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Modeling and optimization of electro-mechanical properties in 18,650 batteries using a hybrid DNN-GWO approach: integrating metrological data and simulation
No full textThe process of optimizing lithium-ion batteries needs precise assessment of all their electro-mechanical and vibrational characteristics. The researchers present a measurement-based framework which enhances the performance of 18,650 lithium-ion cells that use graphene nanoplatelets as reinforcement material. A deep neural network (DNN) serves as the system that establishes complex connections between structural elements and their resulting performance metrics which include mechanical strength and electrical conductivity and vibrational response. The Grey Wolf Optimizer (GWO) establishes a structured approach to measurement space exploration which identifies the best system setups that achieve maximum battery capacity while maintaining dependable measurement results. The method focuses on three key aspects which include accurate measurement results and consistent measurement outcomes and methods of measuring uncertain results. The strong metrological framework establishes assessment procedures for evaluating performance improvements. The framework's ability to predict future outcomes gets confirmed through testing with separate experimental data from academic research while high-fidelity simulations show complete details about the electro-mechanical performance. The approach uses metrological principles which include accuracy and repeatability and uncertainty quantification to create a modeling and optimization system which trains the DNN using a hybrid dataset that combines experimental data with high-fidelity simulations and includes dedicated measurement noise simulation through explicit data augmentation. The measurement-guided approach achieves better results because it combines two advantages: improved accuracy and enhanced computational efficiency which surpass standard predictive and optimization methods. The research introduces an innovative measurement-based technique which uses artificial intelligence together with metrological standards to deliver precise battery performance evaluations and structured development of lithium-ion batteries
Regional differences in microplastic accumulation in the thornback ray (Raja clavata) along the southern Black Sea coast
No full textMicroplastic (MP) pollution is an emerging global concern with potential ecological and human health consequences, yet data on its impacts in demersal cartilaginous fishes remain scarce. This study presents the first assessment of MP ingestion in the thornback ray ( Raja clavata ) specifically focusing on the southern Black Sea coast. A total of 68 individuals collected from 23 stations were examined, yielding 196 MP particles. The mean abundance was 2.88 MPs per individual, with significantly higher values in the eastern Black Sea compared to the western region (Mann–Whitney U, p = 0.036). Fibers dominated the morphological categories (>80 %), while PET and PP were the most prevalent polymer types, and black and blue were the dominant colors. The eastern coast exhibited greater polymer and color diversity, which suggests a potential association with heterogeneous land-based inputs. No significant correlations were found between MP abundance and fish length, weight, or sampling depth. These findings highlight spatial heterogeneity in MP contamination, emphasizing areas of accumulation consistent with regional riverine discharges and coastal pressures. The results highlight the potential importance of targeting land-based sources to support conservation efforts in sensitive marine ecosystems
Nonlinear dynamics and structural stability of arc-auxetic-tapered plates with piezoelectric patches for sports equipment applications
No full textThe research presented here delves into the nonlinear dynamics and structural stability of taper arc-auxetic plates that come with the application of piezoelectric patches, consequently, looking at their applications in sports equipment. The construction consists of an arc-like auxetic core with a negative Poisson’s ratio, along with piezoelectric face sheets to give the structure better mechanical performance. The plates have a variable thickness distribution, which is important in absorbing energy and deforming the material, hence under dynamic loading conditions. Transverse shear deformation plays an essential role in the proper analysis of tapered plates, and for that, higher-order shear deformation theory (HSDT) is used in a way to reveal the effects of this deformation. The main equations are obtained via Hamilton’s principle, which is a strong tool for the dynamic behavior of the system. A numerical solution is achieved using the differential quadrature method (DQM) based on high-order derivatives of the Gauss–Chebyshev–Lobatto function, and an iterative procedure offering high accuracy in the computation for complex geometries and boundary conditions. The results denote the dynamic response change of auxetic design to its energy dissipation and impact resistance properties, thus marking those two as the main characteristics for sport equipment. Besides, the stability analysis has also shown that piezoelectric actuation has an effect on the system’s performance, thus indicating the possibility of applying it for active vibration control. This study opens a new chapter in the field of innovative sports materials, where, besides dynamic stability and energy management, performance optimization is the key factor
Interpetatle data mining for legume crude protein prediction
No full textDespite the critical need for rapid nutritional assessment in diverse legume species, there remains a significant knowledge gap regarding the efficacy and interpretability of advanced data mining algorithms for the non-destructive prediction of Crude Protein (CP) content. The scientific challenge lies in the conventional methods (e.g., Kjeldahl), which are destructive, expensive, and time-intensive, preventing rapid, high-throughput screening necessary for modern crop breeding programs and industrial quality control. This study investigates the efficacy of four data mining algorithms—Multivariate Adaptive Regression Splines (MARS), Support Vector Regression (SVR), k-Nearest Neighbors (KNN), and Artificial Neural Networks (ANN)—for non-destructively predicting CP across 14 legume species. Our objective was to identify a robust and interpretable computational approach that can enhance precision agriculture and industrial legume production. MARS demonstrated superior predictive performance, yielding the lowest Root Mean Squared Error (RMSE) of 0.62 and a Relative Root Mean Squared Error (RRMSE) of 3.38. It also achieved the highest coefficient of determination (R²) of 0.93 and an Akaike Information Criterion (AIC) of −5.51, indicating a strong fit and robustness in modeling complex non-linear relationships and variable interactions inherent in agricultural datasets. SVR showed moderate accuracy with an RMSE of 1.17, exhibiting efficiency with smaller datasets. Conversely, ANN displayed a Mean Absolute Percentage Error (MAPE) of 96.78 %, indicating poor generalization, likely due to overfitting or incompatibility with the dataset size. KNN offered interpretable results but with lower accuracy, reporting an RMSE of 1.92. This research establishes MARS as a precise and interpretable tool for non-destructive CP prediction in legumes, offering a significant improvement over traditional methods for precision agriculture and industrial crop development. The core scientific contribution is the development and validation of a robust, multi-species, non-linear MARS prediction framework that successfully translates complex nutrient interactions (e.g., Ca/P, K/(Ca+Mg) ratios) into actionable, biologically relevant hinge functions. Its integration into crop improvement programs holds potential to accelerate the development of high-protein cultivars through rapid, non-destructive screening. These findings contribute to the application of machine learning in agriculture, providing actionable insights for improving crop trait selection and industrial legume utilization
Pseudoephedrine and acute coronary events: a real-world assessment in acute myocardial infarction patients
No full textAim: The decongestants are frequently prescribed for symptomatic relief to reduce mucosal congestion. However, even in the absence of overt cardiovascular symptoms, patients may subsequently present with serious acute cardiac events. The present study aims to assess the potential association between recent pseudoephedrine exposure and the occurrence of coronary vasospasm in patients presenting with acute myocardial infarction (AMI) to the emergency department, who reported the use of pseudoephedrine-containing products within the preceding week. Materials and Methods: The study population included patients who presented with chest pain and were diagnosed with AMI [either STelevation myocardial infarction (STEMI) or non-STEMI]. The primary objective was to evaluate the history of pseudoephedrine use within the Results: Among patients with a history of pseudoephedrine use, the 1-month incidence of major adverse cardiac events (MACE) was 13% (n=3), compared to 12.7% (n=21) in those without such a history. When comparing age, diagnosis, and MACE rates between patients with and without pseudoephedrine use, no statistically significant differences were observed. Regarding MACE subtypes, the most frequent event was death, occurring in 7.4% (n=14) of all patients. Heart failure was identified in 2.6% (n=5), while recurrent myocardial infarction was observed in 2.1% (n=4) patients. Conclusion: Our findings suggest a clinically relevant association between recent pseudoephedrine use and acute cardiac events in vulnerable patients. This calls for increased awareness among clinicians, pharmacists, and the general public regarding the possible adverse outcomes associated with pseudoephedrine, even when used short-term or at therapeutic doses
Effect of the double-application of one-step universal adhesives on bonding strength to bur-cut enamel
No full textBackground One-step universal adhesives form a thinner adhesive layer, which is more susceptible to oxygen-induced polymerization inhibition, thereby weakening the bond strength. Some studies have suggested strengthening the adhesive layer by applying multiple layers of adhesive. Thus, this study aimed to examine the effect of double application on the bond strength of universal adhesives to bur-cut enamel. Methods The enamel surfaces were prepared using a medium-grit diamond bur. The specimens were randomly distributed into eighteen groups based on three factors: the adhesive used (Optibond Universal, G-Premio Bond, Clearfil S3 Bond Universal), the application method of adhesives (single application and double-application with or without light activation between layers), and the type of composite resin (Clearfil Majesty Flow and Clearfil Majesty Esthetic). The adhesive agents were applied in self-etch mode, and composite blocks were fabricated. After 24 h, the composite blocks were subjected to a shear bond strength test, and the fractured surfaces were examined using a stereomicroscope. Additionally, the thickness of the adhesive layers was assessed using scanning electron microscopy. The data were evaluated by a three-way ANOVA, Weibull analysis, and Kruskal-Wallis tests (alpha = 0.05). Results The double-application of the adhesives did not improve the bonding potential to enamel (p > 0.05). The use of flowable composite resin did not enhance the bond strength. The adhesives demonstrated similar reliability. Optibond Universal exhibited higher bond strength to bur-cut enamel compared to G-Premio Bond and Clearfil S3 Bond Universal, regardless of application method and composite type (p < 0.05). Conclusion The double-application of the adhesives and composite type did not affect the bonding strength to bur-cut enamel, regardless of whether light activation was applied between the applications
Agroecological sustainability analysis of small ruminants farms in the Saharan context of El Oued, Algeria
No full textThis study aims to quantify how structural constraints and management choices together shape agroecological sustainability in small ruminant systems within the arid and oasis contexts of El Oued, Algeria. Using the agroecological scale of the IDEA method combined with multivariate analysis techniques (Principal Component Analysis and K-means clustering); the research constructs a detailed typology of farms. This approach helps isolate the impacts of land use strategies and capital endowment on environmental performance. Five distinct farm clusters were identified, ranging from extensive, low-input systems to intensive, integrated agroecosystems. The analysis shows that while structural factors define the production limits, it is management choices that determine sustainability outcomes. Agroecological performance is maximized in systems that engage in extensive diversification and internal resource recycling. Specifically, integrated systems (Cluster 5), characterized by manure composting and legume fodder, demonstrate how adaptive, knowledge-intensive management practices can overcome the severe physical limitations of the Sahara. On the other hand, farms relying on single strategies, such as pure extensification or heavy reliance on external inputs (Cluster 3), exhibit critical environmental shortcomings. These systems highlight the risks of decoupling productivity from ecosystem functions, leading to long-term sustainability issues. The study concludes that promoting agroecological transitions in the Sahara is achievable with well-targeted interventions. The research advocates for a shift in policy and practice from generic input support to fostering integrated crop-livestock systems. Ensuring the sector's future depends on empowering farmers to optimize on-farm nutrient cycles, promoting resource-conserving, knowledge-intensive, and solidarity-based approaches
Concerns about the methodology used in the paper “Discovery and analysis of microplastics in human bone marrow” (J Hazard Mater 2024)
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Does women’s political participation matter in carbon emissions reduction? A panel data analysis of BRICS-TM
No full textAlthough previous studies have investigated the impact of various determinants on environmental degradation, there has been relatively minimal research on the impact of women’s political participation (WP) on carbon emissions (CO2). Considering the research gaps, we examine the relationship between WP and CO2 as well as their underlying mechanisms in Brazil, Russia, India, China, South Africa, Turkey, and Mexico (BRICS-TM), the world’s largest energy consumers and the largest CO2 emitters. For this purpose, the novel Method of Moment Quantile Regression (MMQR) technique is applied as a robust estimation method alongside Fully Modified Ordinary Least Square (FMOLS), Dynamic Ordinary Least Square (DOLS), and Canonical Cointegration Regression (CCR) estimations. The findings indicate that a 1% rise in WP leads to a reduction in CO2 in the FMOLS, DOLS, and CCR techniques by 0.022%, 0.090%, and 0.024%, respectively. Further, the MMQR technique demonstrates that WP reduces to CO2 by 0.071% to 0.122% across all quantiles from the 10th to 90th. The study is also augmented with additional variables, such as renewable energy consumption (REC), economic growth (EG), energy consumption (EC), and trade openness (TO) to enhance the robustness of the CO2 function. EG, EC, and TO exert a positive impact on CO2, while REC reduces it. The results highlight that increasing women’s political participation can pave the way for legislation that particularly supports green economy and sustainable development in BRICS-TM
Prospective comparative study on postoperative pain after single-visit root canal treatment using rotary and reciprocating kinematics
No full textThis prospective clinical study aimed to compare postoperative pain and analgesic intake following single-visit root canal treatment using two rotary file systems with different kinematic motions: reciprocating (T-endo MUST, TeM) and continuous rotary (Scope RS Narrow, SRN). A total of 121 patients with vital mandibular premolars were prospectively allocated using an alternate, non-randomized approach to single-visit root canal treatment performed by the same operator. The primary outcome was postoperative pain intensity, measured using a 100-mm Visual Analog Scale (VAS) on day 3 after treatment. The secondary outcomes included the VAS scores recorded at 1, 5, and 7 days and the analgesic consumption. A responder analysis was conducted, defining responders as patients who achieved ≥ 30% or ≥ 50% reduction in pain or a VAS score < 10. Pain intensity decreased significantly over time in both groups (p < 0.001). The TeM group was associated with higher pain scores on the third postoperative day (8.31; 95% CI: 3.89–12.72) than the SRN group (3.39; 95% CI: 0.34–6.44) (p = 0.019), whereas no significant differences were observed at other time points. Analgesic use was reported by 12.9% of patients in the TeM group and by none in the SRN group (p = 0.004). Responder analysis confirmed that the SRN system resulted in a higher proportion of pain-free patients on day 3 (RR = 0.83; 95% CI: 0.70–0.98; p = 0.027). Both reciprocating and continuous rotary systems showed a gradual reduction in postoperative pain after single-visit endodontic treatment. A modest and short-lived difference was observed on day 3, with slightly higher pain levels in the reciprocating group, suggesting a temporary variation in the early postoperative response rather than a clinically decisive effect of instrument kinematics