Search Results (58,684)

This paper explores the application of predictive maintenance (PdM) to address paraffin deposition in sucker rod pump systems used for oil production. System maintenance has become critical for enhancing efficiency and reducing costs, while PdM, supported by advanced analytics and sensors, enables downtime prediction and maintenance optimization. Paraffin deposition is a significant problem in the oil industry, as it diminishes production capacity and increases expenses. This paper presents the use of the SCADA system, which enables the collection and analysis of data in real time. Furthermore, it proposes diagnostic methods for early detection of paraffin deposition using predictive maintenance, offering timely warnings to prevent production delays. While the proposed framework relies on interpretable statistical and physics-informed predictive models, the results indicate that further improvements could be achieved by integrating advanced artificial intelligence techniques to enhance adaptability, automation, and decision support in predictive maintenance systems. Full article

Riparian soils co-contaminated with heavy metals and organic pollutants present a formidable environmental challenge; conventional single-target remediation strategies are frequently insufficient due to the synergistic interactions between contaminant classes. This review offers a systematic synthesis of phytoremediation as an integrative and ecologically sustainable paradigm for addressing these complex multi-pollutant scenarios. Through a critical examination of underlying mechanisms—namely phytoextraction, rhizodegradation, phytostabilization, and phytovolatilization—we evaluate the efficacy of selected hyperaccumulator and pollution-tolerant species in simultaneously mitigating inorganic (e.g., Pb, Cd, As) and organic (e.g., PAHs, pesticides) contaminants. Furthermore, the discussion highlights emerging strategic integrations, including genetic engineering for enhanced metal accumulation, the application of engineered nanomaterials to modulate pollutant bioavailability and plant stress tolerance, rhizosphere amendment with low-molecular-weight organic acids, and biochar-mediated immobilization coupled with microbial stimulation. The analysis posits that phytoremediation, particularly when augmented by these advanced synergies, constitutes a viable, multifunctional, and environmentally benign strategy for the holistic restoration of riparian ecosystems. Future inquiries should prioritize the mechanistic elucidation of combined technologies, the development of predictive performance models, and rigorous long-term field validation to guarantee operational efficacy and environmental safety. Full article

This review presents a modern comprehensive analysis of the physicochemical characteristics of carbon materials for the sorption of gases. The main classes of carbon sorbents are considered as follows: nanotubes, activated carbon, graphene, fullerene, composite materials, and organic vapors. Modern methods of modification of carbon materials are systematized. Particular attention is paid to the effect of particle size, morphology, and porous structure on the kinetics and equilibrium characteristics of adsorption. The results of experimental and theoretical studies of the adsorption of the gases (CO₂, SO₂, NO_x, H₂S, NH₃, and CO) are analyzed. A comparative economic analysis of carbon materials is carried out, taking into account the cost of production and estimated costs of modification. Modern areas of application of carbon sorbents are analyzed as follows: industrial gas purification, automotive filters, air conditioning systems, personal protective equipment, and gas sensors. Particular attention is paid to the study of the prospects and future of materials. Prospective development directions are considered, including the creation of hierarchically porous structures, the development of self-healing materials, and integration with artificial intelligence to optimize adsorption processes. The cost of graphene and nanotube production is predicted to decrease by 50–70% by 2030, which will lead to an expansion of their commercial application. Full article

Nahj al-Balāghah is widely recognized as a foundational and authoritative scripture in Shia Islam. One notable aspect of Nahj al-Balāghah is the deliberate selection and structured arrangement of Ḥadiths. According to the book’s introduction, al-Raḍī explains that he chose the Ḥadiths based on literary considerations. An analysis comparing the selected Ḥadiths with their full versions suggests their inclusion was determined not only by eloquence and rhetorical value but also by conceptual significance. Through textual and descriptive analytical methods, this study examines the author’s motives, especially his political and religious aims, in incorporating materials related to ʿĀisha. A comparison of the relevant ḥadīths in Nahj al-Balāghah and other historical sources indicates that Sayyid Raḍī omitted—or at least refrained from including—certain statements attributed to ʿAlī regarding the Prophet Muḥammad’s youngest wife. The omitted parts concern ʿĀisha’s inconsiderate behavior, grudges, sins, following Satan, and ignoring the Prophet’s prediction. Considering sectarian conflicts between Shiites and Sunnis in the 3rd and 4th centuries AH, some arising from criticisms of ʿĀisha’s conduct and sometimes escalating into violence, al-Raḍī, the supreme judge appointed by the ʿAbbāsid Caliphate, was compelled to omit and censor ʿAli’s harsh remarks about ʿĀisha to prevent further sectarian tensions. Full article

Understanding how environmental conditions shape the functional composition of ecological communities is a central goal in community ecology. In this study, we apply this framework to the reptile and amphibian assemblages within Greece’s Mount Chelmos protected area. Based on comprehensive field surveys (2018–2021) across 168 sampling stations, we compiled species trait databases and quantified functional diversity using a corrected Rao’s Q index. We modeled the response of functional diversity to climate, land cover, topography (altitude, slope, aspect), geographic location, and taxonomic diversity, using Generalized Additive Models (GAMs). Additionally, we examined traitspace structure via PCA and evaluated environmental drivers of trait composition with multivariate GAMs. For reptiles, functional diversity was significantly affected by altitude, climate, and aspect, with higher values predicted in water-associated marginal zones surrounding the mountain massif. Traitspace analysis revealed clear ecological structuring along axes related to locomotion, body size, reproductive mode, foraging strategy, and substrate use, shaped by distinct combinations of environmental filters. In amphibians, environmental effects on functional diversity were not statistically significant; however, traitspace showed discernible responses to land cover, climate, and aspect, suggesting weaker—though detectable—filtering processes. Collectively, our findings indicate that Mount Chelmos functions as a system that modulates diversity, with environmental filters operating at fine-to-medium spatial scales to shape the functional composition and diversity of its herpetofauna. Full article

Roadside public transport stops represent localized air pollution hotspots where short-term exposure may differ substantially from levels reported by urban background monitoring. This study investigates the application of low-cost air quality sensors for long-term characterization of particulate matter and gaseous pollutants in a traffic-dominated urban microenvironment. The novelty of this work lies in the combined use of collocated low-cost sensors, energy-independent solar-powered deployment, height-resolved placement representative of different breathing zones, and integrated statistical and predictive analysis to resolve exposure-relevant pollutant dynamics at a single transport stop. Hourly concentrations of particulate matter (PM) PM₁, PM₂.₅, PM₁₀, nitrogen dioxide (NO₂), and ozone (O₃) were measured over one year at a roadside transport stop adjacent to a four-lane urban road carrying approximately 30,000 vehicles per day. Measurements were obtained using two collocated low-cost sensor units based on optical particle sensing for particulate matter and electrochemical sensing for gases, together with concurrent meteorological observations. Strong agreement between the two particulate matter sensors supported the use of averaged concentrations. Mean PM₂.₅ concentrations were substantially higher in winter (32.4 µg/m³) than in summer (10.4 µg/m³), indicating pronounced seasonal variability. PM₁ and PM₂.₅ exhibited closely aligned temporal patterns, while PM₁₀ showed greater variability. NO₂ displayed sharp diurnal peaks associated with traffic activity, whereas O₃ exhibited opposing seasonal and diurnal behavior and was negatively correlated with both PM₂.₅ (r = −0.32) and NO₂ (r = −0.29). One-hour-ahead predictive models incorporating meteorological and temporal variables achieved coefficients of determination up to 0.84. The results demonstrate that energy-independent low-cost sensor systems can robustly capture temporal patterns, pollutant interactions, and short-term predictability in localized roadside environments relevant to exposure assessment. Full article

The yak represents a distinct domestic animal species that predominantly inhabits the Qinghai–Tibet Plateau and adjacent areas, possessing considerable value in both scientific and economic contexts. Compared to animals that mainly dwell on plains, such as cattle, the sperm maturation process in yak exhibits a certain degree of species specificity to adapt to their unique reproductive needs in high-altitude environments. Serving as the main storage site for functionally competent sperm, the cauda epididymis plays an integral role in mediating their post-testicular maturation. MiRNAs are vital regulatory molecules in the epididymis, influencing sperm maturation by modulating gene expression after transcription. To investigate the unique regulatory mechanisms of sperm maturation in yak, this study compared the miRNA expression profiles in the cauda epididymis of yak and cattle using high-throughput small RNA (sRNA) sequencing. The comparative analysis identified and characterized sRNA populations in the cauda epididymis of yak and cattle, revealing a similar length distribution that peaked at 22 nt and a predominance of known miRNAs. Notably, eight miRNAs were found to be highly expressed in both species. Furthermore, the first-nucleotide bias differed significantly between known and novel miRNAs within each species. A total of 31 differentially expressed (DE) miRNAs were identified, with 11 upregulated and 20 downregulated in yak compared to cattle. Among these, bta-miR-1298 exhibited the most significant upregulation, while bta-miR-2344 displayed the most pronounced downregulation. Bioinformatic analysis linked the predicted target genes of these miRNAs to numerous critical signaling pathways, including calcium signaling, the mitogen-activated protein kinase (MAPK) signaling pathway, the Ras-associated protein 1 (Rap1) signaling pathway, and the cyclic guanosine monophosphate-protein kinase G (cGMP-PKG) signaling pathway. Furthermore, eight significantly DE miRNAs, including bta-miR-2443, bta-miR-503-3p, bta-miR-6517, bta-miR-2440, bta-miR-2431-3p, bta-miR-2436-3p, bta-miR-6523a, and bta-miR-6775, were predicted to target genes involved in various aspects of sperm structural and functional maturation. These aspects include flagellum formation, sperm motility, chromatin remodeling, acrosome reaction, acrosome structure, sperm capacitation, chemotaxis, and nuclear chromatin condensation. Multiple miRNAs and their corresponding predicted target genes were analyzed by quantitative real-time PCR (qPCR), demonstrating an inverse correlation between miRNA expression and target gene levels. These findings reveal a distinct, species-specific miRNA signature in the yak cauda epididymis, which suggests a potential contribution to regulating the epididymal luminal environment and the process of sperm maturation. This study provides preliminary foundational data for elucidating the differences in sperm maturation mechanisms between yak and cattle, and offers potential novel targets for improving reproductive efficiency in plateau livestock. Full article

Background/Objectives: Postoperative atrial fibrillation (POAF) is a common and serious complication after coronary artery bypass grafting (CABG), leading to increased morbidity and healthcare utilization. Although systemic inflammation is a well-established driver of POAF pathogenesis, no composite preoperative inflammatory biomarker has been validated for risk stratification in this population. This study aimed to evaluate the novel Inflammatory Burden Index (IBI)—the first composite biomarker combining acute-phase (C-reactive protein, CRP) and chronic cellular (neutrophil-to-lymphocyte ratio, NLR) inflammation—as a preoperative predictor of POAF after CABG. Methods: In this large retrospective cohort study, we included 3481 consecutive patients who underwent isolated CABG at a high-volume cardiac center between 2019 and 2024. Preoperative IBI was calculated as CRP (mg/dL) × NLR. The primary outcome was new-onset POAF within the first 7 postoperative days, confirmed by continuous telemetry on 12-lead ECG. Predictive performance was assessed using multivariable logistic regression, receiver operating characteristic (ROC) curve analysis (area under the curve, AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), and internal validation via bootstrapping (1000 resamples). Results: POAF developed in 866 patients (24.9%). Patients with POAF exhibited significantly higher preoperative IBI levels (39.4 ± 18.6 vs. 26.3 ± 16.7, p < 0.01). In multivariable analysis adjusted for age, hypertension, left atrial diameter, and other clinical covariates, IBI emerged as a strong independent predictor of POAF (adjusted OR 1.041, 95% CI 1.036-1.046, p < 0.01). The IBI alone demonstrated moderate-to-good discriminative performance (AUC 0.72, 95% CI 0.70–0.74), significantly outperforming the Systemic Immune/Inflammation Index (SII; AUC 0.61, DeLong test p < 0.001) and providing superior reclassification (NRI 0.150, IDI 0.032) and model fit (lower AIC). Combining IBI with established clinical risk factors further improved predictive accuracy (combined AUC 0.74, specificity 72.4%). Tertile-based stratification revealed a clear graded relationship with POAF incidence (low IBI: 16.6%, medium: 21.3%, high: 35.1%; p = 0.02). Notably, the medium IBI stratum (11.18-25.44) displayed the highest discriminative power (AUC 0.87, 95% CI 0.85-0.88), with bootstrap validation confirming model stability (minimal bias, robust 95% CI). Conclusions: This study establishes the preoperative Inflammatory Burden Index (IBI) as the first validated composite inflammatory biomarker independently associated with POAF following CABG. Its superior performance over existing indices (SII), graded risk stratification, and peak accuracy in the moderate inflammation window highlight its potential for personalized preoperative risk assessment and targeted perioperative intervention strategies. Full article

In injection molding, advanced numerical modeling tools, such as Moldex3D, can significantly improve product development by optimizing part functionality, structural integrity, and material efficiency. However, the complex and nonlinear interdependencies between the several decision variables and objectives, considering the various operational phases, constitute a challenge to the inherent complexity of injection molding processes. This complexity often exceeds the capacity of conventional optimization methods, necessitating more sophisticated analytical approaches. Consequently, this research aims to evaluate the potential of integrating intelligent algorithms, specifically the selection of objectives using Principal Component Analysis and Mutual Information/Clustering, metamodels using Artificial Neural Networks, and optimization using Multi-Objective Evolutionary Algorithms, to manage and solve complex, real-world injection molding problems effectively. Using surrogate modeling to reduce computational costs, the study systematically investigates multiple methodological approaches, algorithmic configurations, and parameter-tuning strategies to enhance the robustness and reliability of predictive and optimization outcomes. The research results highlight the significant potential of data-mining methodologies, demonstrating their ability to capture and model complex relationships among variables accurately and to optimize conflicting objectives efficiently. In due course, the enhanced capabilities provided by these integrated data-mining techniques result in substantial improvements in mold design, process efficiency, product quality, and overall economic viability within the injection molding industry. Full article

This article examines the statistical relationships between ENUS, defined as per capita energy use, and Environmental, Social, and Governance variables, with particular emphasis on the Environmental dimension and its connections with national energy systems. The study investigates whether systematic associations exist between ESG indicators and the cross-country and temporal variation in ENUS as per capita energy use, and to what extent machine learning methods can contribute to the description and interpretation of these relationships in comparison with panel econometric models. The analysis is based on a large World Bank dataset covering approximately 161 countries over the period 2004–2023 and follows a three-step methodological strategy. First, fixed-effects, random-effects, and Weighted Least Squares panel models are estimated to explore the statistical associations between a broad set of ESG variables and ENUS as per capita energy use, while controlling for unobserved country-level heterogeneity. Second, clustering techniques are applied to identify groups of countries with similar joint patterns in multidimensional variables related to energy systems, emissions, climate conditions, and natural resource use. Third, several machine learning models are implemented, with particular attention to the performance of the K-Nearest Neighbors algorithm evaluated through normalized measures of predictive accuracy and goodness of fit. Model interpretability is enhanced using dropout loss and additive explanation methods to assess the contribution of ESG variables to the prediction of ENUS as per capita energy use. Overall, the results reveal a rich and multidimensional structure of relationships between ESG indicators and ENUS expressed as per capita energy use. In particular, the evidence indicates a close association between ENUS and key environmental variables such as emissions intensity, energy intensity as a control variable, and the use of natural resources, together with Social and Governance factors related to development, institutional quality, and economic structure. These findings suggest that cross-country differences in ENUS as per capita energy use correspond to distinct environmental, social, and governance profiles within the ESG framework. Full article

Symmetry plays a fundamental role in the evolution of mining-induced stress fields and the deformation behavior of roadway surrounding rock. To improve control of roadway deformation under strong mining-induced disturbance, this study takes the 12 Upper 301 face at Buertai Coal Mine and investigates the deformation mechanism and corresponding control methods. Based on an analysis of in situ monitoring data, the key stratum responsible for energy accumulation in the overlying strata was identified. Based on the inherent symmetry of the longwall mining layout, a symmetric predictive model of overburden key-stratum abutment pressure is established, which reveals the spatially symmetric distribution characteristics of the mining-induced stress field. The accuracy of the theoretical model was further verified through a large-scale geomechanical similarity model test, which reproduced the fracture trajectory and stress evolution law of the overburden key strata. To mitigate strong mining pressure, a targeted hydraulic fracturing control technique aimed at specific overburden horizons was proposed and verified through field testing and application. Field monitoring results indicate that roof-to-floor convergence peaked at 235 mm, and rib convergence peaked at 115 mm. Compared with sections without hydraulic fracturing control, the surrounding rock deformation was reduced by 62.3% and 69.7%, respectively, demonstrating a significant pressure relief effect. This approach effectively ensured the roadway stability and enabled safe mining operations. Full article

The transition to renewable energy systems and electric mobility depends on the effectiveness, reliability, and durability of lithium-ion battery technology. Accurate modeling and control of battery systems are essential to ensure safety, efficiency, and cost-effectiveness in electric vehicles and grid storage. In engineering and materials science, battery models depend on physical parameters such as capacity, energy, state of charge (SOC), internal resistance, power, and self-discharge rate. These parameters are affected by measurement uncertainty. Despite the widespread use of lithium-ion cells, few studies quantify how measurement uncertainty propagates to derived battery parameters and affects predictive modeling. This study quantifies how uncertainty in voltage, current, and temperature measurements reduces the accuracy of derived parameters used for simulation and control. This work presents a comprehensive uncertainty analysis of 18650 format lithium-ion cells with nickel cobalt aluminum oxide (NCA), nickel manganese cobalt oxide (NMC), and lithium iron phosphate (LFP) cathodes. It applies the law of error propagation to quantify uncertainty in key battery parameters. The main result shows that small variations in voltage, current, and temperature measurements can produce measurable deviations in internal resistance and SOC. These findings challenge the common assumption that such uncertainties are negligible in practice. The results also highlight a risk for battery management systems that rely on these parameters for control and diagnostics. The results show that propagated uncertainty depends on chemistry because of differences in voltage profiles, kinetic limitations, and temperature sensitivity. This observation informs cell selection and testing for specific applications. Improved quantification and control of measurement uncertainty can improve model calibration and reduce lifetime and cost risks in battery systems. These results support more robust diagnostic strategies and more defensible warranty thresholds. This study shows that battery testing and modeling should report and propagate measurement uncertainty explicitly. This is important for data-driven and physics-informed models used in industry and research. Full article

Background/Objectives: Alterations of the phosphatidylinositol 3-kinase catalytic subunit alpha gene (PIK3CA) are identified in approximately 2–4% of non-small cell lung cancer (NSCLC) cases; however, their biological and clinical relevance in NSCLC remains incompletely understood. This study aimed to comprehensively characterize the clinical and molecular features, as well as outcomes, of patients with PIK3CA-altered NSCLC across different disease stages. Methods: We conducted a retrospective multicenter analysis of 62 patients with histologically confirmed early-stage or advanced NSCLC-harboring PIK3CA alterations (mutations and/or gene amplifications) treated between 2015 and 2022 at three Italian institutions. Demographic, clinical, pathological, and molecular variables were systematically collected and analyzed. Results: PIK3CA mutations accounted for the majority of alterations (90.3%), while amplifications represented 9.7%. The most frequent mutations involved exon 9 (66.1%), predominantly E545K and E542K, followed by exon 20 (16.1%). Most patients were current or former smokers, and concomitant oncogenic alterations were detected in 59.7% of cases, most commonly KRAS mutations. A history of prior malignancy was reported in 24.6% of cases. In the metastatic setting, adenocarcinoma histology was associated with significantly longer overall survival (OS) compared with non-adenocarcinoma histologies (18.4 vs. 5.5 months; p = 0.02). Patients with PD-L1–negative tumors demonstrated a numerically longer OS than those with PD-L1–positive tumors; however, this difference did not reach statistical significance (19.1 vs. 5.4 months; p = 0.05). No statistically significant survival differences were observed according to specific PIK3CA mutation subtypes or treatment strategies. Conclusions: PIK3CA-altered NSCLC represents a molecularly heterogeneous and clinically understudied subgroup, frequently characterized by co-occurring oncogenic alterations. In this study, no definitive prognostic or predictive role for PIK3CA alterations could be established. Nevertheless, these findings provide a descriptive real-world characterization of this molecular subset and support the need for validation in larger, prospectively designed, molecularly stratified studies. Full article

This study tests the performance of 30 tree-level models of literature to predict the aboveground biomass (AGB) of trees in 200 1 ha simulated plots representing the following two successional stages of Amazonian forests: Advanced Secondary Forest (ASF) and Mature Forest (MF). This matters because reliable biomass estimates are fundamental to carbon quantification and climate policy. Ensuring consistency between tree-level and plot-level accuracy strengthens transparency and credibility in global reporting. The aim was twofold: (i) to recommend accurate models to predict biomass in the Amazon and (ii) to detect what characteristics of the model calibration dataset can affect the accuracy of the AGB predicted at the plot level. We considered the characteristics of datasets, sample size, minimum, maximum, and range of tree diameters, as well as the coefficient of determination, root mean square error (RMSE), and number of predictors of the 30 models consulted in the literature. These characteristics were correlated with the biomass error per unit area. We listed 11 models based on their acceptable (overall ± 10%) accuracy, whereas four models overestimated and 15 models underestimated the biomass per unit area beyond the acceptable limit. Our analysis pointed out that the strongest (but moderate) correlation (r) was observed for the RMSE of the models, i.e., precision of model predictions. These correlations were r = 0.60 (p = 0.40) for ASF (kg) and r = 0.40 (p = 0.60) for MF (kg) and r = 0.57 (p = 0.18) for ASF (log) and r = 0.21 (p = 0.64) for MF (log), which means that models with greater uncertainty (higher RMSE) tend to produce larger errors in AGB estimation. As a main conclusion, this study cautions that selecting one model among several based on the lowest RMSE is a misleading practice because the precision of predictions at the tree level is not in agreement with the precision at the plot level. Full article

Background/Objectives: To evaluate the clinical and MRI characteristics of benign solitary schwannomas of the extremities, compare pre- and postoperative neurological symptoms, and identify preoperative and intraoperative risk factors for postoperative complications. Methods: A retrospective review was conducted on 47 patients with histopathologically confirmed benign solitary schwannomas of the extremities who underwent surgical excision. Demographic data, MRI characteristics (tumor volume, perilesional edema, and degenerative changes such as cystic components or intratumoral hemorrhage), fascicular relationship, and use of tru-cut biopsy were recorded. Pre- and postoperative neurological symptoms were compared. Univariate logistic regression analysis was performed to identify factors associated with postoperative complications. Results: The mean age was 38.6 ± 15 years, and the mean follow-up period was 109.8 ± 65.1 months. Lesions were predominantly located in the upper extremity (65.9%), with a mean volume of 9.6 ± 4.8 cm³; perilesional edema and/or degenerative changes were present in 53.1% of cases. Postoperative complications occurred in 19.1% of patients, with intrafascicular involvement being a significant predictor (OR = 5.4, p = 0.037) and a positive preoperative Tinel’s sign showing a trend toward significance (OR = 4.2, p = 0.084). Tumor volume, perilesional edema, degenerative changes, tru-cut biopsy, and anatomical location were not significantly associated with complications. At final follow-up, pain remission was 82.1%, and paresthesia improvement was 63.6%. Conclusions: Intrafascicular involvement was associated with postoperative complications in univariate analysis, whereas preoperative MRI characteristics, biopsy, and Tinel’s sign showed no predictive value for postoperative risk. Full article