Impact of Pet Companionship on Student Development: A Meta-Analysis

Animal companionship has been found to have a positive influence on human well-being, and the presence of pets can have a subtle yet significant impact on the healthy development of students. Pet companionship takes various forms across different fields in China and other regions worldwide, and the impact of such companionship remains uncertain. Hence, it is imperative to investigate the impact of diverse forms of companionship and animals on multiple facets of student growth and development. This study employed meta-analysis methodologies to examine 47 effect sizes derived from 12 domestic and international studies on pet companionship. The aim was to investigate the overall trends of the influence of pet companionship on student development as well as the effects of diverse types of companionship and pets on different aspects of student development, including physical and mental health, social-emotional abilities, and academic performance. The objective was to enhance the exploration of approaches for maximizing the utilization of various forms of pet companionship. Furthermore, this research suggests a systematic and incremental approach to enhancing the function of pets within households, educational institutions, and medical facilities. Adequate content and organization are essential for scientific advancement and the development of students. In this particular context, it is possible to optimize the impact of pet companionship on the development of students

Effects of After-School Programs on Student Cognitive and Non-Cognitive Abilities: A Meta-Analysis Based on 37 Experimental and Quasi-Experimental Studies

The after-school program is a crucial initiative for implementing the Double Reduction policy; however, prior research has not provided conclusive evidence on whether extended school hours contribute to students’ cognitive and non-cognitive development or on which types of after-school services are more beneficial for student development. This study analyzed 37 after-school programs from 18 publications using meta-analytic techniques, and the results indicated that participation in after-school programs had positive effects on student cognitive and non-cognitive development despite the small effect size (d = 0.327, p = 0.000). The decomposition of the effects of after-school programs revealed that they had modestly positive effects on academic achievement (d = 0.369) and social-emotional competence (d = 0.220). In addition, the analysis of moderating variables revealed that socioeconomic status, educational phase, number of after-school service days per week, sample size, and testing instrument all influenced the after-school program effects. This study concludes, based on the results of the meta-analysis, that there should be a balanced consideration of the development of student cognitive and non-cognitive abilities in planning after-school service, a substantial variety of activities in after-school programs, a flexible adoption of diverse after-school programs, and a reasonable participation frequency in after-school service

Quantification and Purification of Mulberry Anthocyanins With Macroporous Resins

Total anthocyanins in different cultivars of mulberry were measured and a process for the industrial preparation of mulberry anthocyanins as a natural food colorant was studied. In 31 cultivars of mulberry, the total anthocyanins, calculated as cyanidin 3-glucoside, ranged from 147.68 to 2725.46 mg/L juice. Extracting and purifying with macroporous resins was found to be an efficient potential method for the industrial production of mulberry anthocyanins as a food colorant. Of six resins tested, X-5 demonstrated the best adsorbent capability for mulberry anthocyanins (91 mg/mL resin). The adsorption capacity of resins increased with the surface area and the pore radius. Residual mulberry fruit juice after extraction of pigment retained most of its nutrients, except for anthocyanins, and may provide a substrate for further processing

Universal enhancement of vacancy diffusion by Mn inducing anomalous Friedel oscillation in concentrated solid-solution alloys

We present a proof-of-principle demonstration of a universal law for the element Mn, which greatly enhances vacancy diffusion through an anomalous Friedel Oscillation effect in a series of Ni-based concentrated solid-solution alloys, regardless of the type of atom involved. The antiferromagnetic element Mn possesses a unique half-filled 3d electron structure, creating split virtual bound states near the Fermi energy level and producing a large local magnetic moment after vacancy formation. The resultant electron spin oscillations reduce the number of electrons involved in charge density oscillations, destroying charge screening and lowering potential interaction at the saddle point between the vacancy and diffusing atom. This ultimately facilitates vacancy diffusion by reducing energy level variations of conduction band electrons during the diffusion process. These findings offer valuable insights into atom diffusion mechanisms and open up new avenues for manipulating defect properties through unique element design, thereby enabling the creation of high-performance alloys in a broad range of fields

Diagnosis of Brain Diseases via Multi-Scale Time-Series Model

The functional magnetic resonance imaging (fMRI) data and brain network analysis have been widely applied to automated diagnosis of neural diseases or brain diseases. The fMRI time series data not only contains specific numerical information, but also involves rich dynamic temporal information, those previous graph theory approaches focus on local topology structure and lose contextual information and global fluctuation information. Here, we propose a novel multi-scale functional connectivity for identifying the brain disease via fMRI data. We calculate the discrete probability distribution of co-activity between different brain regions with various intervals. Also, we consider nonsynchronous information under different time dimensions, for analyzing the contextual information in the fMRI data. Therefore, our proposed method can be applied to more disease diagnosis and other fMRI data, particularly automated diagnosis of neural diseases or brain diseases. Finally, we adopt Support Vector Machine (SVM) on our proposed time-series features, which can be applied to do the brain disease classification and even deal with all time-series data. Experimental results verify the effectiveness of our proposed method compared with other outstanding approaches on Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and Major Depressive Disorder (MDD) dataset. Therefore, we provide an efficient system via a novel perspective to study brain networks

Mass spectrometric and first principles study of Aln_nC−^- clusters

We study the carbon-dope aluminum clusters by using time-of-flight mass spectrum experiments and {\em ab initio} calculations. Mass abundance distributions are obtained for anionic aluminum and aluminum-carbon mixed clusters. Besides the well-known magic aluminum clusters such as Al$_{13}^-$ and Al$_{23}^-$, Al$_7$C$^-$ cluster is found to be particularly stable among those Al$_n$C$^-$ clusters. Density functional calculations are performed to determine the ground state structures of Al$_n$C$^-$ clusters. Our results show that the Al$_7$C$^-$ is a magic cluster with extremely high stability, which might serve as building block of the cluster-assembled materials.Comment: 4 pages, 6 figure