Search Results (5,257)

As industrial automation evolves towards human-centric, adaptable solutions, collaborative robots must overcome challenges in unstructured, dynamic environments. This paper extends our previous work on developing a digital shadow for industrial robots by introducing a comprehensive framework that bridges the gap between physical systems and their virtual counterparts. The proposed framework advances toward a fully functional digital twin by integrating real-time perception and intuitive human–robot interaction capabilities. The framework is applied to a hospital test lab scenario, where a YuMi robot automates the sorting of microscope slides. The system incorporates a RealSense D435i depth camera for environment perception, Isaac Sim for virtual environment synchronization, and a locally hosted large language model (Mistral 7B) for interpreting user voice commands. These components work together to achieve bi-directional synchronization between the physical and digital environments. The framework was evaluated through 20 test runs under varying conditions. A validation study measured the performance of the perception module, simulation, and language interface, with a 60% overall success rate. Additionally, synchronization accuracy between the simulated and physical robot joint movements reached 98.11%, demonstrating strong alignment between the digital and physical systems. By combining local LLM processing, real-time vision, and robot simulation, the approach enables untrained users to interact with collaborative robots in dynamic settings. The results highlight its potential for improving flexibility and usability in industrial automation. Full article

Biochar (BC) demonstrates considerable potential for reducing nitrogen emissions. However, it frequently exhibits a limited capacity for the adsorption of NO₃⁻-N, thereby reducing its effectiveness in mitigating N₂O emissions. Nano-biochar (NBC) is attracting attention due to its higher surface energy, but there is a lack of information on enhancing NO₃⁻-N adsorption and reducing N₂O emissions. Accordingly, this study conducted batch adsorption experiments for NO₃⁻-N and simulated N₂O emissions experiments. The NO₃⁻-N adsorption experiments included two treatments: bulk BC and NBC; the N₂O emissions experiments involved three treatments: a no-biochar control, BC, and NBC. N₂O emissions experiments were incorporated into the soil at mass ratios of 0.3%, 0.6%, 1%, and 3%. The results demonstrate that NBC exhibits nearly twice the NO₃⁻-N adsorption capacity compared to bulk biochar (BC), with adsorption behavior best described by a physical adsorption model. The enhanced adsorption performance was primarily attributed to NBC’s significantly increased specific surface area, pore volume, abundance of surface acidic functional groups, and higher aromaticity, which collectively strengthened multiple sorption mechanisms, including physical adsorption, electrostatic interactions, π–π interactions, and apparent ion exchange. In addition, NBC application (0.3–3%) reduced cumulative N₂O emissions by 11.60–54.77%, outperforming BC (9.16–32.65%). NBC treatments also increased soil NH₄⁺-N and NO₃⁻-N concentrations by 2.4–8.2% and 7.3–59.0%, respectively, indicating improved inorganic N retention. Overall, NBC demonstrated superior efficacy over bulk BC in mitigating N₂O emissions and conserving soil nitrogen, highlighting its promise as a sustainable amendment for integrated nutrient management and greenhouse gas reduction in soil. Full article

As an emerging energy-saving approach, bio-inspired drag reduction technology has become a key research direction for reducing energy consumption and greenhouse gas emissions. This study introduces the latest research progress on bio-inspired microstructured surfaces in the field of underwater drag reduction, focusing on analyzing the drag reduction mechanism, preparation process, and application effect of the three major technological paths; namely, bio-inspired non-smooth surfaces, bio-inspired superhydrophobic surfaces, and bio-inspired modified coatings. Bio-inspired non-smooth surfaces can significantly reduce the wall shear stress by regulating the flow characteristics of the turbulent boundary layer through microstructure design. Bio-inspired superhydrophobic surfaces form stable gas–liquid interfaces through the construction of micro-nanostructures and reduce frictional resistance by utilizing the slip boundary effect. Bio-inspired modified coatings, on the other hand, realize the synergistic function of drag reduction and antifouling through targeted chemical modification of materials and design of micro-nanostructures. Although these technologies have made significant progress in drag reduction performance, their engineering applications still face bottlenecks such as manufacturing process complexity, gas layer stability, and durability. Future research should focus on the analysis of drag reduction mechanisms and optimization of material properties under multi-physical field coupling conditions, the development of efficient and low-cost manufacturing processes, and the enhancement of surface stability and adaptability through dynamic self-healing coatings and smart response materials. It is hoped that the latest research status of bio-inspired drag reduction technology reviewed in this study provides a theoretical basis and technical reference for the sustainable development and energy-saving design of ships and underwater vehicles. Full article

Circularly polarized luminescence (CPL) materials have shown great application potential in the fields of three-dimensional displays, bioimaging, and information encryption and decryption. The chirality enhancement of CPL by a physical chiral environment, involving the delivery of structural asymmetry from helical architectures to luminescent molecules through electromagnetic field resonance, represents an innovative approach for constructing high-performance CPL materials. Liquid crystal polymers (LCPs), possessing helical superstructures, show great potential in constructing CPL systems. By modulating the chirality transfer from the helical structural environment of LCPs to luminescent sources via distinct strategies, the CPL properties of LCP-based composites are readily generated and tailored. This review summarizes the newest construction strategies of LCP-based CPL materials and provides a perspective on their emerging applications and future opportunities. This review can deepen our understanding of the fundamentals of chirality transfer and shed light on the development of functional chiral luminescent materials. Full article

Gravity and gravity gradient data serve as fundamental inputs for geophysical resource exploration and geological structure analysis. However, traditional denoising methods—including wavelet transforms, moving averages, and low-pass filtering—exhibit signal loss and limited adaptability under complex, non-stationary noise conditions. To address these challenges, this study proposes an improved U-Net deep learning framework that integrates multi-scale feature extraction and attention mechanisms. Furthermore, a Laplace consistency constraint is introduced into the loss function to enhance denoising performance and physical interpretability. Notably, the datasets used in this study are generated by the authors, involving simulations of subsurface prism distributions with realistic density perturbations (±20% of typical rock densities) and the addition of controlled Gaussian noise (5%, 10%, 15%, and 30%) to simulate field-like conditions, ensuring the diversity and physical relevance of training samples. Experimental validation on these synthetic datasets and real field datasets demonstrates the superiority of the proposed method over conventional techniques. For noise levels of 5%, 10%, 15%, and 30% in test sets, the improved U-Net achieves Peak Signal-to-Noise Ratios (PSNR) of 59.13 dB, 52.03 dB, 48.62 dB, and 48.81 dB, respectively, outperforming wavelet transforms, moving averages, and low-pass filtering by 10–30 dB. In multi-component gravity gradient denoising, our method excels in detail preservation and noise suppression, improving Structural Similarity Index (SSIM) by 15–25%. Field data tests further confirm enhanced identification of key geological anomalies and overall data quality improvement. In summary, the improved U-Net not only delivers quantitative advancements in gravity data denoising but also provides a novel approach for high-precision geophysical data preprocessing. Full article

A standard approach to design begins with scaling up state-of-the-art machines to new target dimensions, moving towards larger rotors with lower specific energy to maximize revenue and enable power production in lower wind speed areas. This trend is particularly crucial in floating offshore wind in the Mediterranean Sea, where the high levelized cost of energy poses significant risks to the sustainability of investments in new projects. In this context, the conventional approach of scaling up machines designed for fixed foundations and strong offshore winds may not be optimal. Additionally, modern large-scale wind turbines for offshore applications face challenges in achieving high aerodynamic performance in thick root regions. This study proposes a holistic optimization framework that combines multi-fidelity analyses and tools to address the new challenges in wind turbine rotor design, accounting for the novel demands of this application. The method is based on a modular optimization framework for the aerodynamic design of a new wind turbine rotor, where the cost function block is defined with the aid of a model reduction strategy. The link between the full-order model required to evaluate the target rotor’s performance, the physical aspects of blade aerodynamics, and the optimization algorithm that needs several evaluations of the cost function is provided by the definition of a surrogate model (SM). An intelligent SM definition strategy is adopted to minimize the computational effort required to build a reliable model of the cost function. The strategy is based on the construction of a self-adaptive, automatic refinement of the training space, while the particular SM is defined by the use of stochastic radial basis functions. The goal of this paper is to describe the new aerodynamic design strategy, its performance, and results, presenting a case study of a 15 MW wind turbine blades optimized for specific deepwater sites in the Mediterranean Sea. Full article

Wind farm performance monitoring has traditionally relied on deterministic models, such as power curves or machine learning approaches, which often fail to account for farm-wide behavior and the uncertainty quantification necessary for the reliable detection of underperformance. To overcome these limitations, we propose a probabilistic methodology for turbine-level active power prediction and uncertainty estimation using high-frequency SCADA data and farm-wide autoregressive information. The method leverages a Stochastic Variational Gaussian Process with a Linear Model of Coregionalization, incorporating physical models like manufacturer power curves as mean functions and enabling flexible modeling of active power and its associated variance. The approach was validated on a wind farm in the Belgian North Sea comprising over 40 turbines, using only 15 days of data for training. The results demonstrate that the proposed method improves predictive accuracy over the manufacturer’s power curve, achieving a reduction in error measurements of around 1%. Improvements of around 5% were seen in dominant wind directions (200°–300°) using 2 and 3 Latent GPs, with similar improvements observed on the test set. The model also successfully reconstructs wake effects, with Energy Ratio estimates closely matching SCADA-derived values, and provides meaningful uncertainty estimates and posterior turbine correlations. These results demonstrate that the methodology enables interpretable, data-efficient, and uncertainty-aware turbine-level power predictions, suitable for advanced wind farm monitoring and control applications, enabling a more sensitive underperformance detection. Full article

This paper presents a digital twin-driven multi-objective optimization approach for enhancing the performance and productivity of a multi-product manufacturing system under complex operational challenges. More specifically, the concept of digital twin is applied to virtually replicate a physical system that leverages real-time data fusion from Internet of Things devices or sensors. JaamSim serves as the platform for modeling the digital twin, simulating the dynamics of the manufacturing system. The implemented digital twin is a manufacturing system that incorporates a three-stage production line to complete and stockpile two gear types. The production line is subject to unpredictable events, including equipment breakdowns, maintenance, and product returns. The stochasticity of these real-world-like events is modeled using a normal distribution. Manufacturing control strategies, such as CONWIP and Kanban, are implemented to evaluate the impact on the performance of the manufacturing system in a simulation environment. The evaluation is performed based on three key indicators: service level, the amount of work-in-progress items, and overall system profitability. Multiple objective functions are formulated to optimize the behavior of the system by reducing the work-in-progress items and improving both cost-effectiveness and service level. To this end, the proposed approach couples the JaamSim-based digital twins with evolutionary and swarm-based algorithms to carry out the multi-objective optimization under varying conditions. In this sense, the present work offers an early demonstration of an industrial digital twin, implementing an offline simulation-based manufacturing environment that utilizes optimization algorithms. Results demonstrate the trade-offs between the employed strategies and offer insights on the implementation of hybrid production control systems in dynamic environments. Full article

A young adult patient presented to the gastrointestinal outpatient department with a suspected hepatic tumor. The patient was in a traffic accident ten years ago and underwent splenectomy and distal pancreatectomy at another medical institution. The physical examination was unremarkable. The liver function tests and tumor markers were within normal limits, with the alpha-fetoprotein level at 1.38 ng/mL. Both hepatitis B surface antigen and anti-HCV were negative. Based on the clinical history, intrahepatic splenosis was suspected first. Dynamic computed tomography revealed a 2.3 cm lesion exhibiting suspicious early wash-in and early wash-out enhancement patterns. As previous studies have reported, this finding makes hepatocellular carcinoma and metastatic lesions the major differential diagnoses. For further evaluation, dynamic magnetic resonance imaging was performed, and similar enhancing features were observed, along with restricted diffusion. As hepatocellular carcinoma still could not be confidently ruled out, the patient underwent an ultrasound-guided biopsy. The diagnosis of intrahepatic splenosis was confirmed by the pathologic examination. Intrahepatic splenosis is a rare condition defined as an acquired autoimplantation of splenic tissue within the hepatic parenchyma. Diagnosis can be challenging due to its ability to mimic liver tumors in imaging studies. Therefore, in patients with a history of splenic trauma and/or splenectomy, a high index of suspicion and awareness is crucial for accurate diagnosis and for prevention of unnecessary surgeries or interventions. Full article

Upper extremity amputations significantly impact an individual’s physical capabilities, psychosocial well-being, and overall quality of life. The level at which an amputation is performed influences residual limb function, prosthetic compatibility, and long-term patient satisfaction. While surgical guidelines traditionally emphasize maximal limb preservation, prosthetists often advocate for amputation sites that optimize prosthetic fit and function, highlighting the need for a collaborative approach. This review examines the discrepancies between surgical and prosthetic recommendations for optimal amputation levels, from digit amputations to shoulder disarticulations, and explores their implications for prosthetic design, functionality, and patient outcomes. Various prosthetic options, including passive functional, body-powered, myoelectric, and hybrid devices, offer distinct advantages and limitations based on the level of amputation. Prosthetists emphasize the importance of residual limb length, not only for mechanical efficiency but also for achieving symmetry with the contralateral limb, minimizing discomfort, and enhancing control. Additionally, emerging technologies such as targeted muscle reinnervation (TMR) and advanced myoelectric prostheses are reshaping rehabilitation strategies, further underscoring the need for precise amputation planning. By integrating insights from both surgical and prosthetic perspectives, this review highlights the necessity of a multidisciplinary approach involving surgeons, prosthetists, rehabilitation specialists, and patients in the decision-making process. A greater emphasis on preoperative planning and interprofessional collaboration can improve prosthetic outcomes, reduce device rejection rates, and ultimately enhance the functional independence and well-being of individuals with upper extremity amputations. Full article

This work presents a multiscale, microstructure-aware framework for predicting fatigue strength distributions in additively manufactured (AM) alloys—specifically, laser powder bed fusion (L-PBF) AlSi10Mg and Ti-6Al-4V—by integrating density functional theory (DFT), instrumented indentation, and Bayesian inference. The methodology leverages principles common to all 3D printing (additive manufacturing) processes: layer-wise material deposition, process-induced defect formation (such as porosity and residual stress), and microstructural tailoring through parameter control, which collectively differentiate AM from conventional manufacturing. By linking DFT-derived cohesive energies with indentation-based modulus measurements and a MAP-based statistical model, we quantify the effect of additive-manufactured microstructural heterogeneity on fatigue performance. Quantitative validation demonstrates that the predicted fatigue strength distributions agree with experimental high-cycle and very-high-cycle fatigue (HCF/VHCF) data, with posterior modes and 95 % credible intervals of ${\widehat{\sigma }}_f^{\mathrm{AlSi}10\mathrm{Mg}}={86}_{-7}^{+8}\mathrm{MPa}$ and ${\widehat{\sigma }}_f^{\mathrm{Ti}–6\mathrm{Al}–4\mathrm{V}}={115}_{-9}^{+10}\mathrm{MPa}$, respectively. The resulting Woehler (S–N) curves and Paris crack-growth parameters envelop more than 92 % of the measured coupon data, confirming both accuracy and robustness. Furthermore, global sensitivity analysis reveals that volumetric porosity and residual stress account for over 70 % of the fatigue strength variance, highlighting the central role of process–structure relationships unique to AM. The presented framework thus provides a predictive, physically interpretable, and data-efficient pathway for microstructure-informed fatigue design in additively manufactured metals, and is readily extensible to other AM alloys and process variants. Full article

Over the past three turbulent decades, research has profoundly reshaped our understanding of chronic Toxoplasma gondii infection—traditionally regarded as harmless in immunocompetent individuals—unveiling its surprising impact on human health, performance, and behavior. This review emphasizes the effects of chronic Toxoplasma infection on physical and mental health, cognitive performance, and behavioral changes, highlighting key findings from studies investigating these domains, with a particular focus on both ultimate and proximate mechanisms underlying the observed effects. To this end, the primary focus will be on human studies; however, animal model studies will also be thoroughly considered when necessary and appropriate, to provide context and additional important information. Research demonstrates that chronic Toxoplasma infection may contribute to a broad spectrum of physical health issues. Ecological studies have revealed correlations between toxoplasmosis prevalence and increased morbidity and mortality from various conditions, including cardiovascular diseases, neurological disorders, and certain cancers. Large-scale cross-sectional studies have further shown that infected individuals report a higher incidence of numerous health complaints and diagnosed diseases, suggesting a significant impact on overall physical well-being. In addition to physical health, lifelong Toxoplasma infection (subclinical toxoplasmosis) has been implicated in cognitive impairments and behavioral changes. Studies have reported associations between infection and poorer performance in areas such as reaction time, processing speed, working memory, and executive function. Many of these behavioral changes likely relate to worsened health and a shift towards a “fast life history strategy.” These cognitive deficits can have significant implications for daily functioning and performance. Furthermore, the role of Toxoplasma infection in the development or exacerbation of mental health disorders has been extensively investigated. Meta-analyses, ecological studies, and large-scale observational studies have demonstrated associations between Toxoplasma infection and an increased risk of disorders such as schizophrenia and obsessive–compulsive disorder. While the precise mechanisms underlying these associations remain under investigation, research suggests that neuroinflammation and alterations in neurotransmitter systems are likely to play a role. Far from being harmless, subclinical toxoplasmosis is increasingly recognized as a hidden factor influencing human health, behavior, and cognitive performance—with implications that extend well beyond the individual to public health at large. Further research is warranted to elucidate the complex interplay between Toxoplasma infection, host physiology, and the development of various physical, cognitive, behavioral, and mental health conditions. Full article

Introduction: Ureteral stents are widely used in the specialty of urology to preserve renal function and provide ureteral patency in cases of urolithiasis, strictures, malignancy, and trauma. This paper presents a novel application of prophylactic ureteral stents deployed under MRI-guidance for ureteral protection in the setting of in-bore salvage cryoablation therapy for recurrent and metastatic prostate cancer. This is the first known case series of ureteral stent placement using near real-time MRI. Materials and Methods: A retrospective chart review was performed for all patients who underwent MRI-guided ureteral stent placement prior to in-bore cryoablation therapy from 2021 to 2022. Each case was managed by an interdisciplinary team of urologists and interventional radiologists. Preoperative and postoperative data were collected for descriptive analysis. Physics safety testing was conducted on the cystoscope and viewing apparatus prior to its implementation for stent deployment. Results: A total of seven males, mean age 73.4 years (range 65–81), underwent successful prophylactic, cystoscopic MRI-guided ureteral stent placement prior to cryoablation therapy of their prostate cancer. No intraoperative complications occurred. A Grade 2 postoperative complication of pyelonephritis and gross hematuria following stent removal occurred in one case. The majority of patients were discharged the same day as their procedure. Conclusions: This case series demonstrates the feasibility of in-bore cystoscopic aided MRI guidance for ureteral stent placement. Ureteral stents can be used to increase the safety margin of complex cryoablation treatments close to the ureter. Furthermore, by following the meticulous MRI safety protocols established by MRI facility safety design guidelines, MRI conditional tools can aid therapy in the burgeoning interventional MRI space. Full article

Background: High-density lipoprotein cholesterol (HDL-C) is known for its cardiovascular and neuroprotective effects, but its association with cognitive function remains unclear, particularly in relation to genetic factors such as apolipoprotein E ε4 (APOE4). We aimed to investigate the association between serum HDL-C levels and cognition and to examine the moderating effect of APOE4 on this relationship. Methods: This cross-sectional study included 196 dementia-free older adults (aged 65–90) recruited from a memory clinic and the community. Cognitive function was assessed across multiple domains using the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) battery. Serum HDL-C levels were measured, and APOE4 genotyping was performed. Multiple linear regression analyses were conducted, adjusting for age, sex, APOE4 status, education, diagnosis, vascular risk, nutritional status, physical activity, and blood biomarkers. Results: Higher HDL-C levels were significantly associated with better episodic memory (B = 0.109, 95% confidence interval [CI]: 0.029–0.189, p = 0.008) and global cognition (B = 0.130, 95% CI: 0.001–0.261, p = 0.049). These associations were significantly moderated by APOE4 status. In APOE4-positive individuals, HDL-C was strongly associated with both episodic memory (B = 0.357, 95% CI: 0.138–0.575, p = 0.003) and global cognition (B = 0.519, 95% CI: 0.220–0.818, p = 0.002), but no such associations were observed in APOE4-negative participants. Conclusions: This study indicates a significant association between serum HDL-C levels and cognitive function, particularly in episodic memory and global cognition, with APOE4 status potentially moderating this relationship. While these findings may suggest a protective role of HDL-C in individuals at increased genetic risk due to APOE4, they should be interpreted with caution given the cross-sectional design. Future longitudinal and mechanistic studies are warranted to clarify causality and potential clinical implications. Full article

Background: The incidence of anterior cruciate ligament ruptures in football has experienced a marked increase in recent years, affecting both professional and amateur players. This injury is characterised by being highly disabling, causing the player to withdraw from the field of play for prolonged periods and there is no clear consensus on how to carry out the different phases of rehabilitation, which poses a major challenge for health professionals. Case presentation: This study followed a semi-professional player who suffered an anterior cruciate ligament tear following two forced valgus actions without direct contact in the same match. Outcome and follow-up: The patient underwent surgery using an autologous hamstring graft. He followed a progressive rehabilitation programme consisting of one preoperative phase and six phases after the operation. After a 12-month follow-up, with exercises aimed at perfecting step-by-step basic and specific physical skills, the player showed a complete functional recovery, achieving the desired parameters. Conclusions: This case highlights the importance of structured rehabilitation adapted to the specific needs of the football player through an approach with coherent progressions, which considers the muscle chains that determine the movements performed on the football pitch. Full article