Subject Selection Research Articles

Influencing factors comparing different vault groups after phakic implantable collamer lens implantation: review and meta-analysis

BackgroundStudies on the factors affecting vault after posterior chamber phakic Implantable Collamer Lens (ICL) have been carried out, but most of them are single-centered and subjective selections of parameters. The present study aimed to systematically analyze the factors for vault.MethodsA systematic review of case series, case-control, and cohort studies derived from the articles published in PubMed, the Cochrane Library, Embase, Web of Science, CNKI, CBM, Wanfang and VIP, as well as ClinicalTrials, which were conducted to search for studies on factors of vault using four core terms: phakic intraocular lenses, vault, risk factor and observational study, from January 01, 1997, to February 20, 2023. The included studies were meta-analyzed quantitatively and described qualitatively. Subsequently, meta-regression and subgroup analysis were used.ResultsWe identified 13 studies (1,607 subjects), and 14 factors were considered. Meta-analysis showed that anterior chamber depth (ACD), horizontal corneal white-to-white (hWTW), ICL-size, and age are dual effects of the abnormal vaults; anterior chamber volume (ACV) and lens thickness (LT) are a one-way effect; while axial length (AL), ICL- spherical equivalent (ICL-SE) and Km are insignificant. In addition, descriptive analysis of anterior chamber angle (ACA), horizontal sulcus to sulcus (hSTS), ciliary processes height (T value), crystalline lens rise (CLR), and gender showed that all factors except gender tend to have significant effects on vault. Sensitivity analysis showed stable combined results. Country and design respectively affect the heterogeneity in ACD and ICL-size at low vault, while design affects the heterogeneity in ACD at high vault. No publication bias exists.ConclusionsVault after ICL is related to multiple factors, especially anterior segmental biologic parameters, and they are weighted differently. We hope to provide a reference for the selection and adjustment of ICL.

A novel landslide susceptibility prediction framework based on contrastive loss

ABSTRACT Recently, the positive unlabeled (PU) learning algorithms have proven highly effective in generating accurate landslide susceptibility maps. The algorithms categorize samples exclusively into positive samples (landslides) and unlabeled samples for training, eliminating random or subjective selection of non-landslide samples. However, existing PU learning algorithms face limitations in capturing correct negative samples in multi-genesis landslide areas, leading to lower prediction accuracy. To address this issue, the PU-pullbaggingDT algorithm was proposed in this study. This integrated method combines the strengths of two techniques: the superior ranking performance of the PU-baggingDT algorithm and the low bias of the contrastive learning approach. The contrastive learning introduces a new contrastive loss of PU learning (PUpull loss), which pulls the distance of similar landslide samples in the projection space closer based on the validation accuracy threshold, without the need for data augmentation and class probability. The PUpull loss relaxes the tightness toward unlabeled samples, reducing the impact of incorrectly defined non-landslide samples on prediction results in multi-genesis landslide areas. The proposed algorithm outperforms existing PU-learning and machine learning methods (support vector machine, decision tree, logistic regression, AdaBoost, and XGBoost) with random selection of negative samples for predicting landslide susceptibility in China’s Zigui County, as demonstrated by comprehensive evaluation metrics. The landslide susceptibility mapping utilizes equal interval division and ranking, achieving approximately a 90% landslide percentage in areas with very high and high susceptibility in Zigui County. This demonstrates the capability of the proposed algorithm to effectively predict landslide susceptibility in complex geological settings.

Identifying Risks in Management of Educational Institutions: A Case in a Legal Entity Higher Education Institution

Higher education is a legal entity that represents the government to serve society optimally. This research aims to identify, understand and manage risks that can hinder the achievement of organizational goals in terms of organizing higher education. This study uses a quantitative approach with a descriptive type. Research subjects were taken from leaders and employees in the Directorate of Planning and Finance, Directorate of General Affairs, Resources and Law. Directorate of Research and Community Service, Directorate of Cooperation, Information Systems, Innovation and Business, Quality Assurance Directorate, Internal Supervisory Unit, Vocational Faculty. Subject selection was carried out using a purposive technique using considerations of feasibility and competency related to the themes studied in this research. Data collection was carried out using questionnaires and interviews. The data that was collected was analyzed using descriptive quantitative analysis techniques. This risk register management is a document that functions to identify, understand and manage risks that can hinder the achievement of organizational goals. The structured risks are a development of the risk mapping that was prepared during the previous program. Risks that have been identified are not risks that are fixed (given). The risks referred to can change, increase, or even decrease according to the conditions faced by the organization. Communication in efforts to update risk data in risk register management is still very necessary as an effort to enrich the types and impacts of these risks on the organization.

Bounds for selection bias using outcome probabilities

Abstract Objectives Determining the causal relationship between exposure and outcome is the goal of many observational studies. However, the selection of subjects into the study population, either voluntary or involuntary, may result in estimates that suffer from selection bias. To assess the robustness of the estimates as well as the magnitude of the bias, bounds for the bias can be calculated. Previous bounds for selection bias often require the specification of unknown relative risks, which might be difficult to provide. Here, alternative bounds based on observed data and unknown outcome probabilities are proposed. These unknown probabilities may be easier to specify than unknown relative risks. Methods I derive alternative bounds from the definitions of the causal estimands using the potential outcomes framework, under specific assumptions. The bounds are expressed using observed data and unobserved outcome probabilities. The bounds are compared to previously reported bounds in a simulation study. Furthermore, a study of perinatal risk factors for type 1 diabetes is provided as a motivating example. Results I show that the proposed bounds are often informative when the exposure and outcome are sufficiently common, especially for the risk difference in the total population. It is also noted that the proposed bounds can be uninformative when the exposure and outcome are rare. Furthermore, it is noted that previously proposed assumption-free bounds are special cases of the new bounds when the sensitivity parameters are set to their most conservative values. Conclusions Depending on the data generating process and causal estimand of interest, the proposed bounds can be tighter or wider than the reference bounds. Importantly, in cases with sufficiently common outcome and exposure, the proposed bounds are often informative, especially for the risk difference in the total population. It is also noted that, in some cases, the new bounds can be wider than the reference bounds. However, the proposed bounds based on unobserved probabilities may in some cases be easier to specify than the reference bounds based on unknown relative risks.

Model Pengembangan Kurikulum di MI Masalikil Huda 01 Tahunan Jepara

The importance of maintaining the relevance of education to the dynamics of current developments requires real efforts in curriculum development. Apart from that, the application of curriculum development models is key in the process of developing the curriculum. The use of a curriculum development model is necessary to systematically and purposefully design an educational plan. This research aims to determine the curriculum development model implemented at MI Masalikil Huda 01 Annually Jepara. The research method used is descriptive qualitative-case study. The subject of this research is MI Masalikil Huda 01 Tahunan Jepara with the source informant being Mrs. Rujiah, SE as the principal of MI Masalikil Huda 01 Tahunan Jepara. The selection of research subjects was carried out through purposive sampling and snowball sampling techniques. The data analysis technique in this research involves several steps, namely data collection accompanied by analysis during the collection process, data reduction, data segmentation to group the reduction results into smaller units, forming categorization, and drawing conclusions. The results of this research show that MI Masalikil Huda 01 Tahunan Jepara uses a combination curriculum development model between the administrative curriculum development model and the Grass Roots curriculum development model.

Goldman Sachs’s Price Forecast Based on ARIMA and LSTM

The prediction of stock prices is a common and crucial problem in trading. Correctly predicting future stock prices enables traders to determine the optimal time to buy and sell stocks, increasing the probability of making profits. This study focuses on predicting the closing price of Goldman Sachs. Initially, an ARIMA (4,1,6) benchmark model was established based on the AIC information criteria for time series prediction. The model was then applied to make forward predictions. Subsequently, a two-layer LSTM model was constructed. The prediction results of both models were visualized, and the regression indicators were calculated for each model. By comparing the prediction results of the two algorithms, it was determined that LSTM model outperforms ARIMA on the dataset used in this paper. Furthermore, this paper highlights some shortcomings of the ARIMA model, including its unsuitability for long-term forecasting and its subjective parameter selection. In contrast, LSTM performs better in predicting turning points in stock prices.

Lung cancer screening for never smokers: current evidence and future directions.

Screening for lung cancer using low-dose computed tomography is an established means for early lung cancer detection in smokers, but the role of screening for never smokers is unclear. In the 13 lung cancer screening studies involving unselected never smokers, detection rates ranged from 0.1% to 1.1% and positive predictive values ranged from 0.4% to 4.5%. In three lung cancer screening studies involving selected never smokers, selection was based primarily on occupational asbestos exposure, environmental radon exposure and family history of lung cancer in first-degree relatives. Detection rates ranged from 0.3% to 2.6%, and positive predictive values ranged from 0.7% to 15%. Also, 80%-100% of lung cancer cases discovered by screening were early stage, with limited data suggesting survival benefit. Lung cancer screening for never smokers with selected risk factors may achieve detection rates similar to those of screening for high-risk ever smokers. However, further research on optimal subject selection, alternative screening methods and clinical/economic outcomes is needed.

성취평가제 확대 적용에 따른 대입전형 평가의 방향

Objectives The purpose of this study is to explore the problems of the standard-based assessment, which is ex-panded in the high school credit system and the 2022 revised curriculum, and the direction of desirable college admission operation.
 Methods To this end, a survey was conducted on high school teachers and university admissions officers, and the questionnaire was composed of a background variable and a total of 14 questions in 4 areas and was prepared on a 5-point Likert scale. For the collected data, descriptive statistics, t-test, cross-analysis, and one-way var-iance analysis were conducted. In-depth interviews with experts (two high school teachers and admissions offi-cers each) were conducted to secure the validity and reliability of the survey results.
 Results First, the expansion of the standard-based assessment will reduce the student sub-curricular screening and expand the comprehensive student sub-curricular screening. Second, when the standard-based assess-ment is expanded, the boundary between the student sub-curricular screening and the student sub-curricular screening is blurred, and it is necessary to introduce qualitative evaluation in the student sub-curricular screening. Third, admissions officers were more negatively aware of the operation of subject selection and grade inflation according to student wishes than teachers, and teachers preferred to introduce or strengthen interview evaluation and admissions officers preferred to introduce or strengthen the minimum academic standards for the CSAT as a way to compensate for the difficulty of discrimination in student sub-curricular evaluation.
 Conclusions When applying the standard-based assessment requires additional factors, specific methods, spe-cific methods should be applied depending on the characteristics of the universities of universities should be ap-plied according to the characteristics of universities. In order to Evaluation of Desirable University Admissions high school life records, and universities need to strengthen the competitiveness of the evaluation of the evaluation.

Physical Fitness Tests in the Army-Legitimate Diversity or Randomness?

In this article, I present my views on the diversified approaches to assess the physical fitness of soldiers within selected armies worldwide. I discussed the related common features and paradoxes associated with these approaches. For the comparative analysis, national diversity was taken into account and subjective selection of the physical fitness tests conducted in the armies of the United States, Finland, Germany, India, China, Israel, Netherlands, Sweden, Switzerland, United Kingdom, and Poland was done. An analysis of the physical fitness tests reveals significant variations, despite the purpose of the assessment being the same, that is, to evaluate a soldier's physical preparedness for combat tasks. Based on the analysis, I categorize the variation in physical fitness tests of soldiers at four levels: (1) single-system function tests (e.g., push-ups, sit-ups) vs. multi-system tests (obstacle courses), (2) considering health aspects vs. not considering health aspects during physical fitness tests, (3) motor components vs. coordination components (e.g.,balance), and (4) tests differentiated by gender and age vs.tests not differentiated by gender and age. In conclusion, I am in favor of multi-system tests, comprised of obstacle courses mirroring potential situations in combat tasks. In my opinion, multi-system tests are superior compared to single-system function tests since they enable a comprehensive assessment of effort, coordination, and mental predispositions necessary for functioning in real-life conditions. I also support physical fitness evaluation that is not differentiated based on a soldier's gender and age, as there is no logical justification for such distinctions.

The Influence of Role Playing Methods on Early Childhood Language Development

This study aims to determine the influence of role playing learning methods on the language development of group B students in RA Diponegoro 203 Gunungwetan. The approach in this study is Classroom Action Research, which is a controlled investigation process to find and solve classroom learning problems. The problem-solving process is carried out cyclically, with the aim of improving the quality of learning and learning outcomes in certain classes. The subjects of the study were group B RA Diponegoro 203 Gunungwetan Jatilawang which amounted to 33 children. The basis for the selection of research subjects is because it is based on observation and deliberation of teachers about children's language skills. Data analysis is carried out descriptively on student achievement in learning. The results of data analysis show that the learning carried out to improve children's language skills in RA Diponegoro 203 before the role play method was applied was not optimal. The implementation of learning has not been well programmed, teachers carry out routine learning activities with less varied methods, such as storytelling, conversation, and question and answer methods. The media used in learning is less interesting because it only listens to the teacher's story. A considerable increase occurred in cycle II, namely in the indicator that children can respond to speech, can start conversations with the media that plays a role

Establishing a Pregnancy Lyme Disease Biobank.

Availability of relevant biological samples supports both basic science research and patient-centered clinical studies. Establishing a biorepository faces challenges at multiple levels. These tasks include defining mission definition and scope; selection of subjects and sample types; recruitment strategies; timing of collection in the patient's journey; sample logistics and processing; determining what clinical data to collect; ensuring sample integrity on transport, processing, and storage; defining governance structures and oversight responsibilities; clarifying sample provenance and ownership; establishing procedures for sample and data access; selecting testing to be performed routinely versus upon request, and management of results; data security; funding sources; and regulatory compliance. Establishing and maintaining a biorepository therefore requires careful planning, diligent and sustained execution, technical and financial resources, stakeholder support, and flexible and resilient management to respond to changing environments and needs.

Chapter 7 - Ethics of gene and cell therapy development for neurologic disorders

In this chapter, I provide a condensed overview of nine recurring policy and ethical challenges encountered with the development of gene and cell therapies for neurologic disease. These include the question of when to initiate first-in-human trials, the ethics and policy of expanded/special access, the conduct of individualized therapy trials, subject selection in trials, designing trials for negative results, unintended effects of interventions on personal identity, comparator choice in randomized trials, consent and therapeutic misestimation, and cost and access for effective therapies. Broadly speaking, I argue that early in their development, the justification of risk in trials of gene and cell therapies derives from the social and scientific value of a trial and not the therapeutic value for trial participation. This generates strong imperatives to justify, design, and report trials appropriately and select patient populations that incur the least burden and opportunity cost for trial participation. Late in intervention development, policy makers must contend with the fact that proven effective interventions will almost certainly amplify strains in healthcare budgets as well as the ethical justifications standing behind reimbursement decisions.

Research on the Influencing Factors of Artistic Creation of Fine Arts Painting in the Context of Visual Culture

Abstract The creators of fine art painting art are often affected by the conceptualization and imagination factors, expression techniques and methods, as well as the influence of theme and subject selection, etc., and they are unable to commit themselves to the expression of artistic power and infectious power of fine art painting works. This paper constructs the PLS-SEM and BN combined structural equation model in the context of visual culture, analyzes the latent variables in the path relationship with PLS-SEM for empirical testing, and predicts and diagnoses the causal relationship between the variables with the Bayesian network. Finally, a questionnaire survey was conducted among the major fine art painting art creation groups in Guangzhou, China, and the influence factors of fine art painting creation were extracted using principal component analysis. The constructed model is combined with the collected data for empirical analysis. The β coefficients of the influence paths of the factors are all greater than 0.42 and significant at least at the 0.05 level, and the creative motivation, creative environment, personal artistic level, art industry environment, and painting artistic skill all positively influence the fine art painting artistic creation outcome. When the level of artistic achievement in art painting and drawing is “high”, the probability that the level of the art industry environment and creative environment is high is higher, with probabilities of 0.742 and 0.701. When the level of personal artistic level is “high”, the probability that the level of creative motivation is “high” is greater than 0.42 and significant at least at the 0.05 level. When the individual’s artistic level is “high”, the level of creative motivation is more likely to be “high,” with a probability of 0.726. This study allows the timeliness and dynamics of painting to be harmonized and linked with the painter and provides certain references for the improvement of the artistic skills and creative level of the creator of the painting.

The contemporary value and practical strategy of sports education in colleges and universities under the perspective of “five education”

Abstract A physical education and fitness course is a course with physical practice as the main means. Teachers often pay more attention to students’ physical practice in teaching but neglect the development of moral education, aesthetic education, intellectual education, and labor education. Aiming at this kind of teaching problem, this paper combines the concept of “five education” and the characteristics of physical education teaching. It puts forward the physical education teaching mode and practice path that permeates the idea of “five education.” Selection of research subjects, design, and distribution of a humanistic literacy questionnaire to obtain data, reliability test processing to ensure accuracy of research, and analysis of this paper. SPSS software is used to analyze an example of sports education work that integrates the concept of five education. Data presentation: the mean values of the post-test students’ humanistic literacy in the experimental group and the control group are 9.45 and 9.36, respectively, and the P value is 0.009, which indicates that the integration of the concept of five education in college physical education teaching is more advantageous in the cultivation of students’ humanistic literacy in comparison with the traditional physical education teaching mode. This study makes physical education classroom teaching more dynamic and versatile, gives a new meaning to physical education classroom teaching, and improves the effectiveness of physical education teaching.

Anatomical structure and morphometric parameters of the distal segment of the lacrimal drainage system

The article analyzes the literature data concerning the anatomical structure of the tear ducts with a focus on morphometric studies of individual structures. It is noted that, despite the high information content of cadaveric studies, most of the information to date has been obtained with the use of intravital radiation visualization methods. It is shown that the lacrimal drainage system has age, gender, and racial characteristics. Most studies focus on features of the bone nasolacrimal canal, while there is a relative shortage of research devoted to soft-tissue nasolacrimal duct. The analysis showed that most of the studies are devoted to particular morphometric indicators, which makes it difficult to obtain a complete objective picture of the size of the tear duct. Information about its volume can be found only in a couple of studies. The numerical values of the obtained data vary, which can be explained by a different approach to the selection of research subjects, sample sizes, anatomical criteria and the calculation method of morphometric parameters. There are only a few studies on morphometric parameters of the nasolacrimal duct in healthy humans, which is extremely important for determining the pathogenesis of disorders of the patency of the lacrimal drainage system.

A New Brain Network Construction Paradigm for Brain Disorder Via Diffusion-Based Graph Contrastive Learning.

Brain network analysis plays an increasingly important role in studying brain function and the exploring of disease mechanisms. However, existing brain network construction tools have some limitations, including dependency on empirical users, weak consistency in repeated experiments and time-consuming processes. In this work, a diffusion-based brain network pipeline, DGCL is designed for end-to-end construction of brain networks. Initially, the brain region-aware module (BRAM) precisely determines the spatial locations of brain regions by the diffusion process, avoiding subjective parameter selection. Subsequently, DGCL employs graph contrastive learning to optimize brain connections by eliminating individual differences in redundant connections unrelated to diseases, thereby enhancing the consistency of brain networks within the same group. Finally, the node-graph contrastive loss and classification loss jointly constrain the learning process of the model to obtain the reconstructed brain network, which is then used to analyze important brain connections. Validation on two datasets, ADNI and ABIDE, demonstrates that DGCL surpasses traditional methods and other deep learning models in predicting disease development stages. Significantly, the proposed model improves the efficiency and generalization of brain network construction. In summary, the proposed DGCL can be served as a universal brain network construction scheme, which can effectively identify important brain connections through generative paradigms and has the potential to provide disease interpretability support for neuroscience research.

Safe Food Supply Chain as Health Network: An Evolutionary Game Analysis of Behavior Strategy for Quality Investment.

There is a natural relation between human health and the quality of their food and drinks, and elevating the quality input level of food production for all enterprises within the food supply chain system forms the foundation for preventing various potential food safety risks that may be encountered. Unlike the previous research on quality investment of food production by enterprises, this paper probes into the evolutionary routes of the behavior strategy selection of subjects in the food supply chain and the preconditions for the equilibrium points of the social co-governance system. It takes the approach of establishing a tripartite evolutionary game model of food suppliers, food manufacturers and consumers on the basis of the social co-governance framework, in view of the above, this paper focuses on the influence of the reputation mechanism and the market contracts among supply chain subjects on the selection of a behavior strategy for quality investment by enterprises under the condition of lawful regulation by government. The results show that every subject selects their own behavior strategy on the basis of the balance of their respective interests. The net disbursement incurred by enterprises for quality investment and the costs of participation in governance by consumers constitute the dominant factors that influence both enterprises' selection of a behavior strategy and the level of social co-governance. Compared with the increase in economic punishment imposed on suppliers for production of risky food raw materials, it is more efficient to control food safety risks by lowering the costs of quality investment by suppliers. Accordingly, this paper proposes advice on policy in an attempt to provide inspiration for preventing and controlling food safety risks.

Reproducible supervised learning-assisted classification of spontaneous synaptic waveforms with Eventer.

Detection and analysis of spontaneous synaptic events is an extremely common task in many neuroscience research labs. Various algorithms and tools have been developed over the years to improve the sensitivity of detecting synaptic events. However, the final stages of most procedures for detecting synaptic events still involve the manual selection of candidate events. This step in the analysis is laborious and requires care and attention to maintain consistency of event selection across the whole dataset. Manual selection can introduce bias and subjective selection criteria that cannot be shared with other labs in reporting methods. To address this, we have created Eventer, a standalone application for the detection of spontaneous synaptic events acquired by electrophysiology or imaging. This open-source application uses the freely available MATLAB Runtime and is deployed on Mac, Windows, and Linux systems. The principle of the Eventer application is to learn the user's "expert" strategy for classifying a set of detected event candidates from a small subset of the data and then automatically apply the same criterion to the remaining dataset. Eventer first uses a suitable model template to pull out event candidates using fast Fourier transform (FFT)-based deconvolution with a low threshold. Random forests are then created and trained to associate various features of the events with manual labeling. The stored model file can be reloaded and used to analyse large datasets with greater consistency. The availability of the source code and its user interface provide a framework with the scope to further tune the existing Random Forest implementation, or add additional, artificial intelligence classification methods. The Eventer website (https://eventerneuro.netlify.app/) includes a repository where researchers can upload and share their machine learning model files and thereby provide greater opportunities for enhancing reproducibility when analyzing datasets of spontaneous synaptic activity. In summary, Eventer, and the associated repository, could allow researchers studying synaptic transmission to increase throughput of their data analysis and address the increasing concerns of reproducibility in neuroscience research.

Optimizing Academic Journey for High Schoolers in Oman: A Machine Learning-Enabled AI Model

This article presents a comprehensive exploration of the development of an AI-driven Gen-Alpha education guidance indicator for Oman, employing cutting-edge machine learning techniques. The primary objective is to deliver a highly personalized support system and guidance mechanism that accompanies students throughout their academic journey. The research places a spotlight on the pivotal role of Artificial Intelligence (AI) within the realm of education. It skillfully demonstrates AI’s robust capabilities in accurately forecasting student performance through the effective use of data mining and advanced machine learning techniques. An integral aspect of this research is the application of AI in aiding students to navigate the involved process of subject selection for their higher education pursuits. This multidimensional process encompasses exhaustive data collection, the detailed creation of a sophisticated machine learning model, and the application of a diverse array of state-of-the-art algorithms. Among these algorithms, K-Nearest Neighbors (KNN), Decision Trees, Random Forest, and Support Vector Machines (SVM) stand out prominently. Notably, the SVM algorithm emerges as the outright winner, delivering an extraordinary accuracy rate of 77%. This remarkable achievement underscores the model’s unwavering robustness and its potential to redefine the educational landscape in Oman. In essence, this paper transcends the boundaries of conventional research and offers conclusive validation of AI’s revolutionary potential in reshaping educational paradigms. By facilitating data-driven decision-making, the AI-driven Gen-Alpha education guidance indicator empowers students to embark on their educational journeys well-informed and confident, thereby playing a pivotal role in advancing Oman’s education system.

Analysis of Major Filling in Double First-Class Colleges and Universities for Geography Candidates in Yunnan under the Subject Selection Requirements of New College Entrance Examination

Analysis of Major Filling in Double First-Class Colleges and Universities for Geography Candidates in Yunnan under the Subject Selection Requirements of New College Entrance Examination