The Óbuda University (Hungarian: Óbudai Egyetem, Latin: Universitas Budensis), named after Óbuda, a part of Budapest, is a technical university in Budapest, Hungary. It was founded in 2000 as Budapest Tech (Budapesti Műszaki Főiskola) with the merging of three polytechnical institutes (Bánki Donát Technical College, Kandó Kálmán Technical College, Light Industry Technical College). With more than 15,000 students it is one of the largest technical universities in the country. Having complied with the requirements, the institution was promoted to university status on 1 January 2010 under the name of Óbuda University.
HT/Ag nanocomposites were synthesized by combining the sol–gel method for hardystonite preparation with laser ablation in liquid to generate silver nanoparticles, followed by their in-situ incorporation. XRD, FTIR, FESEM, TEM, EDX, and XPS analyses confirmed the successful incorporation of Ag nanoparticles (20–27 nm) into the ceramic substrate, with a mixed Ag⁰/AgO surface composition. HER evaluation based on LSV measurements revealed that HT/Ag-7 exhibited the lowest overpotential of 180 mV at 10 mA cm⁻² and a Tafel slope of 78 mV dec⁻¹. In catalytic reduction experiments, HT/Ag-7 achieved complete degradation of methylene blue and Congo red in 2 min, and complete reduction of 4-nitrophenol in 5 min in the presence of NaBH₄. These findings suggest enhanced interfacial charge transfer, improved accessibility of catalytic interfaces, and synergistic interactions between silver nanoparticles and the hardystonite substrate.
This study investigated the effects of knee braces with differing stiffness on in vivo knee kinematics and neuromuscular control during single-leg lateral landings. 14 healthy males performed landings under three conditions: no brace (Control), low-stiffness (Type-1), and high-stiffness (Type-2). Kinematics were quantified via dual fluoroscopic imaging, and sEMG recorded seven lower-limb muscles. Brace mechanics were assessed via three-point bending. Statistical analysis used repeated-measures ANOVA (α = 0.05). Kinematically, neither brace restricted knee flexion. Both significantly reduced varus angle (Type-1: 27-100% stance, ${p} = 0.043$ ; Type-2: 60-100% stance, ${p} = 0.033$ ), and Type-2 also lowered peak sagittal flexion acceleration (5.0 rad/s2, ${p} = 0.013$ ). Neuromuscularly, Type-1 enhanced multiplanar control, advancing rectus femoris (154.7 ms vs. Type-2, ${p} = 0.005$ ) and vastus lateralis (35.6 ms vs. Control, ${p} = 0.046$ ) activation without increasing rotational instability. Conversely, Type-2 demonstrated a trade-off: despite earlier vastus medialis activation (43.6 ms vs. Control, ${p} = 0.011$ ), it significantly delayed gluteus medius activation (23.9 ms vs. Type-1, ${p} = 0.037$ ) and, critically, exacerbated compensatory internal-rotation acceleration (3.3 rad/s2 vs. Type-1, ${p} = 0.006$ ) at peak flexion. The low-stiffness brace leveraged neuromuscular coordination for multiplanar stability, whereas the high-stiffness brace improved frontal-plane protection at the cost of rotational instability. These findings provide biomechanical evidence for the synergistic optimization of mechanical support and neuromuscular adaptation in knee brace design for populations with similar characteristics to the young male athletes studied herein.
Anterior cruciate ligament (ACL) injury often leads to lasting deficits in joint stability and neuromuscular control, even after reconstruction. Understanding how these deficits manifest during functional tasks is essential for improving rehabilitation strategies. This study recruited 25 male participants who had undergone ACL reconstruction, with assessments conducted at 3, 6, 9, and 12 months postoperatively. Surface electromyography (sEMG) signals from ten lower-limb muscles were collected during a standardized stair-descent task, and non-negative matrix factorization (NNMF) was applied to extract the corresponding muscle synergy patterns. Paired-samples t-tests and Wilcoxon rank-sum tests were performed to evaluate differences in muscle synergies between the ACL-reconstructed and contralateral limbs. Motor modules analysis revealed a preserved four-synergy structure in both limbs, with VAF exceeding 90%. At 3-6 months postoperatively, the reconstructed limb exhibited significantly elevated activation of the medial gastrocnemius (MG), biceps femoris (BF), and rectus femoris (RF) (p <0.005). This was accompanied by reduced contributions from the semitendinosus (ST) and lateral gastrocnemius (LG) in Synergy 4, indicative of a compensatory, quadriceps-dominant control strategy. By <0.005). This was accompanied by reduced contributions from the semitendinosus (ST) and lateral gastrocnemius (LG) in Synergy 4, indicative of a compensatory, quadriceps-dominant control strategy. By 9 months, partial normalization of neuromuscular coordination was observed, with interlimb symmetry across all four synergies largely restored by 12 months. These findings demonstrate that recovery of physiological neuromuscular control following ACL reconstruction is a prolonged and staged process. Longitudinal motor module analysis captures the dynamic reorganization of modular control from early compensatory patterns to restored interlimb coordination and may provide a mechanism-based indicator to guide personalized neuromuscular rehabilitation.
The development of wearable technology and electronic skin (e-skin) depends on flexible pressure sensors because they convert mechanical signals into electrical outputs. The devices which respond quickly and have comfortable designs for skin contact do not resolve the issue of maintaining device reliability during extended use in real-world body-worn applications. This review centers sweat- and moisture-dominated failure pathways that govern stability during extended wear. In addition, examines how occlusive patches create a harsh skin microenvironment during continuous wear. Moreover, examines how these conditions affect different fields which include sports, fitness applications, medical bandaging practices, virtual reality and industrial wearable technology. Notably, we then map moisture-activated degradation mechanisms across key material families, including polymer swelling and plasticization, leaching and ionic migration, interfacial delamination, biofouling, corrosion of silver-based conductors via chloride chemistry, humidity-driven conductivity drift in PEDOT: PSS, drying/swelling instabilities in hydrogels/ionogels, and the water/oxygen oxidation vulnerability of MXenes. The research evaluates different device-level reliability engineering approaches which combine three encapsulation techniques (hermetic and breathable and hybrid) with four structural designs (porous/foam and microfluidic for sweat control and multilayer wettability-gradient stacks and wet-skin adhesion solutions) to prevent edge lift-off and delamination. However, the study uses compiled literature records to demonstrate that there are no established reliability endpoints for retention and drift and hysteresis tests and that essential cycling and environmental test conditions remain unreported. Consequently, the paper concludes by presenting data-based methods which use machine learning to enhance system reliability through drift correction, anomaly identification, multi-objective design optimization and physics-based modeling systems. The research identifies three main areas which need additional work regarding available data collections and performance assessment standards and official industry standards.
Autonomous mobile robot navigation in dynamic and uncertain environments demands control architectures that are simultaneously robust, adaptive, and provably stable. This work introduces a hierarchical predictive navigation framework that combines adaptive fuzzy decision-making with forward-looking motion optimization and explicit stability constraints.Simulation studies conducted in static, mixed, and dynamic environments demonstrate that the proposed framework achieves approximately 30–40% higher average velocity, a 25–35 % reduction in traversal time, and 5–10 % lower energy consumption per unit distance compared with conventional fuzzy–potential field and optimization-tuned fuzzy navigation baselines. Across all evaluated scenarios, the robot maintained collision-free navigation and bounded control behavior. Selective human supervision was required in fewer than 10 % of operating intervals, reducing operator involvement while preserving safety.These results indicate that the proposed framework provides a quantitatively validated and interpretable alternative to existing fuzzy-based and predictive navigation approaches for autonomous mobile robots.