Search Results (9,881)

This study systematically investigates 50 μm-diameter 631 stainless steel fine wires subjected to both sequential and simultaneous electrothermomechanical loading to simulate probe spring conditions in microelectronic test environments. Under cyclic current loading (~10⁴ A/cm²), the 50 μm 631SS wire maintained electrical integrity up to 0.30 A for 15,000 cycles. Above 0.35 A, rapid oxide growth and abnormal grain coarsening resulted in surface embrittlement and mechanical degradation. Current-assisted tensile testing revealed a transition from recovery-dominated behavior at ≤0.20 A to significant thermal softening and ductility loss at ≥0.25 A, corresponding to a threshold temperature of approximately 200 °C. These results establish the endurance limit of 631 stainless steel wire under coupled thermal–mechanical–electrical stress and clarify the roles of Joule heating, oxidation, and microstructural evolution in electrical fatigue resistance. A degradation map is proposed to inform design margins and operational constraints for fatigue-tolerant, electrically stable interconnects in high-reliability probe spring applications. Full article

Background/Objectives: Dental practitioners widely use dental implants to treat traumatic cases. Titanium implants are currently the most popular choice among dental practitioners and surgeons. The discovery of newer polymeric materials is also influencing the interest of dental professionals in alternative options. A comparative study between existing titanium implants and newer polymeric materials can enhance professionals’ ability to select the most suitable implant for a patient’s treatment. This study aimed to investigate material property advantages of high-performance thermoplastic biopolymers such as PEEK and PEKK, as compared to the time-tested titanium implants, and to find the most suitable and economically fit implant material. Methods: Three distinct implant material properties were assigned—PEEK, PEKK, and commercially pure titanium (CP Ti-55)—to dental implants measuring 5.5 mm by 9 mm, along with two distinct titanium (TI6AL4V) abutments. Twelve three-dimensional (3D) models of bone blocks, representing the mandibular right molar area with Osseo-integrated implants were created. The implant, abutment, and screw were assumed to be linear; elastic, isotropic, and orthotropic properties were attributed to the cancellous and cortical bone. Twelve model sets underwent a three-dimensional finite element analysis to evaluate von Mises stress and total deformation under 250 N vertical and oblique (30 degree) loads on the top surface of each abutment. Results: The study revealed that the time-tested titanium implant outperforms PEEK and PEKK in terms of marginal bone preservation, while PEEK outperforms PEKK. Conclusions: This study will assist dental practitioners in selecting implants from a variety of available materials and will aid researchers in their future research. Full article

In a variety of UAV applications, visual–inertial navigation systems (VINSs) play a crucial role in providing accurate positioning and navigation solutions. However, traditional VINS struggle to adapt flexibly to varying environmental conditions due to fixed covariance matrix settings. This limitation becomes especially acute during high-speed drone operations, where motion blur and fluctuating image clarity can significantly compromise navigation accuracy and system robustness. To address these issues, we propose an innovative adaptive covariance matrix estimation method for UAV-based VINS using Gaussian formulas. Our approach enhances the accuracy and robustness of the navigation system by dynamically adjusting the covariance matrix according to the quality of the images. Leveraging the advanced Laplacian operator, detailed assessments of image blur are performed, thereby achieving precise perception of image quality. Based on these assessments, a novel mechanism is introduced for dynamically adjusting the visual covariance matrix using a Gaussian model according to the clarity of images in the current environment. Extensive simulation experiments across the EuRoC and TUM VI datasets, as well as the field tests, have validated our method, demonstrating significant improvements in navigation accuracy of drones in scenarios with motion blur. Our algorithm has shown significantly higher accuracy compared to the famous VINS-Mono framework, outperforming it by 18.18% on average, as well as the optimization rate of RMS, which reaches 65.66% for the F1 dataset and 41.74% for F2 in the field tests outdoors. Full article

Rising volumes of textile waste necessitate the development of more efficient recycling systems, with a primary focus on the optimization of sorting technologies. Near-infrared (NIR) spectroscopy is a state-of-the-art method for fiber identification; however, its accuracy in quantifying textile blends, particularly common polyester/cotton blend textiles, still requires refinement. This study explores the potential and limitations of NIR spectroscopy for quantifying cotton content in post-consumer textiles. A lab-scale NIR sorter and a handheld NIR spectrometer in complementary wavelength ranges were applied to a diverse range of post-consumer textile samples to test model accuracies. Results show that the commonly assumed 10% accuracy threshold in industrial sorting can be exceeded, especially when excluding textiles with <35% cotton content. Identifying and excluding the range of non-linearity significantly improved the model’s performance. The final models achieved an RMSEP of 6.6% and bias of −0.9% for the NIR sorter and an RMSEP of 3.1% and bias of −0.6% for the handheld NIR spectrometer. This study also assessed how textile characteristics—such as color, structure, product type, and alkaline treatment—affect spectral behavior and model accuracy, highlighting their importance for refining quantification when high-purity inputs are needed. By identifying current limitations and potential sources of errors, this study provides a foundation for improving NIR-based models. Full article

Climbing vines have recently induced increasing threats to forest growth under favourable environmental changes. In mangrove forests, the native vine Derris trifoliata became invasive and is now one of the main threats. Yet current management relies on manual removal with low efficiency. Exploring an alternative, cost-effective method is required. To assess the potential of a proposed biological control method, this study performed a pot-plant experiment using Cuscuta japonica to infect D. trifoliata and three common mangrove species in Beihai, China. Results showed that D. trifoliata had a higher infection rate and high host mortality (90%) than mangrove (0%). It also had significantly decreased moisture by 4%, nitrogen by 14%, phosphorus by 27%, potassium by 49% and increased soluble sugar by 49% and protein by 20%, whereas only moisture (2% reduction) and one or two minerals of Excoecaria agallocha and Aegiceras corniculatum were influenced. Only Kandelia obovata had neither effective haustoria nor any nutrients impact from the infection. This study indicated that C. japonica can cause more damage to D. trifoliata than to mangrove species and has the potential to be used as a biological control agent for the threatened mangrove forests of A. corniculatum and K. obovata with monitoring and control. Further field tests are required to bring this method into practice. Full article

To develop high-performance electrode materials for supercapacitors, in this paper, a heterostructured composite material of cerium sulfide and zinc sulfide quantum dots (CeS₂/ZnS QD) was successfully prepared by hydrothermal method. Characterization through scanning electron microscopy (SEM), X-ray diffraction (XRD), and transmission electron microscopy (TEM) showed that ZnS QD nanoparticles were uniformly composited with CeS₂, effectively increasing the active sites surface area and shortening the ion diffusion path. Electrochemical tests show that the specific capacitance of this composite material reaches 2054 F/g at a current density of 1 A/g (specific capacity of about 256 mAh/g), significantly outperforming the specific capacitance of pure CeS₂ 787 F/g at 1 A/g (specific capacity 98 mAh/g). The asymmetric supercapacitor (ASC) assembled with CeS₂/ZnS QD and activated carbon (AC) retained 84% capacitance after 10,000 charge–discharge cycles. Benefited from the synergistic effect between CeS₂ and ZnS QDs, the significantly improved electrochemical performance of the composite material suggests a promising strategy for designing rare-earth and QD-based advanced energy storage materials. Full article

Electric buses (EBs) are increasingly adopted for their environmental and operational benefits, yet their real-world efficiency is influenced by climate, route characteristics, and auxiliary energy demands. While most existing research identifies winter as the most energy-intensive season due to cabin heating and reduced battery performance, this study presents a contrasting perspective based on a three-year longitudinal analysis of the LYMMO fleet in Orlando, Florida—a subtropical U.S. region. The findings reveal that summer is the most energy-intensive season, primarily due to sustained HVAC usage driven by high ambient temperatures—a seasonal pattern rarely reported in the current literature and a key regional contribution. Additionally, idling time exceeds driving time across all seasons, with HVAC usage during idling emerging as the dominant contributor to total energy consumption. To mitigate these inefficiencies, a proxy-based HVAC energy estimation method and an optimization model were developed, incorporating ambient temperature and peak passenger load. This approach achieved up to 24% energy savings without compromising thermal comfort. Results validated through non-parametric statistical testing support operational strategies such as idling reduction, HVAC control, and seasonally adaptive scheduling, offering practical pathways to improve EB efficiency in warm-weather transit systems. Full article

Aiming at the problems of the high multiple- and missed-seeding index and low operation efficiency of current mechanical potato seed-meters in minituber sowing, this study designed an air-suction roller-type minituber seed-metering device for minitubers (mass between 2 and 4 g) in accordance with the agronomic standards for potato cultivation in the single-cropping area of northern China. An account of the device’s structure and operational principle was made, its working process was theoretically analysed, and the three main factors affecting the airflow suction were determined: the seed roller speed, the suction seeding hole diameter, and the air inlet negative pressure. This study adopted the fluid dynamics simulation method and determined that the ideal location of the air inlet was 30° for horizontal inclination and 60° for vertical inclination. Then, based on the CFD-DEM fluid-structure coupling simulation method, the impact of a range of factors on the functionality of the seed-metering device under different conditions was studied and verification tests were carried out. Design-Expert was used to analyse test results. The results showed that when the pressure at the air inlet was −7000 Pa, the speed of the seeding roller was 40.2 r·min⁻¹, the suction seeding hole diameter was 10.37 mm, and the performance was optimal: the qualified index was 92.95%, the multiple-seeding index was 4.16%, and the missed-seeding index was 2.89%. The research results show that the seed-metering device developed under this scheme exhibited satisfactory seeding performance during operation and was able to meet the demands of minituber sowing. Full article

Early crop yield prediction is a major challenge in precision agriculture, and efficient and rapid yield prediction is highly important for sustainable fruit production. The accurate detection of major fruit characteristics, including flowering, green fruiting, and ripening stages, is crucial for early yield estimation. Currently, most crop yield estimation studies based on the YOLO model are only conducted during a single stage of maturity. Combining multi-growth period data for crop analysis is of great significance for crop growth detection and early yield estimation. In this study, a new network model, YOLOv8-RL, was proposed using citrus multigrowth period characteristics as a data source. A citrus yield estimation model was constructed and validated by combining network identification counts with manual field counts. Compared with YOLOv8, the number of parameters of the improved network is reduced by 50.7%, the number of floating-point operations is decreased by 49.4%, and the size of the model is only 3.2 MB. In the test set, the average recognition rate of citrus flowers, green fruits, and orange fruits was 95.6%, the mAP@.5 was 94.6%, the FPS value was 123.1, and the inference time was only 2.3 milliseconds. This provides a reference for the design of lightweight networks and offers the possibility of deployment on embedded devices with limited computational resources. The two estimation models constructed on the basis of the new network had coefficients of determination R² values of 0.91992 and 0.95639, respectively, with a prediction error rate of 6.96% for citrus green fruits and an average error rate of 3.71% for orange fruits. Compared with network counting, the yield estimation model had a low error rate and high accuracy, which provided a theoretical basis and technical support for the early prediction of fruit yield in complex environments. Full article

Background/Objectives: An important new consideration when studying autism spectrum disorder (ASD) is the bioenergetic mechanisms underlying the relatively recent rapid evolutionary expansion of the human brain, which pose fundamental risks for mitochondrial dysfunction and calcium signaling abnormalities and their potential role in ASD, as recently highlighted by insights from the BTBR mouse model of ASD. The rapid brain expansion taking place as Homo sapiens evolved, particularly in the parietal lobe, led to increased energy demands, making the brain vulnerable to such metabolic disruptions as are seen in ASD. Methods: Mitochondrial dysfunction in ASD is characterized by impaired oxidative phosphorylation, elevated lactate and alanine levels, carnitine deficiency, abnormal reactive oxygen species (ROS), and altered calcium homeostasis. These dysfunctions are primarily functional, rather than being due to mitochondrial DNA mutations. Calcium signaling plays a crucial role in neuronal ATP production, with disruptions in inositol 1,4,5-trisphosphate receptor (ITPR)-mediated endoplasmic reticulum (ER) calcium release being observed in ASD patient-derived cells. Results: This impaired signaling affects the ER–mitochondrial calcium axis, leading to mitochondrial energy deficiency, particularly in high-energy regions of the developing brain. The BTBR mouse model, with its unique Itpr3 gene mutation, exhibits core autism-like behaviors and metabolic syndromes, providing valuable insights into ASD pathophysiology. Conclusions: Various interventions have been tested in BTBR mice, as in ASD, but none have directly targeted the Itpr3 mutation or its calcium signaling pathway. This review presents current genetic, biochemical, and neurological findings in ASD and its model systems, highlighting the need for further research into metabolic resilience and calcium signaling as potential diagnostic and therapeutic targets for ASD. Full article

Surface Air-Cooled Oil Coolers (SACOCs) can become a critical component in managing the increasing thermal loads of modern turbofan engines. Installed within the bypass duct, SACOCs utilize high-mass flow bypass air for convective heat rejection, reducing reliance on traditional Fuel-Oil Heat Exchangers. This review explores SACOC design principles, integration challenges, aerodynamic impacts, and performance trade-offs. Emphasis is placed on the balance between thermal efficiency and aerodynamic penalties such as pressure drop and flow distortion. Experimental techniques, including wind tunnel testing, are discussed alongside numerical methods, and Conjugate Heat Transfer modeling. Presented studies mostly demonstrate the impact of fin geometry and placement on both heat transfer and drag. Optimization strategies and Additive Manufacturing techniques are also covered. SACOCs are positioned to play a central role in future propulsion systems, especially in ultra-high bypass ratio and hybrid-electric architectures, where traditional cooling strategies are insufficient. This review highlights current advancements, identifies limitations, and outlines research directions to enhance SACOC efficiency in aerospace applications. Full article

Background: Effective triage of women testing positive for high-risk HPV DNA is essential to reduce unnecessary colposcopies while preserving cancer prevention. Cytology, the current standard, has limited specificity and reproducibility. The genotype-specific 7-type HPV E6/E7 mRNA test (PreTect HPV-Proofer’7), targeting HPV types 16, 18, 31, 33, 45, 52, and 58, detects transcriptionally active infections and may enhance risk stratification. Methods: Between 2019 and 2023, 34,721 women aged 25–69 underwent primary HPV DNA screening with the Cobas 4800 assay at the University Hospital of North Norway, within the national screening program. Of these, 1896 HPV DNA-positive women were triaged with liquid-based cytology with atypical squamous cells of undetermined significance or worse (≥ASC-US) and the 7-type HPV mRNA test. Histological outcomes were followed through October 2024. Diagnostic performance for CIN2+ was evaluated overall and by genotype. Results: CIN2+ prevalence was 13.3%. The mRNA test reduced test positivity from 50.3% to 33.4% while maintaining comparable sensitivity (70.6% vs. 72.2%) and improving specificity (72.3% vs. 53.0%) and PPV (28.1% vs. 19.1%). Genotype-specific PPVs were highest for HPV16 mRNA (47.7%), followed by HPV33 (39.2%) and HPV31 (32.2%), all exceeding corresponding DNA-based estimates. Conclusion: Genotype-specific HPV mRNA triage offers superior risk discrimination compared to cytology, supporting more targeted, efficient, and accessible cervical cancer screening. Full article

Banana Fusarium wilt (BFW, also known as Panama disease) is a highly infectious and destructive disease that threatens global banana production, requiring early recognition for timely prevention and control. Current monitoring methods primarily rely on continuous variable features—such as band reflectances (BRs) and vegetation indices (VIs)—collectively referred to as basic features (BFs)—which are prone to noise during the early stages of infection and struggle to capture subtle spectral variations, thus limiting the recognition accuracy. To address this limitation, this study proposes a discretized enhanced feature (EF) construction method, the automated kernel density segmentation-based feature construction algorithm (AutoKDFC). By analyzing the differences in the kernel density distributions between healthy and diseased samples, the AutoKDFC automatically determines the optimal segmentation threshold, converting continuous BFs into binary features with higher discriminative power for early-stage recognition. Using UAV-based multi-spectral imagery, BFW recognition models are developed and tested with the random forest (RF), support vector machine (SVM), and Gaussian naïve Bayes (GNB) algorithms. The results show that EFs exhibit significantly stronger correlations with BFW’s presence than original BFs. Feature importance analysis via RF further confirms that EFs contribute more to the model performance, with VI-derived features outperforming BR-based ones. The integration of EFs results in average performance gains of 0.88%, 2.61%, and 3.07% for RF, SVM, and GNB, respectively, with SVM achieving the best performance, averaging over 90%. Additionally, the generated BFW distribution map closely aligns with ground observations and captures spectral changes linked to disease progression, validating the method’s practical utility. Overall, the proposed AutoKDFC method demonstrates high effectiveness and generalizability for BFW recognition. Its core concept of “automatic feature enhancement” has strong potential for broader applications in crop disease monitoring and supports the development of intelligent early warning systems in plant health management. Full article

The aim of the current study is to investigate the mechanical properties and ballistic performance of HARDOX 450 steel for defense applications in different conditions: uncoated, alumina-coated, and LINE X polyurea-coated. Tensile tests and Vickers microhardness measurements were conducted, along with fracture surface analysis using stereomicroscopy, scanning electron microscopy, and computed tomography. Experimental results showed that uncoated HARDOX 450 steel exhibited the highest strength and hardness, with ductile fracture features. Polyurea-coated HARDOX 450 steel samples retained good mechanical properties and demonstrated effective ballistic protection, including the containment of fragments. In contrast, alumina-coated HARDOX 450 steel samples exhibited reduced strength and ballistic resistance, attributed to the microstructural changes in HARDOX 450 steel caused by the high-temperature deposition process of alumina. Numerical simulations performed with the 5.56 × 45 mm bullet used in the simulation, along with its ballistic impact interaction with the Hardox 450 target model, aligned well with experimental ballistic impact results for all the samples. Overall, LINE X polyurea coating on HARDOX 450 steel proved to be the more suitable coating for applications requiring a balance of mechanical strength and ballistic impact resistance. Full article

The electrification of road vehicles is a relatively mature sector, while other areas of mobility, such as construction machinery, are just beginning their transition to electric solutions. This work presents the design and realization of an integrated drive system specifically developed for retrofitting fan drives in heavy machinery, like bulldozers and tractors, utilizing existing 48 VDC batteries. By replacing or complementing internal combustion and hydraulic technologies with electric solutions, significant advantages in efficiency, reduced environmental impact, and versatility can be achieved. Focusing on the fan drive system addresses the critical challenge of thermal management in high ambient temperatures and harsh environments, particularly given the high current requirements for $3\mathrm{k}\mathrm{W}$-class applications. A sensorless architecture has been selected to enhance reliability by eliminating mechanical position sensors. The developed fan drive has been extensively tested both on a braking bench and in real-world applications, demonstrating its effectiveness and robustness. Future work will extend this prototype to electrify additional onboard hydraulic motors in these machines, further advancing the electrification of heavy-duty equipment and improving overall efficiency and environmental impact. Full article