Search Results (8,222)

Extant empirical studies investigate social commerce purchase intention from the perspective of swift guanxi dimensions while neglecting to explain how the purchase intention is influenced. This study proposed and tested a research model to unveil the relationship between swift guanxi dimensions (mutual understanding, reciprocity favor, and relationship harmony), trust in the seller and purchase intention while considering the mediation effect of trust in the seller in social commerce settings. Data from 421 social commerce youth consumers in Tanzania were used in PLS-SEM analysis to test the research model. Results revealed that except for reciprocity favor, swift guanxi dimensions positively influence purchase intention. The swift guanxi dimensions also positively influence trust in the seller. Further trust in the seller mediates the relationship between swift guanxi dimensions and purchase intention. This study recommends that social commerce micro, small, and medium traders embrace swift guanxi to drive consumer purchase intention. Swift guanxi dimensions foster a rapid and affirmative connection with the consumers, enhancing their trust in the seller. In turn, trust in the seller significantly enhances the likelihood of purchase intention, since the consumers feel safer and more confident in their buying journey. Therefore, by leveraging swift guanxi dimensions, social commerce sellers can effectively build business relationships based on a strong foundation, which not only drives immediate consumer purchases but also fosters enduring consumer devotion. Full article

This study aimed to explore the health impacts, mechanisms of toxicity, and key gene biomarkers of a mixture of the most prominent perfluoroalkyl/polyfluoroalkyl substances (PFAS) through in silico ADMET and toxicogenomic analysis. The following databases and tools were used: AdmetSAR (2.0), ADMETlab (2.0), Comparative Toxicogenomic Database, ToppGene Suite portal, Metascape (3.5), GeneMANIA server, and CytoHubba and CytoNCA Cytoscape (3.10.3) plug-ins. ADMET analysis showed that PFAS compounds pose risks of organ-specific toxicity, prolonged retention, and metabolic disruptions. Forty mutual genes were identified for all the tested PFAS. The mutual gene set was linked to disruption of lipid metabolism, particularly through nuclear receptors. The most important gene clusters identified were nuclear receptor signaling and PPAR signaling pathways, with kidney and liver diseases, diabetes, and obesity as the most significant related diseases. Phenotype data showed that PFAS compounds impact cell death, growth, inflammation, steroid biosynthesis, and thyroid hormone metabolism. Gene network analysis revealed that 52% of the 40 mutual genes showed co-expression, with co-localization as the next major interaction (18.23%). Eight key genes were extracted from the network: EHHADH, APOA2, MBL2, SULT2A1, FABP1, PPARA, PCK2, and PLIN2. These results highlight the need for further research to fully understand the health risks of PFAS mixtures. Full article

This paper introduces the mixed-strategy Harris hawk optimization (MSHHO) algorithm as an enhancement to address the limitations of the conventional Harris hawk optimization (HHO) algorithm in solving complex optimization problems. HHO often faces challenges such as susceptibility to local optima, slow convergence, and inadequate precision in global solution-seeking. MSHHO integrates four innovative strategies to bolster HHO’s effectiveness in both local exploitation and global exploration. These include a positive charge repulsion strategy for diverse population initialization, a nonlinear decreasing parameter to heighten competitiveness, the introduction of Gaussian random walk, and mutual benefit-based position updates to enhance mobility and escape local optima. Empirical validation on 12 benchmark functions from CEC2005 and comparison with 10 established algorithms affirm MSHHO’s superior performance. Applications to three real-world engineering problems and UAV flight trajectory optimization further demonstrate MSHHO’s efficacy in overcoming complex optimization challenges. This study underscores MSHHO as a robust framework with enhanced global exploration capabilities, significantly improving convergence accuracy and speed in engineering applications. Full article

For feedback to be productive, it relies on the interactions of participants, design elements, and resources. Yet, complexities in clinical education pose challenges for feedback practices in students and teachers, and efforts to improve feedback often ignore the influence of culture and context. A recent sociocultural approach to feedback practices recognized three layers to understand the complexity of productive feedback: the encounter layer, the design layer, and the knowledge layer. This study explores the sociocultural factors that influence productive feedback practices in clinical settings from the clinical teacher–student dyad perspective. A cross-sectional qualitative study in a physiotherapy clerkship involved semi-structured interviews with ten students and eight clinical educators. Convenience sampling was used, and participation was voluntary. Employing thematic analysis from a sociocultural perspective, this study examined feedback practices across the three layers of feedback practices. The analysis yielded different elements along the three layers that enable productive feedback practices in the clinical workplace: (1) the feedback encounter layer: dyadic relationships, mutual trust, continuity of supervision, and dialogue; (2) the feedback design layer: enabled learning opportunities and feedback scaffolding; (3) the knowledge domain layer in the clinical culture: Growing clinical experience and accountability. In the context of undergraduate clinical education, productive feedback practices are shaped by social–cultural factors. Designing feedback practices should consciously integrate these components, such as cultivating relationships, fostering guidance, enhancing feedback agency, and enabling supervised autonomy to promote productive feedback. Full article

The differentiation/maturation trajectories of different blood cell types stemming from a CD34⁺ common ancestor takes place in different biologically relevant multidimensional spaces. Here, we generated microRNA and cytokine profiles from highly purified populations of hematopoietic progenitors/precursors derived from cord blood hematopoietic stem/progenitor cells. MicroRNA and cytokine landscapes were then analyzed to find their mutual relationships under the hypothesis that the highly variable miRNome corresponds to the ‘force field’ driving the goal of a stable phenotype (here corresponding to the cytokine abundance pattern) typical of each cell kind. The high dimensionality and lack of linearity of the hematopoietic process pushed us to adopt a distance–geometry approach to compare different trajectories, while a complex network analysis was instrumental in revealing the fine structure of microRNA–cytokine relations. Importantly, the approach enabled us to identify a limited number of factors (represented either by microRNAs or cytokines) corresponding to crucial nodes responsible for connecting distinct interaction modules. Subtle changes in ‘master nodes’, keeping the connections between different regulatory networks, may therefore be crucial in influencing hematopoietic differentiation. These findings highlight the extremely interconnected network structures underlying hematopoiesis regulation and identify key factors in the microRNA/cytokine landscape that may be potentially crucial for influencing network stability. Full article

(1) Background: The mutual relationship between evolution and learning is a controversial argument among the artificial intelligence and neuro-evolution communities. After more than three decades, there is still no common agreement on the matter. (2) Methods: In this paper, the author investigates whether combining learning and evolution permits finding better solutions than those discovered by evolution alone. In further detail, the author presents a series of empirical studies that highlight some specific conditions determining the success of such combination. Results are obtained in five qualitatively different domains: (i) the 5-bit parity task, (ii) the double-pole balancing problem, (iii) the Rastrigin, Rosenbrock and Sphere optimization functions, (iv) a robot foraging task and (v) a social foraging problem. Moreover, the first three tasks represent benchmark problems in the field of evolutionary computation. (3) Results and discussion: The outcomes indicate that the effect of learning on evolution depends on the nature of the problem. Specifically, when the problem implies limited or absent agent–environment conditions, learning is beneficial for evolution, especially with the introduction of noise during the learning and selection processes. Conversely, when agents are embodied and actively interact with the environment, learning does not provide advantages, and the addition of noise is detrimental. Finally, the absence of stochasticity in the experienced conditions is paramount for the effectiveness of the combination. Furthermore, the length of the learning process must be fine-tuned based on the considered task. Full article

Major depressive disorder (MDD) poses a significant challenge in mental healthcare due to difficulties in accurate diagnosis and timely identification. This study explores the potential of machine learning models trained on EEG-based features for depression detection. Six models and six feature selection techniques were compared, highlighting the crucial role of feature selection in enhancing classifier performance. This study investigates the six feature selection methods: Elastic Net, Mutual Information (MI), Chi-Square, Forward Feature Selection with Stochastic Gradient Descent (FFS-SGD), Support Vector Machine-based Recursive Feature Elimination (SVM-RFE), and Minimal-Redundancy-Maximal-Relevance (mRMR). These methods were combined with six diverse classifiers: Logistic Regression, Support Vector Machine (SVM), Random Forest, Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Light Gradient Boosting Machine (LightGBM). The results demonstrate the substantial impact of feature selection on model performance. SVM-RFE with SVM achieved the highest accuracy (93.54%) and F1 score (95.29%), followed by Logistic Regression with an accuracy of 92.86% and F1 score of 94.84%. Elastic Net also delivered strong results, with SVM and Logistic Regression both achieving 90.47% accuracy. Other feature selection methods yielded lower performance, emphasizing the importance of selecting appropriate feature selection and machine learning algorithms. These findings suggest that careful selection and application of feature selection techniques can significantly enhance the accuracy of EEG-based depression detection. Full article

With the deep integration of psychology and artificial intelligence technology and other related technologies, eye control technology has achieved certain results at the practical application level. However, it is found that the accuracy of the current single-modal eye control technology is still not high, which is mainly caused by the inaccurate eye movement detection caused by the high randomness of eye movements in the process of human–computer interaction. Therefore, this study will propose an intent recognition method that fuses facial expressions and eye movement information and expects to complete an eye control method based on the fusion of facial expression and eye movement information based on the multimodal intent recognition dataset, including facial expressions and eye movement information constructed in this study. Based on the self-attention fusion strategy, the fused features are calculated, and the multi-layer perceptron is used to classify the fused features, so as to realize the mutual attention between different features, and improve the accuracy of intention recognition by enhancing the weight of effective features in a targeted manner. In order to solve the problem of inaccurate eye movement detection, an improved YOLOv5 model was proposed, and the accuracy of the model detection was improved by adding two strategies: a small target layer and a CA attention mechanism. At the same time, the corresponding eye movement behavior discrimination algorithm was combined for each eye movement action to realize the output of eye behavior instructions. Finally, the experimental verification of the eye–computer interaction scheme combining the intention recognition model and the eye movement detection model showed that the accuracy of the eye-controlled manipulator to perform various tasks could reach more than 95 percent based on this scheme. Full article

In this study, I discuss recent studies of human/wildlife mutualisms and suggest that several cases considered to represent domestication that has arisen through commensalism would be better considered as examples of mutualism between humans and various wild species. Species discussed include the only domesticated carnivores: cats (Felis sylvestris) and wolves (Canis lupus and C. dingo). I also discuss species over which there is considerable debate about whether they are domesticated or not: African (Loxodonta) and Asiatic elephants (Elphas). All of these species’ interactions include niche construction on the part of both species and influence human evolution at least a cultural level. I further argue that most contemporary domestic species currently exist in mutualistic relationships with humans because even though all of these species have been selected to benefit humans, all domestica species have also benefitted in terms of increased global and local population sizes and from more secure living conditions than can be found in their wild ancestors. Full article

Deep learning has made significant strides, driving advances in areas like computer vision, natural language processing, and autonomous systems. In this paper, we further investigate the implications of the role of additive shortcut connections, focusing on models such as ResNet, Vision Transformers (ViTs), and MLP-Mixers, given that they are essential in enabling efficient information flow and mitigating optimization challenges such as vanishing gradients. In particular, capitalizing on our recent information bottleneck approach, we analyze how additive shortcuts influence the fitting and compression phases of training, crucial for generalization. We leverage Z-X and Z-Y measures as practical alternatives to mutual information for observing these dynamics in high-dimensional spaces. Our empirical results demonstrate that models with identity shortcuts (ISs) often skip the initial fitting phase and move directly into the compression phase, while non-identity shortcut (NIS) models follow the conventional two-phase process. Furthermore, we explore how IS models are still able to compress effectively, maintaining their generalization capacity despite bypassing the early fitting stages. These findings offer new insights into the dynamics of shortcut connections in neural networks, contributing to the optimization of modern deep learning architectures. Full article

The relationship between soil structure and salt accumulation is unclear; thus, experiments on salt accumulation under different soil structures were conducted in cotton fields in arid areas of northwest China. Thirty-nine sets of soil samples were collected from the 0 to 180 cm profile of three experimental areas. The total salt content of the soil extracts and the particle size distribution of the soil samples were determined using a JENCO TDS and a laser particle size analyzer, respectively, and the fractal dimension of the soil structure was obtained using fractal theory. Pearson’s correlation analysis and Tukey’s test (p < 0.01) were used to analyze the correlation between soil salinity, soil particle size distribution, and fractal dimensions in the three profiles. The results showed soil salinity accumulation was affected mutually by soil texture and soil structure, and soil salinity tended to accumulate in fine-grained soil. The soil fractal dimension (D) could indicate soil texture and quantify soil salinity content. When the sand content was more than 50%, there was a significant positive correlation between the soil fractal dimension and soil salinity (correlation coefficient R = 0.943). The results provide valuable insights into cotton production in arid areas. Full article

This article theorizes the role of local and indigenous culture in its intersection with development initiatives. It argues that Communication for Development and Social Change (CDSC), through a cultural studies framework, strengthens the potentiality of democratization and participation within community-based development and social change settings. We advocate that applied cultural studies can facilitate agency (through voice and self-representation) in social interventions. This is a cultural studies approach that has been recontextualised from the Birmingham origin as read through Marxist development studies, first adapted and mobilized during the anti-apartheid struggle in developing cultural strategy, and more recently with efforts to indigenise research practices with research participants in the southern Kalahari. We draw on an example of the community-owned, state-funded, and privately operated !Xaus Lodge cultural tourism asset. We illustrate how CDSC strategies, influenced by applied cultural studies, can work with an agentic imperative to effect development and mutual understanding in a defined geographical area, where multiple stakeholder agendas, cultural backgrounds, and ontologies are to be negotiated. Full article

Background/Objective: Balanced reciprocal translocations are structural chromosomal anomalies that involve a mutual exchange of segments between two non-homologous chromosomes with a consequent 50–80% risk of conceiving fetuses with unbalanced chromosomal anomalies. This study describes a 37-year-old woman, at 13 + 5 weeks of gestation, who is a balanced reciprocal translocation 46,XX,t(9;18)(q34;q11.2) carrier, with a high-risk non-invasive prenatal screening test, NIPT, for chromosome 18 aneuploidy. Methods: The highlighted aneuploidy was characterized with cytogenetic, FISH and SNP-array techniques. Results: Cytogenetic analysis, performed on flask-cultured amniocytes, indicated a 48,XX,+2mar karyotype on 50 metaphases. SNP array analysis showed a 15.3 Mb duplication of chromosome 18p (arr[hg19]18p11.32-p11.21(12,842-15,303,932)x4), consistent with a partial tetrasomy 18p, and a 926 kbp duplication of chromosome 9q (arr[GRCh37]9q34.3(140,118,286-141,044,489)x3), consistent with partial trisomy 9q. FISH analysis with a 9q34.3 probe was performed on flask-cultured amniocytes’ metaphases, highlighting the presence of a third signal on one of the two marker chromosomes (18p11.32-p11.21). Conclusions: The evidence of such partial aneuploidies suggests that different mutational events may be possible at meiotic segregation or probably post-meiotic segregation. The results obtained highlight the high sensitivity of the screening test, NIPT, with massive parallel sequencing, and the usefulness of cytogenetics, cytogenomics and molecular biology techniques, in synergy, to characterize and confirm positive NIPT results. Full article

Abstract: The Association of Mutual Funds of India (AMFI), under the direction of the Securities and Exchange Board of India (SEBI), provided open access to various risk parameters with respect to MidCap and SmallCap funds for the first time from February 2024. Our study utilizes AMFI datasets from February 2024 to September 2024 which consisted of 14 variables. Among these, the primary variable identified in grading mutual funds is the stress test parameter, expressed as number of days required to liquidate between 50% and 25% of the portfolio, respectively, on a pro-rata basis under stress conditions as a response variable. The objective of our paper is to build and test various neural network models which can help in predicting stress levels with the highest accuracy and specificity in MidCap and SmallCap mutual funds based on AMFI’s 14 parameters as predictors. The results suggest that the simpler neural network model architectures show higher accuracy. We used Artificial Neural Networks (ANN) over other machine learning methods due to its ability to analyze the impact of dynamic interrelationships among 14 variables on the dependent variable, independent of the statistical distribution of parameters considered. Predicting stress levels with the highest accuracy in MidCap and SmallCap mutual funds will benefit investors by reducing information asymmetry while allocating investments based on their risk tolerance. It will help policy makers in designing controls to protect smaller investors and provide warnings for funds with unusually high risk. Full article

The swift expansion of the global population and economy has spurred growing requirements for energy and water in recent decades. Inefficient energy and water consumption, however, has led to an increase in CO₂ emissions. Hence, the socio-economic development of a country must consider the interconnections between energy, water and carbon, as there are mutual dependencies among these three elements. This work considers the nexus between energy, water and carbon in the design of integrated energy–water systems using a new automated targeting modeling (ATM) framework. ATM incorporates the advantages of the insight-based Pinch method and a mathematical programming approach to provide visual understanding for a thorough analysis of the problem while guaranteeing accurate solutions. Minimum targets of power and water based on the integrated network operation were established by the ATM, with corresponding carbon emissions. A specific goal of annual carbon emissions reduction was set as the constraint and the ATM optimized the capacities of the components in the system accordingly to achieve minimum overall cost. The application of ATM on an industrial plant case study shows that a target of 45% reduction in the carbon discharge amount was achieved by shifting to greener fuel in the energy system at a minimum overall cost increase of 0.45% only. The framework can assist users in meeting power and water loads in their plant while planning for the appropriate decarbonization efforts at the minimum possible cost. Full article