Search Results (13,208)

Background: Advances in breast reconstruction have transformed the recovery pathway for women undergoing mastectomy. What was once viewed mainly as a cosmetic option is now recognized as part of modern oncologic care, restoring not only body image but also confidence and quality of life. Yet, surgeons still face the same central dilemma: choosing between implant-based (IBR) and autologous reconstruction (ABR), particularly when postmastectomy radiotherapy (PMRT) is planned. Methods: We reviewed major studies published between 2014 and 2024, combining evidence from observational cohorts and recent meta-analyses that together report on more than 60,000 reconstructed breasts. Outcomes of interest included surgical complications, reconstructive failure, BREAST-Q patient-reported domains, and the impact of PMRT on both techniques. Data were interpreted in light of contemporary reconstructive innovations such as prepectoral implants, acellular dermal matrices, and robotic or sensory-nerve–enhanced autologous procedures. Results: Autologous reconstruction generally provided higher satisfaction and better psychosocial and sexual well-being, particularly in patients who received PMRT. Implant-based reconstruction offered faster recovery and shorter hospitalization but was more vulnerable to capsular contracture and reconstructive loss after irradiation. Across all eligible cohorts, reconstruction—immediate or delayed—did not increase local recurrence or compromise overall survival when adjuvant therapy was delivered without delay. Conclusions: Both IBR and ABR are oncologically safe and contribute meaningfully to recovery after mastectomy. Future progress will depend on combining precise surgical execution with new technologies—prepectoral implant positioning, robotic flap harvest, and sensory nerve coaptation—to achieve durable, natural, and patient-centered reconstruction. Full article

Flywheel energy storage systems play an important role in frequency regulation and power quality control within modern power grids, yet the fault signals generated by defects in their rolling bearings are typically indistinct, making direct diagnosis difficult. Raw noisy signals often yield unsatisfactory diagnostic performance when directly processed by neural networks. Although MOMEDA (Multipoint Optimal Minimum Entropy Deconvolution Adjusted) can effectively extract impulsive fault components, its performance is highly dependent on the selected fault period and filter length. To address these issues, this paper proposes an improved fault diagnosis method that integrates MOMEDA-based periodic extraction with a neural network classifier. The Artificial Fish Swarm Algorithm (AFSA) is employed to adaptively determine the key parameters of MOMEDA using multi-point kurtosis as the optimization objective, and the optimized parameters are used to enhance impulsive fault features. The filtered signals are then converted into image representations and fed into a ResNet-18 network (a compact 18-layer deep convolutional neural network from the residual network family) to achieve intelligent identification and classification of bearing faults. Experimental results demonstrate that the proposed method can effectively extract and diagnose bearing fault signals. Full article

In selective laser melting (SLM), real-time visual inspection of powder spreading quality is essential for maintaining dimensional accuracy and mechanical performance. However, reflections from metallic chamber walls introduce non-uniform illumination and reduce local contrast, hindering reliable defect detection. To overcome this problem, a chamber-reflection-aware image enhancement method is proposed, integrating a physical reflection model with a dual-channel deep network. A Gaussian-based curved-surface reflection model is first developed to describe the spatial distribution of reflective interference. The enhancement network then processes the input through two complementary channels: a Retinex-based branch to extract illumination-invariant reflectance components and a principal components analysis (PCA)-based branch to preserve structural information. Furthermore, a noise-aware loss function is designed to suppress the mixed Gaussian–Poisson noise that is inherent in SLM imaging. Experiments conducted on real SLM monitoring data demonstrate that the proposed method significantly improves contrast and defect visibility, outperforming existing enhancement algorithms in peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and natural image quality evaluator (NIQE). The approach provides a physically interpretable and robust preprocessing framework for online SLM quality monitoring. Full article

Synthetic aperture radar (SAR), with its all-weather and all-day observation capabilities, plays a significant role in the field of remote sensing. However, due to the unique imaging mechanism of SAR, its interpretation is challenging. Translating SAR images into optical remote sensing images has become a research hotspot in recent years to enhance the interpretability of SAR images. This paper proposes a deep learning-based method for SAR-to-optical remote sensing image translation. The network comprises three parts: a global representor, a generator with cascaded multi-head attention, and a multi-scale discriminator. The global representor, built upon InternImage with deformable convolution v3 (DCNv3) as its core operator, leverages its global receptive field and adaptive spatial aggregation capabilities to extract global semantic features from SAR images. The generator follows the classic “encoder-bottleneck-decoder” structure, where the encoder focuses on extracting local detail features from SAR images. The cascaded multi-head attention module within the bottleneck layer optimizes local detail features and facilitates feature interaction between global semantics and local details. The discriminator adopts a multi-scale structure based on the local receptive field PatchGAN, enabling joint global and local discrimination. Furthermore, for the first time in SAR image translation tasks, structural similarity index metric (SSIM) loss is combined with adversarial loss, perceptual loss, and feature matching loss as the loss function. A series of experiments demonstrate the effectiveness and reliability of the proposed method. Compared to mainstream image translation methods, our method ultimately generates higher-quality optical remote sensing images that are semantically consistent, texturally authentic, clearly detailed, and visually reasonable appearances. Full article

This study presents the digitization and integration of Kazakhstan’s legacy gravimetric maps at a scale of 1:200,000 into a modern geospatial database using ArcGIS. The primary objective was to convert analog gravity data into a structured, queryable, and spatially analyzable digital format to support contemporary geoscientific applications, including geoid modeling and regional geophysical analysis. The project addresses critical gaps in national gravity coverage, particularly in underrepresented regions such as the Caspian Sea basin and the northeastern frontier, thereby enhancing the accessibility and utility of gravity data for multidisciplinary research. The methodology involved a systematic workflow: assessment and selection of gravimetric maps, raster image enhancement, georeferencing, and digitization of observation points and anomaly values. Elevation data and terrain corrections were incorporated where available, and metadata fields were populated with information on the methods and accuracy of elevation determination. Gravity anomalies were recalculated, including Bouguer anomalies (with densities of 2.67 g/cm³ and 2.30 g/cm³), normal gravity, and free-air anomalies. A unified ArcGIS geodatabase was developed, containing spatial and attribute data for all digitized surveys. The final deliverables include a 1:1,000,000-scale gravimetric map of free-air gravity anomalies for the entire territory of Kazakhstan, a comprehensive technical report, and supporting cartographic products. The project adhered to national and international geophysical mapping standards and utilized validated interpolation and error estimation techniques to ensure data quality. The validation process by the modern gravimetric surveys also confirmed the validity and reliability of the digitized historical data. This digitization effort significantly modernizes Kazakhstan’s gravimetric infrastructure, providing a robust foundation for geoid modeling, tectonic studies, and resource exploration. Full article

Background/Objectives: Thyroidectomy is a common endocrine procedure associated with postoperative musculoskeletal symptoms such as neck stiffness, pain, and reduced cervical mobility. These sequelae, though often underrecognized, can impair recovery and quality of life. Rehabilitation strategies, including stretching, manual therapy, and kinesio taping, have emerged as potential adjuncts to enhance postoperative outcomes. This scoping review aimed to map and synthesize current evidence on postoperative rehabilitation interventions following thyroidectomy, focusing on stretching exercises, manual therapy, and kinesio taping. Methods: Following the Joanna Briggs Institute methodology and PRISMA-ScR guidelines, a comprehensive search identified studies evaluating physical therapy interventions in adult thyroidectomy patients. Fourteen studies published between 2005 and 2025 met the inclusion criteria, encompassing randomized trials, quasi-experimental designs, and one retrospective cohort study. Interventions were delivered in early postoperative settings and included supervised or home-based programs. Results: Neck stretching and range-of-motion exercises consistently demonstrated benefits in pain reduction, cervical mobility, and functional recovery. These low-cost interventions were feasible for early implementation and continuation post-discharge. Evidence for kinesio taping was mixed, with some studies reporting short-term symptom relief and others showing no significant effect. Manual therapy, assessed in a single large cohort, showed promise when combined with stretching, though its independent efficacy remains unclear. Conclusions: Structured rehabilitation—particularly stretching and mobility exercises—may enhance recovery after thyroidectomy. Kinesio taping and manual therapy appear beneficial as adjunctive measures but require further validation. The findings underscore the need for standardized protocols and high-quality trials to optimize postoperative care and long-term outcomes. Full article

Gears, as critical components of rotating machinery, are prone to wear and fracture due to their complex structural dynamics and harsh operating conditions, leading to catastrophic failures, economic losses, and safety risks. AE technology enables real-time fault diagnosis by capturing stress wave emissions from material defects with high sensitivity. However, mechanical background noise significantly corrupts AE signals, while optimal selection of gear health indicators remains challenging, critically impacting fault feature extraction accuracy. This study develops an adaptive feature extraction method for fault diagnosis using AE. Through gear fault simulation experiments, VMD analyzes mode number and penalty factor effects on signal decomposition. Correlation coefficient-based reconstruction optimization is implemented. For feature selection challenges, SVM-RFE enables adaptive parameter ranking. Finally, SVM with optimized kernel parameters achieves effective fault classification. Optimized VMD enhances signal decomposition, while SVM-RFE reduces feature dimensionality, addressing manual selection uncertainty and computational redundancy. Experimental results demonstrate superior accuracy in gear fault classification. This study proposes an AE-based adaptive feature extraction method with three innovations: (1) establishing VMD parameter–decomposition quality relationships; (2) developing an SVM-RFE feature selection framework; (3) achieving high-accuracy gear fault classification. The method provides a novel technical approach for rotating machinery diagnostics with significant engineering value. Full article

A reliable and data-based teaching quality evaluation is essential for the continuous improvement of higher-education systems. However, the inherent ambiguity of assessment indicators and the subjectivity of evaluators render traditional, crisp-value models insufficient. To address this challenge, we develop a novel intuitionistic fuzzy multi-attribute decision-making framework that integrates Yager triangular norms (t-norms) with the geometric Heronian mean. Specifically, we first introduce intuitionistic fuzzy operations based on Yager t-norms and Yager t-conorms and subsequently construct two aggregation operators: the intuitionistic fuzzy geometric Heronian mean operators and the intuitionistic fuzzy weighted geometric Heronian mean operators. The idempotency, monotonicity, and boundedness properties of these operators are formally proven. Next, the intuitionistic fuzzy weighted geometric Heronian mean operators are employed to develop an approach for multi-attribute decision-making in classroom teaching quality evaluation under intuitionistic fuzzy information. Moreover, an application case study of teaching quality evaluation in an intuitionistic fuzzy environment is presented to demonstrate the practicality and effectiveness of the proposed approach. Additionally, sensitivity and comparative analyses with other techniques are carried out to further confirm the coherence and superiority of the recommended approach. The research results clearly show that our proposed method is highly effective in accurately evaluating teaching quality and can serve as a valuable tool for educational institutions in enhancing their teaching quality management. Full article

Potato (Solanum tuberosum) is an important crop globally, being a starchy, energy-dense food source rich in several micronutrients and bioactive compounds. Achieving food security for everyone is highly challenging in the context of growing populations and climate change. As a highly adaptable crop, potatoes can significantly contribute to food security for vulnerable populations and have outstanding commercial relevance. Specific pre- and post-harvest parameters influence potato quality. It is vital to understand how these factors interact to shape potato quality, minimizing post-harvest losses, ensuring consumer safety, and enhancing marketability. This review highlights how pre-harvest (cultivation approaches, agronomic conditions, biotic and abiotic stresses) and post-harvest factors impact tuber’s microbial stability, physiological behaviour, nutritional, functional attributes and frying quality. Quality parameters, such as moisture content, dry matter, starch, sugar, protein, antioxidants, and color, are typically measured using both traditional and modern assessment methods. However, advanced non-destructive techniques, such as imaging and spectroscopy, enable rapid, high-throughput quality inspection from the field to storage. This review integrates recent advancements and specific findings to identify factors that contribute to substantial quality degradation or enhancement, as well as current challenges. It also examines how pre- and post-harvest factors collectively impact potato quality. It proposes future directions for quality maintenance and enhancement across the field and storage, highlighting research gaps in the pre- and post-harvest linkage. Full article

Mature soybean pods exhibit high homogeneity in color and texture relative to straw and dead leaves, and instances are often densely occluded, posing significant challenges for accurate field segmentation. To address these challenges, this paper constructs a high-quality field-based mature soybean dataset and proposes an adaptive Transformer-based network, PodFormer, to improve segmentation performance under homogeneous backgrounds, dense distributions, and severe occlusions. PodFormer integrates three core innovations: (1) the Adaptive Wavelet Detail Enhancement (AWDE) module, which strengthens high-frequency boundary cues to alleviate weak-boundary ambiguities; (2) the Density-Guided Query Initialization (DGQI) module, which injects scale and density priors to enhance instance detection in both sparse and densely clustered regions; and (3) the Mask Feedback Gated Refinement (MFGR) layer, which leverages mask confidence to adaptively refine query updates, enabling more accurate separation of adhered or occluded instances. Experimental results show that PodFormer achieves relative improvements of 6.7% and 5.4% in mAP50 and mAP50-95, substantially outperforming state-of-the-art methods. It further demonstrates strong generalization capabilities on real-world field datasets and cross-domain wheat-ear datasets, thereby providing a reliable perception foundation for structural trait recognition in intelligent soybean harvesting systems. Full article

Logistics involves planning and managing resources to meet customer demands. Its effectiveness depends not only on time and process coordination but also on the performance of logistics operators, whose actions directly affect customer satisfaction. Although operational risks are inherent to logistics, customer-oriented service failures are often overlooked in traditional risk assessment. To address this gap, this study proposes an integrated approach that combines a Pareto Diagram and Failure Mode and Effects Analysis (FMEA) within the ISO 31000 risk assessment framework. This structured method enables the identification and prioritization of logistics failures based on customer complaints, thereby supporting data-driven decision-making and continuous service improvement. Applied to a real-world case in a ceramic production line specializing in tableware manufacturing, the method identified and evaluated key logistics failures; particularly those related to late deliveries and damaged goods. Based on these findings, improvement actions were proposed to reduce the recurrence of these issues. This study contributes a structured, practical, and replicable approach for organizations to introduce risk assessment practices and enhance the service quality of logistics management. This study advances the literature by shifting the focus from internal production failures to customer-driven service risks, offering strategic insights for improving reliability and operational performance. Full article

(1) Background: Oat (Avena sativa L.) is a crop that is widely used in human nutrition, while it also plays an important role in animal husbandry as a high-quality forage crop. However, this crop is particularly susceptible to combined biotic stressors, including insect pests (Agriotes lineatus) and fungal infections (Fusarium spp.). These stresses act synergistically: root damage caused by wireworms increases the plant’s susceptibility to fungal infection, while pathogens further limit nutrient uptake and root system development. In recent years, the reduced efficacy of chemical pesticides against both insect pests and fungal pathogens has highlighted the need for alternative strategies in oat protection, leading to an increased focus on developing bacterial bio-inoculants as sustainable and effective biocontrol agents. (2) Methods: This study aimed to identify bacterial strains capable of suppressing wireworms (Agriotes lineatus) and Fusarium spp. in oats, while simultaneously promoting plant growth. Bacterial isolates were screened for key Plant Growth Promoting (PGP) and biocontrol traits, including IAA and siderophore production, phosphate solubilization, and the presence of toxin- and antibiotic-coding genes. (3) Results: The highest insecticidal effect against wireworms was recorded for Bacillus velezensis BHC 3.1 (63.33%), while this isolate also suppressed the growth of F. proliferatum for 59%, F. oxysporum for 65%, F. poae for 71%, and F. graminearum for 15%. The most effective Bacillus strains (with insecticidal and antifungal activity) were identified and tested in two pot experiments, where their ability to enhance plant growth in the presence of insects and fungi was evaluated under semi-controlled conditions. An increase in plant biomass, grain yield, and nitrogen content was observed in oat inoculated with B. velezensis BHC 3.1 and B. thuringiensis BHC 2.4. (4) Conclusions: These results demonstrate the strong potential of both strains as multifunctional bio-inoculants for enhancing oat growth and mitigating the adverse effects of wireworm damage and Fusarium infection. Full article

Seaweeds are emerging renewable biomass resources rich in valuable phytochemicals; however, effective stabilization strategies are required to enable their incorporation into sustainable food and bioprocessing applications. This study investigated the effects of convective drying (40–80 °C) and freeze-drying on the chemical composition and functional properties of the underexplored red seaweed Sarcodiotheca gaudichaudii. The drying method significantly modulated nutrient retention, pigment stability, and bioactivity. Freeze-drying and high-temperature convective drying (≥70 °C) resulted in higher protein and saturated fatty acid contents but led to substantial losses of pigments and antioxidant capacity. In contrast, moderate convective drying (40–60 °C) favored the retention of minerals, polyunsaturated fatty acids, essential amino acids, and pigments, while enhancing total phenolic and flavonoid contents and improving antioxidant performance (DPPH and ORAC). All extracts exhibited dose-dependent α-glucosidase inhibition (25–58%) within a concentration range of 0.10–40.0 mg/mL, with freeze-dried samples showing the strongest inhibitory effect. Similarly, cytotoxicity assays conducted on A549 and AGS cancer cell lines at concentrations of 1.25–40.0 mg/mL revealed that freeze-dried extracts consistently displayed the lowest IC₅₀ values. Overall, convective drying better preserved nutritional quality, whereas freeze-drying maintained higher biological functionality, revealing a process-dependent trade-off relevant to industrial biomass stabilization and functional ingredient development. Full article

Background/Objectives: Prolonged fasting—defined as voluntary abstinence from caloric intake for periods exceeding 24 h—is increasingly recognized not only as a metabolic intervention but also as a psycho-behavioral modulator. According to the 2024 international consensus, intermittent fasting encompasses diverse temporal patterns including time-restricted feeding, alternate-day fasting, and periodic fasting of multi-day duration. While metabolic benefits are well documented, the psychoneurobiological and psychiatric consequences remain incompletely characterized. This review critically appraises current evidence on the psychological and psychiatric effects of prolonged and intermittent fasting, including both secular and religious practices. Methods: A narrative synthesis was conducted on clinical trials, observational studies, and translational research published between January 2010 and June 2025 in PubMed, Scopus, and PsycINFO. Search terms included combinations of “prolonged fasting,” “intermittent fasting,” “psychological,” “psychiatric,” “religious fasting,” “Ramadan,” and “Orthodox Church.” Eligible studies required explicit evaluation of mood, cognition, stress physiology, or psychiatric symptoms. Data were analyzed qualitatively, with particular attention to study quality, fasting regimen characteristics, and participant vulnerability. This is a non-registered narrative synthesis drawing on clinical trials, observational studies, and preclinical evidence published between January 2010 and June 2025. Results: Eighty-seven studies met inclusion criteria (39 human; 48 preclinical). In metabolically healthy adults, short-term time-restricted eating and supervised prolonged fasting were associated with modest reductions in depressive symptoms and perceived stress, with small improvements in executive functioning—typically observed in small samples and with limited follow-up. Religious fasting during Ramadan and the Orthodox Christian fasting periods demonstrated similar neuropsychological effects, including greater perceived spiritual meaning and affective modulation, though cultural context played a moderating role. Potential adverse mental-health impacts included mood destabilization, anxiety exacerbation, and rare psychotic or manic decompensations in vulnerable individuals. Randomized trials reported few adverse events and no signal for severe psychiatric harm, whereas observational studies more often noted symptom exacerbations in at-risk groups. Patients with eating disorder phenotypes exhibited increased cognitive preoccupation with food and a heightened risk of behavioral relapse. Methodological heterogeneity across studies—including variation in fasting protocols, psychological assessments, and follow-up duration—limited cross-study comparability. Conclusions: Evidence indicates a bidirectional relationship wherein fasting may foster psychological resilience in select populations while posing significant psychiatric risks in others. Inclusion of religious fasting traditions enriches understanding of culturally mediated outcomes. To enhance rigor and safety, future studies should incorporate clinician-rated outcomes (e.g., HDRS-17, CGI-S/CGI-I), standardized adverse-event tracking using validated psychiatric terminology, and prospective safety monitoring protocols, with ≥6–12-month follow-up. Full article

With the accelerated expansion of the Internet of Things (IoT), massive distributed and heterogeneous devices are increasingly exposed to severe security threats. Traditional centralized intrusion detection systems (IDS) suffer from significant limitations in terms of privacy preservation and communication overhead. Federated Learning (FL) offers an effective paradigm for building the next generation of distributed IDS; however, it remains vulnerable to poisoning attacks in open environments, and existing client selection strategies generally lack robustness and security awareness. To address these challenges, this paper proposes an Uncertainty-Guided Evolutionary Game-Theoretic (UEGT) Client Selection mechanism. Built upon evolutionary game theory, UEGT integrates Shapley value, gradient similarity, and data quality to construct a multidimensional payoff function and employs a replicator dynamics mechanism to adaptively optimize client participation probabilities. Furthermore, uncertainty modeling is introduced to enhance strategic exploration and improve the identification accuracy of potentially high-value clients. Experimental results under adversarial scenarios demonstrate that UEGT maintains stable convergence even under a high fraction of malicious participating clients, achieving an average accuracy exceeding 89%, which outperforms several mainstream client selection and robust aggregation methods. Full article