Search Results (86,862)

This manuscript presents a model of a system implementing individual stages of production for long steel products resulting from rolling. The system encompasses the order registration stage, followed by production planning based on information about the billet inventory status, then offers the possibility of scheduling orders for the melt shop in the form of melt sequences, manages technological knowledge regarding the principles of sequencing, and utilizes machine learning and optimization methods in melt sequencing. Subsequently, production according to the implemented plan is monitored using IoT and vision tracking systems for ladle tracking. During monitoring, predictions of energy demand and energy consumption in LMS processes are made concurrently, as well as predictions of metal overheating at the CST station. The system includes production optimization at two levels: optimization of the heat sequence and at the production level through the prediction of heating time. Optimization models and machine learning tools, including mainly neural networks, are utilized. The system described includes key components: optimization models for sequencing heats using Ant Colony Optimization (ACO) algorithms and neural network-based prediction models for power-on time. The manuscript mainly focuses on process modeling issues rather than implementation or deployment details. Machine learning models have significantly improved process efficiency and quality; the optimization of planning has reduced sequencing plan execution time; and power-on time prediction models estimate the main ladle heating time with 97% precision, enabling precise production control and reducing overheating. The system serves as an example of implementing the concept of a cyber–physical system. Full article

Graphene oxide-silver poly(vinylidene fluoride) membranes (PVDF@GO-Ag) were successfully synthesized by the electrospinning method, which exhibited a high catalytic activity using the hydrogenation of 4-nitrophenol (4-NP) as a model reaction in a batch reaction study. The hybrid membranes doped with 1 wt% GO and 2 wt% Ag (PVDF-1-2) exhibited the most desired performance for the catalytic reduction of 4-NP. Importantly, PVDF-1-2 exhibited excellent cycling stability in 10 catalytic cycle tests and was highly amenable to separation. This property effectively addresses the significant challenges associated with the practical application of nanocatalysts. Furthermore, density-functional theory (DFT) calculations have demonstrated that the GO-Ag nanocomposites exhibit the strongest adsorption capacity for 4-NP⁻ when a specific ratio of GO and Ag is achieved, accompanied by the loading of Ag nanoclusters onto GO. Additionally, the study demonstrated that an increase in temperature significantly accelerated the reaction rate, in line with the van’t Hoff rule. This study provides an effective and environmentally friendly solution for the treatment of 4-NP in wastewater. Full article

The pH- and thermo-responsive behavior of polymeric hydrogels $\left({\mathrm{M}}_{\mathrm{C}}-\mathrm{c}\mathrm{o}-{\mathrm{M}}_{\mathrm{A}}\right)$ have been studied in detail using dynamic light scattering $\left(\mathrm{D}\mathrm{L}\mathrm{S}\right)$, scanning electron microscopy$\left(\mathrm{S}\mathrm{E}\mathrm{M}\right)$, nuclear magnetic resonance (¹H NMR) and rheology to evaluate the conformational changes, swelling–shrinkage, stability, the ability to flow and the diffusion process of nanoparticles at several temperatures. Furthermore, polymeric systems functionalized with acrylic acid $\left({\mathrm{M}}_{\mathrm{C}}\right)$ and acrylamide $\left({\mathrm{M}}_{\mathrm{A}}\right)$ were subjected to a titration process with a calcium chloride $\left({\mathrm{C}\mathrm{a}\mathrm{C}\mathrm{l}}_2\right)$ solution to analyze its effect on the average particle diameter$\left({\mathrm{D}}_{\mathrm{z}}\right)$, polymer structure and the intra- and intermolecular interactions in order to provide a responsive polymer network that can be used as a possible nanocarrier for drug delivery with several benefits. The results confirmed that the structural changes in the sensitive hydrogels are highly dependent on the corresponding critical solution temperature $\left(\mathrm{C}\mathrm{S}\mathrm{T}\right)$ of the carboxylic (–COOH) and amide (–CONH₂) functional groups and the influence of calcium ions $\left({\mathrm{C}\mathrm{a}}^{2+}\right)$ on the formation or breaking of hydrogen bonds, as well as the decrease in electrostatic repulsions generated between the polymer chains contributing to a particle agglomeration phenomenon. The temperature leads to a re-arrangement of the polymer chains, affecting the viscoelastic properties of the hydrogels. In addition, the diffusion coefficients $\left(D\right)$ of nanoparticles were evaluated, showing a closeness among with the morphology, shape, size and temperature, resulting in slower diffusions for larger particles size and, conversely, the diffusion in the medium increasing as the polymer size is reduced. Therefore, the hydrogels exhibited a remarkable response to pH and temperature variations in the environment. During this research, the functionality and behavior of the polymeric nanoparticles were observed under different analysis conditions, which revealed notable structural changes and further demonstrated the nanoparticles promising high potential for drug delivery applications. Hence, these results have sparked significant interest in various scientific, industrial and technological fields. Full article

Type 1 diabetes mellitus (T1D) is an incurable autoimmune disease characterized by the destruction of pancreatic islet cells, resulting in lifelong dependency on insulin treatment. There is an abundance of review articles addressing the prediction of T1D; however, most focus on the presymptomatic phases, specifically stages 1 and 2. These stages occur after seroconversion, where therapeutic interventions primarily aim to delay the onset of T1D rather than prevent it. This raises a critical question: what happens before stage 1 in individuals who will eventually develop T1D? Is there a “stage 0” of the disease, and if so, how can we detect it to increase our chances of truly preventing T1D? In pursuit of answers to these questions, this narrative review aimed to highlight recent research in the field of early detection and prediction of T1D, specifically focusing on biomarkers that can predict T1D before the onset of islet autoimmunity. Here, we have compiled influential research from the fields of epigenetics, omics, and microbiota. These studies have identified candidate biomarkers capable of predicting seroconversion from very early stages to several months prior, suggesting that the prophylactic window begins at birth. As the therapeutic landscape evolves from treatment to delay, and ideally from delay to prevention, it is crucial to both identify and validate such “stage 0” biomarkers predictive of islet autoimmunity. In the era of precision medicine, this knowledge will enable early intervention with the potential for delaying, modifying, or completely preventing autoimmunity and T1D in at-risk children. Full article

Background: The simultaneous occurrence of cancer and acute coronary syndromes (ACSs) presents a complex clinical challenge. This study clarifies variances in diagnostic parameters among ACS patients with and without concurrent cancer. Methods: This retrospective study included 320 individuals diagnosed with ACS, stratified equally into two cohorts—one with cancer and the other cancer-free. We evaluated risk factors, symptom profiles, coronary angiography results, echocardiographic evaluations, and laboratory diagnostics. Statistical analysis was performed using Student’s t-test, the Mann–Whitney U test, and the chi-square test. Results: Cancer patients were older (mean age 71.03 vs. 65.13 years, p < 0.001) and had a higher prevalence of chronic kidney disease (33.1% vs. 15.0%, p < 0.001) but a lower prevalence of hyperlipidemia (59.7% vs. 82.5%, p < 0.001). Chest pain was less frequent in cancer patients (72.5% vs. 90%, p < 0.001), while hypotension was more common (41.9% vs. 28.8%, p = 0.022). NSTEMI was more common in cancer patients (41.9% vs. 30.6%, p = 0.048), while STEMI was less common (20.6% vs. 45.3%, p < 0.001). RCA and LAD involvement were less frequent in cancer patients (RCA: 18.1% vs. 30.0%, p = 0.018; LAD: 18.8% vs. 30.0%, p = 0.026). Conclusions: This study demonstrates differences in the clinical presentation of ACS between patients with and without cancer. Cancer patients were less likely to present with chest pain and more likely to experience hypotension. Additionally, they had a higher prevalence of chronic kidney disease and they were less likely to have hyperlipidemia. These findings highlight the need for a careful approach to diagnosing ACS in oncology patients, considering their distinct symptomatology. Full article

Cardiovascular magnetic resonance (CMR) imaging is widely regarded as the gold-standard technique for myocardial tissue characterization, allowing for the detection of structural abnormalities such as myocardial fatty replacement, myocardial edema, myocardial necrosis, and/or fibrosis. Historically, the identification of abnormal myocardial regions relied on variations in tissue signal intensity, often necessitating the use of exogenous contrast agents. However, over the past two decades, innovative parametric mapping techniques have emerged, enabling the direct quantitative assessment of tissue magnetic resonance (MR) properties on a voxel-by-voxel basis. These mapping techniques offer significant advantages by providing comprehensive and precise information that can be translated into color-coded maps, facilitating the identification of subtle or diffuse myocardial abnormalities. As unlikely conventional methods, these techniques do not require a substantial amount of structurally altered tissue to be visually identifiable as an area of abnormal signal intensity, eliminating the reliance on contrast agents. Moreover, these parametric mapping techniques, such as T1, T2, and T2* mapping, have transitioned from being primarily research tools to becoming valuable assets in the clinical diagnosis and risk stratification of various cardiac disorders. In this review, we aim to elucidate the underlying physical principles of CMR parametric mapping, explore its current clinical applications, address potential pitfalls, and outline future directions for research and development in this field. Full article

The excessive use of electronic devices for prolonged periods has led to problems such as neck pain and pressure injury in sedentary people. If not detected and corrected early, these issues can cause serious risks to physical health. Detectors for generic objects cannot adequately capture such subtle neck behaviors, resulting in missed detections. In this paper, we explore a deep learning-based solution for detecting abnormal behavior of the neck and propose a model called NABNet that combines object detection based on YOLOv5s with pose estimation based on Lightweight OpenPose. NABNet extracts the detailed behavior characteristics of the neck from global to local and detects abnormal behavior by analyzing the angle of the data. We deployed NABNet on the cloud and edge devices to achieve remote monitoring and abnormal behavior alarms. Finally, we applied the resulting NABNet-based IoT system for abnormal behavior detection in order to evaluate its effectiveness. The experimental results show that our system can effectively detect abnormal neck behavior and raise alarms on the cloud platform, with the highest accuracy reaching 94.13%. Full article

Alcohol-blended gasoline is recognized as an effective strategy for reducing carbon emissions during combustion and enhancing fuel performance. However, the carbon footprint associated with its production process in refineries deserves equal attention. This study introduces a refinery modeling framework to evaluate the long-term economic and environmental performance of utilizing alcohols derived from fossil, biomass, and carbon capture sources in gasoline blending processes. The proposed framework integrates Extreme Learning Machine-based models for gasoline octane blending, linear programming for optimization, carbon footprint tracking, and future trends in feedstock costs and carbon taxes. The results indicate that gasoline blended with coal-based alcohol currently exhibits the best economic performance, though its carbon footprint ranges from 818.54 to 2072.89 kgCO₂/t. Gasoline blended with biomass-based alcohol leads to a slight reduction in benefits and an increase in the carbon footprint. Blending gasoline with CCUM (CO₂ capture and utilization to methanol) results in the lowest economic performance, with a gross margin of 8.91 CNY/t_oil at a 30% blending ratio, but achieves a significant 62.4% reduction in the carbon footprint. In long-term scenarios, the additional costs brought by increased carbon taxes result in negative economic performance for coal-based alcohol blending after 2040. However, cost reductions driven by technological maturity lead to biomass-based alcohol and CCUM blending gradually showing economic advantages. Furthermore, owing to the negative carbon emissions characteristic of CCUM, the blending route with CCUM achieves a gross margin of 440.60 CNY/t_oil and a gasoline carbon footprint of 282.28 kgCO₂/t at a 20% blending ratio by 2050, making it the best route in terms of economic and environmental performance. Full article

Terrace erosion has become increasingly pronounced due to the rising incidence of heavy rainfalls resulting from global climate change; however, the processes and mechanisms governing erosion of loess terraces during such events remain poorly understood. A field investigation was performed following a heavy rainfall event in the Tangjiahe Basin to examine the soil erosion characteristics of loess terraces subjected to heavy rainfall events. The results show that various types of erosion occurred on the terraced fields, including rill, gully, and scour hole in water erosion, and sink hole, collapse, and shallow landslide in gravity erosion. Rill erosion and shallow landslide erosion exhibited the highest frequency of occurrence on the new and old terraces, respectively. The erosion moduli of the gully, scour hole, and sink hole on the new terraces were 171.0%, 119.5%, and 308.7% greater than those on the old terraces, respectively. In contrast, lower moduli of collapse and landslide were observed on the new terraces in comparison to the old terraces, reflecting reductions of 34.2% and 23.4%, respectively. Furthermore, the modulus of water erosion (32,102 t/km²) was 4.5 times that of gravity erosion on the new terraces. Conversely, on the old terrace, the modulus of gravity erosion (8804.1 t/km²) exceeded that of water erosion by 14.5%. Gully erosion and collapse dominated the erosion processes, contributing 67.8% and 9.4% to soil erosion on the new terraces and 38.7% and 34.0%, respectively, on the old terraces. In the study area, the new terraces experienced significantly greater erosion (39,252 t/km²) compared to the old terraces (16,491 t/km²). Plastic film mulching, loose and bare ridges and walls, inclined terrace platforms, and high terrace walls, as well as the developing flow paths, might be the key factors promoting the severe erosion of the terraces during heavy rainfall. Improvements in terrace design, construction technologies, temporary protective measures, agricultural techniques, and management strategies could enhance the prevention of soil erosion on terraces during heavy rainfall events. Full article

Background: This study aims to evaluate the role of parvovirus B19 (B19V) in the pathogenesis of myocarditis in a paediatric population, including post-mortem samples from two children. Methods: From 2004 to 2023, endomyocardial biopsies (EMBs) from children under 16 years of age were analyzed using histology, immunohistochemistry, and molecular pathology. A total of 306 children with acute and 1060 children with chronic lymphocytic myocarditis were identified. Results: B19V infection was more frequent in acute myocarditis than in chronic myocarditis (43% vs. 14%), with higher viral loads in acute cases regardless of age. The most prominent cardiac CD3+ T cell infiltration was noted in children < 2 years, correlating with high cardiac B19V loads. In two male infants who died from B19V infection, B19V DNA was localized in the endothelial cells of multiple organs using in situ hybridization. Virus replication was found in the endothelial cells of small cardiac arterioles and venules but not in capillaries. B19V DNA/mRNA was also detected in immune cells, especially in the spleen and lymph nodes, revealing virus replication in B lymphocytes. Conclusions: B19V can induce severe lymphocytic myocarditis, especially in young children. The simultaneous histopathological and molecular assessment of EMBs is important for early diagnosis of viral myocarditis, preventing severe disease, and ensuring appropriate therapy. Full article

Wilson’s disease (WD) is a rare, autosomal recessive disorder of copper metabolism caused by pathogenic mutations in the ATP7B gene. Cellular copper overload is associated with impaired iron metabolism. Oxidative stress, cuproptosis, and ferroptosis are involved in cell death in WD. The clinical picture of WD is variable. Hepatic/neuropsychiatric/other symptoms may manifest in childhood/adulthood and even old age. It has been shown that phenotypic variability may be determined by the type of ATP7B genetic variants as well as the influence of various genetic/epigenetic, environmental, and lifestyle modifiers. In 1976, immunological abnormalities were first described in patients with WD. These included an increase in IgG and IgM levels and a decrease in the percentage of T lymphocytes, as well as a weakening of their bactericidal effect. Over the following years, it was shown that there is a bidirectional relationship between copper and inflammation. Changes in serum cytokine concentrations and the relationship between cytokine gene variants and the clinical course of the disease have been described in WD patients, as well as in animal models of this disease. Data have also been published on the occurrence of antinuclear antibodies (ANAs), antineutrophil cytoplasmic antibodies (ANCAs), anti-muscle-specific tyrosine kinase antibodies, and anti-acetylcholine receptor antibodies, as well as various autoimmune diseases, including systemic lupus erythematosus (SLE), myasthenic syndrome, ulcerative colitis, multiple sclerosis (MS), polyarthritis, and psoriasis after treatment with d-penicillamine (DPA). The occurrence of autoantibodies was also described, the presence of which was not related to the type of treatment or the form of the disease (hepatic vs. neuropsychiatric). The mechanisms responsible for the occurrence of autoantibodies in patients with WD are not known. It has also not been clarified whether they have clinical significance. In some patients, WD was differentiated or coexisted with an autoimmune disease, including autoimmune hepatitis or multiple sclerosis. Various molecular mechanisms may be responsible for immunological abnormalities and/or the inflammatory processes in WD. Their better understanding may be important for explaining the reasons for the diversity of symptoms and the varied course and response to therapy, as well as for the development of new treatment regimens for WD. Full article

Composts produced with kiwifruit waste from the calibration process (KW), mixed with 5%, 10%, and 20% wheat straw (WS), were evaluated as crop fertilizers through a pot experiment with lettuce, arranged as a randomized block design. Highest lettuce yields were achieved with 20 and 40 t·ha⁻¹ 5%WS compost and 40 t·ha⁻¹ 10%WS compost, suggesting that the physical characteristics of the composts increased soil water holding capacity and root growth, whereas chemical characteristics such as pH, organic matter, and nutrient contents contributed to improving soil reaction and nutrient availability. The type of soil amendment used influenced the development of different bacterial consortia in the bulk soil and rhizosphere, leading to increased levels of potentially beneficial bacteria and enhanced levels of relevant functions for plant growth, such as nitrogen fixation. Composted KW as an organic amendment can be used to improve soil quality and the circular economy. Full article

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new emerging coronavirus that caused coronavirus disease 2019 (COVID-19). Whole-genome tracking of SARS-CoV-2 enhanced our understanding of the mechanism of the disease, control, and prevention of COVID-19. Methods: we analyzed 3368 SARS-CoV-2 protein sequences from Iran and compared them with 15.6 million global sequences in the GISAID database, using the Wuhan-Hu-1 strain as a reference. Results: Our investigation revealed that NSP12-P323L, ORF9c-G50N, NSP14-I42V, membrane-A63T, Q19E, and NSP3-G489S were found to be the most frequent mutations among Iranian SARS-CoV-2 sequences. Furthermore, it was observed that more than 94% of the SARS-CoV-2 genome, including NSP7, NSP8, NSP9, NSP10, NSP11, and ORF8, had no mutations when compared to the Wuhan-Hu-1 strain. Finally, our data indicated that the ORF3a-T24I, NSP3-G489S, NSP5-P132H, NSP14-I42V, envelope-T9I, nucleocapsid-D3L, membrane-Q19E, and membrane-A63T mutations might be responsible factors for the surge in the SARS-CoV-2 Omicron variant wave in Iran. Conclusions: real-time genomic surveillance is crucial for detecting new SARS-CoV-2 variants, updating diagnostic tools, designing vaccines, and understanding adaptation to new environments. Full article

SMEs are asked to incorporate sustainable energy solutions into their organizations’ processes to be environmentally friendly and operate more effectively. In this regard, IoT-based technologies seem to have the potential to monitor and optimize energy use. However, more extensive research is required to assess the efficacy of such solutions in the context of SMEs. Despite the growing interest in the Internet of Things (IoT) for renewable energy, there is a lack of information on how well these solutions work for small and medium-sized enterprises (SMEs). While much of the existing literature addresses the application of new technologies in SMEs, the social background underlying their transformation received relatively little attention in previous years. The present research adopts a quantitative approach, employing time series forecasting, specifically long short-term memory networks (LSTM). This paper uses IoT-based approaches to collect and preprocess an energy consumption dataset from various SMEs. The LSTM model is intended to forecast energy consumption in the future based on experience. In terms of analysis, the study adopts Python for data preprocessing, constructing, and assessing models. The main findings reveal a strong positive correlation (r = 0.85) between base energy consumption and overall energy usage, suggesting that optimizing base consumption is crucial for energy efficiency. In contrast, investment in RETs and staff training demonstrate weak correlations (r = 0.25 and r = 0.30, respectively) with energy consumption, indicating that these factors alone are insufficient for significant energy savings. The long short-term memory model used in the study accurately predicted future energy consumption trends with a mean absolute error of 5%. However, it struggled with high-frequency variations, showing up to 15% of mistakes. This research contributes to the literature in line with IoT-based sustainable energy solutions in SMEs, which has not been widely addressed. The findings highlight the critical role of integrating renewable energy technologies (RETs) and fostering a culture of energy efficiency, offering actionable insights for policymakers and business owners. With the application of Python in data analysis and model creation, this research shows a real-world approach to handling issues in sustainable energy management for SMEs. Full article

The skin is vulnerable to damage from ultraviolet rays and oxidative stress, which can lead to aging and pigmentation issues. This study investigates the antioxidant and whitening efficacy of a decapeptide (DP, KGYSSYICDK) derived from marine fish by-products and evaluates its potential as a new skin-whitening agent. DP demonstrated high antioxidant activity, showing comparable or superior performance to Vitamin C (Vit. C) in ferric reducing antioxidant power (FRAP) and 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical scavenging assays. In hydrogen peroxide (H₂O₂)-treated HaCaT cells, DP increased cell viability and reduced reactive oxygen species (ROS) generation. Furthermore, DP inhibited tyrosinase activity and decreased melanin production in α-melanocyte stimulating hormone (α-MSH)-induced B16F10 melanoma cells in a dose-dependent manner. Reverse transcription polymerase chain reaction (RT-PCR) analysis revealed that DP reduces the mRNA expression of MITF, tyrosinase, and MC1R, thus suppressing melanin production. DP exhibits strong binding interactions with multiple amino acid residues of tyrosinase, indicating potent inhibitory effects on the enzyme. These results suggest that DP possesses significant antioxidant and whitening properties, highlighting its potential as a skin-whitening agent. Future research should focus on optimizing DP’s structure and exploring structure–activity relationships. Full article