Search Results (3,216)

Background: Exercise-induced fatigue arises primarily from energy substrate depletion and the accumulation of metabolites such as lactate and ammonia, which impair performance and delay recovery. Emerging evidence implicates gut microbiota modulation—particularly via probiotics—as a means to optimize host energy metabolism and accelerate clearance of fatigue-associated by-products. Objective: This study aimed to determine whether live or heat-inactivated Lacticaseibacillus paracasei NB23 can enhance exercise endurance and attenuate fatigue biomarkers in a murine model. Methods: Forty male Institute of Cancer Research (ICR) mice were randomized into four groups (n = 10 each) receiving daily gavage for six weeks with vehicle, heat-killed NB23 (3 × 10¹⁰ cells/mouse/day), low-dose live NB23 (1 × 10¹⁰ CFU/mouse/day), or high-dose live NB23 (3 × 10¹⁰ CFU/mouse/day). Forelimb grip strength and weight-loaded swim-to-exhaustion tests assessed performance. Blood was collected post-exercise to measure serum lactate, ammonia, blood urea nitrogen (BUN), and creatine kinase (CK). Liver and muscle glycogen content was also quantified, and safety was confirmed by clinical-chemistry panels and histological examination. Results: NB23 treatment produced dose-dependent improvements in grip strength (p < 0.01) and swim endurance (p < 0.001). All NB23 groups exhibited significant reductions in post-exercise lactate (p < 0.0001), ammonia (p < 0.001), BUN (p < 0.001), and CK (p < 0.0001). Hepatic and muscle glycogen stores rose by 41–59% and 65–142%, respectively (p < 0.001). No changes in food or water intake, serum clinical-chemistry parameters, or tissue histology were observed. Conclusions: Our findings suggest that both live and heat-treated L. paracasei NB23 may contribute to improved endurance performance, increased energy reserves, and faster clearance of fatigue-related metabolites in our experimental model. However, these results should be interpreted cautiously given the exploratory nature and limitations of our study. Full article

This study examined the impact of storage and operational aging on the thermal stability, structural degradation, and electrical properties of styrene–butadiene rubber (SBR) compound by analyzing three distinct materials: a laboratory-stored sample, an operationally aged one, and an original unaged reference. Thermal degradation was analyzed through thermogravimetric analysis (TGA), which examined weight loss as a function of temperature and time at different heating rates. Results showed that the onset temperature and peak position in the 457 °C to 483 °C range remained stable. The activation energy (Ea) was determined using the Kissinger–Akahira–Sunose (KAS), Flynn–Wall–Ozawa (FWO), and Friedman methods, with the original unaged sample’s (OUS) Ea averaging 203.7 kJ/mol, decreasing to 163.47 kJ/mol in the laboratory-stored sample (LSS), and increasing to 224.18 kJ/mol in the operationally aged sample (OAS). The Toop equation was applied to estimate the thermal degradation lifetime at a 50% conversion rate. Since the material had been exposed to electricity, the evolution of electrical conductivity was studied and found to have remained stable after storage at around 0.070 S/cm. However, after operational aging, it showed a considerable increase in conductivity, to 0.321 S/cm. Scanning Electron Microscopy (SEM) was employed to analyze microstructural degradation and chemical changes, providing insights into the impact of aging on thermal stability and electrical properties. Full article

The rapid growth of electric vehicles (EVs) and new energy systems has put lithium-ion batteries at the center of the clean energy change. Nevertheless, to achieve the best battery performance, safety, and sustainability in many changing circumstances, major innovations are needed in Battery Management Systems (BMS). This review paper explores how artificial intelligence (AI) and digital twin (DT) technologies can be integrated to enable the intelligent BMS of the future. It investigates how powerful data approaches such as deep learning, ensembles, and models that rely on physics improve the accuracy of predicting state of charge (SOC), state of health (SOH), and remaining useful life (RUL). Additionally, the paper reviews progress in AI features for cooling, fast charging, fault detection, and intelligible AI models. Working together, cloud and edge computing technology with DTs means better diagnostics, predictive support, and improved management for any use of EVs, stored energy, and recycling. The review underlines recent successes in AI-driven material research, renewable battery production, and plans for used systems, along with new problems in cybersecurity, combining data and mass rollout. We spotlight important research themes, existing problems, and future drawbacks following careful analysis of different up-to-date approaches and systems. Uniting physical modeling with AI-based analytics on cloud-edge-DT platforms supports the development of tough, intelligent, and ecologically responsible batteries that line up with future mobility and wider use of renewable energy. Full article

The global energy transition and increasingly rigorous legal regulations aimed at climate protection are driving the search for alternative energy sources, including renewable energy sources (RESs) and waste heat. However, the mismatch between supply and demand presents a significant challenge. Thermal energy storage (TES) technologies, particularly mobile thermal energy storage (M-TES), offer a potential solution to address this gap. M-TES can not only balance supply and demand but also facilitate the transportation of heat from the source to the recipient. This paper reviews the current state of M-TES technologies, focusing on their technology readiness level, key operating parameters, and advantages and disadvantages. It is found that M-TES can be based on sensible heat, latent heat, or thermochemical reactions, with the majority of research and projects centered around latent heat storage. Regarding the type of research, significant progress has been made at the laboratory and simulation levels, while real-world implementation remains limited, with few pilot projects and commercially available systems. Despite the limited number of real-world M-TES implementations, currently existing M-TES systems can store up to 5.4 MWh in temperatures ranging from 58 °C to as high as 1300 °C. These findings highlight the potential of the M-TES and offer data for technology selection, simultaneously indicating the research gaps and future research directions. Full article

This study examines the energy behavior of a strengthening system consisting of a Fiber Reinforced Polymer (FRP) plate bonded to a rigid substrate and subjected to tensile loading, where the adhesive interface is governed by a bilinear bond–slip law with a vertical descending branch. The investigation focuses on the interaction between the elastic energy stored in the FRP and the adhesive interface, as well as the characteristic lengths that control the debonding process. Analytical expressions for the strain energy stored in both the FRP plate and the adhesive interface are derived, enabling the identification and evaluation of two critical characteristic lengths as the bond stress at the loaded end approaches its maximum value $l_c$, at which the elastic energies of the FRP and the adhesive interface converge, signaling energy saturation; and $l_{max}$, where the adhesive interface attains its peak energy absorption. Upon reaching the energy saturation state, the system undergoes failure through the sudden and complete debonding of the FRP from the substrate. The onset of unstable debonding is rigorously analyzed in terms of the first and second derivatives of the total potential energy with respect to the bond length. It is further demonstrated that abrupt debonding may also occur in cases where the length exceeds $l_c$ when the bond stress reaches its maximum, and the bond–slip law is characterized by a vertical branch. The findings provide significant insights into the energy balance and stability criteria governing the debonding failure mode in FRP-strengthened structures, highlighting the pivotal role of characteristic lengths in predicting both structural performance and failure mechanisms. Full article

Minimizing carbon dioxide emissions is crucial due to the generation of energy from fossil fuels. The significance of carbon capture and storage (CCS) technology, which is highly successful in mitigating carbon emissions, has increased. On the other hand, hydrogen is an important energy carrier for storing and transporting energy, and technologies that rely on hydrogen have become increasingly promising as the world moves toward a more environmentally friendly approach. Nevertheless, the integration of CCS technologies into power production processes is a significant challenge, requiring the enhancement of the combined power generation–CCS process. In recent years, there has been a growing interest in process intensification (PI), which aims to create smaller, cleaner, and more energy efficient processes. The goal of this research is to demonstrate the process intensification potential and to model and simulate a hybrid integrated–intensified adsorptive-membrane reactor process for simultaneous carbon capture and hydrogen production. A comprehensive, multi-scale, multi-phase, dynamic, computational fluid dynamics (CFD)-based process model is constructed, which quantifies the various underlying complex physicochemical phenomena occurring at the pellet and reactor levels. Model simulations are then performed to investigate the impact of dimensionless variables on overall system performance and gain a better understanding of this cyclic reaction/separation process. The results indicate that the hybrid system shows a steady-state cyclic behavior to ensure flexible operating time. A sustainability evaluation was conducted to illustrate the sustainability improvement in the proposed process compared to the traditional design. The results indicate that the integrated–intensified adsorptive-membrane reactor technology enhances sustainability by 35% to 138% for the chosen 21 indicators. The average enhancement in sustainability is almost 57%, signifying that the sustainability evaluation reveals significant benefits of the integrated–intensified adsorptive-membrane reactor process compared to HTSR + LTSR. Full article

This article presents a comparative study of the storage of energy produced by photovoltaic panels by means of two types of batteries: Lead–Acid and Lithium-Ion batteries. The work involved the construction of a model in MATLAB-Simulink for controlling the loading/unloading of storage batteries with energy produced by photovoltaic panels through a buck-type DC-DC convertor, controlled by means of the MPPT algorithm implemented through the method of incremental conductance based on a MATLAB function. The program for the MATLAB function was developed by the author in the C++ programming environment. The MPPT algorithm provides maximum energy transfer from the photovoltaic panels to the battery. The electric power taken over at a certain moment by Lithium-Ion batteries in photovoltaic panels is higher than the electric power taken over by Lead–Acid batteries. Two types of batteries were successively used in this model: Lead–Acid and Lithium-Ion batteries. Based on the results being obtained and presented in this work it may be affirmed that the storage battery Lithium-Ion is more performant than the Lead-Acid storage battery. At the Laboratory of Electrical Machinery and Drives of the Engineering Faculty of Bacau, an experimental stand was built for a storing system for electric energy produced by photovoltaic panels. For controlling DC-DC buck-type convertors, a program was developed in the programming environment Arduino IDE for implementing the MPPT algorithm for incremental conductance. The simulation part of this program is similar to that of the program developed in C++. Through conducting experiments, it was observed that, during battery charging, along with an increase in the charging voltage, an increase in the filling factor of the PWM signal controlling the buck DC-DC convertor also occurred. The findings of this study may be applicable to the storage of battery-generated electrical energy used for supplying electrical motors in electric cars. Full article

This study systematically investigates the homogenization-induced Laves phase dissolution kinetics and recrystallization mechanisms in laser powder bed fusion (L-PBF) processed IN718 superalloy. The as-built material exhibits a characteristic fine dendritic microstructure with interdendritic Laves phase segregation and high dislocation density, featuring directional sub-grain boundaries aligned with the build direction. Laves phase dissolution demonstrates dual-stage kinetics: initial rapid dissolution (0–15 min) governed by bulk atomic diffusion, followed by interface reaction-controlled deceleration (15–60 min) after 1 h at 1150 °C. Complete dissolution of the Laves phase is achieved after 3.7 h at 1150 °C. Recrystallization initiates preferentially at serrated grain boundaries through boundary bulging mechanisms, driven by localized orientation gradients and stored energy differentials. Grain growth kinetics obey a fourth-power time dependence, confirming Ostwald ripening-controlled boundary migration via grain boundary diffusion. Such a study is expected to be helpful in understanding the microstructural development of L-PBF-built IN718 under heat treatments. Full article

The energy generated from renewable sources has an intermittent nature since solar irradiation and wind speed vary continuously. Hence, their energy should be stored to be utilized throughout their shortage. There are various forms of energy storage systems while the most widespread technique is the battery storage system since its cost is low compared to other techniques. Therefore, batteries are employed in several applications like power systems, electric vehicles, and smart grids. Due to the merits of the lithium-ion (Li-ion) battery, it is preferred over other kinds of batteries. However, the accuracy of the Li-ion battery model is essential for estimating the state of charge (SOC). Additionally, it is essential for consistent simulation and operation throughout various loading and charging conditions. Consequently, the determination of real battery model parameters is vital. An innovative application of the red-billed blue magpie optimizer (RBMO) for determining the model parameters and the SOC of the Li-ion battery is presented in this article. The Shepherd model parameters are determined using the suggested optimization algorithm. The RBMO-based modeling approach offers excellent execution in determining the parameters of the battery model. The suggested approach is compared to other programmed algorithms, namely dandelion optimizer, spider wasp optimizer, barnacles mating optimizer, and interior search algorithm. Moreover, the suggested RBMO is statistically evaluated using Kruskal–Wallis, ANOVA tables, Friedman rank, and Wilcoxon rank tests. Additionally, the Li-ion battery model estimated via the RBMO is validated under variable loading conditions. The fetched results revealed that the suggested approach achieved the least errors between the measured and estimated voltages compared to other approaches in two studied cases with values of 1.4951 × 10⁻⁴ and 2.66176 × 10⁻⁴. Full article

The current study explores the changes in beef quality following partial freezing and during superchilled storage, alongside chilled storage comparisons. Kinetic models were developed to predict changes in colour difference (∆E), thiobarbituric acid−reactive substances (TBARS), total volatile basic nitrogen (TVB−N), drip loss and firmness. Beef samples were partially frozen in an air blast freezer at −30 °C for 9 min prior to storage at −5 °C, −4 °C, −2.8 °C, −1.8 °C. Chilled beef samples were directly stored at 2 °C and 6 °C without partial freezing. All samples were stored for 21 days. The lightness (L*), redness (a*), yellowness (b*) and colour difference (∆E) were significantly lower in superchilled storage samples compared to chilled storage samples. The pH of beef samples increased gradually over time (p < 0.05). TBARS, TVB−N and drip loss increased while firmness decreased with the increase in storage time in both storage conditions (p < 0.05). Overall, beef quality was affected by both storage duration and temperature. Firmness followed the first order kinetic model; drip loss, TVB−N, TBARS and colour difference (∆E) fitted the zero−order kinetic model. Temperature dependence was adequately modelled using Arrhenius−type equation with the activation energy values of 110.111, 52.870, 68.553, 119.480, 47.301 kJ/mol for drip loss, firmness, TBARS, TVB−N and colour difference (∆E), respectively. The models demonstrated strong predictive performance, with RMSE and MAPE values within ±10%. The developed kinetic models successfully predicted quality changes within the −5 °C to 6 °C temperature range. Full article

Renewable energy sources (RES) are the foundation of the ongoing energy transition in Poland and worldwide. However, increased use of RES has brought several challenges, as most of these sources are dependent on weather conditions. The instability and lack of control over electricity production lead to both overloads and power shortages in transmission and distribution networks. A significant advantage of biogas plants over sources such as photovoltaics or wind turbines is their ability to control electricity generation and align it with actual demand. Biogas produced during fermentation can be temporarily stored in a biogas tank above the digester and later used in an enlarged CHP unit to generate electricity and heat during peak demand periods. While demand-driven biogas plants operate similarly to traditional installations, their development requires navigating regulatory and administrative procedures, particularly those related to the grid connection of the generated electricity. In Poland, it has only recently become possible to obtain grid connection conditions for such installations, following the adoption of the Act of 28 July 2023, which amended the Energy Law and certain other acts. However, the biogas sector still faces challenges, particularly the need for effective incentive mechanisms and the removal of regulatory and economic barriers, especially given its estimated potential of up to 7.4 GW. Full article

Flood control infrastructure is essential for the development of cities and the population’s well-being. The goal is to protect human and economic resources by reducing the inundation area and controlling the flood level and peak discharges. Detention basins can do this by storing a large volume of water to be released after the peak discharge. By doing this, a large amount of energy is stored, which can be recovered via micro-hydropower. In addition, as the release flow is controlled and almost constant, Pumps as Turbines (PAT) could be a feasible and economic option in these cases. Thus, this study investigates the feasibility of micro-hydropower (MHP) in urban detention basins, using the Santa Lúcia detention basin in Belo Horizonte as a case study. The methodology involved hydrological modeling, hydraulic analysis, and economic and environmental assessment. The results demonstrated that PAT selection has a crucial role in the feasibility of the MHP, and exploiting rainfall with lower intensities but higher frequencies is more attractive. Using multiple PATs with different operating points also showed promising results in improving energy production. In addition to the economic benefits, the MHP in the detention basin produces minimal environmental impact and, as it exploits a wasted energy source, it also reduces the carbon footprint in the urban water cycle. Full article

Advanced air defense methods are essential to address the growing complexity of aerial threats. The increasing number of targets necessitates better defensive coordination, and a promising strategy involves the use of interceptors together to protect a specific area. This task fundamentally depends on accurately predicting their kinematic envelopes, or reachable sets. This paper presents a novel approach to determine the boundaries of reachable sets for aerodynamic interceptors, accounting for energy loss from drag, energy gain from thrust, variable acceleration limits, and autopilot dynamics. The devised numerical method approximates reachable sets for nonlinear problems using a constrained model predictive programming concept. Results demonstrate that explicitly accounting for input constraints, such as acceleration limits, significantly impacts the shape and area of the reachable boundaries. Furthermore, a sensitivity analysis was conducted to demonstrate the impact of parameter variations on the reachable set. Revealing the reachable set’s sensitivity to variations in thrust and drag coefficients, this analysis serves as a framework for considering parameter uncertainty and enables the evaluation of these effects prior to embedding the reachability boundaries into an offline database for guidance applications. The resulting boundaries, representing minimum and maximum ranges for various initial parameters, can be stored offline, allowing interceptors to estimate their own or allied platforms’ kinematic capabilities for cooperative strategies. Full article

Strategic petroleum reserves are critical for energy security. In Japan, 0.5 million kiloliters of crude oil (12% of the state-owned reserves) is stored using underground rock-cavern tanks, which comprise unlined horizontal tunnels bored into bedrock. Crude oil is held within the tank by water inside the tank, the pressure of which is kept higher than that of the crude oil by natural groundwater and irrigation water. This study applied microbial DNA-based monitoring to assess the water environments in and around national petroleum-stockpiling bases (the Kuji, Kikuma, and Kushikino bases) using the rock-cavern tanks. Forty-five water samples were collected from the rock-cavern tanks, water-supply tunnels, and observation wells. Principal-component analysis and hierarchical clustering indicated that microbial profiles of the water samples reflect the local environments of their origins. Particularly, the microbial profiles of water inside the rock-cavern tanks were distinct from other samples, revealing biological conditions and hence environmental characteristics within the tanks. Moreover, the clustering analysis indicated distinct features of water samples that have not been detected by other monitoring methods. Thus, microbial DNA-based monitoring provides valuable information on the in situ environments of rock-cavern tanks and can serve as an extremely sensitive measurement to monitor the underground oil storage. Full article

Digital Twins (DTs) are transforming manufacturing by bridging the physical and digital worlds, enabling real-time insights, predictive analytics, and enhanced decision making. In Industry 4.0, DTs facilitate automation and data integration, while Industry 5.0 emphasizes human-centric, resilient, and sustainable production. However, implementing DTs in robotic metal additive manufacturing (AM) remains challenging because of the complexity of the wire-based laser metal deposition (LMD) process, the need for real-time monitoring, and the demand for advanced defect detection to ensure high-quality prints. This work proposes a structured DT architecture for a robotic wire-based LMD cell, following a standard framework. Three DT implementations were developed. First, a real-time 3D simulation in RoboDK, integrated with a 2D Node-RED dashboard, enabled motion validation and live process monitoring via MQTT (message queuing telemetry transport) telemetry, minimizing toolpath errors and collisions. Second, an Industrial IoT-based system using KUKA iiQoT (Industrial Internet of Things Quality of Things) facilitated predictive maintenance by analyzing motor loads, joint temperatures, and energy consumption, allowing early anomaly detection and reducing unplanned downtime. Third, the Meltio dashboard provided real-time insights into the laser temperature, wire tension, and deposition accuracy, ensuring adaptive control based on live telemetry. Additionally, a prescriptive analytics layer leveraging historical data in FireStore was integrated to optimize the process performance, enabling data-driven decision making. Full article