Search Results (277)

Early and accurate detection of mechanical damage in prunes is crucial for preserving postharvest quality and enabling automated sorting. This study proposes a practical and reproducible method for identifying cumulative bruising in prunes using visible–near-infrared (Vis–NIR) reflectance spectroscopy coupled with machine learning techniques. A self-developed impact simulation device was designed to induce progressive damage under controlled energy levels, simulating realistic postharvest handling conditions. Spectral data were collected from the equatorial region of each fruit and processed using a hybrid modeling framework comprising continuous wavelet transform (CWT) for spectral enhancement, uninformative variable elimination (UVE) for optimal wavelength selection, and support vector machine (SVM) for classification. The proposed CWT-UVE-SVM model achieved an overall classification accuracy of 93.22%, successfully distinguishing intact, mildly bruised, and cumulatively damaged samples. Notably, the results revealed nonlinear reflectance variations in the near-infrared region associated with repeated low-energy impacts, highlighting the capacity of spectral response patterns to capture progressive physiological changes. This research not only advances nondestructive detection methods for prune grading but also provides a scalable modeling strategy for cumulative mechanical damage assessment in soft horticultural products. Full article

Graph theory models relationships by representing entities as vertices and their interactionsas edges. To handle directionality and multiple head–tail assignments, various extensions—directed, bidirected, and multidirected graphs—have been introduced, with the multidirected graph unifying the first two. In this work, we further enrich this landscape by proposing the Multidirected hypergraph, which merges the flexibility of hypergraphs and superhypergraphs to describe higher-order and hierarchical connections. Building on this, we introduce five uncertainty-aware Multidirected frameworks—fuzzy, neutrosophic, plithogenic, rough, and soft multidirected graphs—by embedding classical uncertainty models into the Multidirected setting. We outline their formal definitions, examine key structural properties, and illustrate each with examples, thereby laying groundwork for future advances in uncertain graph analysis and decision-making. Full article

Industrial production often involves complex time-varying operating conditions that result in continuous time-series production data. The traditional soft sensor approach has difficulty adjusting to such dynamic changes, which makes model performance less optimal. Furthermore, online analytical systems have significant operational and maintenance costs and entail a substantial delay in measurement output, limiting their ability to provide real-time control. In order to deal with these challenges, this paper introduces a multivariate multi-step predictive multilayer perceptron regression soft-sensing model, referred to as incremental MVMS-MLP. This model incorporates incremental learning strategies to enhance its adaptability and accuracy in multivariate predictions. As part of the method, a pre-trained MVMS-MLP model is developed, which integrates multivariate multi-step prediction with MLP regression to handle temporal data. Through the use of incremental learning, an incremental MVMS-MLP model is constructed from this pre-trained model. The effectiveness of the proposed method is demonstrated by benchmark problems and real-world industrial case studies. Full article

With the development of modern power systems, AC distribution networks face increasing demands for supply flexibility and reliability. Energy storage-based soft open points (E-SOPs), which integrate energy storage systems into the DC side of traditional SOP connecting AC distribution networks, not only maintain power flow control capabilities but also enhance system supply performance, providing a novel approach to AC distribution network fault recovery. To fully leverage the advantages of E-SOPs in handling faults in flexible interconnected AC distribution networks (FIDNs), this paper proposes an E-SOP-based FIDN islanding recovery method. First, the basic structure and control modes of SOPs for AC distribution networks are elaborated, and the E-SOP-based AC distribution network structure is analyzed. Second, with maximizing total load recovery as the objective function, the constraints of E-SOPs are comprehensively considered, and recovery priorities are established based on load importance classification. Then, a multi-dimensional improvement of the dung beetle optimizer (DBO) algorithm is implemented through Logistic chaotic mapping, adaptive parameter adjustment, elite learning mechanisms, and local search strategies, resulting in an efficient solution for AC distribution network power supply restoration. Finally, the proposed FIDN islanding partitioning and fault recovery methods are validated on a double-ended AC distribution network structure. Simulation results demonstrate that the improved DBO (IDBO) algorithm exhibits a superior optimization performance and the proposed method effectively enhances the load recovery capability of AC distribution networks, significantly improving the self-healing ability and operational reliability of AC distribution systems. Full article

Background: The World Health Organization defines myopia as a global epidemic. Its growing prevalence and the increasingly early age onset all raise a major concern for public health due to the elevated risk of loss and deterioration of visual function as a result of myopia-related ocular pathological complications. However, it remains unclear whether the vision-related quality of life of patients with myopia is the same as in healthy individuals. The aim of the present study is to assess the quality of life in children and adolescents with myopia between the ages of 8 and 16 years, who underwent observation at USBOBAL-Varna. Methods: This study prospectively included 190 patients with myopia between −1.00 and −5.50 D, corrected with different optical aids. After a thorough physical ocular examination and inquiry into the best visual acuity with and without distance correction, specially designed questionnaires were completed by the patients and their parents/guardians for the purpose of the study. The data from the questionnaires was statistically processed. The mean age of the patients in the study was 11.65 years, 101 were female and 89 were male. Of these, 83 wore monofocal glasses, 50 were monofocal and 47 were multifocal contact lenses, and 10 wore ortho-K lenses. Results: No significant difference in best corrected visual acuity (BCVA) was found among the three types of optical correction (p-value > 0.05). Cronbach’s alpha of the questionnaire for all 10 factors was higher than 0.6, indicating acceptable internal consistency. Significantly higher scores were reported for overall, near, and distance vision, symptoms, appearance, attitude, activities and hobbies, handling, and perception for soft contact lens wearers than for spectacle wearers (p-value < 0.05). Ortho-K wearers performed better than spectacle wearers in all aspects except for pronounced symptoms (p = 0.74). No significant difference was found between ortho-K wearers and soft contact lens wearers for any factor (p > 0.05). Conclusions: Patients wearing spectacles and with myopia above −5.00 D had the highest anxiety scores and lower quality of life among all myopic participants. The research on the quality of life in children with myopia with different refractive errors and optical correction devices is crucial for improving corrective devices and meeting the needs of patients. Full article

This study presents the design, fabrication, and experimental validation of a two-finger robotic gripper featuring a 135° V-shaped fingertip profile tailored for lightweight waste collection in laboratory-scale environmental robotics. The gripper was developed with a strong emphasis on cost-effectiveness and manufacturability, utilizing a desktop 3D printer and off-the-shelf servomotors. A four-bar linkage mechanism enables parallel jaw motion and ensures stable surface contact during grasping, achieving a maximum opening range of 71.5 mm to accommodate common cylindrical objects. To validate structural integrity, finite element analysis (FEA) was conducted under a 0.6 kg load, yielding a safety factor of 3.5 and a peak von Mises stress of 12.75 MPa—well below the material yield limit of PLA. Experimental testing demonstrated grasp success rates of up to 80 percent for typical waste items, including bottles, disposable cups, and plastic bags. While the gripper performs reliably with rigid and semi-rigid objects, further improvements are needed for handling highly deformable materials such as thin films or soft bags. The proposed design offers significant advantages in terms of rapid prototyping (a print time of approximately 10 h), modularity, and low manufacturing cost (with an estimated in-house material cost of USD 20 to 40). It provides a practical and accessible solution for small-scale robotic waste-collection tasks and serves as a foundation for future developments in affordable, application-specific grippers. Full article

Background: Resorbable biopolymers are increasingly explored for use in regenerative procedures within dental surgery. Their ability to degrade naturally, minimize surgical reinterventions, and potentially reduce immunogenicity makes them appealing in guided bone and tissue regeneration applications. However, despite these advantages, uncertainties persist regarding their comparative effectiveness and associated risks. For example, polyethylene glycol (PEG)-based membranes have shown comparable outcomes to porcine-derived collagen membranes in bone regeneration procedures, yet studies have reported a higher incidence of soft tissue healing complications associated with PEG-based materials. Similarly, while polycaprolactone (PCL) and dextrin-based hydrogels have demonstrated promising clinical handling and bone fill capabilities, their long-term performance and consistency across different anatomical sites remain under investigation. These findings highlight the need for further well-powered clinical trials to establish standardized guidelines for their safe and effective use. Methods: A systematic review protocol was registered with the PROSPERO database and developed in alignment with PRISMA guidelines. Database searches were conducted in PubMed, Medline, Scopus, and Cochrane from June to December 2024. Only randomized controlled trials (RCTs) focusing on synthetic resorbable biopolymers in bone augmentation procedures were considered. Bias was evaluated using the Cochrane Risk of Bias tool. Results: Eleven RCTs were included, totaling 188 patients. The findings suggest that materials such as polylactic acid (PLA), polycaprolactone (PCL), and polyethylene glycol (PEG) contributed effectively to new bone formation. PEG-based membranes were found to perform on par with or occasionally better than traditional collagen membranes derived from porcine sources. Additionally, the application of 3D-printable polymers demonstrated promise in site-specific healing. Conclusions: Resorbable biopolymers are effective and safe for GBR procedures, with clinical outcomes comparable to traditional materials. Advances in 3D-printing technology and bioactive coatings may further enhance their regenerative potential. However, the incidence of soft tissue healing complications suggests the need for further long-term studies to optimize material properties and clinical application. Full article

This study investigates the full penetration simulation of piles from the ground surface, focusing on frictional contact modeling without mesh distortion. To overcome issues related to mesh distortion and improve solution convergence, the Arbitrary Lagrangian–Eulerian (ALE) adaptive mesh technique was implemented within both explicit and implicit time integration schemes. The numerical model was validated against field experiments conducted at Bothkennar, Scotland, using the Imperial College instrumented displacement pile (ICP) in soft clay, where the soil behavior was effectively represented using the modified Cam-Clay model and the Mohr–Coulomb model. The primary objectives of this study are to evaluate the ALE method performance in handling mesh distortion; analyze the effects of soil–pile interface friction, pile dimensions, and various dilation angles on pile resistance; and compare the effectiveness of explicit and implicit time integration schemes in terms of stability, computational efficiency, and solution accuracy. The ALE method effectively modeled pile penetration in Bothkennar clay, validating the numerical model against field experiments. Comparative analysis revealed the explicit time integration method as more robust and computationally efficient, particularly for complex soil–pile interactions with higher friction coefficients. Full article

Inverter-based resources (IBRs) are becoming more prominent due to the increasing penetration of renewable energy sources that reduce power system inertia, compromising power system stability and grid support services. At present, optimal coordination among generation technologies remains a significant challenge for frequency control services. This paper presents a novel networked multi-agent deep reinforcement learning (N—MADRL) scheme for optimal dispatch and frequency control services. First, we develop a model-free environment consisting of a photovoltaic (PV) plant, a wind plant (WP), and an energy storage system (ESS) plant. The proposed framework uses a combination of multi-agent actor-critic (MAAC) and soft actor-critic (SAC) schemes for optimal dispatch of active power, mitigating frequency deviations, aiding reserve capacity management, and improving energy balancing. Second, frequency stability and optimal dispatch are formulated in the N—MADRL framework using the physical constraints under a dynamic simulation environment. Third, a decentralised coordinated control scheme is implemented in the HPP environment using communication-resilient scenarios to address system vulnerabilities. Finally, the practicality of the N—MADRL approach is demonstrated in a Grid2Op dynamic simulation environment for optimal dispatch, energy reserve management, and frequency control. Results demonstrated on the IEEE 14 bus network show that compared to PPO and DDPG, N—MADRL achieves 42.10% and 61.40% higher efficiency for optimal dispatch, along with improvements of 68.30% and 74.48% in mitigating frequency deviations, respectively. The proposed approach outperforms existing methods under partially, fully, and randomly connected scenarios by effectively handling uncertainties, system intermittency, and communication resiliency. Full article

This study introduces a novel magnetorheological (MR) damper for semi-active vehicle suspension systems that enhance ride comfort and handling stability. The proposed damper integrates reverse and normal damping modes, enabling independent control of rebound and compression strokes through an external MR valve. This configuration supports four damping modes—Soft/Soft, Hard/Soft, Soft/Hard, and Hard/Hard—allowing adaptability to varying driving conditions. Magnetic circuit optimization ensures rapid damping force adjustments (≈10 ms), while a semi-active control algorithm incorporating skyhook logic, roll, dive, and squat control strategies was implemented. Experimental validation on a mid-sized sedan demonstrated significant improvements, including a 30–40% reduction in vertical acceleration and pitch/roll rates. These enhancements improve vehicle safety by reducing body motion during critical maneuvers, potentially lowering accident risk and driver fatigue. In addition to performance gains, the simplified MR damper architecture and modular control facilitate easier integration into diverse vehicle platforms, potentially streamlining vehicle design and manufacturing processes and enabling cost-effective adoption in mass-market applications. These findings highlight the potential of MR dampers to support next-generation vehicle architectures with enhanced adaptability and manufacturability. Full article

Soft robotics represents a transformative approach to automation, focusing on the development of robots constructed from flexible, compliant materials that mimic biological systems. Being different from traditional rigid robots, soft robots are engineered to adapt and operate efficiently in complex, unstructured environments, making them highly appropriate for applications that require delicate manipulation, safe human–robot interaction, and mobility on unstable terrain. The key principles, materials, and fabrication techniques of soft robotics are explored in this study, highlighting their versatility in industries such as healthcare, agriculture, and search-and-rescue operations. The essence of soft robotic systems lies in their ability to deform and respond to environmental stimuli. The system enables new paradigms in automation for tasks that demand flexibility, such as handling fragile objects, navigating narrow spaces, or interacting with humans. Emerging materials, such as elastomers, hydrogels, and shape-memory alloys, are driving innovations in actuation and sensing mechanisms, expanding the capabilities of soft robots in applications. We also examine the challenges associated with the control and energy efficiency of soft robots, as well as opportunities for integrating artificial intelligence and advanced sensing to enhance autonomous decision-making. Through case studies and experimental data, the potential of soft robotics is reviewed to revolutionize sectors requiring adaptive automation, ultimately contributing to safer, more efficient, and sustainable technological advancements than present robots. Full article

The separation of palladium from radioactive waste streams represents a critical aspect of the secure handling and disposal of such hazardous materials. Palladium, in addition to its radioactive nature, holds intrinsic value as a resource. Despite the urgency, prevailing adsorbents fall short in their ability to effectively separate palladium under highly acidic environments. To surmount this challenge, our research has pioneered the development of 1,3,5-tris(4-aminophenyl)benzene-2,5-Bis(methylthio)terephthalaldehyde COF (TAPB-BMTTPA-COF), a novel material distinguished by its remarkable stability and an abundance of sulfur-containing functional groups. Leveraging the pronounced affinity of the soft ligands’ nitrogen and sulfur within its molecular architecture, TAPB-BMTTPA-COF demonstrates an exceptional capability for the selective adsorption of palladium. Empirical evidence underscores the material’s swift adsorption kinetics, with equilibrium achieved in as little as ten minutes, and its broad tolerance to varying acidity levels ranging from 0.1 to 3 M HNO₃. Furthermore, TAPB-BMTTPA-COF boasts an impressive adsorption capacity, peaking at 343.6 mg/g, coupled with high selectivity in 13 interfering ions’ environment and the ability to be regenerated, making it a sustainable solution. Comprehensive analyses, including Fourier Transform Infrared Spectroscopy (FT-IR) and X-ray photoelectron spectroscopy (XPS), alongside Density Functional Theory (DFT) calculations, have corroborated the pivotal role played by densely packed nitrogen and sulfur active sites within the framework. These sites exhibit a robust affinity for Pd(II), which is the cornerstone of the material’s outstanding adsorption efficacy. The outcomes of this research underscore the immense potential of COFs endowed with resilient linkers and precisely engineered functional groups. Such COFs can adeptly capture metal ions with high selectivity, even in the face of severe environmental conditions, thereby paving the way for the more effective and environmentally responsible management of radioactive waste. Full article

Background/Objectives: Hospital readmissions within 30 days are a major challenge in general internal medicine (GIM), impacting patient outcomes and healthcare costs. This study aimed to develop and evaluate machine learning (ML) models for predicting 30-day readmissions in patients admitted under a GIM unit and to identify key predictors to guide targeted interventions. Methods: A prospective study was conducted on 443 patients admitted to the Unit of General Internal Medicine at Sultan Qaboos University Hospital between May and September 2023. Sixty-two variables were collected, including demographics, comorbidities, laboratory markers, vital signs, and medication data. Data preprocessing included handling missing values, standardizing continuous variables, and applying one-hot encoding to categorical variables. Four ML models—logistic regression, random forest, gradient boosting, and support vector machine (SVM)—were trained and evaluated. An ensemble model combining soft voting and weighted voting was developed to enhance performance, particularly recall. Results: The overall 30-day readmission rate was 14.2%. Among all models, logistic regression had the highest clinical relevance due to its balanced recall (70.6%) and area under the curve (AUC = 0.735). While random forest and SVM models showed higher precision, they had lower recall compared to logistic regression. The ensemble model improved recall to 70.6% through adjusted thresholds and model weighting, though precision declined. The most significant predictors of readmission included length of hospital stay, weight, age, number of medications, and abnormalities in liver enzymes. Conclusions: ML models, particularly ensemble approaches, can effectively predict 30-day readmissions in GIM patients. Tailored interventions using key predictors may help reduce readmission rates, although model calibration is essential to optimize performance trade-offs. Full article

In recent years, deep-learning-based speech synthesis has garnered substantial attention, achieving remarkable advancements in generating human-like speech. However, synthesized speech often lacks naturalness, primarily because models excessively depend on fine-grained text–speech alignment. To address this issue, we propose MixDiff-TTS, a novel non-autoregressive model. MixDiff-TTS incorporates a linguistic encoder based on a mixture alignment mechanism, which combines word-level hard alignment with phoneme-level soft alignment. This design reduces reliance on fine-grained alignment, enabling the model to handle ambiguous phonetic boundaries more robustly. Additionally, we introduce a Word-to-Phoneme Attention module with a relative position bias mechanism to improve the model’s capacity for processing long text sequences. We evaluate the performance of MixDiff-TTS on the LJSpeech dataset. The experimental results show that MixDiff-TTS scores 0.507 for SSIM (Structural Similarity Index) and 6.652 for MCD (Mel Cepstral Distortion). This suggests that the synthesized speech is closer to real speech in spectral structure and exhibits lower spectral distortion than state-of-the-art baselines (such as FastSpeech2 and DiffSpeech). MixDiff-TTS also achieves a MOS (Mean Opinion Score) of 3.95, which is close to that of real speech. These results indicate that MixDiff-TTS can synthesize speech with high naturalness and quality. Ablation studies demonstrate the effectiveness of our method. Full article

Background: Tooth loss results in hard- and soft-tissue volume loss over time. We compared the handling of three different soft tissue substitutes (STS) to the subepithelial connective tissue graft (SCTG) for soft tissue volume augmentation in a pig ex vivo model. Methods: Five dentists simultaneously shaped, placed and sutured randomized four graft types in single-tooth soft tissue defects created in pig mandibles. The STS, produced from slightly crosslinked collagen fibres (VCMX), were either 3 mm or 6 mm thick blocks or a newly developed pre-customized saddle-shape. Each graft type was handled 20 times. The time required for shaping, placement, and suturing was recorded. Dentists reported outcomes on the grafts’ handling were evaluated with a visual-analogue-scale (VAS). Statistical analysis included calculating means and medians and testing significance. Results: The mean time of 0.72 min for shaping the pre-customized saddle-shape STS was significantly lower than 1.31 min for SCTG, 1.73 min for 3 mm STS and 2.17 min for 6 mm STS. Placement/suturing time was similar for all grafts. The dentists mainly preferred the saddle-shape STS and the SCTG. Conclusions: The saddle-shape STS required less time for graft-shaping and, therefore, reduced the overall treatment time, suggesting a more efficient and less invasive workflow for soft tissue augmentation. Full article