Search Results (388,172)

The scorpion genus Hottentotta Birula, 1908 is widely distributed across Africa and the Middle East, extending to Pakistan, India and Sri Lanka. The processes which resulted in their evolution and diversification across this vast area are poorly understood. The present study investigated the phylogeny and historical biogeography of the genus in the Iranian Plateau and the Zagros Mountains based on nuclear and mitochondrial DNA sequences from four African species, an Arabian species and eight species from the Middle East, most of which are endemic to Iran. Phylogenetic analyses confirmed the monophyly of all species included in the analysis and recovered a clade comprising Iranian and Afro-Arabian species. S-DIVA and BBM analyses demonstrated that the species of Hottentotta occurring in the Iranian Plateau and the Zagros Mountains originated from an African ancestor and then dispersed to their current geographical ranges. Further divergence coincided with the orogeny of the Zagros Mountains and climatic changes during the Miocene epoch. The results support the hypothesis that the Zagros Mountains formed a geographical barrier which promoted vicariance and diversification on the Iranian Plateau. Full article

In this paper, we develop an accelerated three-step iterative scheme for the approximation of fixed points of contraction mappings in Banach spaces, with a particular focus on applications to fractional models. Strong convergence of the proposed iteration is established under standard contraction assumptions, together with stability and data dependence results. A refined rate of convergence analysis shows that the new scheme achieves a smaller effective contraction factor and converges faster than several classical two- and three-step iterative methods, including the Picard, Mann, Ishikawa, and S-iteration processes. The theoretical results are applied to Caputo-type fractional differential equations by reformulating the associated boundary value problems as fixed-point equations. Existence and uniqueness of solutions follow from the Banach contraction principle, while the accelerated convergence of the proposed iteration leads to improved numerical efficiency. Extensive numerical experiments, including fractional differential equations and nonlinear contraction mappings on the real line, are presented to validate the theoretical findings. The results demonstrate that the proposed three-step iteration provides an effective and reliable computational tool for fractional and non-local models. Full article

Despite global efforts to reduce landfill use for municipal waste, many sites remain active, and older closed sites still require management, particularly regarding leachate. Landfill leachate contains varying levels of organic and inorganic pollutants, generated through biological and physicochemical processes following water infiltration. Its complex composition—including COD, inorganic macro-components, heavy metals, and xenobiotics—necessitates effective treatment technologies to enable safe discharge into surface waters. This study compares low-cost, eco-sustainable adsorbents for the removal of ammonium, trace elements (Cd, Be, Fe, Cu, Ni, Pb, Cr, As, Sn, Sb, Se), and color (as an indirect measure of organic compounds) from urban landfill leachate. In more detail, six biochars from different biomass feedstocks and pyro-gasification conditions as well as natural chabazite and synthetic zeolite 13X (FAU-type) were investigated. After characterization, biochars were characterized and adsorption performance was assessed. Removal performance was comparatively evaluated after 24 h batch contact under fixed experimental conditions. Results showed that gasified biochars achieved high removal efficiency for metals and color but were ineffective for ammonium. Instead, both zeolites demonstrated efficient ammonium removal (~50%) but were less efficient for metals, reflecting the mechanism-driven selectivity of the adsorbents studied. Finally, a principal component analysis (PCA) revealed correlations between biochar physicochemical properties and contaminant retention, providing insight into key factors governing adsorption and informing the design of sustainable leachate treatment strategies. Full article

Background: Early assessment of cranial deformities in newborns, such as plagiocephaly, brachycephaly, dolichocephaly, turricephaly, and trigonocephaly, requires precise and non-invasive methods. Methodology: This study presents a methodology using a 3D scanning smartphone application to capture three-dimensional head point clouds. A total of 60 3D point cloud cases were classified according to six classes of head deformities. These 60 real 3D point clouds were expanded to 3600 semi-synthetic point clouds via controlled geometric transformations simulating realistic cranial variations. A total of 138 morphometric descriptors were extracted per class, representing spatial head features as distances from the centre of the point cloud to the head surface. These descriptors were used to train and compare three machine learning models: decision tree, random forest, and multilayer perceptron, enabling the automatic classification of six infant’s head deformities. Results: The machine learning models achieved high classification accuracy, with F1-scores up to 0.98, demonstrating the effectiveness of the approach. Conclusions: The results demonstrate the potential of combining mobile 3D sensors, image-based modelling, semi-synthetic data, and artificial intelligence to provide predictive support in clinical applications, highlighting the usefulness of low-cost portable optical sensors. Full article

Carbon Fiber-Reinforced Polymer (CFRP) composites represent a transformative class of structural materials, combining low density, high specific strength, and excellent fatigue resistance. This review provides a comprehensive overview of CFRPs, addressing their structure, manufacturing routes, mechanical performance, and functional behavior, with particular emphasis on damage tolerance, tribological properties, and environmental durability. The discussion begins with the classification and morphology of carbon fibers, highlighting their influence on composite anisotropy and interlaminar behavior. The effects of impact loading, delamination, and environmental conditioning on residual strength and fatigue life are then examined, with reference to established evaluation methods such as ASTM D7136 and compression-after-impact (CAI) testing. From a tribological perspective, the incorporation of nanoscale additives, such as graphite nanoplatelets and TiO₂ nanoparticles, and their contribution to enhancing wear resistance by promoting the formation of stable tribofilms, is explored. Advances in processing techniques, including low-pressure curing and improved resin systems, are also discussed for their roles in enhancing manufacturability and energy efficiency. Overall, the review underscores that optimal CFRP performance is achieved through the synergistic integration of fiber architecture, matrix design, and precise processing control, while future progress in nanomodification, recycling, and sustainable curing technologies is expected to further expand CFRP applications in the aerospace, automotive, and high-performance engineering sectors. Full article

Green consumption is a key path to dealing with environmental problems and promoting sustainable development. Green brand anthropomorphism can build an emotional bond between green brands and consumers by giving brands human characteristics, providing a new framework for promoting green consumption. This study uses a between-subject experimental design to explore the impact of green brand anthropomorphism on consumers’ purchasing intentions. Study 1 constructed an experimental framework of the impact of anthropomorphism (competence-oriented vs. warmth-oriented) on purchasing intentions and introduced non-anthropomorphism as a control group. It was found that brand anthropomorphism could improve consumers’ green purchasing intentions more than non-anthropomorphism, and the overall effect of warmth-oriented anthropomorphism was better than that of competence-oriented anthropomorphism. It further confirmed the mediating role of psychological distance, that is, green brand anthropomorphism shortened the psychological distance between consumers and brands, thereby improving purchasing intentions. Study 2 adopted a 2 (anthropomorphism: competence-oriented vs. warmth-oriented) × 2 (product attributes: functional vs. hedonic) experimental framework to reveal the moderating role of product attributes. It was found that functional attributes were suitable for competence-oriented anthropomorphism, and hedonic attributes were suitable for warmth-oriented anthropomorphism, which could more effectively improve green purchasing intentions. It was further verified that the mediating effect of psychological distance was regulated by product attributes. This study verifies the applicability of the Stereotype Content Model in the field of green consumption, addressing the gap in green brand anthropomorphism research regarding insufficient evidence on the differences between “anthropomorphism types (competence-oriented vs. warmth-oriented)” and their underlying mechanisms. It further reveals how psychological distance serves as a key behavioral mechanism linking brand stimuli to consumers’ green purchasing decision-making processes. Through two studies, it validates the effects of anthropomorphism types and their mediating mechanisms while also providing evidence for the moderating role of product attributes. This contributes a clearer explanatory framework for advancing brand anthropomorphism theory and informing sustainable marketing practices within the green consumption context. Full article

This study investigates the pyrolysis behavior of loblolly pine through thermogravimetric (TGA) and derivative thermogravimetric (DTG) analysis under varying nitrogen flow rates of 5–40 mL min⁻¹ and heating rates of 5–20 °C min⁻¹. The pyrolysis proceeded through three distinct phases: Phase I: initial moisture release, Phase II: active devolatilization, and Phase III: char formation. Kinetic modeling using both integral and differential forms of the Coats–Redfern method revealed distinct mechanistic interpretations. The integral approach primarily identified diffusion-controlled models (D1, D3) during moisture and char stages and reaction-order or contraction models (F2, R2) during devolatilization, with activation energies ranging from 8.89 to 70.48 kJ mol⁻¹. In contrast, the differential method captured sharper transitions and favored complex nucleation and growth mechanisms (A3, A4) and power laws (P3, P4), yielding higher activation energies up to 111.29 kJ mol⁻¹ in Phase II. These results underscore the influence of both inert gas flow and thermal ramp on pyrolysis reactivity and demonstrate that kinetic model selection significantly affects activation energy interpretation. The findings contribute to a more nuanced understanding of biomass pyrolysis and offer insights into reactor design and process optimization in thermochemical conversion systems. Full article

Cooling curve analysis enables accurate determination of aluminum alloy solidification parameters while capturing important non-equilibrium phenomena that are difficult to resolve using thermodynamic models alone. Modern casting simulation tools such as MAGMASOFT and ProCAST provide advanced capabilities, including user-defined material databases and microstructure models, but their predictive accuracy depends strongly on the quality of alloy-specific input data. In particular, the effects of trace element variations and chemical modification treatments, such as strontium-induced depression of the Al–Si eutectic temperature, are not always quantitatively represented in generic databases. This study demonstrates that thermal analysis provides experimentally based solidification data under controlled cooling conditions representative of foundry practice. Cooling curve analysis directly records undercooling, recalescence, and modification-induced temperature shifts, including eutectic temperature changes of ~10 °C after strontium treatment, which significantly influence solidification kinetics and defect formation. A short industrial thermal analysis test enables the extraction of key parameters, including liquidus, eutectic, coherency, rigidity, and solidus temperatures; fraction-solid evolution; and latent heat release. When integrated into casting simulation databases, these experimentally derived parameters support improved modeling of feeding behavior, shrinkage porosity risk, hot tearing tendency, and microstructure development. The proposed approach positions cooling curve analysis as a practical complementary tool for calibrating and enhancing simulation input data under real alloy and process conditions. Full article

Polyamide 6 (PA6) is limited in its application in precision and high-temperature fields due to its high moisture absorption, low heat resistance, and poor barrier properties. To overcome these intrinsic deficiencies, a rigid 9-(carboxyphenyl)carbazole-based diacid monomer (CzIPA) was incorporated into the PA6 backbone via one-step melt polycondensation. Structural analyses confirmed successful copolymer formation and effective modulation of hydrogen-bonding interactions and chain rigidity. The introduction of the bulky carbazole units markedly enhanced the thermal and physical properties of PA6. The glass transition temperature increased by up to 35.5 °C, while the maximum decomposition temperature rose by 23.8 °C, reflecting the reduced chain mobility and strengthened thermal resistance. The decreased amide-group density led to a 15% reduction in water absorption, improving dimensional stability. The Young’s modulus, flexural strength, and flexural modulus of the prepared copolymers were significantly improved compared to PA6, while the toughness was slightly reduced. Furthermore, oxygen and water-vapor permeabilities were simultaneously reduced by 30–35%, attributed to restricted diffusion pathways in the modified microstructure. Despite the increased rigidity, the copolymers maintained good melt processability with clear shear-thinning behavior. This study demonstrates CzIPA copolymerization as an efficient structural design strategy for producing high-performance PA6 materials with enhanced thermal stability, lower hygroscopicity, and superior barrier properties. Full article

Lactate dehydrogenase (LDH) is widely used as a nonspecific marker of tissue damage and cellular turnover and has been associated with metabolic and inflammatory processes, but its relationship with automated monitoring data and blood biochemical indicators in early-lactation dairy cows is still not well described. The aim of this study was to evaluate associations between LDH activity, blood biochemical parameters, and automated monitoring indicators in early-lactation Holstein cows. A total of 91 clinically healthy cows were classified into two groups according to LDH activity: Group 1 (LDH < 1364 U/L; n = 53) and Group 2 (LDH ≥ 1364 U/L; n = 38). Blood samples were collected once per cow during early lactation, whereas automated monitoring parameters were continuously recorded and daily averages corresponding to the sampling day were used for analysis. Cows with higher LDH activity had significantly higher aspartate aminotransferase (AST) activity and moderate increases in albumin (ALB), creatinine (CREA), gamma-glutamyl transferase (GGT), calcium (Ca), phosphorus (PHOS), and iron (Fe). Correlation analysis showed a strong positive association between LDH and AST (r = 0.799, p < 0.001), while moderate positive correlations were observed with ALB, alanine aminotransferase (ALT), CREA, Ca, GGT, Fe, and PHOS. Receiver operating characteristic (ROC) analysis showed the best discrimination ability for AST, while CREA, ALB, Fe, PHOS, Ca, and GGT showed moderate classification performance. Automated monitoring parameters did not differ significantly between groups; however, cows with higher LDH activity tended to show lower rumination time together with higher milk electrical conductivity, higher milk yield, higher fat-to-protein ratio (FPR), and higher somatic cell count (SCC). Overall, the results indicate that LDH is more closely related to systemic biochemical variation than to immediate changes in production or behavioral indicators, and support the use of biochemical markers together with automated monitoring data when evaluating physiological adaptation during early lactation. Full article

Cadmium (Cd) contamination and rising temperatures pose significant challenges to rice growth and food safety. Here, we investigated growth responses, Cd accumulation, physiological adaptations, and transcriptomic profiles of two rice cultivars, Yuzhenxiang (YZX) and Xiangwanxian 12 (XWX12), under combined Cd (0, 5, 20 μmol L⁻¹) and temperature (25 °C, 30 °C) stress. Moderate warming (30 °C) generally promoted seedling growth and enhanced Cd uptake, with YZX showing greater increases in plant height and biomass, whereas XWX12 developed longer roots. At maturity, the temperature-induced growth advantage persisted in YZX, accompanied by a 60% increase in root Cd concentration, compared with 36% in XWX12. Antioxidant enzyme activities (POD, SOD, CAT) were significantly induced under combined stress, with XWX12 exhibiting stronger enzymatic responses and broader activation of ABC transporter genes, supporting reduced Cd accumulation in shoots. Malondialdehyde content indicated milder oxidative damage in YZX despite higher Cd accumulation. Transcriptomic analyses revealed extensive early transcriptional reprogramming, with enrichment of antioxidant metabolism, ABC transporters, MAPK signaling, and Cd transport-related genes, demonstrating coordinated physiological and molecular responses. XWX12 favored intracellular Cd sequestration and sustained antioxidant activation, whereas YZX relied more on uptake and translocation processes. Overall, these results highlight genotype-specific strategies in coping with combined Cd and temperature stress, providing mechanistic insights for improving rice tolerance and safety under warming and contaminated environments. Full article

High-precision thermal regulation in semiconductor process equipment is critical for product quality, yet it is challenged by actuator transport delays, limited actuator bandwidth due to hardware dynamics, and broadband inlet disturbances in temperature-controlled process fluids. This paper presents a systematic solution integrating architecture optimization with a physics-informed hybrid prediction model to enable effective feedforward compensation. Frequency-domain analysis justifies placing the temperature fluctuation attenuator (TFA) upstream of the heater to filter mid-to-high-frequency disturbances without compromising feedback stability. To address actuation delays, a Physics-CNN-LSTM predictor is developed using a residual learning strategy. This framework employs a mechanism model for baseline estimation and a deep learning network to correct persistent low-frequency residuals caused by unmodeled dynamics. Comparative experiments on industrial data demonstrate that the model achieves a Root Mean Square Error (RMSE) of $3.56\times {10}^{-5}$ K under low-to-mid-frequency inlet disturbances, reducing error by approximately 51.8% compared to a standard LSTM. The model also exhibits strong robustness against disturbance frequency shifts ($R^2>0.996$ on unseen data). Furthermore, closed-loop simulations confirm that the proposed feedforward compensation enhances temperature stability in high-precision thermal control. Full article

This study assesses whether the mainstreaming hypothesis, derived from cultivation frameworks developed during the mass audience era, remains operative in a digital media environment characterized by fragmenting media and cultural taste publics. In particular, we consider evolving conceptions of mainstreaming that stimulated our research questions and hypotheses in four surveys conducted from 2015 to 2024. We broaden our view of media to see if entertainment content—especially film genres—can provide common ground in attracting people with little else in common. Results suggest that such “cultural mainstreaming” may occur by providing common gratifications and impact global indictors of our lives—happiness, community attachment, feelings about our quality of life, and perceived cosmopoliteness. But the results are limited to a general adult population, not the younger students studied. The findings apply only to the general adult population and not to the younger student sample examined. Overall, the results indicate that the cultivation effect is relatively weak; the small number of significant relationships observed does not appear to exceed what might be expected by chance. Taken together, these findings suggest that mainstreaming and media influence operate as more complex processes in the digital era. Full article

The complexity of internal control in commercial banks continues to increase, and relevant reports exhibit notable lag and template issues. In response to the demand to transform unstructured disclosures into actionable insights, this study proposes an “augmented Business Intelligence (BI) framework” that integrates a text-based internal control quality assessment system, a dual-validation process, and the resulting Intelligent Internal Control Decision Support System (IIC-DSS). By combining large language models and neural-symbolic models of regulatory prototypes, a quality evaluation system for internal control based on complex text is constructed using a mixed probability mechanism to reduce interference from defensive disclosures. A dual validation process is designed with Partial Least Squares Structural Equation Modeling (PLS-SEM). PLS-SEM verification confirms the construct validity of this evaluation system, while XGBoost verification indicates that internal control quality has incremental predictive ability for asset quality deterioration. The IIC-DSS uses SHapley Additive exPlanations (SHAP) to explain XGBoost outputs, quantifying the marginal contribution of each control factor to the predicted risk. Overall, this study advances internal-control measurement by establishing a neural-symbolic, text-to-indicator representation within an augmented BI architecture and empirically demonstrating its utility in improving predictive power for bank asset quality deterioration and in enhancing decision transparency via explainable AI. Full article

It is proposed that the process of learning may be represented as a trajectory within the phase space of long-term memory. The research uses an artificial neural network design to explore, in theory, if starting from different points within the phase space affects how quickly learning occurs. Using a Monte Carlo method, 1000 virtual agents were trained using the Levenberg–Marquardt algorithm to recognise a large set of Arabic digits at ten different skill levels. The simulations replicated the typical learning curves observed in human learning and were successful in distinguishing ten levels of skill. First, and in line with previous research, the results provide convincing evidence that learning consolidates a selected set of pathways within the network. Second, and critical to the hypothesis, the distance in the phase space, calculated as the difference in average connectivity between skill levels, is highly predictive of both learning time and performance. The findings strongly support the hypothesis that learning represents progression along a trajectory connecting two points within the phase state landscape. As these properties may be more pronounced in biological systems because of their greater complexity, these results shed new light on individual variance in learning. Full article