Search Results (99)

Predictive maintenance (PdM) is a cornerstone of smart manufacturing, enabling the early detection of equipment degradation and reducing unplanned downtimes. This study proposes an advanced machine learning framework that integrates the Extreme Learning Machine (ELM) with a novel hybrid metaheuristic optimization algorithm, the Polar Lights Salp Cooperative Optimizer (PLSCO), to enhance predictive modeling in manufacturing processes. PLSCO combines the strengths of the Polar Light Optimizer (PLO), Competitive Swarm Optimization (CSO), and Salp Swarm Algorithm (SSA), utilizing a cooperative strategy that adaptively balances exploration and exploitation. In this mechanism, particles engage in a competitive division process, where winners intensify search via PLO and losers diversify using SSA, effectively avoiding local optima and premature convergence. The performance of PLSCO was validated on CEC2015 and CEC2020 benchmark functions, demonstrating superior convergence behavior and global search capabilities. When applied to a real-world predictive maintenance dataset, the ELM-PLSCO model achieved a high prediction accuracy of 95.4%, outperforming baseline and other optimization-assisted models. Feature importance analysis revealed that torque and tool wear are dominant indicators of machine failure, offering interpretable insights for condition monitoring. The proposed approach presents a robust, interpretable, and computationally efficient solution for predictive maintenance in intelligent manufacturing environments. Full article

Background/Objectives: Sodium-glucose cotransporter-2 inhibitors (SGLT2i) have revolutionized heart failure (HF) therapies and are an essential component of guideline-directed medical therapy (GDMT); however, their significance in arrhythmia prevention is still uncertain. This meta-analysis evaluates the benefits of SGLT2i on arrhythmias in HF. Methods: A comprehensive examination was performed with PubMed, ScienceDirect, PLOS One, Cochrane, Google Scholar, and ClinicalTrials.gov from January 2014 to March 2025, complying with PRISMA guidelines. Randomized controlled trials (RCTs) comparing SGLT2i with placebo were incorporated. Primary results included ventricular arrhythmias (VA), sudden cardiac death (SCD), atrial arrhythmias, and conduction disorders. Subgroup analyses investigated the effects on arrhythmias in HF with reduced ejection fraction (HFrEF) and preserved ejection fraction (HFpEF). Results: A total of 11 RCTs involving 23,701 patients, 11,848 on SGLT2i (mean age: 68.26 ± 10 yrs, 53.5% males) and 11,853 on placebo (mean age: 67.91 ± 10 yrs, 53% males), were analyzed with a mean follow-up of 2.71 yrs. No significant differences were reported between SGLT2i and placebo for VA [relative risk (RR): 1.02, 95% confidence interval (CI): 0.83–1.25], I² =0%), atrial arrhythmias (RR: 0.92 [CI: 0.67–1.27], I² = 65.3%), or conduction disorders (RR:1.22 [CI: 0.86–1.73], I² = 10.4%). Notably, significant reductions in risk of SCD (RR: 0.68 [CI: 0.49–0.93], I² = 0%) and in the risk of atrial arrhythmias in HFrEF (RR: 0.66 [CI: 0.49–0.89], I² = 10.3%) were witnessed, although no such reduction was seen in HFpEF (RR: 1.14 [CI: 0.94–1.40], I² = 33.8%). Conclusions: SGLT2i do not reduce overall arrhythmia or conduction disorder risk in HF but significantly reduce the risk of SCD and atrial arrhythmias in HFrEF patients. These results highlight potential arrhythmia prevention benefits in HFrEF, warranting further targeted studies. Full article

The thermal properties and flammability of rigid polyurethane foams (RPUFs) containing various flame retardants, including solid (melamine, expanded graphite (EG), Exolit OP 935, ammonium polyphosphate (APP)) and liquid (Roflam B7, Roflam PLO) types, added at 30 wt.% and 60 wt.% by weight have been evaluated. Thermogravimetric analysis (TGA) demonstrated enhanced thermal stability, with the maximum 10% weight loss temperature (292 °C, +34 °C vs. reference) observed for foams containing 60 wt.% Exolit OP 935 and APP. The limiting oxygen index (LOI) test demonstrated the optimal performance for 30 wt.% APP and melamine (26.4 vol.% vs. 18.7 vol.% reference). In the UL-94 test, Exolit OP 935 and APP achieved a V-0 rating. The 60 wt.% Exolit with an EG blend also demonstrated a substantial reduction in heat release rate. These findings underscore the cooperative effects of hybrid flame retardants, thereby supporting their utilization in fire-safe RPUFs for construction and transport. Full article

Background: Artificial intelligence and machine learning are playing crucial roles in digital transformation, aiming to improve the efficiency, effectiveness, equity, and responsiveness of primary health systems and their services. Method: Using synthetic knowledge synthesis and bibliometric and thematic analysis triangulation, we identified the most productive and prolific countries, institutions, funding sponsors, source titles, publications productivity trends, and principal research categories and themes. Results: The United States and the United Kingdom were the most productive countries; Plos One and BJM Open were the most prolific journals; and the National Institutes of Health, USA, and the National Natural Science Foundation of China were the most productive funding sponsors. The publication productivity trend is positive and exponential. The main themes are related to natural language processing in clinical decision-making, primary health care optimization focusing on early diagnosis and screening, improving health-based social determinants, and using chatbots to optimize communications with patients and between health professionals. Conclusions: The use of machine learning in primary health care aims to address the significant global burden of so-called “missed diagnostic opportunities” while minimizing possible adverse effects on patients. Full article

This study investigates the evolution and prospective directions of big data applications within the global digital economy over the past twelve years. A comprehensive bibliometric analysis was conducted using Biblioshiny and included 752 documents authored by 1748 scholars and published in 416 specialized journals and academic books between 2013 and 2024. The findings reveal that scholarly interest in this area peaked in 2024. Co-occurrence network mapping highlights three dominant thematic trends in the applicability of big data within the digital economy: technological innovations, conceptual frameworks, and the role of China. Influential academic publications—such as Sustainability, PLoS ONE, and the proceedings of the 8th International Conference on Information Technology and Quantitative Management—have played a pivotal role in advancing research in this domain. Moreover, leading institutions, including the University of the Chinese Academy of Sciences, Shenzhen University, and Guizhou University, have emerged as pivotal contributors to advancing research in this field. China is the primary driving force and key player in reshaping the digital economy through big data, a role that is expected to contribute to global technological advancement in the future. Full article

The aims of the present study were to analyze the taxonomic profile and to evaluate the functional effects of sheep FF cultivable microbiota on prepubertal lamb oocytes PLOs developmental potential. Ovarian FFs were recovered from slaughtered adult sheep via the aspiration of developing follicles and used for microbiota propagation. Bacterial pellets underwent 16S rRNA gene sequencing and targeted culturomics, whereas cell-free supernatants were used as supplements for the in vitro maturation (IVM) of slaughtered PLOs. For the first time, bacteria presence in adult sheep FF was detected, with the first report of Streptococcus infantarius subsp. infantarius (as a species) and Burkholderia cepacia (as a genus and species) in either animal or human FF. The short- and long-term effects of bacterial metabolites on PLO maturation and embryonic development were demonstrated. As short-term effects, the addition of FF microbiota metabolites did not affect the oocyte nuclear maturation and mitochondria distribution pattern, except in one of the examined supernatants, which reduced all quantitative bioenergetic/oxidative parameters. As long-term effects, one of them reduced the total cleavage rate after in vitro embryo culture (IVC). In conclusion, microbiota/bacteria are present in adult sheep FF and may influence reproductive outcomes in vitro. Future studies may reveal the beneficial in vitro effects using the microbiome from preovulatory follicles. Full article

Despite having the world’s largest HIV burden, South Africa has yet to attain the 95-95-95 targets. Accurate, complete, and timely data are critical for monitoring a country’s HIV progress. The integration of unique identifier codes (UICs) for key populations (KPs) into routine health information management systems (RHIMS) strengthens data accuracy and completeness, facilitating more targeted HIV interventions and greater accountability. This systematic review assessed how Sub-Saharan African (SSA) countries have integrated KPs’ UICs into RHIMS, highlighting key enablers, challenges, and opportunities. A comprehensive search was conducted across PubMed, Scopus, Google Scholar, MEDLINE, PLOS ONE, and various government and non-government websites to identify the published and grey literature relevant to the study objective from June 2013 to December 2024. References were managed using Zotero version 6.0.36. Two authors independently screened studies using Covidence software. The review was done in accordance with the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA) guidelines and was registered with the “International Prospective Register of PROSPERO) Systematic Reviews” with the registration number CRD42023440656. Out of 1735 studies screened, 361 duplicates were removed. The review found that only nine of the fifty-three SSA countries have incorporated UICs for KPs into their RHIMS through alphanumeric codes. They include Burundi, Burkina Faso, Ghana, Mali, Kenya, Uganda, Togo, Malawi, and Liberia. Facilitators for KPs’ UIC adoption included strong data security and political will, whereas barriers encompassed compromised privacy, stigma and discrimination. In South Africa, the UIC for KPs can be integrated into the new electronic medical record (EMR) system. Full article

Surface target salvage is a crucial component of marine emergencies. Although unmanned surface vehicles (USVs) have emerged as effective alternative platforms to traditional artificial salvage, salvage target tracking remains a challenging issue. Therefore, this paper proposes a salvage target tracking (STT) algorithm which enables rapid approach (RA) to the salvage target, while maintaining an appropriate salvage distance that keeps the surface target within the operational range in the terminal tracking (TT) phase. In the RA phase, the model predictive line-of-sight (PLOS) guidance algorithm is proposed to estimate and compensate for the drift angle encountered when following a curved path. In the TT phase, a guidance algorithm based on the key point is proposed to track the salvage target. To achieve the goals in the RA phase and the TT phase, a heading and speed controller based on proportional–integral–derivative control is proposed to track the desired signals computed by the PLOS and key point guidance algorithms. To verify the effectiveness of the STT algorithm, simulation analysis is conducted for the PLOS guidance algorithm and key point guidance algorithm. The simulation results show that the proposed PLOS guidance algorithm has the lowest cross-tracking error compared with the traditional LOS, integral LOS, and adaptive error constraint LOS. Moreover, the distance between the USV and the salvage target is less than the operating radius of the salvage operation. The results demonstrate that the proposed STT algorithm is capable of maintaining the appropriate salvage distance while tracking the salvage target. Full article

The global oat harvest area occupied by China has been increasing annually. In this study, the fatty acid and triacylglycerol compositions, lipid concomitants, and antioxidant capacities of 16 oat oil cultivars in China were compared. All oat oils were found to be rich in unsaturated fatty acids (UFA), particularly oleic acid and linoleic acid. The main triacylglycerols in oat oil were first reported, including 1-palmitoyl-2-linoleoyl-3-oleyl-glycerol (PLO, 16.50–18.69%), 1,3-dioleoyl-2-linoleoyl-glycerol (OLO, 14.97–18.44%), and 1-palmitoyl-2,3-dioleoyl-glycerol (POO, 11.00–13.45%). Significant variations were observed among the cultivars in lipid concomitants, including tocochromanols (0–124.83 mg/kg), phytosterols (3380.94–5735.96 mg/kg), squalene (17.39–59.33 mg/kg), and polyphenols (255.47–513.99 mg GAE/kg). The antioxidant capacities of the different cultivars varied for DPPH (154.34–189.80 μmol VE/kg), ABTS (124.40–343.97 μmol VE/kg), and FRAP (834.32–2746.09 μmol VE/kg). Pearson correlation analysis showed a positive correlation between antioxidant capacity and the contents of polyphenols, squalene, and campesterol. Hierarchical cluster analysis classified the oat oils into distinct groups based on their phytosterol, polyphenol, monounsaturated fatty acids (MUFA), triacylglycerol, squalene, polyunsaturated fatty acid (PUFA), and tocochromanol contents. This study confirms that oat oil has potential as a functional oil and dietary supplement, and sheds light on the relationship between its nutritional quality and functionality, which may aid in the screening of beneficial oat oil cultivars. Full article

Background/Objectives: Various studies have investigated the importance of perceptual–cognitive skills in decision-making and the expert performance of athletes. However, bibliometric study has yet to identify research trends on this topic. The objective of this study was to perform a bibliometric review to identify the research trends in the study of soccer decision-making. Method. A total of 172 studies were included in the databases. Results. The year 2021 was the year with the highest number of published studies (n = 23), and 2016 was the year with the highest number of citations (n = 692). The average number of citations per document was 19.79. The concepts that have the greatest occurrence in the investigations are performance (n = 68), decision-making (n = 54), expertise (n = 32), skill (n = 23), and anticipation (n = 22). The journals with the highest number of published documents are the Journal of Sport Sciences (10 documents and 437 citations) and PLoS One (11 documents and 349 citations). The countries with the highest number of published documents and citations are England (n = 46 documents and 996 citations), Germany (n = 32 documents and 749 citations), and Spain (n = 38 documents and 597 citations). German Sport University Cologne is the organization that has the most publications and citations (n = 19 and 531). Conclusions. Existing knowledge production on decision-making in soccer has a preference for the study of two major categories: one related to the analysis of the factors associated with perceptual–cognitive skills, mental fatigue, anticipation, creativity, and memory, whereas the second is more related to the study that has decision-making in the manifestations of specific game performance, between experts and novices, in the precision of technical actions, such as the pass, as well as in a methodology for the selection of athletes. Full article

This paper explores the intersection of water quality management and advanced metaheuristic algorithms (MAs) by optimizing the location of water quality sensors in urban water networks. A comparative analysis of ten cutting-edge MAs, Harris Hawk Optimization (HHO), Artemisinin Optimization (AO), Educational Competition Optimizer (ECO), Fata Morgana Algorithm (FATA), Moss Growth Optimization (MGO), Parrot Optimizer (PO), Polar Lights Optimizer (PLO), Rime Optimization Algorithm (RIME), Runge Kutta Optimization (RUN), and Weighted Mean of Vectors (INFO), was conducted to determine their effectiveness in minimizing the risk of contaminated water consumption. Both benchmark and real-world water network serve as case studies to assess algorithmic performance. The optimization process focuses on reducing the volume of contaminated water by treating sensor placement as a critical design variable. EPANET 2.2 software was integrated with the optimization algorithms to simulate water quality and hydraulic behavior within the networks. The obtained results from analysis of two urban water networks revealed that the newer algorithms, such as the RIME and FATA, exhibit superior convergence rates and stability compared to traditional methods. While all tested algorithms demonstrated satisfactory performance, this study provides foundational insights for future research, paving the way for more effective algorithmic solutions in water quality management. Full article

The increasing generation of agro-industrial waste and its improper disposal have raised significant environmental concerns, highlighting the urgent need for sustainable alternatives which would repurpose these materials. In this context, enzymes such as endoglucanase play a critical role in degrading lignin–cellulose biomass by catalyzing the breakdown of β-1,4-glycosidic bonds in cellulose, thereby converting it into fermentable sugars with diverse industrial applications. This study aimed to investigate the production, purification, and characterization of an endoglucanase produced by the fungus Pleurotus djamor PLO13, using coconut fiber, sugarcane bagasse, wheat bran, and pineapple crown as substrates. Endoglucanase activity was measured by the Miller method (1959), using 2% (w/v) carboxymethyl cellulose (CMC) as substrate. Solid-state fermentation (SSF) was found to be highly efficient for enzyme synthesis, with wheat bran emerging as the most effective substrate, yielding an enzyme production of 7.19 U after 120 h of cultivation. The endoglucanase was purified through ethanol precipitation and ion-exchange chromatography using DEAE-Sepharose, achieving a recovery rate of 110%, possibly due to removal of inhibitors present in the crude extract. The purified enzyme exhibited stability across a broad pH range and thermostability, with optimal activity at pH 5.0 and 50 °C. Furthermore, the enzyme was activated by EDTA, Mn²⁺, and Ca²⁺, while being inhibited by Mg²⁺. Notably, the enzyme demonstrated halotolerance, with activity increasing by 60% upon the addition of 3 M NaCl. Kinetic analysis revealed that the purified enzyme showed affinity to the CMC substrate at the analyzed parameters (pH 5.0 and 50 °C), with Km and Vmax values of 0.0997 mg/mL and 112.2 µg/min/mL, respectively. These findings suggest that the endoglucanase from P. djamor PLO13 has promising potential for biotechnological applications, underscoring the feasibility of the use of lignocellulosic waste as sustainable substrates in industrial processes. Full article

Gut microbiota plays an important role in gut health, and its dysbiosis is closely related to the pathogenesis of various intestinal diseases. The field of gut microbiota and intestinal diseases has not yet been systematically quantified through bibliometric methods. This study conducted bibliometric analysis to delineate the evolution of research on gut microbiota and intestinal diseases. Data were sourced from the Web of Science Core Collection database from 2009 to 2023 and were scientometrically analyzed using CiteSpace. We have found that the number of annual publications has been steadily increasing and showing an upward trend. China and the Chinese Academy of Sciences are the country and institution with the most contributions, respectively. Frontiers in Microbiology and Nutrients are the journals with the most publications, while Plos One and Nature are the journals with the most citations. The field has shifted from focusing on traditional descriptive analysis of gut microbiota composition to exploring the causal relationship between gut microbiota and intestinal diseases. The research hotspots and trends mainly include the correlation between specific intestinal diseases and gut microbiota diversity, the mechanism of gut microbiota involvement in intestinal diseases, the exploration of important gut microbiota related to intestinal diseases, and the relationship between gut microbiota and human gut health. This study provides a comprehensive knowledge map of gut microbiota and intestinal diseases, highlights key research areas, and outlines potential future directions. Full article

Nitrous oxide produced during wastewater treatment is a major greenhouse gas, and accurate prediction and control of N₂O emissions are crucial for achieving carbon neutrality. In this study, aiming to address the complex issues of N₂O emission prediction in wastewater treatment, large-scale multidimensional data from the Altenrhein wastewater treatment plant was used to build a sample database. The role of symmetry in model architecture and data analysis was discussed, and six intelligent prediction models for N₂O emissions were proposed based on deep learning technology. The results showed that the PLO-CNN-BiLSTM-Attention model achieved the best performance, with an R² of 0.99 on the test set. Engineering validation using 48 subsequent datasets confirmed the model’s strong generalization ability and robustness. Feature importance analysis based on SHAP revealed that water temperature was the most critical factor influencing N₂O emissions, while dissolved oxygen concentration and inlet flow rate also had impacts but showed a certain symmetrical change between summer and winter. This study provides efficient and reliable technical support for monitoring and predicting N₂O emissions in urban wastewater treatment plants and offers a scientific basis for developing strategies to reduce greenhouse gas emissions. Full article

Optimization algorithms play a crucial role in solving complex problems across various fields, including global optimization and feature selection (FS). This paper presents the enhanced polar lights optimization with cryptobiosis and differential evolution (CPLODE), a novel improvement upon the original polar lights optimization (PLO) algorithm. CPLODE integrates a cryptobiosis mechanism and differential evolution (DE) operators to enhance PLO’s search capabilities. The original PLO’s particle collision strategy is replaced with DE’s mutation and crossover operators, enabling a more effective global exploration and using a dynamic crossover rate to improve convergence. Furthermore, a cryptobiosis mechanism records and reuses historically successful solutions, thereby improving the greedy selection process. The experimental results on 29 CEC 2017 benchmark functions demonstrate CPLODE’s superior performance compared to eight classical optimization algorithms, with higher average ranks and faster convergence. Moreover, CPLODE achieved competitive results in feature selection on ten real-world datasets, outperforming several well-known binary metaheuristic algorithms in classification accuracy and feature reduction. These results highlight CPLODE’s effectiveness for both global optimization and feature selection. Full article