Tomas Bata University in Zlín (TBU), (Czech Univerzita Tomáše Bati ve Zlíně), is a Czech public university in the Moravian city of Zlín, comprising six faculties offering courses in technology, economics, humanities, arts and health care. The university was named after the entrepreneur Tomáš Baťa, the founder of the shoe industry in Zlín. With a current student population of more than 9,200, TBU is among the medium-sized Czech universities.
Electrospinning is a breakthrough technique in materials science that enables the fabrication of polymer yarns with extraordinary nanoscale precision previously unattainable. Nanofiber-based yarns produced by the electrospinning process show unique structural and functional properties differing from traditional materials, such as an incredibly high surface area, mechanical properties leading to significantly increased durability, and architectures that can be designed according to specific requirements. This review details the transformative influence of electrospinning on yarn technology, emphasizing its incredible potential to go beyond the conventional constraints that have previously limited this field and thus fuel innovation in a vast range of applications across many industries. Thanks to innovative nanotechnology applications and high-end materials engineering, electrospun nanofibrous yarns have seriously redefined standards in many important areas, such as environmental sustainability, energy solutions, wearable technology, and biomedicine. However, significant challenges persist, requiring further scale-up and process optimization. This review emphasizes advancements made so far in the art of the electrospinning process for yarn development in pivotal positions for next-generation progress. The future of nanofiber-based yarns and their integration underscores the importance of such yarns in solving current global challenges.
The contemporary digital economy requires businesses to utilise creative skills to boost their performance. Utilising the Dynamic Managerial Capability (DMC) theory, we examine the influence of managerial capabilities—human capital (MHC), social capital (MSC), and cognition (MC) —on digital innovation (DI) and perceived financial performance (PFP) within the banking sector. The mediating influence of MC and DI on the link among MSC, MHC, and PFP was assessed. To achieve this, 728 bankers in Ghana were purposefully sampled, adopting a seven-point Likert scale for data collection regarding managers and employees. The analysis was conducted using the Partial Least Squares – Structural Equation Modelling (PLS-SEM). The results demonstrate that managerial cognition influences the interaction among managerial social capital, managerial human capital, digital innovation, and performance, with digital innovation not serving as a mediator. Managerial cognition serves as a conduit to human managerial capital, impacting perceived financial performance. The findings compel managers and policymakers to integrate DMC’s initiatives to enhance banks’ digital innovation, which is essential in the contemporary dynamic and digital landscape. The assessment has expanded the framework of dynamic capability theory in nascent communities, underscoring the influence of DMC on advancing digital innovation within the banking sector.
The accelerating growth of electric mobility and stationary storage has intensified demand for lithium-ion batteries, where raw materials represent 50–70% of total cost. Volatility in Ni, Co and Li markets therefore poses major risks for affordability and investment decisions. This study systematically evaluates existing battery-cost models with a specific focus on how raw-material price inputs are represented, standardized and propagated into final $/kWh outputs.A comparative review of sixteen representative models, including BatPaC, GREET®, EverBatt and CellEST, investigates methodological assumptions, cost-element structure, and transparency of data sources. This work provides the first review that jointly analyses deterministic cost models alongside probabilistic validation using real-world commodity volatility. Robustness is assessed through a 1,000-run Monte Carlo simulation based on historical price distributions of key metals.Results indicate clear differentiation among chemistries. NMC-111 exhibits the widest 5–95% uncertainty range due to cobalt sensitivity, whereas NMC-811 and NMC-9525 show narrower bands reflecting reduced cobalt exposure. Bottom-up process-based models offer stronger cost traceability and better uncertainty representation than top-down macro forecasts, which often lack explicit linkages to $/kWh outputs. Findings demonstrate that raw-material price treatment strongly influences forecast accuracy, and that scenario-free point estimates systematically under-represent market risk.To address these gaps, the study proposes a structured multi-source data acquisition strategy integrating public databases, commodity-market feeds and industry publications. The review enhances transparency, establishes reproducible comparison criteria, and quantifies price-volatility effects, offering actionable guidance for researchers, analysts and policymakers aiming to improve reliability and reduce uncertainty in LIB cost assessment.
Evaluation of motion symmetry is an important aspect of rehabilitation, fitness monitoring, and the detection of neurological disorders. In the simplest case, objective assessment of movement can be performed through the analysis of accelerometric data acquired by a single accelerometer. This paper presents a methodology for data acquisition using a wearable sensor embedded in a smartphone, wireless signal transmission, and subsequent computational analysis based on spectral analysis and wavelet decomposition at a selected level. The proposed methodology is applied to the estimation of gait symmetry using data collected during walking along a selected route. The study focuses on the analysis of motion data relevant to the detection of stability disorders associated with neurological problems, with the aim of providing additional information for biomedical specialists. Symmetry estimation is performed by analyzing the ratio of energy in selected frequency regions and in wavelet bands corresponding to odd and even steps during walking, combined with artificial intelligence techniques. Neural networks provided the best performance in distinguishing left and right steps, achieving an accuracy higher than 90%. The results demonstrate the potential of computational intelligence for the detection of specific neurological disorders and for diagnostic support in clinical practice. In addition, the proposed general tools may also find applications in engineering and robotics.
The objective of this study was to evaluate the effect of citrus fiber (1% wt/wt) on the physicochemical, rheological, textural, tribological, and sensory properties of quark cheese spreads with different fat content (10%, 15%, and 20% wt/wt). Physicochemical properties, including pH, DM, and water activity, were not affected by citrus fiber addition. Furthermore, rheological and textural analyses showed that citrus fiber increased samples' hardness and viscoelastic moduli, probably by reinforcing the protein-polysaccharide matrix, especially in low-fat variants. Tribological measurements indicated that higher fat improved lubrication, whereas fiber-containing quark cheese spread samples exhibited slightly higher coefficients of friction due to increased structural rigidity. Additionally, color analysis revealed minor shifts toward darker and more yellow tones when citrus fiber was used. Moreover, sensory evaluation confirmed that citrus fiber addition did not affect most of the tested organoleptic attributes, with slight improvement in flavor. The findings indicate that incorporating citrus fiber into quark cheese spreads can improve their functional properties, facilitate fat reduction, and promote the sustainable utilization of byproducts in dairy products.
