Search Results (3,938)

This study aimed to optimize the inoculation dosage and fermentation duration to enhance the protein content and reduce soluble oligosaccharides in soybean meal using Aspergillus oryzae and assessed its performance in dog food extrusion. A 3 × 5 factorial design was used to determine the optimal fermentation conditions. These conditions were applied to ferment soybean meal in bulk for nutritional analysis. Finally, the impact of fermentation on extrusion processing was assessed by formulating and extruding four diets: SBM (30% soybean meal), AMF (30% soybean meal with 1% Amaferm^®—A. oryzae biomass), FSBM (30% fermented soybean meal), and SPI (18% soy protein isolate). Diets were extruded with a single-screw extruder, and physical characteristics of kibbles, particle size distribution, and viscosity of raw mixes were analyzed. The optimal fermentation conditions were 1 × 10⁴ spore/g substrate for 36 h, which increased the crude protein content by 4.63% DM, methionine and cysteine total content by 0.15% DM, and eliminated sucrose, while significantly reducing stachyose, raffinose, and verbascose (95.22, 87.37, and 41.82%, respectively). The extrusion results showed that FSBM had intermediate specific mechanical energy (SME), in-barrel moisture requirements, and sectional expansion index (198.7 kJ/kg, 28.2%, and 1.80, respectively) compared with SBM (83.7 kJ/kg, 34.5%, and 1.30, respectively) and SPI (305.3 kJ/kg, 33.5%, and 2.55, respectively). The FSBM also exhibited intermediate particle size distribution and the least raw mix viscosity. These findings demonstrate that A. oryzae fermentation enhances the nutrient profile of soybean meal while improving extrusion efficiency and kibble quality, supporting its potential use as a sustainable pet food ingredient. Full article

Lattice-based metamaterials have attracted much attention due to their excellent mechanical properties. Nevertheless, designing lattice materials with desired properties is still challenging, as their mesoscopic topology is extremely complex. Herein, the bidirectional evolutionary structural optimization (BESO) method is adopted to design lattice structures with maximum bulk modulus and elastic isotropy. Various lattice configurations are generated by controlling the filter radius during the optimization processes. Afterwards, the optimized lattices are fabricated using Stereo Lithography Appearance (SLA) printing technology. Experiments and numerical simulations are conducted to reveal the mechanical behavior of the topology-optimized lattices under quasi-static compression, which are compared with the traditional octet-truss (OT) and body-centered cubic (BCC) lattice structures. The results demonstrate that the topology-optimized lattices exhibited superior mechanical properties, including modulus, yield strength, and specific energy absorption, over traditional OT and BCC lattices. Moreover, apart from the elastic modulus, the yield stress and post-yield stress of the topology-optimized lattice structures with elastically isotropic constraints also present lower dependence on the loading direction. Accordingly, the topology optimization method can be employed for designing novel lattice structures with high performance. Full article

Background: Midfacial fractures are common outcomes of facial trauma. While younger individuals typically sustain these injuries through high-energy events like assaults and traffic or sports accidents, elderly patients increasingly present with fractures from low-energy mechanisms, primarily falls. Purpose: The aim of this study was to analyze age- and gender-specific patterns in midfacial fractures over a 10-year period, with emphasis on elderly individuals and low-energy trauma. Methods: A retrospective review was performed of proven midfacial fractures between 2013 and 2022 at the Department of Oral and Maxillofacial Surgery (University of Pécs, Hungary). The patients were stratified by age (<65 vs. ≥65 years) and gender. The variables included the injury mechanism, fracture localization, the dental status, hospitalization, and the presence of associated injuries. Bivariate analyses were performed, and the significance level was set to p < 0.05. Results: A total of 957 radiologically confirmed midfacial fracture cases were evaluated, of whom 344 (35.9%) were ≥65 years old. In the elderly group, females had a 19-fold higher risk for midfacial trauma than younger females (OR: 19.1, 95%CI: 9.30–39.21). In the older group, a fall was significantly the most frequent injury mechanism (OR: 14.5; 95%CI: 9.9–21.3), responsible for 89.5% of the cases, while hospitalization (OR: 0.36; 95%CI: 0.23–0.56) was less characteristic. Most of the fractures occurred in the zygomatic bone, in the zygomaticomaxillary complex, or in the anterior wall of the maxilla. Associated injuries in the elderly group included mostly lower limb injuries—particularly pertrochanteric femoral fractures in females—and upper limb injuries, with a slight male dominance. Conclusions: Low-energy falls are the primary cause of midfacial fractures in elderly patients, particularly in women. Tailored prevention and management strategies are essential for improving the outcomes in this growing demographic group. Full article

This study presents a comparative analysis of the annual performances of stationary and dual-axis sun-tracked photovoltaic–thermal (PVT) systems. The experimental research was conducted at a demonstration site in Oświęcim, Poland, where both systems were evaluated in terms of electricity and heat production. The test installation consisted of thirty stationary PVT modules and five dual-axis sun-tracking systems, each equipped with six PV modules. An innovative cooling system was developed for the PVT modules, consisting of a surface-mounted heat sink installed on the rear side of each panel. The system includes embedded tubes through which a cooling fluid circulates, enabling efficient heat recovery. The results indicated that the stationary PVT system outperformed a conventional fixed PV installation, whose expected output was estimated using PVGIS data. Specifically, the stationary PVT system generated 26.1 kWh/m² more electricity annually, representing a 14.8% increase. The sun-tracked PVT modules yielded even higher gains, producing 42% more electricity than the stationary system, with particularly notable improvements during the autumn and winter seasons. After accounting for the electricity consumed by the tracking mechanisms, the sun-tracked PVT system still delivered a 34% higher net electricity output. Moreover, it enhanced the thermal energy output by 85%. The findings contribute to the ongoing development of high-performance PVT systems and provide valuable insights for their optimal deployment in various climatic conditions, supporting the broader integration of renewable energy technologies in building energy systems. Full article

Sanicro 25 (X7NiCrWCuCoNb25-23-3-3-2) steel is specifically designed for use in superheater components within the latest generation of conventional power plants. These power plants operate under conditions often referred to as super-ultra-supercritical, with steam parameters that can reach up to 30 MPa and temperatures of 653 °C for fresh steam and 672 °C for reheated steam. While last-generation supercritical power plants still rely on fossil fuels, they represent a significant step forward in more sustainable energy production. The most sophisticated facilities of this kind can achieve thermodynamic efficiencies exceeding 47%. This study aimed to conduct a detailed analysis of the initial precipitation processes occurring in Sanicro 25 steel within the temperature range of 700–900 °C. The temperature of 700 °C corresponds to the operational conditions of this material, particularly in secondary steam superheaters in thermal power plants that operate under ultra-supercritical parameters. Understanding precipitation processes is crucial for optimizing mechanical performance, particularly in terms of long-term strength and creep resistance. To accurately assess the microstructural changes that occur during the early stages of service, a digital twin approach was employed, which included CALPHAD simulations and experimental heat treatments. Experimental annealing tests were conducted in air within the temperature range of 700–900 °C. Precipitation behavior was simulated using the Thermo-Calc 2025a with Dictra software package. The results from Prisma simulations correlated well with the experimental data related to the kinetics of phase transformations; however, it was noted that the predicted sizes of the precipitates were generally smaller than those observed in experiments. Additionally, computational limitations were encountered during some simulations due to the complexity arising from the numerous alloying elements present in Sanicro 25 steel. The microstructural evolution was investigated using various methods, including light microscopy (LM), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). Full article

The wave spectrum, as a key statistical feature describing wave energy distribution, is crucial for understanding wave propagation mechanisms and supporting ocean engineering applications. This study, based on ERA5 reanalysis spectrum data, proposes a model combining CNN and xLSTM for rapid gridded wave spectrum prediction over the Bohai and Yellow Seas domain. It uses 2D gridded spectrum data rather than a spectrum at specific points as input and analyzes the impact of various input factors at different time lags on wave development. The results show that incorporating water depth and mean sea level pressure significantly reduces errors. The model performs well across seasons with the seasonal spatial average root mean square error (SARMSE) of spectral energy remaining below 0.040 m²·s and RMSEs for significant wave height (SWH) and mean wave period (MWP) of 0.138 m and 1.331 s, respectively. At individual points, the spectral density bias is near zero, correlation coefficients range from 0.95 to 0.98, and the peak frequency RMSE is between 0.03 and 0.04 Hz. During a typical cold wave event, the model accurately reproduces the energy evolution and peak frequency shift. Buoy observations confirm that the model effectively tracks significant wave height trends under varying conditions. Moreover, applying a frequency-weighted loss function enhances the model’s ability to capture high-frequency spectral components, further improving prediction accuracy. Overall, the proposed method shows strong performance in spectrum prediction and provides a valuable approach for regional wave spectrum modeling. Full article

β-hydroxybutyrate (BHB) is the most abundant ketone body produced during ketosis, a process initiated by glucose depletion and the β-oxidation of fatty acids in hepatocytes. Traditionally recognized as an alternative energy substrate during fasting, caloric restriction, and starvation, BHB has gained attention for its diverse signaling roles in various physiological processes. This review explores the emerging therapeutic potential of BHB in the context of sarcopenia, metabolic disorders, and neurodegenerative diseases. BHB influences gene expression, lipid metabolism, and inflammation through its inhibition of Class I Histone deacetylases (HDACs) and activation of G-protein-coupled receptors (GPCRs), specifically HCAR2 and FFAR3. These actions lead to enhanced mitochondrial function, reduced oxidative stress, and regulation of inflammatory pathways, with implication for muscle maintenance, neuroprotection, and metabolic regulation. Moreover, BHB’s ability to modulate adipose tissue lipolysis and immune responses highlight its broader potential in managing chronic metabolic conditions and aging. While these findings show BHB as a promising therapeutic agent, further research is required to determine optimal dosing strategies, long-term effects, and its translational potential in clinical settings. Understanding BHB’s mechanisms will facilitate its development as a novel therapeutic strategy for multiple organ systems affected by aging and disease. Full article

Facial expression recognition (FER) is a critical research direction in artificial intelligence, which is widely used in intelligent interaction, medical diagnosis, security monitoring, and other domains. These applications highlight its considerable practical value and social significance. Face expression recognition models often need to run efficiently on mobile devices or edge devices, so the research on lightweight face expression recognition is particularly important. However, feature extraction and classification methods of lightweight convolutional neural network expression recognition algorithms mostly used at present are not specifically and fully optimized for the characteristics of facial expression images, yet fail to make full use of the feature information in face expression images. To address the lack of facial expression recognition models that are both lightweight and effectively optimized for expression-specific feature extraction, this study proposes a novel network design tailored to the characteristics of facial expressions. In this paper, we refer to the backbone architecture of MobileNet V2 network, and redesign LightExNet, a lightweight convolutional neural network based on the fusion of deep and shallow layers, attention mechanism, and joint loss function, according to the characteristics of the facial expression features. In the network architecture of LightExNet, firstly, deep and shallow features are fused in order to fully extract the shallow features in the original image, reduce the loss of information, alleviate the problem of gradient disappearance when the number of convolutional layers increases, and achieve the effect of multi-scale feature fusion. The MobileNet V2 architecture has also been streamlined to seamlessly integrate deep and shallow networks. Secondly, by combining the own characteristics of face expression features, a new channel and spatial attention mechanism is proposed to obtain the feature information of different expression regions as much as possible for encoding. Thus improve the accuracy of expression recognition effectively. Finally, the improved center loss function is superimposed to further improve the accuracy of face expression classification results, and corresponding measures are taken to significantly reduce the computational volume of the joint loss function. In this paper, LightExNet is tested on the three mainstream face expression datasets: Fer2013, CK+ and RAF-DB, respectively, and the experimental results show that LightExNet has 3.27 M Parameters and 298.27 M Flops, and the accuracy on the three datasets is 69.17%, 97.37%, and 85.97%, respectively. The comprehensive performance of LightExNet is better than the current mainstream lightweight expression recognition algorithms such as MobileNet V2, IE-DBN, Self-Cure Net, Improved MobileViT, MFN, Ada-CM, Parallel CNN(Convolutional Neural Network), etc. Experimental results confirm that LightExNet effectively improves recognition accuracy and computational efficiency while reducing energy consumption and enhancing deployment flexibility. These advantages underscore its strong potential for real-world applications in lightweight facial expression recognition. Full article

With the rapid development of intelligent transportation systems (ITSs) and Internet of Things (IoT), vehicle-mounted edge computing (VEC) networks are facing the challenge of handling increasingly growing computation-intensive and latency-sensitive tasks. In the UAV-assisted VEC network, by introducing mobile edge servers, the coverage of ground infrastructure is effectively supplemented. However, there is still the problem of decision-making lag in a highly dynamic environment. This paper proposes a deep reinforcement learning framework based on the long short-term memory (LSTM) network for trajectory prediction to optimize resource allocation in UAV-assisted VEC networks. Uniquely integrating vehicle trajectory prediction with the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, this framework enables proactive computation offloading and UAV trajectory planning. Specifically, we design an LSTM network with an attention mechanism to predict the future trajectory of vehicles and integrate the prediction results into the optimization decision-making process. We propose state smoothing and data augmentation techniques to improve training stability and design a multi-objective optimization model that incorporates the Age of Information (AoI), energy consumption, and resource leasing costs. The simulation results show that compared with existing methods, the method proposed in this paper significantly reduces the total system cost, improves the information freshness, and exhibits better environmental adaptability and convergence performance under various network conditions. Full article

The majority of studies of fungal utilization of chitosan are associated with the production of a specific enzyme, chitosanase, which catalyzes the hydrolytic cleavage of the macrochain. In our opinion, the development of approaches to obtaining materials with new functional properties based on non-destructive chitosan transformation by living organisms and their enzyme systems is promising. This study was conducted using a wide range of classical and modern methods of microbiology, biochemistry, and physical chemistry. The ability of the ascomycete Fusarium oxysporum Schltdl. to modify films of chitosan with average-viscosity molecular weights of 200, 450, and 530 kDa was discovered. F. oxysporum was shown to use chitosan as the sole source of carbon/energy and actively overgrew films without deformations and signs of integrity loss. Scanning electron microscopy (SEM) recorded an increase in the porosity of film substrates. An analysis of the FTIR spectra revealed the occurrence of oxidation processes and crosslinking of macrochains without breaking β-(1,4)-glycosidic bonds. After F. oxysporum growth, the resistance of the films to mechanical dispersion and the degree of ordering of the polymer structure increased, while their solubility in the acetate buffer with pH 4.4 and sorption capacity for Fe²⁺ and Cu²⁺ decreased. Elemental analysis revealed a decrease in the nitrogen content in chitosan, which may indicate its inclusion into the fungal metabolism. The film transformation was accompanied by the production of extracellular hydrolase (different from chitosanase) and peroxidase, as well as biosurfactants. The results obtained indicate a specific mechanism of aminopolysaccharide transformation by F. oxysporum. Although the biochemical mechanisms of action remain to be analyzed in detail, the results obtained create new ways of using fungi and show the potential for the use of Fusarium and/or its extracellular enzymes for the formation of chitosan-containing materials with the required range of functional properties and qualities for biotechnological applications. Full article

In this work, we provide a promising way to alleviate the Hubble tension within the framework of Myrzakulov $f(R,T)$ gravity. The latter incorporates both curvature and torsion under a non-special connection. We consider the $f(R,T)=R+\alpha R^2$ class, which leads to modified Friedmann equations and an effective dark energy sector. Within this class, we make specific connection choices in order to obtain a Hubble function that coincides with that of $\Lambda$CDM at early times while yielding higher $H_0$ values at late times. The reason behind this behavior is that the dark energy equation of state exhibits phantom behavior, which is known to be a sufficient mechanism for alleviating the $H_0$ tension. A full observational comparison with various datasets, including the Cosmic Microwave Background (CMB), is required to test the viability of this scenario. Strictly speaking, the present work does not provide a complete solution to the Hubble tension but rather proposes a promising way to alleviate it. Full article

Marine–continental transitional shale is a promising unconventional gas reservoir, playing an increasingly important role in China’s energy portfolio. However, compared to marine shale, research on marine–continental transitional shale’s fractal characteristics of pore structure and complete pore size distribution remains limited. In this work, high-pressure mercury intrusion, N₂ adsorption, and CO₂ adsorption techniques, combined with fractal geometry modeling, were employed to characterize the pore structure of the Shanxi Formation marine–continental transitional shale. The shale exhibits generally high TOC content and abundant clay minerals, indicating strong hydrocarbon-generation potential. The pore size distribution is multi-modal: micropores and mesopores dominate, contributing the majority of the specific surface area and pore volume, whereas macropores display a single-peak distribution. Fractal analysis reveals that micropores have high fractal dimensions and structural regularity, mesopores exhibit dual-fractal characteristics, and macropores show large variations in fractal dimension. Characteristics of pore structure is primarily controlled by TOC content and mineral composition. These findings provide a quantitative basis for evaluating shale reservoir quality, understanding gas storage mechanisms, and optimizing strategies for sustainable of oil and gas development in marine–continental transitional shales. Full article

At a critical juncture in the global low-carbon transition, the role of artificial intelligence (AI) in facilitating low-carbon growth has become increasingly significant. To accelerate the integration of AI with socio-economic development, China has established National New Generation Artificial Intelligence Innovation and Development Pilot Zones (AIPZ). However, the specific impact of these zones on low-carbon development remains unclear. This study utilized panel data from 30 provinces in China from 2013 to 2022 and employed the multi-period difference-in-differences (DID) model and the spatial autoregressive difference-in-differences (SARDID) model to examine the carbon emissions reduction effects of the AIPZ policy and its spatial spillover effects. The findings revealed that the policy significantly reduced carbon emissions intensity (CEI) across provinces, with an average reduction effect of 6.9%. The analysis of the impact mechanism confirmed the key role of human, technological, and financial resources. Heterogeneity analysis indicated varying effects across regions, with more significant reductions in eastern and energy-rich areas. Further analysis using the SARDID model confirmed spatial spillover effects on CEI. This paper aims to enhance understanding of the relationship between AIPZ and CEI and provide empirical evidence for policymakers during the low-carbon transition. By exploring the potential of the AIPZ policy in emissions reduction, it proposes targeted strategies and implementation pathways for policymakers and industry participants to promote the sustainable development of China’s low-carbon economy. Full article

The advancement of next-generation lubricants is pivotal for enhancing energy efficiency and mitigating environmental impacts across diverse industrial applications. This review systematically examines recent developments in lubricant technologies, with a particular focus on sustainable strategies incorporating bio-based feedstocks, nanostructured additives, and hybrid formulations. These innovations are designed to reduce friction and wear, decrease energy consumption, and prolong the operational lifespan of mechanical systems. A critical assessment of tribological behavior, environmental compatibility, and functional performance is presented. Furthermore, the integration of artificial intelligence (AI) into lubricant formulation and performance prediction is explored, highlighting its potential to accelerate development cycles and enable application-specific optimization through data-driven approaches. The findings emphasize the strategic role of eco-innovative lubricants in supporting low-carbon technologies and facilitating the transition toward sustainable energy systems. Full article

Improving the energy efficiency of residential buildings is essential for achieving global climate goals and reducing environmental impact. This study analyzes the Total Performance approach using the example of a modern semi-detached house built by a Polish developer, as an example. The building is designed with integrated systems that minimize energy consumption while maintaining resident comfort. The building is equipped with an air-to-water heat pump, underfloor heating, mechanical ventilation with heat recovery, and automatic temperature control systems. Energy efficiency was assessed using ArCADia–TERMOCAD 8.0 software in accordance with Polish Technical Specifications (TS) and verified by monitoring real-time electricity consumption during the heating season. The results show a PED from non-renewable sources of 54.05 kWh/(m²·year), representing a 23% reduction compared to the Polish regulatory limit of 70 kWh/(m²·year). Real-time monitoring conducted from December 2024 to April 2025 confirmed these results, indicating an actual energy demand of approximately 1771 kWh/year. Domestic hot water (DHW) preparation accounted for the largest share of energy consumption. Despite its dependence on grid electricity, the building has the infrastructure to enable future photovoltaic (PV) installation, offering further potential for emissions reduction. The results confirm that Total Performance strategies are not only compliant with applicable standards, but also economically and environmentally viable. They represent a scalable model for sustainable residential construction, in line with the European Union’s (EU’s) decarbonization policy and the goals of the European Green Deal. Full article