Search Results (288)

Obesity and its related diseases severely threaten people’s health, causing persistently high morbidity and mortality worldwide. The abnormal proliferation and hypertrophy of adipocytes mediate the expansion of adipose tissue, which is the main cause of obesity-related diseases. Inhibition of cell proliferation during the mitotic clonal expansion (MCE) period of adipogenesis may be a promising strategy for preventing and treating obesity. Arthrocolins are a series of fluorescent dye-like complex xanthenes from engineered Escherichia coli, with potential anti-tumor and antifungal activities. However, the role and underlying mechanisms of these compounds in adipocyte differentiation remain unclear. In this study, we discovered that arthrocolin B, a member of the arthrocolin family, significantly impeded adipogenesis by preventing the accumulation of lipid droplets and triglycerides, as well as by downregulating the expression of key factors involved in adipogenesis, such as SREBP1, C/EBPβ, C/EBPδ, C/EBPα, PPARγ, and FABP4. Moreover, we revealed that this inhibition might be a consequence of cell cycle arrest during the MCE of adipocyte differentiation, most likely by modulating the p53, AKT, and ERK pathways, upregulating the expression of p21 and p27, and repressing the expression of CDK1, CDK4, Cyclin A2, Cyclin D1, and p-Rb. Additionally, arthrocolin B could promote the expression of CPT1A during adipocyte differentiation, implying its potential role in fatty acid oxidation. Overall, our research concludes that arthrocolin B has the ability to suppress the early stages of adipocyte differentiation mainly by modulating the signaling proteins involved in cell cycle progression. This work broadens our understanding of the function and mechanisms of arthrocolins in regulation of adipogenesis and might provide a potential lead compound for treating the obesity. Full article

The use of waste, recycled, and modified materials is increasingly popular in roadway construction for sustainability and pavement longevity. This research examines the combination of steel slag (SS) and low-density polyethylene (LDPE), commonly used in plastic bags and steel manufacturing by-products, to mitigate environmental pollution. LDPE was tested as a binder modifier in two bitumen grades, 60–70 and 80–100, at concentrations of 3%, 5%, and 7% by weight. SS was used as a replacement for coarse aggregate. The physical properties of both modified and unmodified bitumen grades and SS were analyzed before creating and testing hot-mix asphalt (HMA) samples. The dynamic modulus of these samples was measured at temperatures of 4.4 °C, 21.1 °C, 37.8 °C, and 54.4 °C with frequencies of 0.1 Hz, 0.5 Hz, 1 Hz, 5 Hz, 10 Hz, and 25 Hz. Master curves were developed, and the dynamic modulus data underwent design of experiment (DOE) and computational intelligence (CI) analyses. Using KENPAVE, a mechanistic–empirical tool, the analysis assessed the design life and enhancements in damage ratio for each modifier and grade. The results showed that adding LDPE increases the softening point and penetration grade but decreases ductility due to increased bitumen stiffness, leading to premature fatigue failure at higher LDPE levels. Both 3% LDPE and 3% SS-modified LDPE improved Marshall Stability and dynamic modulus across all temperature and frequency ranges. Specifically, 3% LDPE enhanced stability by 13–16% and 3% SS-LDPE by 30–32%. The KENPAVE results for 3% LDPE showed a design life improvement of 19–25% and a damage ratio reduction of 15–18%. In comparison, 3% SS-LDPE demonstrated a design life improvement of 50–60% and a damage ratio reduction of 25–35%. Overall, this study concludes that 3% LDPE- and 3% SS-LDPE-modified HMA in both bitumen grades 60–70 and 80–100 provide optimal results for improving pavement performance. Full article

This study was performed to determine the effects of dietary supplementation of Bacillus amyloliquefaciens FD777 (BA) and Macleaya cordata extract (MCE) on the performance, morbidity and mortality rates, body measurements, immunity, rumen parameters, antioxidant parameters, microbiome level, and profitability of calves during the pre-weaning period. In the study, 51 calves were divided into three groups as one control and two treatment groups considering their age (1 day old), gender (nine females and eight males in each group), and birth weight (37.7 ± 0.4 kg). The calves in the control group (CON) were fed milk without supplements whereas the first treatment group (BA) was fed milk containing 10 mL/day/head of Bacillus amyloliquefaciens FD777 and the second treatment group (MCE) was fed milk containing 2 g/day/head of MCE. As a result, supplementing BA and MCE to calf milk had no significant effect on body weight (BW), dry matter intake (DMI), feed efficiency (FE), morbidity and mortality rates, rumen pH, IgG, IgA, and IgM values, and gastrointestinal microbiota (p > 0.05). On the other hand, it was determined that body weight gain (BWG), body length, body depth, rump width, withers height change, rump height change, rump width change, and serum GSH level increased significantly in the BA group compared to the control group (p < 0.05). According to the partial budget analysis, despite the additional cost of supplementing BA to the calf milk, no calf deaths and lower disease were observed in this group, unlike the other groups, resulting in a lowest calf rearing cost and highest profit. In calves receiving MCE, withers height, rump height, body length, rump width, body depth, chest circumference change, withers height change, rump height change, and rump width change values increased significantly compared with the control group (p < 0.05). In conclusion, the results obtained not only reveal the positive effects of BA and MCE on calves during the pre-weaning period, but also encourage the necessity of investigating their effects on the long-term performance of animals and farm economies. Full article

Today, cloud computing is a widely used technology that provides a wide range of services to numerous sectors around the world. This technology depends on the interaction and cooperation of virtual machines (VMs) to complete various computing tasks, propagating malware attacks quickly due to the complexity of cloud computing environments and users’ interfaces. As a result of the rising demand for cloud computing from multiple perspectives for complete analysis and decision-making across a range of life disciplines, multi-cloud environments (MCEs) are established. Therefore, in this work, we discuss impacted mathematical modeling for the MCEs’ network dynamics using two deterministic and stochastic approaches. In both approaches, appropriate assumptions are considered. Then, the proposed networks’ VMs are classified to have six different possible states covering media, healthcare, finance, and educational servers. After that, the two developed modeling approaches’ solution existence, uniqueness, equilibrium, and stability are carefully investigated. Using an optimal control strategy, both proposed models are tested for sustaining a certain level of security of the VMs’ states and reducing the propagation of malware within the networks. Finally, we verify the theoretical results by employing numerical simulations to track the malware’s propagation immunization. Results showed how the implemented control methods maintained the essential objectives of managing malware infections. Full article

Adipocytes secrete adipokines, bioactive molecules crucial for various physiological processes, such as enhancing insulin sensitivity, promoting wound healing, supporting hair growth, and exhibiting anti-aging effects on the skin. With the growing global demand for sustainable and alternative protein sources, insect-derived proteins, particularly from Tenebrio molitor (mealworms), have gained attention due to their high nutritional value and functional bioactivities. This study aims to explore the potential of mealworm-derived protein hydrolysates as novel bioactive materials for promoting adipogenesis and improving adipokine expression, with applications in metabolic health and skin regeneration. Protein hydrolysates (<1 kDa) were prepared using enzymatic hydrolysis with three proteases (alcalase, flavourzyme, and neutrase) and evaluated for their adipogenic activity in 3T3-L1 preadipocytes. Among them, the flavourzyme-derived hydrolysate (Fh-T) exhibited the most significant effects, enhancing adipogenic differentiation and lipid accumulation. Fh-T facilitated adipogenesis by promoting mitotic clonal expansion (MCE) during the early stage of differentiation, which was associated with the upregulation of C/EBPδ and the downregulation of p27. These findings underscore the potential of mealworm-derived protein hydrolysates, particularly Fh-T, as sustainable and functional ingredients for use in glycemic control, skin health, and tissue regeneration. This study provides valuable insights into the innovative use of alternative protein sources in functional foods and cosmeceuticals. Full article

This research article proposes a new method for an enhanced Flexible Manufacturing System (FMS) using a combination of smart methods. These methods use a set of three technologies of Industry 4.0, namely Artificial Intelligence (AI), Digital Twin (DT), and Wi-Fi-based indoor localization. The combination tackles the problem of asset tracking through Wi-Fi localization using machine-learning algorithms. The methodology utilizes the extensive “UJIIndoorLoc” dataset which consists of data from multiple floors and over 520 Wi-Fi access points. To achieve ultimate efficiency, the current study experimented with a range of machine-learning algorithms. The algorithms include Support Vector Machines (SVM), Random Forests (RF), Decision Trees, K-Nearest Neighbors (KNN) and Convolutional Neural Networks (CNN). To further optimize, we also used three optimizers: ADAM, SDG, and RMSPROP. Among the lot, the KNN model showed superior performance in localization accuracy. It achieved a mean coordinate error (MCE) between 1.2 and 2.8 m and a 100% building rate. Furthermore, the CNN combined with the ADAM optimizer produced the best results, with a mean squared error of 0.83. The framework also utilized a deep reinforcement learning algorithm. This enables an Automated Guided Vehicle (AGV) to successfully navigate and avoid both static and mobile obstacles in a controlled laboratory setting. A cost-efficient, adaptive, and resilient solution for real-time tracking of assets is achieved through the proposed framework. The combination of Wi-Fi fingerprinting, deep learning for localization, and Digital Twin technology allows for remote monitoring, management, and optimization of manufacturing operations. Full article

Cryogenic magnetic refrigerants based on the magnetocaloric effect (MCE) hold significant potential as substitutes for the expensive and scarce He-3. Gd(III)-based complexes are considered excellent candidates for low-temperature magnetic refrigerants. We have synthesized a series of Ln(III)-based metal-organic framework (MOF) Ln-3D (Ln = Gd/Dy) by the slow release of oxalates in situ from organic ligands (disodium edetate dehydrate (EDTA-2Na) and thiodiglycolic acid). Structural analysis shows that the Ln-3D is a neutral 3D framework with one-dimensional channels connected by [Ln(H₂O)₃]³⁺ as nodes and C₂O₄²⁻ as linkers. Magnetic measurements show that Gd-3D exhibits very weak antiferromagnetic interactions with a maximum −ΔS_m value of 36.6 J kg⁻¹ K⁻¹ (−ΔS_v = 74.47 mJ cm⁻³ K⁻¹) at 2 K and 7 T. The −ΔS_m value is 28.4 J kg⁻¹ K⁻¹ at 2 K and 3 T, which is much larger than that of commercial Gd₃Ga₅O₁₂ (GGG), indicating its potential as a low-temperature magnetic refrigerant. Full article

Developmental dysplasia of the hip (DDH) is a prevalent developmental condition that necessitates early detection and treatment. Follow–up, as well as therapeutic decision-making in children younger than four years, is challenging because the center–edge (CE) angle of Wiberg is not reliable in this age group. The authors propose a modification of the CE angle (MCE) to achieve comparable reliability with the CE among children younger than four and set diagnostic thresholds for the diagnosis of DDH. 952 anteroposterior pelvic radiographs were retrospectively reviewed. The MCE is defined on X-ray pelvic overview images as the angle between the line connecting the epiphyseal joint center and the outer edge of the acetabulum, and perpendicular to the Hilgenreiner line. The MCE angle exhibited high sensitivity and specificity, as well as intrarater variability comparable to the CE among children younger and older than four years. The authors recommend cut-off values for the MCE angle; for children under four years old, the angle should be equal to or greater than 15 degrees; for those under eight years old, it should be equal to or greater than 20 degrees; and for those eight years old and older, it should be equal to or greater than 25 degrees. However, the MCE angle’s reliability diminishes around the age of nine due to the curvature of the growth plate, which complicates accurate measurement. This study showed that the MCE angle can be used adequately in children under four years and could be used as a progression parameter to diagnose DDH. Full article

In this work, we study the magnetocaloric effect (MCE) in a working substance corresponding to a square lattice of spins with Q possible orientations, known as the “Q-state clock model”. When the Q-state clock model has $Q\ge 5$ possible configurations, it presents the famous Berezinskii–Kosterlitz–Thouless (BKT) phase associated with vortex states. We calculate the thermodynamic quantities using Monte Carlo simulations for even Q numbers, ranging from $Q=2$ to $Q=8$ spin orientations per site in a lattice. We use lattices of different sizes with $N=L\times L=8^2,{16}^2,{32}^2,{64}^2,\mathrm{and}{128}^2$ sites, considering free boundary conditions and an external magnetic field varying between $B=0$ and $B=1.0$ in natural units of the system. By obtaining the entropy, it is possible to quantify the MCE through an isothermal process in which the external magnetic field on the spin system is varied. In particular, we find the values of Q that maximize the MCE depending on the lattice size and the magnetic phase transitions linked with the process. Given the broader relevance of the Q-state clock model in areas such as percolation theory, neural networks, and biological systems, where multi-state interactions are essential, our study provides a robust framework in applied quantum mechanics, statistical physics, and related fields. Full article

The contamination of water by heavy metals poses an escalating risk to human health and the environment, underscoring the critical need for efficient removal methods to secure safe water resources. This study evaluated the performance of four cationic exchange materials (labeled “PS—DVB”, “PA—DVB”, “TFSA”, and “OGL”) in removing or harvesting metals such as copper, silver, lead, cobalt, and nickel from aqueous solutions, several of which are precious and/or classified as Critical Raw Materials (CRMs) due to their economic importance and supply risk. The objective was to screen and benchmark the four ion exchange materials for water treatment applications by investigating their metal sequestration capacities. Experiments were conducted using synthetic solutions with controlled metal concentrations, analyzed through ICP-OES, and supported by kinetic modeling. The adsorption capacities (qe) obtained experimentally were compared with those predicted by pseudo-first-order and pseudo-second-order models. This methodology enables high precision and reproducibility, validating its applicability for assessing ion exchange performance. The results indicated that PS—DVB and PA—DVB resins proved to be of “wide range”, exhibiting high efficacy for most of the metals tested, including CRM-designated ones, and suggesting their suitability for water purification. Additionally, the second-life Nafion-based “TFSA” material demonstrated commendable performance, highlighting its potential as a viable and technologically advanced alternative in water treatment. Lastly, the lignin-based material, “OGL”, representing the most innovative and sustainability apt option, offered relevant performance only in selected cases. The significant differences in performance among the resins underscore the impact of structural and compositional factors on adsorption efficiency. This study offers valuable insights for investigating and selecting new sustainable materials for treating contaminated water, opening new pathways for targeted and optimized solutions in environmental remediation. Full article

This study aimed to evaluate the effects of Mosla chinensis extract (MCE) on broiler intestinal health. A total of 240 1-day-old Arbor Acres (AA) broilers (balanced for sex) were randomly allocated into four treatment groups, each with six replicates of 10 chickens. The study comprised a starter phase (days 1–21) and a grower phase (days 22–42). The control group (C) received a basal diet, while the experimental groups were supplemented with low (S1, 500 mg/kg), medium (S2, 1000 mg/kg), and high doses (S3, 2000 mg/kg) of MCE. The results showed that MCE supplementation significantly improved average daily gain in broilers (p < 0.05) and reduced the feed-to-gain ratio in broilers. Additionally, MCE enhanced the anti-inflammatory and antioxidant capacity of broilers. In the duodenum and cecum, MCE significantly upregulated the expression of tight junction proteins Claudin-1, and Occludin, with the high-dose group showing the strongest effect on intestinal barrier protection (p < 0.05). There was no significant difference in ZO-1 in dudenum (p > 0.05). Microbial analysis indicated that MCE supplementation significantly reduced the Chao and Sobs indices in both the small and large intestines (p < 0.05). At the same time, the Coverage index of the small intestine increased, with the high-dose group demonstrating the most pronounced effect. Beta diversity analysis revealed that MCE had a significant modulatory effect on the microbial composition in the large intestine (p < 0.05), with a comparatively smaller impact on the small intestine. Furthermore, MCE supplementation significantly increased the relative abundance of Ruminococcaceae and Alistipes in the large intestine, along with beneficial genera that promote short-chain fatty acid (SCFA) production, thus optimizing the gut microecological environment. Correlation analysis of SCFAs further confirmed a significant association between the enriched microbiota and the production of acetate, propionate, and butyrate (p < 0.05). In conclusion, dietary supplementation with MCE promotes healthy growth and feed intake in broilers and exhibits anti-inflammatory and antioxidant effects. By optimizing gut microbiota composition, enhancing intestinal barrier function, and promoting SCFA production, MCE effectively maintains gut microecological balance, supporting broiler intestinal health. Full article

Land utilization—a crucial resource for human survival and development—reflects the outcomes of intricate interactions between human communities and their respective environments. The Jianmen Shu Road Heritage Area presents both opportunities and challenges in terms of protection and development. Any alterations in its land use and landscape patterns directly impact the sustainable development of the regional environment and heritage sites. In this study, we considered three cities along the Jianmen Shu Road, analyzed the evolution characteristics of land use and landscape patterns from 2012 to 2022, and used the multi-criteria evaluation–cellular automata-Markov (MCE-CA-Markov) model to predict the land use and landscape patterns in 2027. The results show the following: (1) From 2012 to 2022, forest land was at its greatest extent, the growth rate of forest land increased, the loss rate of cropland increased, and impervious land continued to expand. (2) From 2012 to 2022, the degrees of fragmentation in cropland, impervious land, and grassland increased; water area had the highest connectivity; forest land had the lowest connectivity; and barren land had the highest degree of separation. The degree of fragmentation and connectivity of the landscape patterns decreased, the degree of complexity increased, and landscape diversity increased and gradually stabilized. (3) Predictions for 2022–2027 indicate that forest land, impervious land, grassland, and barren land will increase, whereas cropland and the water area will decrease. The growth rate of grassland will increase, the loss rates of cropland and water area will decrease, and the growth rates of impervious land and forest land will decrease. (4) Further predictions for 2022–2027 indicate that the density and complexity of the grassland edge will decrease, whereas the fragmentation and complexity of the remaining patches will increase. The degree of fragmentation, complexity, connectivity, and separation of landscape patterns will increase significantly, whereas landscape diversity will remain stable. This study deepens our understanding of how land use and landscape patterns change in the heritage area from a long-term perspective that involves both the past and future. Such research can provide crucial information for tourism management, heritage protection, and spatial planning in the heritage area and, thus, has important management implications for the study area and similar heritage areas in other regions. Full article

Accurate classification of three-dimensional (3D) point clouds in real-world environments is often impeded by sensor noise, occlusions, and incomplete data. To overcome these challenges, we propose SMCNet, a robust multimodal framework for 3D point cloud classification. SMCNet combines multi-view projection and neural radiance fields (NeRFs) to generate high-fidelity 2D representations with enhanced texture realism, addressing occlusions and lighting inconsistencies effectively. The Mamba model is further refined within this framework by integrating a depth perception module to capture long-range point interactions and adopting a dual-channel structure to enhance point-wise feature extraction. Fine-tuning adapters for the CLIP and Mamba models are also introduced, significantly improving cross-domain adaptability. Additionally, an intelligent voting mechanism aggregates predictions from multiple viewpoints, ensuring enhanced classification robustness. Comprehensive experiments demonstrate that SMCNet achieves state-of-the-art performance, outperforming the PointNet++ baseline with a 0.5% improvement in mean overall accuracy (mOA) on ModelNet40 and a 7.9% improvement on ScanObjectNN. In corruption resistance, SMCNet reduces the mean corruption error (mCE) by 0.8% on ModelNet40-C and 3.6% on ScanObjectNN-C. These results highlight the effectiveness of SMCNet in tackling real-world classification scenarios with noisy and corrupted data. Full article

Understanding the convergence characteristics of manufacturing carbon emissions (MCEs) in China is essential for aligning regional carbon reduction efforts and achieving national climate goals. This study investigates the spatiotemporal evolution and convergence of MCEs across China and its eastern, central, and western regions, using panel data from 30 provinces spanning 2001 to 2020. A spatial panel model is applied to analyze convergence trends and influencing factors. The findings reveal three key insights: (1) Nationwide, the disparity in MCEs is expanding, with significant spatial imbalances; intra-regionally, emission disparities are highest in the eastern region and lowest in the western region. (2) Both nationally and regionally, MCEs lacks a converging trend, complicating coordinated carbon reduction efforts. Less economically developed regions exhibit higher degrees and rates of spatial divergence. (3) Technological advancement and energy structure optimization accelerate spatial divergence, while reduced disparities in manufacturing output and urbanization levels help mitigate it. These results underscore the need for a gradient-based, region-specific approach to achieve carbon peaking and neutrality in China. Full article

Green infrastructure (GI) is increasingly prioritised in landscape policy and planning due to its potential to benefit ecosystems and enhance wildlife conservation. However, due to the uneven distribution of protected areas (PAs) and the fragmentation of habitats more generally, multi-level policy strategies are needed to create an integrated GI network bridging national, regional and local scales. In the province of Almeria, southeastern Spain, protected areas are mainly threatened by two land use/land cover changes. On the one hand, there is the advance of intensive greenhouse agriculture, which, between 1984 and 2007, increased in surface area by more than 58%. On the other hand, there is the growth of artificial surfaces, including urban areas (+64%), construction sites (+194%) and road infrastructures (+135%). To address this challenge, we present a proposal for green infrastructure deployment in the province of Almeria. We combine Geographic Information Systems (GISs) and multi-criteria evaluation (MCE) techniques to identify and evaluate suitability for key elements to be included in GI in two key ways. First, we identify the most suitable areas to form part of the GI in order to address vulnerability to degradation and fragmentation. Second, we propose 15 ecological corridors connecting the 35 protected areas of the province that act as core areas. The proposed GI network would extend along the western coast of the province and occupy the valleys of the main rivers. The river Almanzora plays a leading role. Due to its remoteness from the coast and its climatic conditions, it has not attracted intensive greenhouse agriculture and urban development, the main drivers of the transformation and fragmentation of traditional land uses. Around 50% of the area occupied by the proposed corridors would be located in places of medium and high suitability for the movement of species between core areas. Full article