Search Results (122,900)

Objectives: Arthroscopic surgery requires complex visuospatial coordination and psychomotor skills, which are traditionally acquired through mentorship and cadaveric training. High-fidelity simulators are effective but often costly and inaccessible. This study evaluates the technical effectiveness of a novel home-based 3D-printed arthroscopic simulator (“Arthrozero”) for improving basic arthroscopic skills among orthopedic residents. Methods: Thirty-three orthopedic residents (25–36 years) from two Italian university centers were randomized into three groups: ZERO (Arthrozero training), ARTHRO (real arthroscope training), and CONTROL (theoretical session). Training was performed on a FAST-like workstation through four progressively complex tasks. Performance metrics included task completion time, number of looks down, and skill progression during a final Shoulder Challenge (SHO-CHA) assessment. A web-based Likert questionnaire evaluated participant satisfaction and perceived educational value. Results: Both ZERO and ARTHRO groups demonstrated significant improvement across training sessions (p < 0.05) for all tasks, while the CONTROL group showed minimal gains. In the SHO-CHA assessment, mean completion times were 394.1 ± 140.7 s (ZERO), 456.1 ± 123.2 s (ARTHRO), and 745.5 ± 190.7 s (CONTROL) (p < 0.01). No significant difference was observed between ZERO and ARTHRO groups (p = 0.276). Conclusions: The home-based Arthrozero simulator demonstrated improvements in basic arthroscopic skill performance, suggesting that it may represent an accessible training tool to support early arthroscopic skill acquisition alongside traditional training methods. Full article

The integration of whole-cell biosensors in miniaturized measuring devices to exploit synergetic effects as small, rapid, cost-effective, sensitive, and highly specific platforms with point-of-care applicability was often discussed in recent years and many different setups have been presented to date. In many cases these setups were envisaged as powerful systems in their respective fields; however, the anticipated success often failed to materialize, and the systems remained a proof-of-concept. We elaborate on the hurdles and possible challenges that have to be overcome for the successful development and application of such systems. Further, we critically discuss and rank the impact of different challenges during system development, application, and commercialization. Finally, we point out possible future applications and conclude future perspectives for whole-cell biosensors integrated into microfluidic platforms. Full article

DC motors are widely used in mechatronic systems; however, their performance degrades significantly in the presence of nonlinear mechanical loads, parameter variations and sensing uncertainties. This paper proposes three control strategies (i.e., PID, optimal, and hybrid controllers) for discrete-time DC motor systems to overcome the disturbances caused by nonlinear mechanical loads and parameter variations. Optimal control of nonlinear discrete-time systems is formally characterized by the Hamilton–Jacobi–Bellman (HJB) equation, whose analytical solution is generally intractable. To address this challenge, a learning-based optimal control strategy based on the Heuristic Dynamic Programming (HDP) framework is developed to approximate the HJB equation, supported by a formal convergence proof. For that purpose, Neural Networks (NNs) are employed to approximate both the cost function and the optimal control policy, enabling near-optimal performance with manageable computational complexity. Although the resulting optimal control achieves fast convergence, it may introduce overshoot and steady-state offset under nonlinear disturbances. To address this limitation, a hybrid control framework is proposed, where nonlinear optimal corrections are integrated with the robustness and adaptability of Proportional–Integral–Derivative (PID) control through error-dependent gating and gain-scheduling mechanisms. A structured evaluation framework is conducted, including nominal analysis, motor-parameter stress testing across nine nonlinear scenarios, controller-design sensitivity analysis, and stochastic measurement-noise assessment under filtered sensing conditions. Results demonstrate that the hybrid controller preserves transient speeds within 5–10% of the optimal controller while effectively eliminating overshoot and steady-state offset under nominal conditions. The hybrid design reduces the accumulated tracking error by more than 95% compared to the optimal controller, while incurring only negligible additional control effort. Under aggressive supply-sag disturbances, the hybrid controller significantly limits peak deviation and reduces accumulated tracking error by over 90%, while maintaining comparable control cost. Overall, the hybrid framework provides a convergence-proven and practically deployable control solution that combines near-optimal convergence speed with robust, overshoot-free performance for intelligent motion-control and robotics applications. Full article

This paper presents a zone-based method for the interim verification and spatial metrological characterization of a 2D vision measurement system. The approach relies on a system calibrated along a single axis and employs a stable yet non-calibrated artifact, demonstrating that spatial performance assessment can be achieved without the need for fully calibrated artifacts distributed across the entire field of view. To enable this process, a custom-designed reference standard was developed, providing a straightforward, robust, and cost-effective solution for performing interim verification tasks. The proposed method provides a structured framework for evaluating both precision and spatial consistency across the measurement surface, even in the absence of fully calibrated standards distributed across the surface. The method is applicable to a wide range of vision-based measurement systems, including those supporting industrial Optical Character Recognition (OCR), while maintaining alignment with established metrological principles. When combined with complementary optical performance tests, the approach supports robust and repeatable interim verification strategies in advanced manufacturing metrology. Full article

Background/Objectives: Radiological assessment has become indispensable for modern clinical decision-making. Image quality plays a critical role in the reliability of radiological interpretation. Unlike most previous studies, this study investigated the effect of monitor type on diagnostic accuracy and ease of diagnosis under physical conditions outside the radiology unit. Methods: Three image sets were prepared for the study, consisting of emergency radiological images, each containing 50 computed tomography, magnetic resonance imaging, and digital radiography images. The image sets were examined by five emergency specialists, who were blinded to each other’s work, under emergency service conditions on a standard monitor (SM), medical monitor (MM), and advanced monitor (AM). The accuracy and ease of diagnosis were analyzed statistically according to the type of monitor used. Results: Overall diagnostic accuracy rates were 98.7% for SM, 100% for AM, and 100% for MM. Cochran’s Q test demonstrated a statistically significant difference between monitor types (p = 0.002), with significant pairwise differences for SM–AM and SM–MM comparisons. The absolute risk difference between SM and AM/MM was 1.3%, corresponding to a relative risk of 1.013 and a number needed to benefit (NNB) of 77. Ease of diagnosis scores increased progressively across monitor types (SM: 7.6 [IQR 7–8], AM: 9.4 [IQR 9–9.8], MM: 9.8 [IQR 9.6–10]; p < 0.001), with a large overall effect size (Kendall’s W = 0.81). Multilevel modeling confirmed that these associations persisted after adjustment for clustering effects. Conclusions: In situations where medical monitors cannot be used due to cost and operational constraints, opting for advanced monitors instead of standard monitors may modestly improve diagnostic accuracy while substantially enhancing perceived ease of diagnosis. Full article

The analysis of water and emission efficiency in cropping systems is vital for sustainable agriculture in Mediterranean regions, which face increasing water shortages. This study offers a site-specific assessment of the Water Footprint (WFP) and Carbon Footprint (CFP) of organic cotton grown under Mediterranean conditions, integrating environmental indicator measurements with economic valuation of greenhouse gas (GHG) emissions via the EU Emissions Trading System (ETS) and the Social Cost of Carbon (SCC). Experiments were carried out at three sites with different soil types, testing two cultivars (Armonia and ST-318) under three irrigation scenarios: severe water deficit (I30), moderate water deficit (I70), and full irrigation (I100). The results reveal significant site-specific variability, with average WFP_lint values ranging from about 1.440 m³ per ton at the most productive site to over 4.100 m³ per ton at the least productive site. Similarly, CFP_lint is lower under high-yield conditions, emphasizing the strong influence of yield on mass-based indicators. At the Carboj and Primosole sites, shifting from (I30) to I100 results in roughly a 50% reduction in emissions, while at Buonfornello, increased irrigation does not consistently produce benefits. The cultivar response is key: Armonia shows greater resilience to water stress, while ST-318 performs best with full irrigation. Overall, the findings highlight that the sustainability of the Mediterranean cotton system depends on factors such as yield performance, site-specific conditions, and cultivar choice. Full article

The integration of truck platooning and electrification presents a promising avenue for improving operational efficiency and environmental sustainability in freight transportation. Realizing the energy and cost saving as well as emission reduction benefits requires a holistic design of truck routing, scheduling, and platooning strategies that account for practical operational constraints. This study investigates the integrated planning problem of routing, scheduling, and platooning for a mixed fleet of conventional trucks (CTs) and electric trucks (ETs), referred to as mixed fleet truck platooning (MFTP) problem. The MFTP incorporates charging scheduling and key operational factors, such as platooning leader–follower positioning under the battery constraints of ETs, charging station availability and capacity, and the positional configuration of trucks within a platoon. The objective is to minimize the total operation cost of the MFTP system, including charging cost, fuel cost, travel labor cost, charging labor cost, and platoon formation labor cost, while ensuring timely arrivals across multiple origin–destination (OD) pairs. The proposed MFTP is formulated as a novel mixed-integer linear program (MILP). Extensive numerical experiments on the simplified Illinois interstate highway network are conducted to examine the effectiveness and efficiency of the proposed model. Numerical results show that incorporating platooning reduces the total operational cost by 7.6% relative to the non-platooning scenario. The findings also shed some light on planning mixed fleets of CTs and ETs with platooning, offering valuable managerial insights for decision-makers. Full article

Indoor air quality (IAQ) in educational buildings plays a critical role in the health, cognitive performance, and well-being of occupants. Aging university facilities often rely on outdated ventilation systems that are not designed to meet current demands or respond to dynamic occupancy levels. This study investigates the performance and feasibility of various advanced ventilation strategies in comparison to an existing balanced mechanical ventilation (BMV) system in a university classroom accommodating 100 students. Using a Dynamic Building Energy Simulation Program, simulations were conducted to evaluate IAQ (using CO₂ levels), energy consumption, and thermal comfort under three retrofitting scenarios: BMV, demand-controlled ventilation (DCV), and hybrid ventilation combining natural and mechanical airflow. The simulations indicate that DCV cuts annual HVAC energy use by 33% relative to the baseline, while the hybrid strategy achieves the greatest reduction of 42% and maintains CO₂ levels and thermal comfort within recommended limits. Although hybrid systems provide seasonal advantages, their complexity may limit applicability. In addition to technical analysis, this study also explores the financial and tax-related challenges associated with retrofitting ventilation systems in university buildings. Investment payback periods, operational costs, and potential tax incentives are discussed to evaluate economic viability. Overall, the endorse hybrid ventilation as the most cost-effective strategy where mixed-mode control is feasible, and DCV as a practical alternative for buildings unable to employ natural ventilation. Full article

A substantial number of students with hearing impairments are enrolled in higher education, motivating the development of inclusive assistive technologies that reduce communication barriers. This study developed and evaluated a prototype electronic glove that translates Mexican Sign Language (LSM) signs into Spanish text using machine learning. Eight participants (four deaf and four hearing with LSM proficiency) completed four sessions involving 12 signs; three sessions (S1–S3) were used for model development and one session (T) was held out for evaluation. Models were trained on S1–S3 and tested on T using a session-level split without window mixing across sessions; therefore, results represent a speaker-dependent, inter-session pilot assessment rather than a speaker-independent generalization test. The glove integrates flex sensors and an inertial measurement unit IMU MPU6050 connected to an ESP32-C3 SuperMini microcontroller. These components were selected due to their low cost, availability, and ease of integration, making them suitable for the development of accessible wearable assistive technologies. Under this protocol, the system achieved a window-level overall test accuracy of 97.0% (95% CI computed at the window level: 96.00–97.00), with higher performance for the dynamic subset (98.0%) than for the static subset (95.0%), and an algorithmic decision delay of 1.2 s. Usability and acceptance were evaluated using the System Usability Scale (SUS) and a Technology Acceptance Model (TAM)-based questionnaire. The mean SUS score was 50.6 ± 1.8 (marginal usability), while participants reported positive perceptions across TAM constructs. Overall, findings demonstrate technical feasibility under controlled inter-session conditions and provide a foundation for iterative user-centered refinement, followed by strict speaker-independent validation and classroom deployment studies in future work. Full article

An improved optimizer based on projection-iterative methods (IPIMO) is proposed to address the optimal configuration problem of multi-type energy storage systems (MT-ESS), with the objective of achieving synergistic minimization of comprehensive costs, including both investment and operational expenditures. A comprehensive energy system model is established, integrating photovoltaic power, wind power, and six typical energy storage technologies—lithium-ion battery, flywheel energy storage, supercapacitors, valve-regulated lead-acid battery, compressed air energy storage, and redox flow battery. Four typical operational scenarios are designed to validate the adaptability and robustness of the algorithm. A systematic evaluation of IPIMO’s comprehensive performance is conducted by comparing it with the weighted average method (WA), the single-energy storage optimization method (SEO), the projection-iterative-methods-based optimizer algorithm (PIMO), and the genetic algorithm (GA). Simulation results demonstrate that IPIMO exhibits superior convergence performance, achieving stable convergence rapidly and significantly outperforming PIMO and GA. Moreover, IPIMO achieves the lowest total cost across all four scenarios, with an average of $46,837, representing reductions of 6.54% compared to the benchmark weighted average method and 11.8% compared to the SEO. Additionally, IPIMO adaptively adjusts the allocation ratios of energy storage types based on scenario characteristics, prioritizing energy-type storage in stable scenarios while increasing the proportion of fast-response storage to 49.1% in fluctuating scenarios, thereby demonstrating its strong scenario adaptability. Full article

Countries around the world are committed to achieving the Sustainable Development Goals (SDGs). However, significant challenges remain—particularly the economic consequences of climate change. Using a GVAR model for 17 economies over the period 2001M1–2021M12, we explore how temperature anomalies affect industrial production through four potential mechanisms: food prices, credit costs, exchange rates and investment. Our theoretical model demonstrates that temperature anomalies lower agricultural production, which drives up food prices and reduces real wages. This in turn leads to lower investment and production in the industrial sector. Our empirical results indicate that rising temperature anomalies are associated with a decrease in industrial production and investment, as well as the depreciation of domestic currencies relative to the U.S. dollar. Additionally, we observe that the influence of temperature anomalies is more pronounced in hot regions than in cold regions. Our investigation underscores the importance of financial markets and investment as potential transmission channels for the impact of climate change on industrial production. This study provides empirical evidence to support policymaking aimed at mitigating the adverse impacts of climate change, thereby helping countries to advance toward key SDGs such as no poverty, zero hunger, and climate action. Full article

Corrosion in steel pipelines can cause critical failures in industrial systems, while conventional inspection methods such as radiography and ultrasonic testing are costly and require specialized personnel. This study presents a mobile computer vision system for automated corrosion detection inside steel pipes using deep learning-based visual analysis. The proposed system consists of a Raspberry Pi 4-based mobile robot equipped with a high-resolution camera for internal inspection. Acquired images were processed using color-space transformations (RGB–HSV), filtering, and segmentation. Convolutional neural networks and semantic segmentation models, including YOLOv8-seg (Instance segmentation) and DeepLabV3 (Semantic segmentation), were trained on a custom corrosion image dataset to identify corroded regions. Real-time visualization was implemented via Flask-based video streaming. Experimental results demonstrated high detection accuracy for uniform corrosion, achieving a mean Intersection over Union (mIoU) above 0.98 and a precision of 0.99 with the YOLOv8-seg model. These results indicate that the proposed system enables reliable and automated corrosion inspection, with the potential to reduce inspection costs and improve operational efficiency. Future work will focus on enhancing real-time performance through hardware optimization. Full article

This study introduces an efficient and accurate two-stage explicit computational scheme for solving partial differential equations (PDEs) containing first-order time derivatives. The suggested method is a modification of the classical Runge–Kutta scheme that introduces a new first-stage formulation. This minimizes numerical error with moderate step sizes while preserving the stability region of the classical method. Spatial discretization is performed using a sixth-order compact finite-difference scheme to obtain high-resolution solutions. The analysis of stability and convergence is strictly determined for both scalar and system forms of convection–diffusion-type equations. To illustrate the suitability of the method, a dimensionless mathematical model of the unsteady, incompressible, laminar flow of a Prandtl-type non-Newtonian nanofluid over a Riga plate is considered, accounting for viscous dissipation, thermophoresis, Brownian motion, and a magnetic field. Here, the Prandtl ternary nanofluid is defined as a non-Newtonian nanofluid that follows the Prandtl rheological model, and it exhibits three critical transport phenomena: heat conduction, viscous dissipation, and nanoparticle diffusion. Representative values of the Prandtl number P_r = 3 and Reynolds number R_e = 5 are used to perform the simulation, and other parameters, including but not limited to the Hartmann number H_a, Williamson number W_e, thermophoresis N_t and Brownian motion N_b, are varied to evaluate the flow behavior. Moreover, an artificial neural network (ANN)-developed surrogate model is used to calculate the skin friction coefficient and the local Sherwood number, using five input parameters: the Reynolds number, Prandtl number, Schmidt number, Brownian motion parameter, and thermophoresis parameter. The governing partial differential equations yield high-fidelity numerical data used to train the surrogate model. The data is split into 80% for training, 10% for validation, and 10% for testing. The ANN is tested using regression analysis and error histograms, which demonstrate high accuracy and generalization capacity. Numerical simulation combined with AI-based prediction is a cost-efficient method for real-time estimation of complex non-Newtonian nanofluid systems. Full article

In pursuit of energy decarbonization and supply security, the integrated energy production unit (IEPU) is regarded as a notable multi-technology energy production model integrating coal-fired power, carbon capture, and renewable energy. As a core component of the IEPU, Power-to-X (P2X) technology encompasses various technical routes with distinct economic performance and technological maturity at different development stages. Thus, selecting the most techno-economically optimal route is critical. In view of this, this paper proposes an integrated decision-making framework for the selection of P2X technology routes in IEPU, which combines “technology selection—economic analysis—risk assessment”. Firstly, a decision model for key P2X processes is established, with the levelized cost of hydrogen and unit hydrogen conversion revenue as core performance metrics to identify the optimal technology combination for hydrogen production and utilization. Secondly, integrating the aforementioned optimized technical route, a life-cycle economic benefit evaluation model is constructed for IEPU retrofit projects to systematically assess the overall economic feasibility of the IEPU project. Thirdly, an investment risk assessment model for P2X-integrated IEPU retrofits is established based on interval number theory, which can quantify project risks under fluctuations of critical parameters such as electricity and carbon prices. Finally, a case study of a 600 MW coal-fired unit retrofit demonstrates that “alkaline electrolysis + methane synthesis” constitutes the optimal P2X technology combination. However, its profitability is relatively sensitive to fluctuations in external market parameters, necessitating the implementation of corresponding risk management strategies. Full article

Ion-selective electrodes (ISEs) are widely used analytical tools for the determination of specific ions in a variety of analytical applications due to their simplicity, selectivity, and low cost. Recent developments in materials science and digital fabrication have opened new opportunities for redesigning ISEs using modern manufacturing techniques. Here, we present a new application of 3D printing for fabricating potassium-selective electrodes using a simplified membrane composition. The 3D printing cocktail was prepared by mixing potassium tetraphenylborate, silver sulfide or graphite, and industrial ABS (acrylonitrile Butadiene Styrene) polymer. Membranes were tested both without and with the addition of ZnO nanoparticles. Incorporation of ZnO NPs significantly enhanced the electrode slope, while graphite-based membranes exhibited faster response, with potential stabilizing within 3–7 s across a concentration range of 4.88 × 10⁻⁵ mol L⁻¹ to 1.00 × 10⁻² mol L⁻¹. The optimized 3D printed membrane containing 0.6% ZnO NPs showed near-Nernstian behaviour (slope: 59.178 mV per decade and R² = 0.9989), a limit of detection of 2.06 × 10⁻⁵ mol L⁻¹ and high selectivity against common interfering ions. These results demonstrate that 3D printing combined with a suitable membrane composition and nanoparticle incorporation provides a versatile platform for rapid, reproducible, and high-performance potassium ISEs. Full article