Validation Data Research Articles

The Implementation of Cambridge Curriculum in the International Elementary School

Development of the era affects education to keep on growing and its triggers schools for persist in being innovative, and one of the alternatives was using international curriculum, among others is Cambridge Curriculum. This research aims to analyze the implementation of the Cambridge Curriculum at International Elementary School from the school and class level in terms of the planning, implementation, and evaluation. It used the case study approach with the type of descriptive research design with the subject consisting of teachers, principals, curriculum teams, students, and Cambridge Curriculum subject classes. The data collection was conducted through interviews, observations, and documentation. The data validation was through triangulation and increased persistence using the interactive model data analysis technique. The results of this research indicate (1) there is an inter-teacher discussion forum entitled Curriculum Day, used in planning both levels of curriculum implementation and school-level evaluation, (2) implementation of the Cambridge Curriculum in the grade level is filled with hands on activities and uses sources and digital media such as Twinkl and Grammarly, and (3) besides the written test, there are non-written tests for learning evaluation such as I-STEAM EXPO and SLC, and the way of how Cambridge Curriculum’s implemented showed relevance between the development of education with their needs. This research concluded the implementation of Cambridge Curriculum in the school executed well for all stages in both levels and give advantages to the school’s development.

Evaluation of Reliability Between Race and Ethnicity Data Obtained from Self-report Versus Electronic Health Record.

Disparities based on perceived race and ethnicity exist in all fields of medicine. Accurate data collection is crucial to addressing these disparities, yet few studies have evaluated the validity of data gathered. This study compares self-reported race and ethnicity data, considered the gold standard, with data documented in the electronic health record (EHR), to assess the validity of that data. Data from self-reported questionnaires was collected from adolescents admitted to a psychiatric inpatient unit from February 2019 to July 2022. Demographic questionnaires were self-administered as part of a larger battery completed during the admission process. Data was compared to demographic information collected from the hospital's EHR for the same patients and time. In a sample of 1191 patients (ages 11-18, 61.9% female, 89% response rate), substantial agreement was observed for Hispanic ethnicity (κ = 0.64), while agreement for specific racial groups ranged from slight to substantial (κ = 0.10-0.63). In addition, it was noted that there was discrepancy between multiracial identification, with 17.1% of patients identifying as more than one race in self-reported data compared to 3.1% in EHR data. The findings from this data set highlight the need for caution when using EHR data to draw conclusions about health disparities. It also suggests that the method of data collection meaningfully influences the responses patients provide. Addressing these challenges is essential for advancing equitable healthcare and mitigating disparities among patients.

Sensing Volatile Pollutants with Spin-Coated Films Made of Pillar[5]arene Derivatives and Data Validation via Artificial Neural Networks.

Different types of solvents, aromatic and aliphatic, are used in many industrial sectors, and long-term exposure to these solvents can lead to many occupational diseases. Therefore, it is of great importance to detect volatile organic compounds (VOCs) using economic and ergonomic techniques. In this study, two macromolecules based on pillar[5]arene, named P[5]-1 and P[5]-2, were synthesized and applied to the detection of six different environmentally volatile pollutants in industry and laboratories. The thin films of the synthesized macrocycles were coated by using the spin coating technique on a suitable substrate under optimum conditions. All compounds and the prepared thin film surfaces were characterized by NMR, Fourier transform infrared (FT-IR), elemental analysis, atomic force microscopy (AFM), scanning electron microscopy (SEM), and contact angle measurements. All vapor sensing measurements were performed via the surface plasmon resonance (SPR) optical technique, and the responses of the P[5]-1 and P[5]-2 thin-film sensors were calculated with ΔI/Io × 100. The responses of the P[5]-1 and P[5]-2 thin-film sensors to dichloromethane vapor were determined to be 7.17 and 4.11, respectively, while the responses to chloroform vapor were calculated to be 5.24 and 2.8, respectively. As a result, these thin-film sensors showed a higher response to dichloromethane and chloroform vapors than to other harmful vapors. The SPR kinetic data for vapors validated that a nonlinear autoregressive neural network was performed with exogenous input for the best molecular modeling by using normalized reflected light intensity values. It can be clearly seen from the correlation coefficient values that the nonlinear autoregressive with exogenous input artificial neural network (NARX-ANN) model for dichloromethane converged more successfully to the experimental data compared to other gases. The correlation coefficient values of the dichloromethane modeling results were approximately 0.99 and 0.98 for P[5]-1 and P[5]-2 thin-film sensors, respectively.

Understanding the Social Dimensions and Context in HIV Research: Instrument Adaptation Is More Than Just Translation

Abstract There is a shortage of health-related questionnaires developed specifically for African countries' cultural contexts and local languages. Researchers have translated some tools to the target local languages. However, poor-quality or even high-quality translation can lead to an instrument that is not comparable with the original language if linguistic nuances, and cultural differences are not considered. This can affect the validity and reliability of the research data. The objective of this commentary was to highlight limitations of the commonly accepted forward–backward translation approach and the importance of more rigorous cultural and linguistic adaptation processes in social and behavioral research related to HIV.

Cerebrovascular Effects of Sildenafil in Small Vessel Disease: The OxHARP Trial.

Vascular cognitive impairment due to cerebral small vessel disease is associated with cerebral pulsatility, white matter hypoperfusion, and reduced cerebrovascular reactivity (CVR), and is potentially improved by endothelium-targeted drugs such as cilostazol. Whether sildenafil, a phosphodiesterase-5 inhibitor, improves cerebrovascular dysfunction is unknown. OxHARP trial (Oxford Haemodynamic Adaptation to Reduce Pulsatility) was a double-blind, randomized, placebo-controlled, 3-way crossover trial after nonembolic cerebrovascular events with mild-moderate white matter hyperintensities (WMH), the most prevalent manifestation of cerebral small vessel disease. The primary outcome assessed the superiority of 3 weeks of sildenafil 50 mg thrice daily versus placebo (mixed-effect linear models) on middle cerebral artery pulsatility, derived from peak systolic and end-diastolic velocities (transcranial ultrasound), with noninferiority to cilostazol 100 mg twice daily. Secondary end points included the following: cerebrovascular reactivity during inhalation of air, 4% and 6% CO2 on transcranial ultrasound (transcranial ultrasound-CVR); blood oxygen-level dependent-magnetic resonance imaging within WMH (CVR-WMH) and normal-appearing white matter (CVR-normal-appearing white matter); cerebral perfusion by arterial spin labeling (magnetic resonance imaging pseudocontinuous arterial spin labeling); and resistance by cerebrovascular conductance. Adverse effects were compared by Cochran Q. In 65/75 (87%) patients (median, 70 years;79% male) with valid primary outcome data, cerebral pulsatility was unchanged on sildenafil versus placebo (0.02, -0.01 to 0.05; P=0.18), or versus cilostazol (-0.01, -0.04 to 0.02; P=0.36), despite increased blood flow (∆ peak systolic velocity, 6.3 cm/s, 3.5-9.07; P<0.001; ∆ end-diastolic velocity, 1.98, 0.66-3.29; P=0.004). Secondary outcomes improved on sildenafil versus placebo for CVR-transcranial ultrasound (0.83 cm/s per mm Hg, 0.23-1.42; P=0.007), CVR-WMH (0.07, 0-0.14; P=0.043), CVR-normal-appearing white matter (0.06, 0.00-0.12; P=0.048), perfusion (WMH: 1.82 mL/100 g per minutes, 0.5-3.15; P=0.008; and normal-appearing white matter, 2.12, 0.66-3.6; P=0.006) and cerebrovascular resistance (sildenafil-placebo: 0.08, 0.05-0.10; P=4.9×10-8; cilostazol-placebo, 0.06, 0.03-0.09; P=5.1×10-5). Both drugs increased headaches (P=1.1×10-4), while cilostazol increased moderate-severe diarrhea (P=0.013). Sildenafil did not reduce pulsatility but increased cerebrovascular reactivity and perfusion. Sildenafil merits further study to determine whether it prevents the clinical sequelae of small vessel disease. URL: https://www.clinicaltrials.gov; Unique identifier: NCT03855332.

Point prevalence surveys of antibiotic prescribing in children at a tertiary hospital in a resource constraint, low-income sub-Saharan African country—the impact of an antimicrobial stewardship program

BackgroundResistance to multiple antibiotics by several pathogens has been widely described in children and has become a global health emergency. This is due to increased use by parents, caregivers, and healthcare providers. This study aims to describe the prevalence rates of antibiotic prescribing, ascertain the impact of antimicrobial stewardship programs, and target improving the quality of antibiotic prescribing in the paediatric population over time in a hospital.MethodA point prevalence survey of antibiotic use was performed yearly for 4 years to monitor trends in antibiotic prescribing. Data from all patients admitted before 8 a.m. on the day of the PPS were included. A web-based application designed by the University of Antwerp was used for data entry, validation, and analysis (http://www.global-pps.com).ResultsA total of 260 children, including 90 (34.6%) neonates and 170 (65.4%) older children, were admitted during the four surveys. Overall, 179 (68.8%) patients received at least one antibiotic. In neonates, the prevalence of antibiotic use increased from 78.9 to 89.5% but decreased from 100 to 58.8% in older children. There was a reduction in the use of antibiotics for prophylaxis from 45.7 to 24.6%. The most frequently prescribed antibiotic groups were third generation cephalosporins and aminoglycosides. The most common indications for antibiotic prescription were sepsis in neonates and central nervous system infection in older children. The documentation of reason in notes increased from 33 to 100%, while the stop-review date also increased from 19.4 to 70%.ConclusionThe indicators for appropriate antibiotic prescription improved over time with the introduction of antibiotic stewardship program in the department.

Neural Architecture Search Using Covariance Matrix Adaptation Evolution Strategy.

Evolution-based neural architecture search methods have shown promising results, but they require high computational resources because these methods involve training each candidate architecture from scratch and then evaluating its fitness, which results in long search time. Covariance Matrix Adaptation Evolution Strategy (CMA-ES) has shown promising results in tuning hyperparameters of neural networks but has not been used for neural architecture search. In this work, we propose a framework called CMANAS which applies the faster convergence property of CMA-ES to the deep neural architecture search problem. Instead of training each individual architecture seperately, we used the accuracy of a trained one shot model (OSM) on the validation data as a prediction of the fitness of the architecture, resulting in reduced search time. We also used an architecture-fitness table (AF table) for keeping a record of the already evaluated architecture, thus further reducing the search time. The architectures are modeled using a normal distribution, which is updated using CMA-ES based on the fitness of the sampled population. Experimentally, CMANAS achieves better results than previous evolution-based methods while reducing the search time significantly. The effectiveness of CMANAS is shown on two different search spaces using four datasets: CIFAR-10, CIFAR-100, ImageNet, and ImageNet16-120. All the results show that CMANAS is a viable alternative to previous evolution-based methods and extends the application of CMA-ES to the deep neural architecture search field.

Use of Optical and Radar Imagery for Crop Type Classification in Africa: A Review.

Multi-source remote sensing-derived information on crops contributes significantly to agricultural monitoring, assessment, and management. In Africa, some challenges (i.e., small-scale farming practices associated with diverse crop types and agricultural system complexity, and cloud coverage during the growing season) can imped agricultural monitoring using multi-source remote sensing. The combination of optical remote sensing and synthetic aperture radar (SAR) data has emerged as an opportune strategy for improving the precision and reliability of crop type mapping and monitoring. This work aims to conduct an extensive review of the challenges of agricultural monitoring and mapping in Africa in great detail as well as the current research progress of agricultural monitoring based on optical and Radar satellites. In this context optical data may provide high spatial resolution and detailed spectral information, which allows for the differentiation of different crop types based on their spectral signatures. However, synthetic aperture radar (SAR) satellites can provide important contributions given the ability of this technology to penetrate cloud cover, particularly in African tropical regions, as opposed to optical data. This review explores various combination techniques employed to integrate optical and SAR data for crop type classification and their applicability and limitations in the context of African countries. Furthermore, challenges are discussed in this review as well as and the limitations associated with optical and SAR data combination, such as the data availability, sensor compatibility, and the need for accurate ground truth data for model training and validation. This study also highlights the potential of advanced modelling (i.e., machine learning algorithms, such as support vector machines, random forests, and convolutional neural networks) in improving the accuracy and automation of crop type classification using combined data. Finally, this review concludes with future research directions and recommendations for utilizing optical and SAR data combination techniques in crop type classification for African agricultural systems. Furthermore, it emphasizes the importance of developing robust and scalable classification models that can accommodate the diversity of crop types, farming practices, and environmental conditions prevalent in Africa. Through the utilization of combined remote sensing technologies, informed decisions can be made to support sustainable agricultural practices, strengthen nutritional security, and contribute to the socioeconomic development of the continent.

Movement Quality Assessment of Army Reserve Officers' Training Corps Cadets: A Report of Validity and Normative Data.

Movement quality screening in early-career military populations, like Army Reserve Officers' Training Corps (AROTC) cadets, could decrease the negative impact of musculoskeletal injury observed within the military. Movement quality screening techniques should be valid before being pursued in the field. Normative data describing movement quality of AROTC cadets are also needed. Therefore, the aims of this study were to determine criterion validity of several movement quality assessments and report normative jump-landing kinematics of AROTC cadets. This cross-sectional research was approved by the Institutional Review Board. As part of a larger study, 20 AROTC cadets (21.3 ± 3.4 years; 1.7 ± 0.1 m; 73.8 ± 14.8 kg) had 3-dimensional (3D) and 2-dimensional (2D) kinematic data collected simultaneously while performing a jump-landing task. Variables of interest were 3D hip and knee sagittal, frontal, and transverse joint angles at maximum knee flexion. An experienced rater calculated sagittal and frontal 2D joint angles at maximum knee flexion. Averages of 2D and 3D angles were calculated to describe normative data and for further data analysis. Bivariate correlations between 3D and 2D variables were used to determine criterion validity. Moderate correlations were found between 2D and 3D hip frontal plane angles (P = .05, r =-0.33), 2D and 3D knee sagittal plane angles (P = .04, r = 0.35), and 2D and 3D knee frontal plane angles (P = .03, r = -0.36). Normative values of knee and hip kinematics demonstrated averages of 17.58° of knee adduction, 16.48° of knee external rotation, 11.57° of hip abduction, 10.76° of hip internal rotation, and 103.47° of knee flexion during landings. However, ranges demonstrated that landing patterns vary within AROTC cadets. The normative values of 3D jump-landing kinematic data indicate that movement quality varies greatly within AROTC cadets, and some cadets display potentially injurious movements. Therefore, screening movement quality could be beneficial to determine musculoskeletal injury risk in AROTC cadets. Based on the correlations discovered in this study, we recommend the 2D techniques used in this study be researched further as they may serve as alternatives to expensive, timely 3D techniques that could be better utilized in military environments.

Sociodemographic and Health Factors of the Alcohol Treatment-seeking Population in New South Wales, Australia.

Although factors associated with alcohol use have been researched at a population level, descriptions of the alcohol and other drug (AOD) treatment-seeking population in New South Wales (NSW), Australia, are limited. This study addresses this gap by analyzing sociodemographic and health characteristics in the NSW AOD treatment-seeking population. Self-reported Australian Treatment Outcomes Profile data on substance use, health ratings, and sociodemographic factors were acquired from public AOD services (offering services from counseling to ambulatory/inpatient withdrawal management) in 6 administrative health districts from 2016 to 2019 (n = 14,287). Gaussian and multiple logistic regressions were conducted to examine associations between these factors and alcohol consumption quantity. Data were analyzed for patients seeking treatment for alcohol consumption specifically (n = 5929; median age, 44 years; 65% male). Valid alcohol consumption data were available for 5460 patients, among whom the mean volume of alcohol consumed was 311 standard drinks (3110 grams of ethanol) over the past 28 days and 15 standard drinks (150 grams of ethanol) per occasion. Higher volumes were consumed by males and those with recent experiences of violence and/or injecting drug use. Caring for children younger than 5 years and having above-median health ratings were associated with lower alcohol consumption. This study contributes to the characterization of the NSW public AOD treatment population and identifies associations between alcohol consumption, sociodemographic factors, and health ratings among people seeking treatment for alcohol consumption. Findings point towards multilevel assessment and comprehensive interventions for people engaging in treatment for alcohol use. Future research should address barriers to treatment.

Risk calculators for the detection of prostate cancer: a systematic review.

Prostate cancer (PCa) (early) detection poses significant challenges, including unnecessary testing and the risk of potential overdiagnosis. The European Association of Urology therefore suggests an individual risk-adapted approach, incorporating risk calculators (RCs) into the PCa detection pathway. In the context of 'The PRostate Cancer Awareness and Initiative for Screening in the European Union' (PRAISE-U) project ( https://uroweb.org/praise-u ), we aim to provide an overview of the currently available clinical RCs applicable in an early PCa detection algorithm. We performed a systematic review to identify RCs predicting detection of clinically significant PCa at biopsy. A search was performed in the databases Medline ALL, Embase, Web of Science Core Collection, Cochrane Central Register of Controlled Trials and Google Scholar for publications between January 2010 and July 2023. We retrieved relevant literature by using the terms "prostate cancer", "screening/diagnosis" and "predictive model". Inclusion criteria included systematic reviews, meta-analyses, and clinical trials. Exclusion criteria applied to studies involving pre-targeted high-risk populations, diagnosed PCa patients, or a sample sizes under 50 men. We identified 6474 articles, of which 140 were included after screening abstracts and full texts. In total, we identified 96 unique RCs. Among these, 45 underwent external validation, with 28 validated in multiple cohorts. Of the externally validated RCs, 17 are based on clinical factors, 19 incorporate clinical factors along with MRI details, 4 were based on blood biomarkers alone or in combination with clinical factors, and 5 included urinary biomarkers. The median AUC of externally validated RCs ranged from 0.63 to 0.93. This systematic review offers an extensive analysis of currently available RCs, their variable utilization, and performance within validation cohorts. RCs have consistently demonstrated their capacity to mitigate the limitations associated with early detection and have been integrated into modern practice and screening trials. Nevertheless, the lack of external validation data raises concerns about numerous RCs, and it is crucial to factor in this omission when evaluating whether a specific RC is applicable to one's target population.

Reaction Rate Ratios for Recent Fast Metal Experiments with Large Plutonium Masses

Reaction rate ratios are integral responses that are used within the criticality experiments field because they contain spectral information. While these types of measurements have been utilized for nuclear data validation with historic experiments, few experiments of this type have been utilized for recent experiments, as few exist. This work focuses on measured reaction rate ratios for two nearly bare plutonium critical assemblies with different geometries: one that is cube like (with a Pu mass of 40 kg) and one that is slab like (with a Pu mass of 109 kg). Irradiations were performed with both configurations in which foils were placed near the center of the assembly. Plutonium, highly enriched uranium, depleted uranium, and Au foils were included in the irradiation and counted via high-purity germanium detectors. From these measurements, reaction rate ratios were calculated. Measured and simulated values and uncertainties are presented for the reaction rate ratios. Ratios utilizing the following reactions are given in this work: 197Au(n, γ ), 197Au(n,2n), 235U(n,fission), 238U(n,fission), 238U(n,2n), 238U(n, γ ), and 239Pu(n,fission). Uncertainties for the measured reaction rate ratios ranged from 4% to 7%, and the contribution of various parameters to this uncertainty was investigated. The results are compared to historical experiments and should be used for nuclear data validation for future nuclear data library releases. These measurements are part of the EUCLID (Experiments Underpinned by Computational Learning for Improvements in Nuclear Data) project, which utilizes measurement responses in addition to keff (such as these reaction rate ratios) to help reduce uncertainties in 239Pu nuclear data.

A validated LC-MS/MS method for simultaneous quantitation of piperacillin, cefazolin, and cefoxitin in rat plasma and twelve tissues

BackgroundThe investigation of drug disposition in tissues is critical to improving dosing strategy and maximizing treatment effectiveness, yet developing a multi-tissue bioanalytical method could be challenging due to the differences among various matrices. Herein, we developed an LC-MS/MS method tailored for the quantitation of piperacillin (PIP), cefazolin (CFZ), and cefoxitin (CFX) in rat plasma and 12 tissues, accompanied by validation data for each matrix according to the FDA and EMA guidelines. ResultsThe method required only a small sample volume (5 μL plasma or 50–100 μL tissue homogenates) and a relatively simple protocol for simultaneous quantitation of PIP, CFZ, and CFX within different biological matrices. Mobile phase A was composed of 5 mM ammonium formate and 0.1 % formic acid in water, while mobile phase B contained 0.1 % formic acid in acetonitrile. The mobile phase was pumped through a Synergi Fusion-RP column equipped with a guard column with a gradient elution program at a 0.3 mL/min flow rate. The mass spectrometer was operated in positive ionization mode (ESI+) using multiple reaction monitoring. SignificanceThe validated method has been successfully applied to quantify PIP, CFZ, and CFX from the plasma and tissue samples collected in a pilot rat study and will further be used in a large pharmacokinetic study. To our knowledge, this is also the first report presenting long-term, freeze-thaw, and autosampler stability data for PIP, CFZ, and CFX in rat plasma and multiple tissues.

Location-Specific Microstructure Characterization Within AM Bench 2022 Laser Tracks on Bare Nickel Alloy 718 Plates

Additive manufacturing of metal alloys produces microstructures that are typically very different from those produced by more traditional manufacturing approaches. Computer simulations are useful for connecting processing, structure, and performance for these materials, but validation data that span this full range is difficult to produce. This research is part of a broad effort by the Additive Manufacturing Benchmark Test Series to produce such datasets for laser powder bed fusion builds of nickel Alloy 718. Here, single laser tracks produced with variations in laser power, scan velocity, and laser diameter, and arrays of adjacent laser tracks on bare wrought Alloy 718 plates are examined using optical microscopy, electron backscatter diffraction, and energy dispersive spectroscopy.

Legalization of Community Organizations in Indonesia from the Perspective of Legal Protection: A Problematic Legal Choice

The research examines the legalization of community organizations in Indonesia, focusing on Law Number 17 of 2013. Using legal dogmatic research, the study finds that while legalizing community organizations provides greater legal certainty and protection, it also leads to issues like leadership dualism and internal conflicts due to differing views on legal status. Additionally, the law's digital registration system offers efficiency but causes conflicts over data validity as it lacks field verification. The findings highlight the need for fair, inclusive mechanisms to ensure equal participation in community development for all legally registered organizations.

Discrimination of maize transgenic and non-transgenic varieties by laser induced spectroscopy (LIBS) and machine learning algorithms

BackgroundIn the last decades, the production and consumption of genetically modified agricultural products have increased markedly due to the worldwide population growth and rising demand for food and feed. Consequently, genetically modified crops have been extensively produced and consumed, which required identifying and discriminating transgenic and non-transgenic products. ResultsLaser-induced breakdown spectroscopy (LIBS) combined with chemometric methods was applied to identify and discriminate two varieties of conventional (not-genetically modified, NGM) maize from four varieties of transgenic maize (genetically modified, GM). The LIBS spectra acquired under reduced pressure (100 Torr) conditions over two ranges, i.e., 175–330 nm and 275–770 nm, were subjected to Standard Normal Variation (SNV) and multivariate methods such as Principal Component Analysis (PCA) to reduce data matrices dimensionality and spectral noise. The supervised machine learning algorithms k-nearest neighbor (k-NN) and support vector machine (SVM) have been applied to discriminate among NGM and GM maize reserving 25 % of data for external validation. The training data were employed for hyperparameter optimization of classifiers using the Leave-One-Out Cross-Validation (LOOCV) method. Considering all six maize varieties simultaneously, the highest training accuracy achieved was 90.56 %, with an external validation accuracy of 88.33 %. In an alternative approach based on pairwise combinations of one GM variety against one NGM variety, the best outcome achieved was 100 % LOOCV and external validation accuracy. ConclusionsThese results showed that LIBS supported by appropriate chemometric methods represents an alternative screening technique for identifying and discriminating transgenic from non-transgenic maize.

Validity and Reliability of the Residency and Immersion Program (PRIme) Assessment Instrument in Enhancing the Quality of New Principals

The Residency and Immersion Program (PRIme) is an induction for new school principals serving their first year. PRIme was implemented in 2014, and it requires a comprehensive evaluation for program improvement. Hence, this study aims to assess the validity and reliability of the PRIme assessment instrument. The assessment data of content validity by professional experts and face validity by new principals were analyzed using CVR, CVI, and FVI methods. Meanwhile, pilot study data was obtained from 53 respondents and further analyzed using the Rasch measurement model. The questionnaire includes 102 items for assessing the domains of input (courses, coaching, and mentoring), process (type of leadership practiced), and product (leadership and organizational management) in the implementation of PRIme. The Rasch analysis results showed that the items in the questionnaire have good content validity, construct validity, and reliability. Therefore, the Rasch analysis has proven that this questionnaire can be used as an instrument to assess PRIme in an effort to enhance the quality of new principals in Malaysia.

Assessing the Accuracy of Substance Use Disorder Treatment Search Tools: A Cross-sectional Analysis of National and State-Level Directories

BackgroundAddressing the critical public health crisis of substance use disorder (SUD), this study evaluates the accuracy of SUD treatment search tools, such as FindTreatment.gov, to connect patients with appropriate care. MethodsTo ensure geographic diversity, we randomly selected one state from four distinct US regions (Arizona, Florida, Massachusetts, Ohio) and then randomly selected counties of varying sizes (one large, two medium, three small) within each state using a random number generator. Contact information for practices was extracted from the tools and validated through phone calls. The primary outcome measures were exact accuracy rate (wherein all information was accurate) and functional accuracy rate (wherein enough information was provided to facilitate care establishment). ResultsA total of 697 practices from within ten SUD treatment search tools were assessed. Accuracy of the ten SUD treatment search tools varied considerably, with exact accuracy rates ranging from 9.1% to 76.0% (mean: 56.0%) and functional accuracy rates from 50.0% to 92.0% (mean: 82.8%). National tools exhibited higher accuracy for both exact accuracy rate (66.3% v. 49.0%; p = 0.2864) and functional accuracy rate (83.8% v. 82.2%; p = 0.9148) than state tools, while privately funded tools demonstrated higher accuracy for both exact accuracy rates (66.8% v. 48.9%; p = 0.2008) and functional accuracy rates (83.8% v. 82.2%; p = 0.9148), but none of these differences were statistically significant. ConclusionsThis study found that SUD treatment search tools commonly list inaccurate information, underscoring the need for systematic improvements in data management and validation practices.

The Use of Islamic Attribute in Food Product Marketing: Expert Views

The vast number of Muslim consumers in Malaysia, which accounts for 63.5% of the total Malaysian population, offers a vast market segment to food producers—the food producers employ various marketing strategies to compete in the market. Among the strategies employed is the use of Islamic attributes in marketing activities. Food product marketers used Islamic attributes such as Muslim names, pictures, emblems, Quranic verses, and Hadith in their marketing campaigns. The free use of the Islamic attributes has drawn attention to knowing the Islamic perspective of the use. Through in-depth interviews, this research paper aimed to reveal the views of 7 Shariah experts in a rich and contextualized account of using Islamic attributes in food product marketing. The experts were selected through purposive and snowball sampling methods. Thematic analysis was used to analyze the data, which resulted in the emergence of six themes in this study. These themes include (1) protection of Islamic honor and dignity, (2) prohibition of using Islam for personal gain, (3) prohibition of unjust practices, (4) prohibition of superstitious or syirik acts, (5) promotion of good business practices, and (6) promotion of a healthy diet, revealing the Islamic perspective on its use. This study contributes knowledge to Islamic marketing and the rulings on using Islamic attributes according to the Shariah principle. This research sheds light on the ethical implications of using Islamic attributes in scholarly and marketing domains. Further, this research explores the triangulation method of data analysis and validity via Shariah and socio-research.

Palembang songket fabric motif image detection with data augmentation based on ResNet using dropout

A good way to spread knowledge about Palembang songket woven cloth patterns is to use information technology, especially artificial intelligence technology. This study's main goal is to develop a ResNet model with dropout regularization methods and find out how dropout regularization affects the ResNet model for detecting Palembang songket fabric motif with more data. Data was collected in places like tujuh saudara songket, Zainal songket, songket PaSH, AMS songket, and batik, Ernawati songket, Nabilah collections, Ilham songket, and Marissa songket. We used eight class of data for this research. A dataset of 7,680 data for training, 960 data for validation, and 960 data for testing is a dataset that has been prepared to be implemented in experiments. In the final results, the experimental results for DResNet demonstrated that accuracy at the training stage was 92.16%, accuracy at the validation stage was 78.60%, and accuracy at the submission stage was 80.3%. The experimental results also show that dropouts are able to increase the accuracy of the ResNet model by adding +1.10% accuracy in the training process, adding +1.80% accuracy in the validation process, and adding +0.40% accuracy in the testing process.