Tool For Evaluation Research Articles

Tracheal Tumours

Malignant primary tracheal tumours are rare. The most common histological subtypes are squamous cell carcinoma and adenoid cystic carcinoma. These two entities have different prognoses and growth patterns. Tracheobronchoscopy and thoracic sectional imaging are standard diagnostic tools for tumour staging and local evaluation. Complete surgical resection of the affected tracheal segment is the treatment of choice for limited disease without distant metastases. Incomplete gross tumour resection with additional irradiation is an acceptable therapeutic option for adenoid cystic carcinoma. Interventional endoscopy with tumour debulking or tracheal stenting and/or definitive mediastinal radiotherapy are treatment alternatives in either a locally advanced or palliative setting.

In Silico Prediction of Chemical Acute Dermal Toxicity Using Explainable Machine Learning Methods.

The research on acute dermal toxicity has consistently been a crucial component in assessing the potential risks of human exposure to active ingredients in pesticides and related plant protection products. However, it is difficult to directly identify the acute dermal toxicity of potential compounds through animal experiments alone. In our study, we separately integrated 1735 experimental data based on rabbits and 1679 experimental data based on rats to construct acute dermal toxicity prediction models using machine learning and deep learning algorithms. The best models for the two animal species achieved AUC values of 78.0 and 82.0%, respectively, on 10-fold cross-validation. Additionally, we employed SARpy to extract structural alerts, and in conjunction with Shapley additive explanation and attentive FP heatmap, we identified important features and structural fragments associated with acute dermal toxicity. This approach offers valuable insights for the detection of positive compounds. Moreover, a standalone software tool was developed to make acute dermal toxicity prediction easier. In summary, our research would provide an effective tool for acute dermal toxicity evaluation of pesticides, cosmetics, and drug safety assessment.

Effectiveness of eHealth Interventions Promoting Physical Activity in Children and Adolescents: Systematic Review and Meta-Analysis.

eHealth interventions have been postulated as a feasible, acceptable, and possibly effective tool to promote physical activity (PA) among children and adolescents; however, a comprehensive quantitative analysis of the effects of eHealth interventions promoting PA is lacking. This study aims to conduct a systematic review and meta-analysis on experimental studies reporting the effects of eHealth interventions aimed at promoting PA on PA parameters and sedentary behavior parameters in children and adolescents. The CENTRAL, MEDLINE, Embase, and Web of Science databases were searched from inception to February 2022 for randomized controlled trials that analyzed the effects of eHealth interventions aimed at promoting PA on PA and sedentary parameters in children and adolescents. The Hartung-Knapp-Sidik-Jonkman random effects method was used to determine the mean differences (MDs) with their respective 95% CIs. The risk of bias was assessed using the Risk of Bias 2 (RoB2; Cochrane) tool and its extension for cluster randomized controlled trials. The certainty of evidence was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) tool. A total of 20 trials reporting the effects of different eHealth interventions aimed at promoting PA were included. Results for each parameter were as follows: counts per minute (MD -16.11 counts, 95% CI -122.76 to 90.53; k=3; n=402; I2=69%; favoring control), steps per day (MD 593.46 steps, 95% CI -2102.27 to 3289.19; k=2; n=152; I2=0%; favoring intervention [FI]), moderate to vigorous PA (MD -1.99 min/d, 95% CI -8.95 to 4.96; k=14; n=2336; I2=86%; favoring control), light PA (MD 3.28 min/d, 95% CI -15.48 to 22.04; k=5; n=355; I2=67%; FI), screen time (MD -31.48 min/d, 95% CI -68.62 to 5.65; k=5; n=904; I2=0%; FI), and sedentary time (MD -33.12 min/d, 95% CI -57.27 to -8.97; k=8; n=819; I2=75%; FI). Our results should be interpreted cautiously because of important limitations such as the scarcity of evidence, overall risk of bias, and low to very low certainty of evidence. We did not find conclusive evidence regarding the impact of PA-targeted eHealth interventions on PA parameters, but the very low certainty of evidence suggests that eHealth interventions may reduce sedentary time in children and adolescents. Our results may have important scientific implications as they highlight that the rapid development of eHealth interventions to promote PA lacks robust supporting evidence. PROSPERO CRD42020211020; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=211020.

Month of Birth and Cognitive Effort: A Laboratory Study of the Relative Age Effect among Fifth Graders

Abstract All around the world, school-entry cohorts are organized on an annual calendar so that the age of students in the same cohort differs by up to one year. It is a well-established finding that this age gap entails a consequential (dis)advantage for academic performance referred to as the relative age effect (RAE). This study contributes to a recent strand of research that has turned to investigate the RAE on non-academic outcomes such as personality traits. An experimental setup is used to estimate the causal effect of monthly age on cognitive effort in a sample of 798 fifth-grade students enrolled in the Spanish educational system, characterized by strict enrolment rules. Participants performed three different real-effort tasks under three different incentive conditions: no rewards; material rewards; and material and status rewards. We observe that older students outwork their youngest peers by two-fifths of a standard deviation, but only when material rewards for performance are in place. Despite the previously reported higher taste for competition among the older students within a school-entry cohort, we do not find that the RAE on cognitive effort increases after inducing competition for peer recognition. Finally, the study also provides suggestive evidence of a larger RAE among boys and students from lower social strata. Implications for sociological research on educational inequality are discussed. To conclude, we outline policy recommendations such as implementing evaluation tools that nudge teachers toward being mindful of relative age differences.

Comparison of the performance of the AUB-HAS2 Cardiovascular Risk Index in emergency vs elective surgeries

BackgroundThe AUB-HAS2 Cardiovascular Risk Index is a newly derived tool for preoperative cardiovascular evaluation. It is based on six data elements: history of heart disease, symptoms of angina or dyspnea, age ≥ 75 years, hemoglobin < 12 g/dl, vascular surgery, and emergency surgery. This study compares the performance of this new index among emergency and elective surgeries. Methods and resultsThe study population consisted of 1,167,414 non-cardiac surgeries registered in the American College of Surgeons National Surgical Quality Improvement Program database (153,715 were emergency and 1,013,699 were elective). Each patient was given an AUB-HAS2 score of 0, 1, 2, 3, or >3 depending on the number of data elements s/he has. The outcome measure (death, myocardial infarction, or stroke at 30 days after surgery) was higher in emergency than elective surgeries (7.0 % vs 1.4 %, p < 0.0001). The AUB-HAS2 index was able to stratify risk in both types of surgeries with a gradual increase in risk as the score increased (p < 0.0001). The discriminatory power of the AUB-HAS2 index, measured by the area under the receiver operator characteristic curves, was good and similar in the two types of surgeries (0.804 for emergency vs 0.791 for elective surgeries). ConclusionThe AUB-HAS2 index is a versatile tool that can effectively and equally stratify risk in both emergency and elective surgeries with a good discriminatory power.

Chemometric Analysis Combined with GC × GC-FID and ESI HR-MS to Evaluate Ultralow-Sulfur Diesel Stability.

Diesel has been the most employed fuel in highway and nonhighway transportation systems. Many studies over the past years have attempted to classify diesel as a stable or unstable composition since this fuel can still degrade during storage or thermal oxidative processes. Products generated because of such degradation are the reason for the formation of soluble gums and insoluble organic particulates, which in turn cause a negative influence on engine performance. This work reports a detailed composition of nonpolar and polar compounds in many ultralow-sulfur diesel (ULSD) samples by comprehensive two-dimensional gas chromatography with a flame ionization detector (GC × GC-FID) and electrospray ionization high-resolution mass spectrometry (ESI HR-MS). In addition, chemometric approaches were applied for ULSD storage stability investigation. GC × GC-FID experiments achieved the nonpolar chemical characterization for the ULSD samples, including all main hydrocarbon classes: paraffins, mono- and dinaphthenics and olefins, and aromatics. The GC × GC-FID data combined with principal component analysis (PCA) described that the separation of the samples' concerning storage stability was mainly due to the contents of mono- and diaromatic compounds in the unstable ULSD samples. Moreover, PCA was also applied to the ESI (±) data set, and the results highlight the presence of compounds belonging to O class (natural antioxidants), which decrease the rate of oxygen consumption in the fuel, characterizing it as stable composition. The basic nitrogen compounds are mostly present in the stable ULSD samples indicating that they did not affect the stability of the fuel. On the other hand, the HC classes presented pronounced abundance among unstable ULSD samples suggesting that the fuel degradation may go through the oxidation of hydrocarbons and the formation of Ox compounds as byproducts. Furthermore, MS/MS experiments point to the formation of CcHhNnOo-like precursor species, which can react with each other and lead to the formation of gums and insoluble sediments in the fuel. In summary, the results express the potential of using the GC × GC-FID and ESI (±) Orbitrap MS techniques as valuable tools for diesel stability evaluations.

A probabilistic model based on the peak-over-threshold approach for risk assessment of airport controllers' performance

Airport tower control plays an instrumental role in ensuring airport safety. However, obtaining objective, quantitative safety evaluations is challenging due to the unavailability of pertinent human operation data. This study introduces a probabilistic model that combines aircraft dynamics and the peak-over-threshold (POT) approach to assess the safety performance of airport controllers. We applied the POT approach to model reaction times extracted from a radiotelephony dataset via a voice event detection algorithm. The model couples the risks of tower control and aircraft operation to analyze the influence of human factors. Using data from radiotelephony communications and the Base of Aircraft Data (BADA) database, we compared risk levels across scenarios. Our findings revealed heightened airport control risks under low demand (0.374) compared to typical conditions (0.197). Furthermore, the risks associated with coupling under low demand exceeded those under typical demand, with the final approach stage presenting the highest risk (4.929×10−7). Our model underscores the significance of human factors and the implications of mental disconnects between pilots and controllers for safety risks. Collectively, these consistent findings affirm the reliability of our probabilistic model as an evaluative tool for evaluating the safety performance of airport tower controllers. The results also illuminate the path toward quantitative real-time safety evaluations for airport controllers within the industry. We recommend that airport regulators focus on the performance of airport controllers, particularly during the final approach stage.

Prediction of suicidal ideation among preadolescent children with machine learning models: A longitudinal study

BackgroundMachine learning (ML) has been widely used to predict suicidal ideation (SI) in adolescents and adults. Nevertheless, studies of accurate and efficient models of SI prediction with preadolescent children are still needed because SI is surprisingly prevalent during the transition into adolescence. This study aimed to explore the potential of ML models to predict SI among preadolescent children. MethodsA total of 4691 Chinese children (54.89 % boys, Mage = 10.92 at baseline) and their parents completed relevant measures at baseline and the children provided 6-month follow-up data for SI. The current study compared four ML models: Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), to predict SI and to identify variables with predictive value based on the best-performing model among Chinese preadolescent children. ResultsThe RF model achieved the highest discriminant performance with an AUC of 0.92, accuracy of 0.93 (balanced accuracy = 0.88). The factors of internalizing problems, externalizing problems, neuroticism, childhood maltreatment, and subjective well-being in school demonstrated the highest values in predicting SI. ConclusionThe findings of this study suggested that ML models based on the observation and assessment of children's general characteristics and experiences in everyday life can serve as convenient screening and evaluation tools for suicide risk assessment among Chinese preadolescent children. The findings also provide insights for early intervention.

PERCEPTIONS OF HIGHER EDUCATION PROFESSIONALS ON THE UTILITY OF THE ACTIVITIES, PROGRAMS, OR POLICIES TOOL TO PROMOTE SELF-DETERMINATION FOR COLLEGE STUDENTS WITH DISABILITIES

Self-determination has been associated with academic success for college students with and without disabilities. The APP Tool was designed to allow higher education professionals to examine which campus Activities, Programs, or Policies (APPs) promote, and which might hinder, the development of student self-determination. This study used the qualitative basic interpretive approach (Merriam &amp; Tisdell, 2016) to analyze data from semi-structured interviews of three focus groups of higher education professionals (practitioners) that were conducted to ascertain their impressions of the utility of the tool. Use of the APP Tool led practitioners to reflect on what self-determination included and what campus efforts were currently fostering these skills. Implications of the APP Tool included use as (1) a progress monitoring tool for student self-determination skills and (2) an evaluation tool for current campus programming.

Development and Comparison of Treatment Decision Tools for Glucocorticoid-Induced Osteoporosis.

Long-term Glucocorticoid (GC) use results in compromised bone strength and fractures, and several treatment recommendations have been developed to prevent fractures, but none have been validated in a real-world setting. This study aims to create a treatment decision tool and compares this tool to the treatment suggestions from the American College of Rheumatology (ACR), International Osteoporosis Foundation and European Calcified Tissue Society (IOF-ECTS), and GC-adjusted Fracture Risk Assessment Tool (GC-FRAX), above the intervention threshold. We utilized registry data gathered at Chang Gung Memorial Hospital at Kaohsiung, Taiwan, between September 2014 and April 2021. This research is a single-center, observational, and case-controlled study. We recruited participants using prednisone for at least 2.5 mg/day or the equivalent dose for over 3 months, excluding those younger than 40, those with malignancies, or those currently undergoing anti-osteoporosis therapy. The primary endpoint was new fragility fractures within 3 years, including morphometric vertebral fractures detected at baseline and with a follow-up thoracic-lumbar spine X-ray. Participants were randomly allocated into derivation and validation sets. We developed the Steroid-Associated Fracture Evaluation (SAFE) tool in the derivation cohort by assessing the weights of exploratory variables via logistic regression. Prediction performance was compared in the validation set by the receiver operating characteristic (ROC) curve, the area under the curve (AUC), and sensitivity and specificity. A total of 424 treatment-naïve subjects were enrolled, and 83 (19.6%) experienced new fractures within 3 years. The final formula of the SAFE tool includes osteoporosis (1 point), an accumulated GC dose ≥ 750 mg within 6 months (or equivalent prednisolone of ≥4.5 mg/day for 6 months) (1 point), a BMI ≥ 23.5 (1 point), previous fractures (1 point), and elderliness of ≥70 years (2 points). In the validation set, a treatment decision based on the SAFE ≥ 2 points demonstrated an AUC of 0.65, with a sensitivity/specificity/accuracy of 75.9/54.0/58.9, with an ACR of 0.56 (100.0/11.0/31.0), IOF-ECTS 0.61 (75.9/46.0/52.7), and GC-FRAX 0.62 (82.8/42.0/51.2). Among current GIOP recommendations, the SAFE score serves as an appropriate treatment decision tool with increased accuracy and specificity.

Feasibility of High-Cellular-Resolution Full-Field, Artificial-Intelligence-Assisted, Real-Time Optical Coherence Tomography in the Evaluation of Vitiligo: A Prospective Longitudinal Follow-Up Study.

Vitiligo, a psychologically distressing pigmentary disorder characterized by white depigmented patches due to melanocyte loss, necessitates non-invasive tools for early detection and treatment response monitoring. High-cellular-resolution full-field optical coherence tomography (CRFF-OCT) is emerging in pigmentary disorder assessment, but its applicability in vitiligo repigmentation after tissue grafting remains unexplored. To investigate the feasibility of CRFF-OCT for evaluating vitiligo lesions following tissue grafting, our investigation involved ten vitiligo patients who underwent suction blister epidermal grafting and laser ablation at a tertiary center between 2021 and 2022. Over a six-month period, clinical features, dermoscopy, and photography data were recorded. Utilizing CRFF-OCT along with artificial intelligence (AI) applications, repigmentation features were captured and analyzed. The CRFF-OCT analysis revealed a distinct dark band in vitiligo lesion skin, indicating melanin loss. Grafted areas exhibited melanocytes with dendrites around the epidermal-dermal junction and hair follicles. CRFF-OCT demonstrated its efficacy in the early detection of melanocyte recovery and accurate melanin quantification. This study introduces CRFF-OCT as a real-time, non-invasive, and in vivo evaluation tool for assessing vitiligo repigmentation, offering valuable insights into pigmentary disorders and treatment responses.

Identifying and Evaluating Young Children with Developmental Central Hypotonia: An Overview of Systematic Reviews and Tools.

Children with developmental central hypotonia have reduced muscle tone secondary to non-progressive damage to the brain or brainstem. Children may have transient delays, mild or global functional impairments, and the lack of a clear understanding of this diagnosis makes evaluating appropriate interventions challenging. This overview aimed to systematically describe the best available evidence for tools to identify and evaluate children with developmental central hypotonia aged 2 months to 6 years. A systematic review of systematic reviews or syntheses was conducted with electronic searches in PubMed, Medline, CINAHL, Scopus, Cochrane Database of Systematic Reviews, Google Scholar, and PEDro and supplemented with hand-searching. Methodological quality and risk-of-bias were evaluated, and included reviews and tools were compared and contrasted. Three systematic reviews, an evidence-based clinical assessment algorithm, three measurement protocols, and two additional measurement tools were identified. For children aged 2 months to 2 years, the Hammersmith Infant Neurological Examination has the strongest measurement properties and contains a subset of items that may be useful for quantifying the severity of hypotonia. For children aged 2-6 years, a clinical algorithm and individual tools provide guidance. Further research is required to develop and validate all evaluative tools for children with developmental central hypotonia.

Evaluation of kinetic constants uncertainty for the metrologically sound comparison of photodegradation efficiency using TiO2 catalysts

The comparison of the efficiency of a pair of catalysts for the photodegradation of a compound in the same irradiation conditions is affected by many independent and correlated random and systematic effects. These comparisons become objective and indisputable if the efficiency difference is estimated with uncertainty, producing an interval encompassing the “true” difference value with a known probability. This work presents a tool for the detailed evaluation of the uncertainty of estimated kinetic constant values and differences by the Monte Carlo Method simulation of all relevant uncertainty components. First-order kinetics were quantified from regression of the concentrations of studied compounds over various degradation times. The efficiency of methylene blue (MB) and sulfamethazine (SMZ) photodegradation using various solid-state catalysts was quantified and compared. Synthesised TiO2 nanoparticles are more efficient than the purchased TiO2 for MB degradation. For SMZ degradation, ruthenium-doped titanate nanowires and ruthenium-doped titanate nanotubes are equally efficient and more efficient than pristine titanate nanowires. The efficiency equivalence can be challenged by quantifying larger and/or less uncertain SMZ concentrations. First-order kinetics was tested by taking the simulated confidence limits of the intercept of the linear regression used to describe this kinetic order. Kinetic constants with asymmetric distribution and a relative expanded uncertainty for a 95 % confidence level between 3.6% and 17% were quantified. Three successively more uncertain procedures for preparing calibrators for the quantification of MB by UV/Vis spectroscopy were tested, concluding that since the same calibration curve is used to quantify all MB solutions, kinetic quantification is not proportionally or significantly affected by this uncertainty component.

Enhancing Airway Assessment with a Secure Hybrid Network-Blockchain System for CT &amp; CBCT Image Evaluation

Our investigation explored the intricacies of airway evaluation through Cone-Beam Computed Tomography (CBCT) and Computed Tomography (CT) images. By employing innovative data augmentation strategies, we expanded our dataset significantly, enabling a more comprehensive analysis of airway characteristics. The utility of these techniques was evident in their ability to yield a diverse array of synthetic images, each representing different airway scenarios with high fidelity. A notable outcome of our study was the effective categorization of the initial image as "Class II" under the Mallampati Classification system. The augmented images further enhanced our understanding by exhibiting a spectrum of airway parameters. Moreover, our approach included training a Recurrent Neural Network (RNN) model on a dataset of CT images. This model, fortified with pseudo-labels created via K-means clustering, showcased its proficiency by accurately predicting airway assessment categories in various test scenarios. These results underscore the model's potential as a tool for swift and precise airway evaluation in clinical settings, marking a significant advancement in medical imaging technologies.

Metabolomics integration with chemometrics for the quality assessment: A case study with commercially important Himalayan medicinal plant Aconitum heterophyllum Wall.

Herein, targeted and non-targeted metabolomics was coupled with chemometrics tools for easy and reliable quality assessment of commercially important Himalayan medicinal plant Aconitum heterophyllum Wall. having annual demand of 100–200 metric tons in India. Seventeen root (tubers) (sixteen from different geographical locations and one marketed sample) of this plant were evaluated for the quality assessment using ultra-high performance liquid chromatography. Targeted metabolomics involves quantification of specialized metabolites of A. heterophyllum, revealing phytochemical variation among different locations. The root sample from Chitkul (Himachal Pradesh) was found to have a high content of major metabolites atisinium and aconitic acid. Non-targeted metabolomics includes chemical profiling followed by MZmine-based data processing and chemometrics tools for the quality evaluation. Chemometrics tools such as principal component analysis (PCA), orthogonal partial least square-discriminant analysis (OPLS-DA), and variable importance plot (VIP) serve as excellent tools facilitating quality evaluation revealing samples with chemical similarity, compounds responsible for phytochemical similarity/dissimilarity and identifying samples with high content of desired compounds. Fingerprint chromatograms authenticate all samples to be of A. heterophyllum indicating hetisine and atisinium to be the major constituents however, similarity/dissimilarity was observed in term of presence/absence or high/low mass intensity. Hence developed method can be easily implemented to authenticate plant material and quality assessment with respect to desired marker compounds. Further, chemical profiling suggests the presence of high-intensity unidentified compounds which underscores the potential for further research to uncover unique and previously unknown metabolites in the plant.

The role of social media literacy in infodemic management: a systematic review.

The term infodemic refers to the proliferation of both accurate and inaccurate information that creates a challenge in identifying trustworthy and credible sources. Among the strategies employed to mitigate the impact of the infodemic, social media literacy has emerged as a significant and effective approach. This systematic review examines the role of social media literacy in the management of the infodemic. Six databases, including SID, Magiran, Scopus, PubMed, Google Scholar and Web of Science were systematically searched using relevant keywords. We included the relevant publications between 2012 and 2023 in our analysis. To ensure a qualitative assessment of the studies, we used the STROBE and AMSTAR checklists as evaluation tools. The Preferred Reporting Items for Systematic Reviews (PRISMA) guideline was used for the design of this review study. Finally, we organized the studies into groups based on similarities and retrieved and analyzed evidence pertaining to the challenges and opportunities identified. Eleven papers were included in this study after reviewing the retrieved studies. Five of them examined the effect of social media literacy and health literacy on acceptance of health behaviors. Four studies investigated the role of media literacy in managing misinformation and fake news related to health. Two studies focused on infodemic management and promoting citizen engagement during health crises. Results showed that health-related infodemics are derived from the users' lack of media knowledge, distrust of government service systems, local influencers and peers, rapid circulation of information through mass media messages, weakness of solutions proposed by health care providers, failure to pay attention to the needs of the audience, vertical management, and inconsistency of published messages. The findings of this study highlight the importance of increasing social media literacy among the general public as a recognized strategy for managing the infodemic. Consequently, it is recommended that relevant organizations and institutions, such as the Ministry of Health, develop targeted training programs to effectively address this need.

Validating aircraft noise models: Aviation environmental design tool at Heathrow

Aircraft noise models are fundamental tools for noise abatement, control, enforcement, evaluation, and policy-making. Validation of aircraft noise models is necessary to ensure their reliability and credibility, particularly given their significant impact on society, the economy, and public health. However, validating such models is often a complex undertaking, and an acceptable validation methodology still needs to be developed. In this study, the Federal Aviation Administration's (FAA) Aviation Environmental Design Tool (AEDT) aircraft noise model is validated by correlating the calculated and measured noise levels for a specific aircraft flying in a particular flight path at Heathrow Airport. The validation results suggest that the AEDT noise model estimates the actual noise level quite accurately for landings, with a variation less than 2 dB(A), but might be inaccurate for takeoffs for certain aircraft types, with variations reaching 10 dB(A), resulting in a considerable difference between the measured and calculated noise levels.

A breakthrough in circular economy: Using a closed-loop framework to assess the circularity of supply chains

The concept of the circular economy (CE) proposes eco-friendly principles for dealing with circularity problems in production systems and supply chains (SCs). To achieve circularity goals in SCs, CE principles assist decision-makers with reusing, remanufacturing, and recycling initiatives in the production process. However, previous meso- and macro-level circularity investigations reveal shortcomings in practical closed-loop solutions. This study aims to develop a closed-loop framework for assessing the circularity of sustainable SCs using network data envelopment analysis (NDEA). The closed-loop framework is created utilizing real-life indices of sustainable SCs, such as "recyclable undesirable outputs". To make the appraisal more realistic and enhance the validity and reliability of the results, the circularity results of SCs are assessed under data uncertainty. This allows SCs to be ranked based on their real circularity level, identifying sustainable and unsustainable SCs. The proposed framework offers decision-makers a practical evaluation tool to identify the highly circular SCs and establish circularity benchmarks for inefficient SCs. The proposed approach is scalable and applicable to real-world circularity evaluations in multistage SCs and production systems. The study concludes with a two-stage case study to show the practicality of the new framework.

TATHBIQU I’DADI BUNUDI AL-IKHTIBARI LI MAHARAH AL-ISTIMA’ WA AL-KALAM

Preparing test items is an important step in developing an educational curriculum. However, teachers usually do not take it seriously regarding the implementation of the preparation of these items, so that compiling assessment tools in evaluation has not carried out the steps for developing the tools as they should, namely improving the items. Therefore, the preparation and development of instruments is an important thing that influences the decision making of learning assessment results. The purpose of writing this article is to describe steps and application of the preparation of items to measure good listening and speaking skills. This research uses a qualitative approach with the type of library research. As for data collection techniques based on reading results as well as books, journals and documents related to the theme of this article. The items designed for class XII Madrasah Aliyah with the topic "Sport". The results are the stages of preparing test followed the steps: 1) determining the purpose of assessment, 2) determining test material, 3) choosing a title, 4) determining the form of the questions and how to answer them, 5) determining the number of items, 6) making a grid test grid, 7) arrange test items. Furthermore, the listening and speaking skill test grids are arranged following the sports material in class XII. The number of items for listening skills is 5 multiple choice questions and 5 filled-in questions, while speaking skills are only 3 questions in the form of conversations, telling pictures and telling about sports.

Impact of ChatGPT on ESL students’ academic writing skills: a mixed methods intervention study

This paper presents a study on the impact of ChatGPT as a formative feedback tool on the writing skills of undergraduate ESL students. Since artificial intelligence-driven automated writing evaluation tools positively impact students’ writing, ChatGPT, a generative artificial intelligence-propelled tool, can be expected to have a more substantial positive impact. However, very little empirical evidence regarding the impact of ChatGPT on writing is available. The current mixed methods intervention study tried to address this gap. Data were collected from tertiary level ESL students through three tests and as many focus group discussions. The findings indicate a significant positive impact of ChatGPT on students' academic writing skills, and students’ perceptions of the impact were also overwhelmingly positive. The study strengthens and advances theories of feedback as a dialogic tool and ChatGPT as a reliable writing tool, and has practical implications. With proper student training, ChatGPT can be a good feedback tool in large-size writing classes. Future researchers can investigate the impact of ChatGPT on various specific genres and micro aspects of writing.