Perfect Fit Research Articles

THE DEVELOPMENT OF COVID-19 BRIEF ANXIETY SCALE IN OMAN

Introduction: The global COVID-19 pandemic has caused increased levels of anxiety and stress. A recent study reported that 99% of its sample experienced anxiety, stress, stigma, and concerns about potential social isolation. Aims: This study aims to address the lack of a specific COVID-19 anxiety scale in Oman. Methods: The online scale was distributed via Google Form to 431 students, representing 3% of the total student population at Sultan Qaboos University (SQU). The students were enrolled in different colleges of Sultan Qaboos University. Exploratory factor analysis revealed a unidimensional factorial structure consisting of four items. Item loadings ranged from 0.617 (item 4) to 0.847 (item 3). Results: The results indicated that the validity fit indices were excellent. The RMSEA and SRMR values were greater than 0.05 indicating a perfect fit. The X2/DF value was 0.854, with a p-value of less than 0.653, while the GFI, AGFI, IFI, NFI, TLI, CFI, RMR, and RMSEA values were 0.999, 0.995, 1.003, 0.998, 0.959, 1.00, 0.015, 0.000, respectively (90% CI [0.000; 0.074]). The COBAS-4 showed convergent validity through its significant association with similar scales. Discriminant validity was established via its insignificant correlation to distinct constructs. McDonald’s omega and Cronbach’s alpha showed adequate reliability at 0.810 each. The results of multiple group CFA indicated configural, metric, and scalar invariance between male and female students (ΔCFI ≤ 0.01, ΔRMSEA ≤ 0.015). No significant differences were found between the two sexes. The scale was calibrated using the Rasch rating scale, which provided robust results. The Mantel-Haenszel test showed no differential item functioning (DIF) across sexes. Conclusion: The COBAS-4 is a valid, reliable, simple, and easy-to administer self-report instrument for assessing COVID-19-related anxiety.

Doumou Douala Therapeutic Theorems for Modulo 3 Congruence Memory

Three mathematical theorems invented by Doumou in the medical field, aimed at curing and relieving people. Its proposition can be demonstrated by logical reasoning based on established facts or justifiable hypotheses, which is of crucial importance in the treatment of illnesses, aiming to cure them. This theorem enables the individual to find a perfect fit, a harmony between his thoughts, feelings, words and actions.

Development and external validation of a novel score for predicting postoperative 30‑day mortality in tumor craniotomy patients: A cross‑sectional diagnostic study.

The identification of patients with craniotomy at high risk for postoperative 30-day mortality may contribute to achieving targeted delivery of interventions. The present study aimed to develop a personalized nomogram and scoring system for predicting the risk of postoperative 30-day mortality in such patients. In this retrospective cross-sectional study, 18,642 patients with craniotomy were stratified into a training cohort (n=7,800; year of surgery, 2012-2013) and an external validation cohort (n=10,842; year of surgery, 2014-2015). The least absolute shrinkage and selection operator (LASSO) model was used to select the most important variables among the candidate variables. Furthermore, a stepwise logistic regression model was established to screen out the risk factors based on the predictors chosen by the LASSO model. The model and a nomogram were constructed. The area under the receiver operating characteristic (ROC) curve (AUC) and calibration plot analysis were used to assess the model's discrimination ability and accuracy. The associated risk factors were categorized according to clinical cutoff points to create a scoring model for postoperative 30-day mortality. The total score was divided into four risk categories: Extremely high, high, intermediate and low risk. The postoperative 30-day mortality rates were 2.43 and 2.58% in the training and validation cohort, respectively. A simple nomogram and scoring system were developed for predicting the risk of postoperative 30-day mortality according to the white blood cell count; hematocrit and blood urea nitrogen levels; age range; functional health status; and incidence of disseminated cancer cells. The ROC AUC of the nomogram was 0.795 (95% CI: 0.764 to 0.826) in the training cohort and it was 0.738 (95% CI: 0.7091 to 0.7674) in the validation cohort. The calibration demonstrated a perfect fit between the predicted 30-day mortality risk and the observed 30-day mortality risk. Low, intermediate, high and extremely high risk statuses for 30-day mortality were associated with total scores of (-1.5 to -1), (-0.5 to 0.5), (1 to 2) and (2.5 to 9), respectively. A personalized nomogram and scoring system for predicting postoperative 30-day mortality in adult patients who underwent craniotomy were developed and validated, and individuals at high risk of 30-day mortality were able to be identified.

The relationship between environmental factors and dust accumulation by machine learning

Abstract This study aims to explore the relationship between dust accumulation on a glass and various environmental factors including temperature, humidity, atmospheric pressure, and wind speed. The data was analyzed using Python, a popular language for data science and artificial intelligence, and regression algorithms from the scikit-learn library. The data was divided into training (80 %) and test (20 %) sets and different models were used, such as linear regression, decision tree, K-neighbor regression, random forest regression, and decision tree regression. The accuracy of the models was determined using R 2 scores, where a score of 1.0 indicates a perfect fit and negative values suggest that the model is worse than predicting the mean value. The accuracy of the selected models was calculated as a percentage by multiplying the obtained R 2 scores by 100. Graphs were used to visualise the data and determine the appropriate analysis model. The study found that the amount of dust is directly proportional to temperature and humidity. The accuracy levels of the linear models were suboptimal, leading to the use of nonlinear models like random forest regressor, decision tree regressor, and gradient boosting regressor, which showed improved performance.

Socialization and Practice of Processing Organic Waste into Eco-Enzyme at Sungai Nanam

The village of Sungai Nanam, Lembah Gumanti, is an area in Solok Regency, West Sumatra, that produces significant organic waste from horticultural agriculture. If not managed properly, this waste can cause environmental and aesthetic issues. However, this waste can be converted into an eco-enzyme. An eco-enzyme is a liquid enzyme produced by fermenting organic waste. Eco-enzyme has many benefits, including being used as a liquid organic fertilizer, cleaner, and pest repellent. This community service aims to educate and train the community, especially the women farmers group, named Kelompok Wanita Tani (KWT) Rimbo Data Sejahtera, on converting organic waste into eco-enzymes. The program used three methods: socialization, demonstration, and mentoring. It has been proven to be an effective way to educate and train the community, particularly KWT Rimbo Data Sejahtera, on proper waste management. This program is a perfect fit for their needs and is very useful in reducing the impact of pollution.

Find, Evaluate and Negotiate The Perfect Speaker Fit

Find, Evaluate and Negotiate The Perfect Speaker Fit

Equilibrium, kinetic, and thermodynamic study of Direct Yellow 12 dye adsorption by biomass-derived porous graphitic activated carbon

AbstractCompetent treatment techniques were explored to curb the environmental pollution of dye-laden wastewater. In the current study, eucalyptus biomass contemplated as agricultural waste is translated into eucalyptus graphitic activated carbon (EPGAC) using ZnCl2 at 600 °C in the N2 atmosphere. The present investigation illustrated awareness about the nature of EPGAC’s dye elimination by employing Direct Yellow 12 dye (DY12) as a model dye. EPGAC was characterized using multiple characterization tools such as Fourier transform infrared spectroscopy (FTIR), Boehm titrations, pHzpc, X-ray diffraction (XRD), Raman, field emission scanning electron microscopy (FESEM), energy dispersive X-ray analysis (EDX), high-resolution transmission electron microscopy (HRTEM), and Brunauer-Emmett-Teller (BET) surface area analysis techniques. Electron micrographs disclosed the availability of high pore density for the adsorption of DY12 dyes. BJH analysis reported the distribution of mesopores having a 3 nm diameter on the EPGAC surface. Further, the surface area available for adsorption per gram of the adsorbent is estimated as 178.35 m2 employing BET analysis. XRD and Raman’s data revealed the graphitic nature of EPGAC. Influences of adsorbent parameters such as EPGAC mass, initial dye concentration, contact time, solution pH, and temperature on the eviction of DY12 by EPGAC were examined to achieve a deeper insight into the adsorption mechanism. The optimum EPGAC adsorbent dose was found to be 0.15 g. The equilibrium was attained at 120 min for DY12 dye. Pseudo-second-order kinetics entirely relates to the perfect fit associated with the investigational results. The aptness of the equilibrium data relevant to the Langmuir adsorption isotherm eventually recommends a maximum unilayer adsorption capacity of 42.01 mg/g for EPGAC. Thermodynamic studies further reveal the spontaneous, endothermic, and chemisorption nature of adsorption. Adsorbent viability was established through stability and recyclability studies carried out up to 5 run cycles with 0.15 g of EPGAC. Adsorption mechanisms were explained considering hydrogen bonding, π-π interactions, and electrostatic interactions, ultimately confirming the adsorption tendency displayed by EPGAC for the eviction of DY12 dye present in industrial wastewater.

Adaptation of the scale of effects of social media on eating behavior in Hungarian university students.

People live in a technological world, where social media is used very commonly. Social media has effects on eating behaviors, as in other aspects. For this reason, it is important to measure social media effect. This study aimed to adapt the Scale of Effects of Social Media on Eating Behaviour (SESMEB) that examines the effect of social media on eating behavior in Hungarian university students. The SESMEB was translated into the target language by taking various stages. The online questionnaire including general information, social media use, and the eighteen-item SESMEB was used to collect data. The scale was administered to the study group consisting of 213 Hungarian university students, and data from 203 of them were analyzed. Confirmatory factor analyses were performed to test construct validity, and the Cronbach alpha coefficient was calculated for the reliability of the scale in Hungarian. Total correlation value was higher than 0.50 for all items of the scale. The fit indices were at an acceptable level or had a perfect fit. The t-values were significant at the level of 0.1 and ranged between 2.927 and 5.706. The Spearman-Brown coefficient was calculated at 0.894. The reliability coefficient of the scale was calculated to be 0.866. SESMEB scores were different according to spending time daily, sharing content, and using filters or Photoshop on social media (p < 0.05). Higher than 0.80 Cronbach's alpha coefficient and other results show that Hungarian SESMEB is a valid and reliable tool. Therefore, Hungarian SESMEB will be useful for further studies to determine the impact of social media on eating behaviors.

High Concordance of Different Assays in the Determination of Homologous Recombination Deficiency-Associated Genomic Instability in Ovarian Cancer.

Poly(ADP-ribose) polymerase inhibitors (PARPi) have shown promising clinical results in the treatment of ovarian cancer. Analysis of biomarker subgroups consistently revealed higher benefits for patients with homologous recombination deficiency (HRD). The test that is most often used for the detection of HRD in clinical studies is the Myriad myChoice assay. However, other assays can also be used to assess biomarkers, which are indicative of HRD, genomic instability (GI), and BRCA1/2 mutation status. Many of these assays have high potential to be broadly applied in clinical routine diagnostics in a time-effective decentralized manner. Here, we compare the performance of a multitude of alternative assays in comparison with Myriad myChoice in high-grade serous ovarian cancer (HGSOC). DNA from HGSOC samples was extracted from formalin-fixed paraffin-embedded tissue blocks of cases previously run with the Myriad myChoice assay, and GI was measured by multiple molecular assays (CytoSNP, AmoyDx, Illumina TSO500 HRD, OncoScan, NOGGO GISv1, QIAseq HRD Panel and whole genome sequencing), applying different bioinformatics algorithms. Application of different assays to assess GI, including Myriad myChoice, revealed high concordance of the generated scores ranging from very substantial to nearly perfect fit, depending on the assay and bioinformatics pipelines applied. Interlaboratory comparison of assays also showed high concordance of GI scores. Assays for GI assessment not only show a high concordance with each other but also in correlation with Myriad myChoice. Thus, almost all of the assays included here can be used effectively to assess HRD-associated GI in the clinical setting. This is important as PARPi treatment on the basis of these tests is compliant with European Medicines Agency approvals, which are methodologically not test-bound.

An integrated model exploring the relationship between self-efficacy, technology integration via Blackboard, English proficiency, and Saudi EFL students’ academic achievement

The proliferation of technology in educational settings and its impact on learning outcomes has become a focal point in educational research. In language education, the interplay among technological tools, learner self-efficacy, and language proficiency is critical for academic success. This study aims to shed light on these dynamics by presenting a comprehensive structural model that elucidates the relationships and causal effects among students’ academic achievement, English proficiency, self-efficacy, and the utilization of instructional technology while focusing on the Blackboard learning management system. Employing a quantitative correlational design, this study used three questionnaires to measure the primary variables. The study sample included 590 university students from two universities purposively selected using random stratified sampling to ensure representativeness. Statistical analyses—including descriptive statistics, correlation coefficients, and structural equation modeling (path analysis)—were employed to investigate the data. The emergent model demonstrated a perfect fit to the sample data, exhibiting robust goodness-of-fit indicators. The findings highlight the direct positive influence of self-efficacy on academic achievement and the beneficial effects of Blackboard integration on English proficiency and academic success. These insights emphasize the importance of self-efficacy in educational achievement and the pivotal role of e-learning platforms in enhancing students’ motivation and linguistic skills. The implications of these results are profound, suggesting avenues for future research to examine the applicability of the structural model across diverse educational contexts and incorporate additional variables for a more granular understanding of the factors driving academic achievement in technology-enhanced learning environments.

DOACs: A perfect fit for patients with bariatric surgery?

DOACs: A perfect fit for patients with bariatric surgery?

İlkokul Dönemi Üstün Yetenekli Çocuklar İçin Aday Bildirim Ölçeği (İDABÖ) Geliştirme Çalışması

Introduction: The aim of this study is to develop a teacher nomination scale for gifted students in primary schools. The developed scale is theoretically based on Renzulli’s Three-Ring Conception of Giftedness Theory. Method: In this study, a Teacher Nomination Scale for Gifted Children in Primary Schools (TNSG-P) was developed to identify gifted children attending primary school. The descriptive survey design was used in this study. In study 1, the exploratory factor analysis (EFA) using principal axis method was conducted. In study 2, the confirmatory factor analysis (CFA) using robust maximum likelihood estimation method was carried out. In study 3, the validity of the scale was tested. Findings: A pilot study involving 507 participants was carried out using the 89-item trial form. After the pilot phase, 50 items remained in the scale. A confirmatory factor analysis was conducted on the 50-item scale in a sample of 225 participants. This showed that the fit indices of the scale had a perfect fit and the second level three-factor structure of the scale with 27 items was confirmed. In the validity analysis of the scale, gifted students and typical students were compared and a statistically significant difference was obtained in favor of the gifted students. Cronbach’s α and stratified Cronbach’s α, McDonald’s ω, composite reliability coefficients and average variance extracted (AVE) values regarding the reliability of the scale were also examined. The reliability coefficients for both the overall scale and its subscales were found to be above .95. Discussion: The findings have revealed that the Teacher Nomination Scale for Gifted Children in Primary Schools is a valid assessment instrument and gives reliable scores in identifying gifted children.

Representasi Budaya Bali pada Film A Perfect Fit: Analisis Semiotika Roland Barthes

A film is a literary work in the form of moving images or videos that can be seen, heard, and shown to convey information or a message to the general public. Films have various ways of depicting various realities of life, including social realities related to daily life, politics, psychology, culture, and others. This research aims to examine films that represent the reality of life in Balinese culture, namely the film A Perfect Fit. The method used in this research is a qualitative descriptive method, with a study of Roland Barthes' semiotic analysis. Data collection techniques were carried out using reading and note-taking techniques. Based on the research results, there are eight pieces of Balinese culture in the film A Perfect Fit, namely Canang Sari, Melukat, Kecak Dance, Balinese Karawitan, Prayer, Traditional Balinese Wedding Clothing, Mepantigan or Mud Wrestling, and Lontar Reading.

Aging in light of digitalization of healthcare

With the advancements in digital technologies, notions such as aging in place have gained currency. But, next to technical issues concerning the extent to which full-blown aging in place is possible, philosophical and ethical questions have been also raised. An important dimension of the digitalization of healthcare is how would aging look to both older adults and the public in the wake of such systems. In this article, I will suggest integrating postphenomenology into Age Studies to explore how aging comes to be conceptualized. An advantage of postphenomenology is that it is anchored in a first-person perspective, and in this sense, it may be a perfect fit to enhance our awareness about the impacts of the digitalization of health on older adults from their own point of view. I will also argue that postphenomenlogy can throw light on digital technologies in their actual use. This may help researchers go beyond merely exploring conditions of use and adoption implemented through notions such as usability, trust, privacy, dignity, and the like, and gain knowledge of how users’ relationship with their surroundings reconfigures after exposure to digital assistive technologies.

An AI-based framework for earthquake relief demand forecasting: A case study in Türkiye

Accurately forecasting the demand for relief assistance is crucial for efficient post-disaster relief operations. This paper presents an AI-based framework for earthquake relief demand forecasting, aiming to optimize the distribution of emergency resources. To identify higher-risk areas, the framework integrates historical earthquake data with socioeconomic information. This information aids in identifying regions that require immediate relief assistance. The initial dataset for relief demand quantification includes essential parameters such as Latitude, Longitude, Population, Demand, Supply, Cost, Deaths, Injuries, Infrastructural Damage, and Cascading Disaster. By analyzing these inputs and factors, AI algorithms can quantify the relief demand scenario accurately, resulting in accurate and reliable forecasts of relief supply requirements. The iterative ML techniques in generating comprehensive data for Türkiye greatly enhance the quality and accuracy of relief demand prediction. The prediction models used in the framework, namely Least Angle Regression and Linear Regression, demonstrate exceptional performance in forecasting relief demand. They achieve a remarkable R2 score of 1, indicating a perfect fit to the data, and a Loss Error function value of zero, signifying highly accurate predictions. Additionally, the Decision Tree algorithm showcases strong capabilities in iterative learning, achieving an accuracy rate of 88.99 %. In addition to providing valuable insights for tailored relief strategies, the framework extends beyond relief demand by predicting outcomes such as casualties, injuries, and infrastructure damage. With a comprehensive understanding of the earthquake’s impact, relief organizations can strategize their response and allocate resources accordingly. The thorough methodology and innovative application of ML algorithms within our pioneering AI-based framework significantly contribute to the field. This signifies a fundamental shift in the approach to envisioning and executing relief operations following seismic events.

Philip O’Flaherty and the Irish roots of Uganda’s Protestantism

Despite exiting history only a few years after his death, Philip O’Flaherty, the Irish missionary sent to Uganda by the Church Missionary Society (CMS) in 1880, planted Uganda’s Protestantism and, with others, established the foundation that its present success can be understood. Upon arriving on March 18, 1881, he renewed the enthusiasm of a mission station struggling to find its footing in the East African country by conceptualizing a long-term vision for the mission and ensuring it was implemented. According to the historian J.A. Rowe, O’Flaherty possessed just those qualities of tact and savoir-faire that his colleagues lacked, making him a perfect fit for the mission field in Uganda. Among others, he performed the landmark first Protestant baptism in 1882, which had been five years in waiting. He was also a key member in forming the first church councils (one for men and another for women) whose membership included only Ugandans, a move that indigenized the church’s leadership for the first time.

Exploring Job Satisfaction Behavior as an Antecedent and Moderating Variable of Work Engagement In the Public Sector

This study investigates the role of multifaceted job satisfaction as an antecedent variable and moderating factor influencing work engagement in the public sector. Drawing upon Bakker and Demerouti's (2007) model, the research adapts Spector's (1997) job satisfaction measurement constructs and tests them on a sample of 1484 employees. Employing a mixed-methods approach with a cross-sectional design, the study focuses on identifying key drivers of work engagement in the aftermath of the COVID-19 pandemic. All dimensions are constructed based on qualitative exploration through Focus Group Discussions (FGD) involving 122 respondents, and item formulation is guided by the mapping of dimensions using In-Vivo software. The study employs bootstrapping techniques, revealing a Structural Equation Modeling (SEM) result with a Standardized Root Mean Residual (SRMR) value of 0.054 (&lt;0.08), indicating a perfect fit for the model. All path coefficient indices exhibit a P-Value of 0.000 (&lt;0.05), signifying the acceptance of all proposed relationships in the study. The findings contribute to understanding the intricate interplay between job satisfaction, personal factors, organizational factors, and work engagement, providing valuable insights for practitioners and policymakers in the public sector post-COVID-19.

Synergistic potency of ultrasound and solar energy towards oxidation of 2,4-dichlorophenol: a chemometrics approach.

Industrial units based on chemical processes-the textile and paper industries-are major sources of chlorophenols in the environment, and chlorophenolic compounds persist within the environment for a long time with high toxicity levels. The photo-assisted Fenton's and photocatalysis processes were investigated for the degradation of chlorophenols in the present study. Response surface methodology was employed to get optimised conditions for photocatalysis and photo-Fenton process-governing factors, thus, yielding a profound removal efficiency. Under optimised conditions, with a photocatalyst dose of 0.2 g/L, oxidant concentration of 10.0 mM and pH 5.0, complete removal of 2,4-dichlorophenol (2,4-DCP) was observed in 210 minutes in photocatalytic treatment. In the case of the photo-Fenton process, at an H2O2 dose of 5.0 mM and Fe2+ concentration of 0.5 mM, the organic pollutant was eliminated within 5 minutes of reaction time under acidic conditions (pH 3.0). The RSM model reported the perfect fit of experimental data with the predicted response. Among different isotherm models, the Langmuir isotherm was the best fit. The process followed pseudo-first order rate kinetics among various kinetics models. For the obtained optimised conditions, sonication and solar energy-driven processes were incorporated to study enhanced mineralisation. The solar-assisted Fenton process reported maximum mineralisation (90%) and cost-effective ($0.01/litre for 100 mg/L 2,4-DCP) treatment among different hybrid oxidation processes. The work provides insight into harnessing the naturally available solar energy, reducing the overall treatment cost and opting for a sustainable treatment method.

Integrated Bulk and Single-cell RNA Sequencing Data Constructs and Validates a Prognostic Model for Non-small Cell Lung Cancer.

Background: Most of the current research on prognostic model construction for non-small cell lung cancer (NSCLC) only involves in bulk RNA-seq data without integration of single-cell RNA-seq (scRNA-seq) data. Besides, most of the prognostic models are constructed by predictive genes, ignoring other predictive variables such as clinical features. Methods: We obtained scRNA-seq data from GEO database and bulk RNA-seq data from TCGA database. We construct a prognostic model through the Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression. Furthermore, we performed ESTIMATE, CIBERSORT, immune checkpoint-related analyses and compared drug sensitivity using pRRophetic method judged by IC50 between different risk groups. Results: 14 tumor-related genes were extracted for model construction. The AUC for 1-, 3-, and 5 years overall survival prediction in TCGA and three validation cohorts were almost higher than 0.65, some of which were even higher than 0.7, even 0.8. Besides, calibration curves suggested no departure between model prediction and perfect fit. Additionally, immune-related and drug sensitivity results revealed potential targets and strategies for treatment, which can provide clinical guidance. Conclusion: We integrated traditional bulk RNA-seq and scRNA-seq data, along with predictive clinical features to develop a prognostic model for patients with NSCLC. According to the constructed model, patients in different groups can follow precise and individual therapeutic schedules based on immune characteristics as well as drug sensitivity.

An Approach to balance Learning and Procrastination through a Strategic Model in the context of Higher Education

Abstract— My own behaviour baffles me. For I find myself doing what I really hate, and not doing what I really want to do! ST PAUL The statement given by St. Paul regarding the frequently changing mindset of Humans is a perfect fit. Sometimes we as Humans face a lot of confusion in our minds about what to do and what not to do. Due to some reasons, we try to postpone things related to our work often which is known as Procrastination. There are Procrastination Models having dimensions Psychologic, Personal and Pedagogic in the academic scenario. The study in this paper focuses on two research questions which analyze the cause of academic procrastination and proposes a strategic model, along with the concept of Deadline, to overcome it. This work also focuses on the pedagogic dimension considering both student and teacher perspectives of learning and avoiding procrastination. Keywords— Procrastination; Pedagogic Strategic Model; Learning; Deadline