in the last week of curative ion therapy, when there are few remaining viable tumor clonogens randomly spread in the target volume, the patient should preferably receive the last 10 GyE via low LET, ensuring perfect tumor coverage, a high cure probability, and a reduced risk for adverse normal tissue reactions. Interestingly, such an approach would also ensure a steeper rise in tumor cure probability and a higher complication-free cure, as the few remaining clonogens are often fairly well oxygenated, eliminating a shallower tumor response due to inherent ion beam heterogeneity. With the improved fractionation proposal, these approaches may improve the complication-free cure probability by about 10–25% or even more. Full article

Federated learning has attracted much attention in fault diagnosis since it can effectively protect data privacy. However, efficient fault diagnosis performance relies on the uninterrupted training of model parameters with massive amounts of perfect data. To solve the problems of model training difficulty and parameter negative transfer caused by data corruption, a novel cross-device fault diagnosis method based on repaired data is proposed. Specifically, the local model training link in each source client performs random forest regression fitting on the fault samples with missing fragments, and then the repaired data is used for network training. To avoid inpainting fragments to produce the wrong characteristics of faulty samples, joint domain discrepancy loss is introduced to correct the phenomenon of parameter bias during local model training. Considering the randomness of the overall performance change brought about by the local model update, an adaptive update is proposed for each round of global model download and local model update. Finally, the experimental verification was carried out in various industrial scenarios established by three sets of bearing data sets, and the effectiveness of the proposed method in terms of fault diagnosis performance and data privacy protection was verified by comparison with various currently popular federated transfer learning methods. Full article

High Dynamic Range (HDR) images are widely used in automotive, aerospace, AI, and other fields but are limited by the maximum dynamic range of a single data acquisition using CMOS image sensors. High dynamic range images are usually synthesized through multiple exposure techniques and image processing techniques. One of the most challenging task in multiframe Low Dynamic Range (LDR) images fusion for HDR is to eliminate ghosting artifacts caused by motion. In traditional algorithms, optical flow is generally used to align dynamic scenes before image fusion, which can achieve good results in cases of small-scale motion scenes but causes obvious ghosting artifacts when motion magnitude is large. Recently, attention mechanisms have been introduced during the alignment stage to enhance the network’s ability to remove ghosts. However, significant ghosting artifacts still occur in some scenarios with large-scale motion or oversaturated areas. We proposea novel Distilled Feature TransformerBlock (DFTB) structure to distill and re-extract information from deep image features obtained after U-Net downsampling, achieving ghost removal at the semantic level for HDR fusion. We introduce a Feature Distillation Transformer Block (FDTB), based on the Swin-Transformer and RFDB structure. FDTB uses multiple distillation connections to learn more discriminative feature representations. For the multiexposure moving scene image fusion HDR ghost removal task, in the previous method, the use of deep learning to remove the ghost effect in the composite image has been perfect, and it is almost difficult to observe the ghost residue of moving objects in the composite HDR image. The method in this paper focuses more on how to save the details of LDR image more completely after removing the ghost to synthesize high-quality HDR image. After using the proposed FDTB, the edge texture details of the synthesized HDR image are saved more perfectly, which shows that FDTB has a better effect in saving the details of image fusion. Futhermore, we propose a new depth framework based on DFTB for fusing and removing ghosts from deep image features, called TransU-Fusion. First of all, we use the encoder in U-Net to extract image features of different exposures and map them to different dimensional feature spaces. By utilizing the symmetry of the U-Net structure, we can ultimately output these feature images as original size HDR images. Then, we further fuse high-dimensional space features using Dilated Residual Dense Block (DRDB) to expand the receptive field, which is beneficial for repairing over-saturated regions. We use the transformer in DFTB to perform low-pass filtering on low-dimensional space features and interact with global information to remove ghosts. Finally, the processed features are merged and output as an HDR image without ghosting artifacts through the decoder. After testing on datasets and comparing with benchmark and state-of-the-art models, the results demonstrate our model’s excellent information fusion ability and stronger ghost removal capability. Full article