Score Statistics Research Articles

STAFF DEVELOPMENT PROGRAMMES AND TEACHERS’ INSTRUCTIONAL TASK PERFORMANCE IN KWARA STATE PRIMARY SCHOOLS, NIGERIA

ABSTRACTThe study adopted the descriptive survey research design. The Population of the study consisted of 14,206 public primary school teachers and 1,406 head teachers in the three senatorial districts of Kwara State. The sample for the study comprised 400 teachers and 40 head teachers which were selected using a multistage sampling procedure. A self-designed validated research instrument titled “Staff Development and Teachers’ Instructional Task Performance” (SDTITP) was used to collect data for the study. The reliability of the instrument was ensured with a coefficient index of 0.77. Data collected were analyzed using descriptive statistics of frequency counts and percentage scores. The results showed that in-service training 76.1; mentoring 68.6%, seminars 63.9%; and workshops 62.7%) were the types of development programmes available for the teachers; frequencies of conducting the programme were once every session and the mode used by the management in selecting staff for the development programmes was based on bias/prejudice 68.2%, seniority 65%, and the subject taught 72%. The study concluded that the development programmes of in-service training, mentoring, seminars, and workshops were staff development programmes used by the Kwara state Government for their primary school teachers. However, it was discovered that not all the teachers had the privilege of being selected to go for the programme. It was recommended among others that: the management should endeavour to add conferences as part of the professional development programmes made available for primary school teachers in Kwara State to enhance their pedagogic skills and competence towards meeting the expectations of the general public. Keywords: development; programmes; primary; schools; teachers’ task.

Prediction of hospital mortality among critically ill patients in a single centre in Asia: comparison of artificial neural networks and logistic regression-based model.

This study compared the performance of the artificial neural network (ANN) model with the Acute Physiologic and Chronic Health Evaluation (APACHE) II and IV models for predicting hospital mortality among critically ill patients in Hong Kong. This retrospective analysis included all patients admitted to the intensive care unit of Pamela Youde Nethersole Eastern Hospital from January 2010 to December 2019. The ANN model was constructed using parameters identical to the APACHE IV model. Discrimination performance was assessed using area under the receiver operating characteristic curve (AUROC); calibration performance was evaluated using the Brier score and Hosmer-Lemeshow statistic. In total, 14 503 patients were included, with 10% in the validation set and 90% in the ANN model development set. The ANN model (AUROC=0.88, 95% confidence interval [CI]=0.86-0.90, Brier score=0.10; P in Hosmer-Lemeshow test=0.37) outperformed the APACHE II model (AUROC=0.85, 95% CI=0.80-0.85, Brier score=0.14; P<0.001 for both comparisons of AUROCs and Brier scores) but showed performance similar to the APACHE IV model (AUROC=0.87, 95% CI=0.85-0.89, Brier score=0.11; P=0.34 for comparison of AUROCs, and P=0.05 for comparison of Brier scores). The ANN model demonstrated better calibration than the APACHE II and APACHE IV models. Our ANN model outperformed the APACHE II model but was similar to the APACHE IV model in terms of predicting hospital mortality in Hong Kong. Artificial neural networks are valuable tools that can enhance real-time prognostic prediction.

A note on the covariate-dependent kink threshold regression model for panel data

This article provides new estimating and testing procedures for the panel covariate-dependent kink threshold regression model. By utilizing a linearization technique, we develop an efficient estimator that offers a computationally viable alternative to the grid-search approach. Then we propose a novel testing procedure based on the score statistic to examine the existence of the covariate-dependent kink effect. The test statistic only requires fitting the model in the absence of varying kink threshold, greatly reducing computing time. Extensive simulation studies demonstrate that the proposed method outperforms existing methodologies. To further illustrate their performance, we utilize a real-data analysis. The results demonstrate the practicality and effectiveness of our approach in a wide range of scenarios.

Selection Signatures Reveal Candidate Genes for the Cornish Rex Breed-Specific Phenotype.

Many coat color, behavioral and morphological traits are specific and fixed across cat breeds, with several variants influencing these traits being common among different breeds. In the domestic cat, rexoid mutations have been documented in several breeds. In the Cornish Rex, four bp deletion in the LPAR6 gene has been found to cause a frame shift and a premature stop codon. In addition to the rexoid coat, Cornish Rex cats also have a characteristic head, ear shape and body type. Analysis of the selection signatures in the Cornish Rex genome revealed several regions that are under selective pressure. One of these is located in CFA B4, in the region where the ALX1 gene is located. The ALX1 gene in Burmese cats disrupts the cranial morphogenesis and causes brachycephaly in the heterozygous state. In our study, we confirmed the presence of a deletion in LPAR6 in 20 Cornish Rex and in four F1 hybrids between Cornish Rex and domestic cat. However, we did not confirm the presence of the deletion in ALX1 in Cornish Rex cats. Genome-wide selection signature analysis was performed using ROH islands and integrated haplotype score (iHS) statistics based on publicly available SNP array data of 11 Cornish Rex cats. The selection signatures were detected on chromosomes A1, A3, C2, B1, B4 and D1.

Comprehensive study of thunderstorm indices threshold favorable for thunderstorms during monsoon season using WRF–ARW model and ERA5 over India

IntroductionThe current research investigates into the application of various thunderstorm indices to predict severe thunderstorm occurrences during the monsoon season across four distinct regions in India. Methods: The study assesses the prediction model’s efficacy using various skill scores and the Weather Research and Forecasting (WRF) model has been integrated for 30 h with double moment microphysics scheme NSSL-17 which accurately reproduces vertical and meteorological measures.ObjectiveFurthermore, it investigates fifteen thunderstorm indices derived from the ERA5 dataset to identify the most effective index for forecasting severe thunderstorms.ResultsThe results indicate that combining thunderstorm indices with skill scores, such as the Heidke Skill Score and True Skill Statistic, enhances the accuracy of severe thunderstorm predictions in the Indian monsoon season. The accurate predictions rely on determining optimal thresholds for each index. The study emphasizes the importance of using multiple indices rather relying solely on single measure for predicting severe thunderstorms. Advanced indices like the Energy Helicity Index (EHI) and Supercell Composite Parameter (SCP) perform well in forecasting extreme severe thunderstormsdue to their strong reliance on wind shears. The EHI (> 1), and SCP (≥ 3.5), STP (≥ 1.2) along with low SRH at 3 km (100 m2/s2), indicated no evidence of helicity or tornado activity during the event. On the other hand, the CAPE, K Index, and VT Index demonstrate robust predictive capabilities for non-severe category thunderstorms.ConclusionsIntegrating numerous thunderstorm indices improves meteorologists’ forecasts, ensuring public safety.Based on this work, future research can improve severe weather forecasting models’ accuracy and reliability.

Performance of the Asia-Pacific Colorectal Screening score in stratifying the risk of advanced colorectal neoplasia: A meta-analysis and systematic review.

This study systematically reviewed and meta-analyzed the performance of the Asia-Pacific Colorectal Screening (APCS) score and its incorporation with the fecal immunochemical test (FIT) in stratifying the risk of advanced colorectal neoplasia (ACN). We systematically searched for relevant articles in 12 electronic databases and registers on October 20, 2021, and updated the search to September 1, 2023. Random-effect models were used to obtain the pooled performance statistics of the APCS score for ACN risk. From the 101 records screened, 13 eligible studies in the Asia-Pacific region involving 69762 subjects who had undergone colonoscopy were enrolled. The pooled prevalences of ACN in the average-risk (AR) tier (APCS 0-1), moderate-risk (MR) tier (APCS 2-3), and high-risk (HR) tier (APCS≥4) groups were 0.9%, 3.1%, and 8.1%, respectively. Compared with the combined AR-MR group, the HR group was significantly associated with a higher ACN risk (pooled diagnostic odds ratio: 2.84, 95% confidence interval [CI]: 2.35-3.45, P<0.001). The APCS score showed a sensitivity of 0.42 (95% CI: 0.40-0.44) and a specificity of 0.86 (95% CI: 0.85-0.86) for predicting the ACN risk, with a weighted area under the curve of 0.642 (95% CI: 0.610-0.657). The combination of the APCS score and FIT substantially improved ACN risk identification, demonstrating pooled diagnostic odds ratios of 4.02 (95% CI: 2.50-6.49) in the AR-MR groups and 5.44 (95% CI: 1.89-15.63) in the MR-HR groups. The APCS score could effectively stratify the ACN risk in the Asia-Pacific population. Incorporating FIT further improves its performance in identifying high-risk subjects who should be prioritized for colonoscopy screenings.

Carbon fibre reinforced triboelectric nanogenerator for self-powered sporting events monitoring

Porous materials such as metal-organic frameworks (MOFs) are promising platforms for the design of highly efficient and economical triboelectric nanogenerators (TENGs) due to their extremely high surface area, tunable pore size, large porosity, and flexible functionality. In this report, Co2+ and Ni2+ doped MOF-74 triboelectric active materials were synthesized in situ on the surface of carbon cloth (CoNi-MOF@CC) via a simple one-step solvothermal method. The as-fabricated TENG (C-TENG) is not only lightweight and wear-resistant, but also shows an open circuit voltage and a short circuit current density of as high as 108.6 V and 4.11 mA/m2, respectively. In addition, by introducing 1N4007 diodes on the back of CoNi-MOF@CC as energy collector, an excellent electrical output performance of output voltage up to 4500 V and output current density up to 2.84 mA/m2 was thus obtained, which is capable powering directly a one-watt lamp for reading at night. Furthermore, self-powered fencing score statistics system and an edge ball judgement system were developed on the base of the as-designed C-TENG for training guidance and real-time competition assistance, which promote the application of TENGs in intelligent sports industry by providing a new way to improve the fairness of sports.

Risk adjustment-based statistical control charts with a functional covariate

ABSTRACT In real applications, there are situations in which quality characteristics are significantly affected by function-valued covariates such as electrocardiogram (ECG) signals; however, previous surveillance methods with covariate adjustment mainly use scalar-valued covariates to construct control charts to investigate the stability of nonindustrial processes, such as in the medical and public health fields. Few existing approaches address the infinite-dimensional regression encountered with function-valued covariates. Thus, an applicable scheme needs to be developed. Here, by relaxing the assumption of normality, a novel surveillance strategy for functional logistic regression (FLR) is proposed. In this approach, the response is a binary variable, and the covariate information is obtained from functional data. To address the above problems, functional principal component analysis (FPCA) is used to extend the score test to situations with functional covariates. Moreover, the score statistic is integrated into an exponentially weighted moving average (EWMA) charting scheme to detect abnormal changes in the location and scale parameters based on additional information. The simulation results show that the proposed approach is more efficient than previous approaches in detecting small to moderate shifts. Finally, the proposed charting scheme is applied to a real case study to demonstrate its utility and applicability.

The Influence of HLA Polymorphisms on the Severity of COVID-19 in the Romanian Population.

In this study, we aimed to investigate whether specific HLA alleles found in patients from Romania and the Republic of Moldova were associated with the severity of COVID-19 infection and its associated mortality. We analyzed the HLA alleles at the -A, -B, -C, -DRB1, and -DQB1 loci in a cohort of 130 individuals with severe and extremely severe forms of COVID-19, including 44 individuals who died. We compared these findings to a control group consisting of individuals who had either not been diagnosed with COVID-19 or had experienced mild forms of the disease. Using multivariate logistic regression models, we discovered that the B*27 and B*50 alleles were associated with an increased susceptibility to developing a severe form of COVID-19. The A*33 and C*15 alleles showed potential for offering protection against the disease. Furthermore, we identified two protective alleles (A*03 and DQB1*02) against the development of extremely severe forms of COVID-19. By utilizing score statistics, we established a statistically significant association between haplotypes and disease severity (p = 0.021). In summary, this study provides evidence that HLA genotype plays a role in influencing the clinical outcome of COVID-19 infection.

Comparative efficacy of cognitive behavior therapy and interpersonal therapy in the treatment of depression: A randomized controlled study

Background: Depressive disorders are one of the most common psychiatric disorders that occur in people of all ages. Aim: To assess the efficacy of cognitive behavior therapy (CBT) and interpersonal therapy (IPT) in cases of depression. Materials and Methods: The sample for the study comprised 52 diagnosed cases of major depressive disorder (MDD) based on the 10th revision of the International Classification of Diseases (ICD-10) criteria in the age range of 21 to 50 years. They were randomized with the help of the app and assigned to CBT and IPT groups. Two patients had dropped out of the CBT group. Hence, finally, 50 cases were taken for study. A total of 12 therapy sessions were given. All patients were given standard treatment as prescribed. Results: Findings of paired samples t-test to assess the within-group differences in both the groups (CBT and IPT) as well as gains score statistics or difference score statistics (pre-test minus post-test) were applied to compare group differences. Results of paired samples ‘t’ test suggest that there was a statistically significant difference in the scores of both the groups from pre-test to post-test. IPT group had shown significantly better outcomes as quantified by the percentage of patients scoring less on the outcome variable of the Beck Depression Inventory (BDI), Montgomery–Asberg Depression Rating Scale (MADRS), Hopelessness Scale, and General Health Questionnaire (GHQ). Conclusion: Both CBT and IPT are efficacious in the treatment of MDD. IPT yielded better results on MADRS as compared to CBT on parameters of outcome in the present study. The findings indicate that interventions were effective in reducing the symptoms of depression.

Enhancing stock market trend reversal prediction using feature-enriched neural networks

According to several previous studies, neural network-based stock price predictors perform better for plunging patterns of stock prices than normal stock price patterns. Focusing on this issue, this study proposes a novel method that uses a neural network-based stock price predictor to predict the upward trend-reversal of the plunging market itself. To achieve more consistent prediction results for plunging patterns, newly designed input features are added to improve the performance of traditionally used neural network-based predictors. The statistics of the prediction scores for past plunging markets and analyzed, and the results are used to predict the upward trend-reversal in the plunging market that occurred during the test period. We demonstrate the superiority of the proposed method through the simulation results of 3-year trading on KOSDAQ, a representative stock market in South Korea.

Diversity of Learners, their Special Learning Needs and Inclusive Education: Perspectives of Teachers in Public Secondary Schools in Kwara State, Nigeria.

The study was aimed at building knowledge base for addressing learners’ special learning needs and for promoting inclusive education. The purpose of the study was to provide a rich description of public secondary schools’ teachers’ perspectives on their learners and on inclusive education. The study adopted quantitative design to investigate teachers’ awareness and knowledge of diversity of learners in their classrooms, including the teachers’ understanding of their learners’ special learning needs as well as the teachers’ perspectives on their experiences with implementation of inclusive education The population for the study comprised all teachers in public secondary schools in Kwara State, Nigeria, and by convenient sampling method drew a sample of one hundred and forty-five teachers. The instrument for data gathering was a questionnaire which contained descriptive statements responded to by respondents who indicated their degrees of agreement on a four-point; liker-type scale. Data collected were analysed and with the use of the descriptive statistics of percentage frequencies and mean response scores to answer the research questions and to arrive at the achievement of the study objectives. The results revealed that teachers involved in the study were generally aware of learners’ diversity in their classrooms. To strengthen this awareness, the teachers also indicated their knowledge regarding the manifestations of the diversity in terms of the learners’ ability and activity levels as well as behavioural tendencies. Teacher-respondents reported some learners as gifted and talented, some as more active than others, some aggressive or withdrawn in behaviors, some with speech and language difficulties as well as auditory, visual and orthopaedic impairments, while some are with other disabilities. The results of the study have implications for pre-service and in-service training of teachers as well as better structuring of schools’ physical infrastructure in order to promote better access, participation and belongingness for pupils with disabilities.

A Study of Quality Related Project Control Measures on Building Construction Projects in Delta State

This paper investigated quality-related project control measures on building construction projects in Delta State. Three research questions guided the study while three null hypotheses were formulated. The design to be adopted for this study is a descriptive survey. The population of this study comprised 157 construction professionals in relevant professional bodies in Delta State. The required sample size from this sample frame was 157, which represents the entire population of each group of professional bodies stated above. Data for this study were collected by means of a structured questionnaire titled “Questionnaire on Quality-Related Project Control Measures on Building Construction Project” (QQRPCMBCP) developed by the researcher. The questionnaire was subjected to face validation. Cronbach Alpha method was used to test the internal consistency of the instrument and an overall co-efficient of 0.78 was obtained. Descriptive statistics of the mean score and standard deviation were used to analyze the data to answer the research questions while a one-sample t-test was used to determine the significant difference between the mean scores. The SPSS (version 24) will be used to analyze the data. The paper found that construction professionals agreed on the quality project control measures for planning, monitoring, and reporting on building construction projects in Delta State. Based on the findings, it was recommended amongst others that construction professionals in Delta State should formalize and standardize project control measures across the construction industry to ensure consistency and uniformity in planning, monitoring, and reporting practices.

Adolescent Perspectives on a School-Based Mind-Body Intervention

Background Poor mental health among adolescents increased over the last decade prompting recommendations for school-based primary prevention programs that promote mental health. Mind-body modalities offer an evidence-based approach to support well-being, however few studies have evaluated the adolescent experience of participating in these groups. Objective To engage high school students in a mind-body skills group (MBSG) to assess acceptability in an afterschool setting, learn about the adolescent experience of participating in a MBSG, and get recommendations to enhance program acceptability. To collect preliminary data on mindfulness, social support, emotional regulation, and quality of life. Methods Ten high school students participated in an afterschool MBSG. A focus group was conducted 1-week post-intervention. Surveys were administered at baseline, 1-week post-intervention (T1), and 10-weeks post-intervention (T2). The research team used qualitative thematic analysis with inductive and thematic coding. Descriptive statistics of survey scores were calculated, but the study was not powered to detect statistical significance. Results Participants deemed the group to be acceptable. They valued the sense of community derived from the group and enjoyed learning a variety of mind-body skills. Participants recommended adaptations to build connections sooner within the group, offer more interactive experiences with mind-body skills, and prioritize social, emotional, and physical comfort. Mindfulness, emotional regulation, and quality of life scores increased from baseline to T1. All survey scores improved from baseline to T2. Conclusion This study offers insights into the adolescent experience and recommendations for program adaptations for a MBSG in an afterschool setting. Understanding the adolescent perspective is essential for the development of an acceptable MBSG, which has implications for program implementation, utilization, and effectiveness.

HOUSING AFFORDABILITY AMONG CIVIL SERVANTS IN OGUN STATE, NIGERIA

Housing has been acknowledged generally as a key human necessity. One of its problems may be claimed that it is not affordable for the ordinary Nigerian worker, whose earnings and wages are now strongly depressed and unable to fulfill their fundamental necessities. From the perspective of the above, this study studied the affordability of government workers in Ogun State, Nigeria. Purposive method of sampling was used to sample two government agencies and parastatals. A total of One Hundred and Ten (110) government officials were picked. 94 questionnaires were returned. The necessary data was gathered using the questionnaire, which was then, analyzed using descriptive and medium item score statistics. The results showed that, on average, government workers in Ogun State could afford to pay their rent because most of them, particularly those in the medium and high income brackets, spend less than 30% of their annual salary on housing. Public and private engagements were suggested in the study as a method of providing state housing that is both inexpensive and sustainable.

The Effect of Organizational Silence of Individuals Working in Private Sports Centers on Job Satisfaction and Job Performance (Evidence from Istanbul Province)

Purpose –It is aimed to determine the relationship between organizational silence, job satisfaction, and job performance. The research population consists of individuals working in private sports centers in Istanbul. It is aimed to reveal the effects of organizational silence of private sector employees on job satisfaction and performance.Design/methodology/approach –The sample size is determined as "n=464". In the research sample, the convenience sampling method, one of the random sampling methods, is preferred. Data were collected from 423 employees through an online survey form by applying the survey technique. The collected data were analyzed in the SPSS 27.0 statistical program. Frequency, percentage, mean, and standard deviation values were used to make descriptive statistics of demographic factors and scale scores. Skewness and kurtosis values were examined to determine the normality distribution of the scales. Pearson correlation analysis and simple and multiple linear regression analysis were performed to test the hypotheses.Findings –The finding that defensive silence affects job performance significantly and negatively is not compatible with the generally accepted findings in the literature that there is a positive relationship. Discussion –Unlike studies in which silence is found to be positive and significant for the organization's benefit, the same issue is found to be negative and significant in this study

A new method for fully automated detection of algae blooms in Antarctica using Sentinel-2 satellite images

The melting of Antarctic glaciers has become a significant issue as a result of global climate change. Algae on the Antarctic ice/snow is an important part of terrestrial photosynthetic organisms. Monitoring and tracking these algal blooms is crucial for understanding the melting of glaciers in the region. Due to the climatic and natural conditions of the region, traveling to and arranging logistics for monitoring and observing snow algae in the Antarctic continent becomes extremely challenging. To overcome these challenges, a novel algorithm has been developed and designed to automatically detect and analyze green algae (Chlorella sp.) from satellite images. Leveraging the vast and free available data from the Sentinel-2 satellite, the algorithm utilizes its high spectral resolution capabilities, capturing invaluable information from various spectral bands. The algorithm was formulated based on the image obtained on February 28, 2017, where green algae formations were intensively seen in the Ryder Bay. The algorithm was developed based on rule-based detection of algae, with the usage of reflection values from the areas where ground truth was established on this date. The developed algorithm was coded and tested using Python version 3.9. The accuracy analysis of the algorithm was conducted using overall accuracy (OA), F1 score, and Kappa statistical test. As a result of the analysis, the OA, F1 score, and Kappa statistic values were calculated as %91, %88.82-%95.27, and 0.901, respectively.

The Johns Hopkins Hope at Hopkins Clinic: Supporting the comprehensive needs of individuals with metastatic breast cancer.

452 Background: Individuals with metastatic breast cancer (MBC) may live with their disease for many years. We initiated the Johns Hopkins Hope at Hopkins Clinic, a multi-pronged quality improvement initiative that aims to optimize care for individuals with MBC. Methods: Individuals with MBC were offered participation in the Clinic, in addition to their usual care. Participants completed baseline patient reported outcome (PRO) surveys through REDCap. They were offered a menu of services, including cancer rehabilitation, supportive oncology, social work, nutrition, navigation, and access to optional services based on baseline PRO data and preferences. A medical oncologist assembled and delivered a comprehensive Care Plan synthesizing recommendations from all consultants with a focus on optimizing treatment, managing symptoms, and promoting wellness. Patients were asked to complete an evaluation survey 3 days after the clinic session, and follow-up PRO surveys at 3 and 6 months after the Care Plan was delivered. We report summary statistics of baseline demographics, tumor characteristics, and PRO scores at the pre-clinic visit. Results: From 11/2020 to 6/2022, 53 patients were referred, and 44 (83%) completed baseline surveys and participated in the Hope at Hopkins Clinic. Median age (N=44) was 58 (32-86); 32 (73%) were white, and 6 (14%) were Black; 24 (55%) had BMI&gt;25. Tumors were mostly estrogen receptor-positive (85%) and HER2-negative (68%). Physical and mental health was not good for ≥14 days for 9/44 (20%) and 4/44 (9%), respectively. PROMIS measure scores were &gt;1 standard deviation below average for 14/44 (32%) for Physical Health, 7/44 (16%) for Mental Health, and 10/44 (23%) for Physical Function. PHQ8 scores indicated depression for 10/44 (23%); GAD7 scores indicated anxiety for 3/44 (7%). 37 (84%) participants received an individualized Care Plan and educational materials based on identified symptoms, concerns, and interests. We will report the number and types recommendations by domain. Follow-up PRO data are available for 10 and 4 patients at 3- and 6-months, respectively. Conclusions: We have provided individualized assessment, education, and recommendations and created a comprehensive Care Plan to guide patients living with MBC. We have used the experience to implement a new multidisciplinary model, and to extend support services to patients living with MBC and their care teams.

The effectiveness of iot-based learning media for supporting the expertise program of renewable energy engineering in vocational high school

The objectives of this study are: (1) the performance of developing renewable energy based on IoT (Internet of Things) in the renewable energy engineering expertise program in the subject of solar hydro-wind energy (Tesha) engineering at Vocational Schools, (2) the level feasibility of development renewable energy based IoT in the subject Tesha's learning at Vocational Schools, (3) level effectiveness development renewable energy based IoT in the subject Tesha learning at Vocational Schools. This research is a research and development research with ADDIE approach (Analyze, Design, Develop, Implement, Evaluate) (Branch (2009) with approach experiments that will used is Quasi Experimental Design with form Nonequivalent Control Group Design. Results from study this show: (1) the feasibility of learning media " Eligible " category. Analysis response user get "Very feasible " category with Cronbach's Alpha get "Very Strong" category so that this media is declared fit for use as a learning media, (2) different and difficulty test on the questions pre-test with medium category of 96.875%, difficult 3.125% and post-test with moderate category of 93.75%, easy 3.125%, and difficult 3,125%, (3) Learning media more IoT based effective in comparison without using IoT because score statistic 0.05 that is 0.044 with the post-test average value is greater than the specified KKM value (85.42 75), while without using IoT, the post-test average value almost same KKM value (75.96 75).Keywords: learning media, renewable energy, Internet of Things (IoT)

Data-adaptive and pathway-based tests for association studies between somatic mutations and germline variations in human cancers.

Cancer is a disease driven by a combination of inherited genetic variants and somatic mutations. Recently available large-scale sequencing data of cancer genomes have provided an unprecedented opportunity to study the interactions between them. However, previous studies on this topic have been limited by simple, low statistical power tests such as Fisher's exact test. In this paper, we design data-adaptive and pathway-based tests based on the score statistic for association studies between somatic mutations and germline variations. Previous research has shown that two single-nucleotide polymorphism(SNP)-set-based association tests, adaptive sum of powered score (aSPU) and data-adaptive pathway-based(aSPUpath) tests, increase the power in genome-wide association studies (GWASs) with a single disease trait in a case-control study. We extend aSPU and aSPUpath to multi-traits, that is, somatic mutations of multiple genes in a cohort study, allowing extensive information aggregation at both SNP and gene levels. -values from different parameters assuming varying genetic architecture are combined to yield data-adaptive tests for somatic mutations and germline variations. Extensive simulations show that, in comparison with some commonly used methods, our data-adaptive somatic mutations/germline variations tests can be applied to multiple germline SNPs/genes/pathways, and generally have much higher statistical powers while maintaining the appropriate type I error. The proposed tests are applied to a large-scale real-world International Cancer Genome Consortiumwhole genome sequencing data set of 2583 subjects, detecting more significant and biologically relevant associations compared with the other existing methods on both gene and pathway levels. Our study has systematically identified the associations between various germline variations and somatic mutations across different cancer types, which potentially provides valuable utility for cancer risk prediction, prognosis, and therapeutics.