Interpretation Techniques Research Articles

Problem Faced by Indonesian Internship Students in Thai English Language Teaching Classroom Context

What are the problems faced by Indonesian internship students in Thai English Language Teaching Classrooms Based on the problem statement, the proposed objective is to find out the problem or the obstacles experienced by internship students in English language teaching classrooms while teaching in Thailand School. Considering the different cultural backgrounds, this research is expected to explore the internship problems and whether internship students experience any cultural issues. This research uses a qualitative method that explores cross-cultural communication issues in participants, according to Creswell (2018, p. 294) In most cases, qualitative research presents the data, interprets the data, validates it, and shows the potential of the research results in order to expand the steps of data analysis and the methods used. In qualitative research, the social context is taken into consideration while examining events from the perspectives of the individuals being studied. This includes research that uses interpretative naturalistic techniques (Denzin & Lincoln, 2005 Kihlgren, 2016). The findings of the interviews from the three domains of problems faced by the internship students during the teaching internship program are based on the findings of the interview results that have been conducted by the researcher. 1. Teaching Learning Aspects as the Problem in ELT Classroom, 2. Personal Aspects as the Problem in ELT Classroom, 3. Communication Aspects as the Problem in ELT Classroom. Based on the findings that the researcher has found, the difficulties experienced by internship students during the internship program are quite varied, so it is in accordance with the problems found by the researcher that the main problems experienced by internship students in the internship program come from the cross-cultural problems that occur in English language teaching classrooms.

Linguistic-discursive peculiarities of pronominal usage in political discourse (based on the material of the political talk show "60 Minutes")

This paper considers the specific features of the functioning of pronouns in political discourse. We analyse their role in the speech of the presenters and guests of the "60 Minutes" talk show. It belongs to the television discussion genre whose function is to form a collective political world picture by projecting and broadcasting certain images and meanings. The relevance of the research is due to the need not only for studying communication strategies and tactics in political actors’ speech, but also for analysing the impact of linguistic means on public opinion formation and transformation. Considering pronouns in the functional aspect enables us to identify the characteristic features that make pronouns one of the most effective means of speech influence (persuasion). Possessing manipulative potential, words of this morphological class can participate in the implementation of numerous communicative strategies and tactics, including the upward and downward strategies, the tactics of self-presentation, cooperation, impersonal accusation, discrediting, and the "us–them" opposition. The research uses discourse analysis, interpretation and contextual analysis techniques, and the communicative-pragmatic approach. As the obtained data show, pronouns can be employed to impose certain beliefs and opinions, to enhance the sense of unity and solidarity between the speaker and the audience, to shift the emphasis in the event under discussion. Moreover, they also prove to be a significant characteristic of politicians, reflecting their worldview.

Assessment of land cover degradation due to mining activities using remote sensing and digital photogrammetry

Appropriate environment management requires an understanding of how mining activity alters environmental characteristics and how those changes affect an area. Therefore, to reduce the adverse effects of mining activity on the land, it becomes crucial to have relevant information about responses to environmental degradation. This study aims to assess the impact of semi-mechanised and artisanal mining activities on the land cover using remote sensing data and photogrammetric analysis, in the Mbale locality, Northern Cameroon. For this purpose, the maximum likelihood classification algorithm of the supervised classification method combined with field surveys was used to map environmental changes, based on Sentinel-2 images of 2019, 2021, and 2023. Normalized Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), Brithness index (BI), and Soil crust index (SCI), were calculated to assess changes in vegetation, bare soil, water body, and exploited area. The orthophoto obtained from photogrammetric processing was performed to outline river network change through visual interpretation techniques and to calculate the volume of pits created by mining. The result of classified images indicated that vegetation cover decreased by 11.74% over the studied years. However, bare soil and exploited areas increased by 9.2% and 5.4% respectively. The calculated spectral indices show that between 2019 and 2023 the locality of Mbale considerably lost its vegetation cover, in favor of bare soil. The color of the soil and the granulometric size of the topsoil have also changed. The photogrammetry analysis highlighted the deviation of the main river and estimated the volume of pits created by mining activity to 22188.7 m3. The mining activities caused a loss of the vegetation cover, generated big pits, and multiple deviations of the Lom River from its natural course, which have a substantial negative influence on the ecosystem. Such data can be used for long-term environmental management, reclamation and rehabilitation monitoring, and mining area restoration.

BEDAWANG NALA: ARTISTIC INSPIRATION FOR A FICTION FILM TO STRENGTHEN ENVIRONMENTAL EDUCATION AWARENESS

This research explores the philosophical and symbolic value of Bedawang Nala in Balinese architectural art and the development of its narrative in fiction films based on Hindu mythology. Bedawang Nala, depicted as a giant fire-haired turtle with dragons Anantabhoga and Basuki, reflects cosmic balance and natural harmony in Tri Hita Karana. This qualitative research uses an interdisciplinary perspective involving the views of cultural studies and the creativity of film art creation. Data was obtained through in-depth interviews to explore the meaning and connection of the Bedawang Nala concept with environmental themes, audiovisual observations of traditional Balinese architecture, and literature studies on Bedawang Nala and ecological issues. Data were analyzed using interview transcript interpretation techniques to identify main themes related to the philosophical and symbolic values of Bedawang Nala, audiovisual analysis, and literature synthesis that connects findings from interviews and observations with current discussions about ecological education. The findings show that the Bedawang Nala mythology is not just an aesthetic ornament but has an educational value relevant to contemporary environmental issues. The development of this mythology-based film narrative can be an effective academic and cultural reflection tool, strengthening ecological awareness in society. This research suggests making and evaluating films based on Bedawang Nala and exploring other local mythologies to increase the educational impact and environmental awareness through film media.

Icehouse mixed carbonate and siliciclastic sequence evolution based on 3D seismic analysis: Insights from the Eastern Shelf of the Permian Basin, Texas

Reciprocal model was introduced for mixed carbonate and siliciclastic sequences within the Virgilian (Upper Pennsylvanian – Gzhelian) and Wolfcampian (Lower Cisuralian) on the Eastern Shelf of the Permian Basin in Texas. This model emphasizes carbonate dominance during transgressive and highstand on the slope, and siliciclastic dominance in the basin during lowstand. Contradictory observations from other mixed basins, such as compositionally mixed lithology in slope cores, accumulation of siliciclastics behind carbonate highs on the outer shelf, off-shelf transport of siliciclastics during highstands, and carbonate deposition on the slope during falling stage, require a re-evaluation of the reciprocal model. This research examines mixed sequences in the Cisco Group and maps lithology distribution by integrating geological and geophysical data, using advanced seismic interpretation techniques, wireline-log crossplot analyses, model-based post-stack inversion, probabilistic neural networks, and supervised Bayesian classification. Observations from the investigation are presented at two scales. In ∼400kyr sequences, relative sea level fall starts with carbonate deposition on the slope while siliciclastics persist near the inner shelf. As the falling stage progresses, siliciclastics reach the shelf edge, forming offlapping fans at the toe of the slope, likely truncating part of the siliciclastic topsets. Subsequently, mixed facies were deposited during the early stages of rising relative sea level, followed by carbonate deposition on the slope and shelf throughout transgression. The mixed facies found inland of transgressive carbonates indicate that prodelta siliciclastics mixed with carbonate mud. Occasionally, transgressive carbonates create shelf-edge build ups, hindering the transport of highstand siliciclastics to the slope. In the composite sequence spanning ∼1200kyr, siliciclastic input decreases significantly with the onset of sea-level fall. The fall truncates topsets with limited siliciclastics, preserving carbonate and mixed facies-dominated foresets with offlapping fans at the toe of the slope. This study advances our understanding of mixed sequences, challenges existing models and lays the groundwork for future investigations in similar geological settings.

Evaluation of Settlement Distribution on Detailed Spatial Plan in Sewon District, Bantul Regency, Special Region of Yogyakarta 2018 – 2038

Settlement as a place to live is one of the main needs that need to be met for the survival of human life. The need for settlement will increase along with the increase in population. The development of settlements in Sewon District. Bantul, Indonesia which is very dynamic requires monitoring to ensure that the distribution of settlements is in accordance with the spatial pattern plan that has been determined by the local government. Imagery as a remote sensing product which is then processed with the help of GIS [HV1] can be used in monitoring the distribution of settlements because it can provide more detailed information regarding land use, including settlements. This research aims to evaluate the distribution of settlements against the detailed spatial plan (RDTR) for the Sewon Urban Area (BWP) of Bantul Regency in 2018-2038. This study uses visual interpretation techniques of SPOT-7 PMS imagery in mapping existing settlement land, field surveys, and GIS processing. The research results show that mapping settlement land using imagery produces an overall accuracy of 95.20%. The settlement evaluation shows that there is a suitability of settlement land with the detailed spatial plan reaching 579.88 hectares or 87.26%, while the settlement land that is not in accordance with the detailed spatial plan is 9.62 hectares or 1.45%, and the settlement land that is temporarily not in accordance with the detailed spatial plan is 75.05 hectares or 11.29% of the total settlement area in Sewon District. Local governments must pay more attention to existing settlements with regular monitoring so that the existing settlements that are not in accordance with the detailed spatial plan will not expand.

Machine Learning‐Guided Design and Synthesis of Eco‐Friendly Poly(ethylene oxide) Membranes for High‐Efficacy CO2/N2 Separation

AbstractMachine learning (ML)‐guided polymer design and synthesis will enable the next‐generation membrane material discovery for CO2 capture. Herein, ML is leveraged to establish a structure‐performance relationship for the eco‐friendly poly(ethylene oxide) (PEO) membrane and guide its design for high‐efficacy CO2/N2 separation. Through a rational fragment representation method and knowledge sharing across membranes fabricated by different methods, the precise prediction of CO2/N2 separation performance for PEO membranes with high Pearson correlation coefficients (0.973 for permeability and 0.875 for selectivity) despite data scarcity is demonstrated. Expertise knowledge and external monomer databases are then utilized in a human‐in‐the‐loop workflow to effectively explore high‐performance PEO membranes in the design space. Several discovered thermally crosslinked PEO membranes achieve CO2/N2 separation performances close to the 2019 Robeson upper bound, which are promising for practical large‐scale carbon capture applications. Model interpretation techniques are employed to provide data‐driven insights into the design of PEO membranes for high‐efficacy CO2/N2 separation. Further life cycle assessment results reveal the outstanding advantage of discovered PEO membranes in terms of environmental friendliness. The work highlights the enormous potential of ML in expediting the discovery of high‐performance carbon capture membrane materials.

The linear canonical W-transform

The time-frequency spectrum of seismic data is commonly used as an attribute in the analysis and interpretation of nonstationary seismic signals. To improve the capability of the W-transform to analyze nonstationary data, we introduce the linear canonical W-transform (LCWT) by implementing an affine matrix along with scaling parameters to generate high resolution and energy-concentrated time-frequency spectra. More features of nonstationary signals can be explored in the time-linear canonical frequency domain by transforming the time-frequency plane, which makes the algorithm more applicable to seismic signals. For the completeness of the LCWT, we also develop the inverse LCWT to extend its applications. Examples of synthetic and field seismic data show that a highly resolved and energy-concentrated time-frequency spectrum can be estimated with the LCWT with negligible additional computational burden. In addition, our algorithm is promising for several seismic data processing and interpretation techniques, such as reservoir characterization and thin layer identification.

Relasi Penafsiran Amin Al-Khuli tentang Puasa dalam Al-Quran dengan Kondisi Sosiologi dan Psikologi

The literary exegesis of the Qur'an developed by Amin Al-Khuli offers a new approach to interpreting the sacred text as a response to critiques of classical exegesis and modern cultural and intellectual developments. This study aims to explore the evolution of Qur'anic interpretation in the contemporary era, with a focus on Amin Al-Khuli’s literary exegesis of the verses on fasting and its relevance to the sociological and psychological conditions of Muslims who fast. The research uses a qualitative approach with library research methods, where data is gathered through literature review and analyzed using techniques of inventory, classification, and interpretation. The findings reveal that Al-Khuli’s literary exegesis provides a deeper and more contextual understanding of the fasting verses, linking the aspects of language and Qur'anic meaning with contemporary social and psychological conditions. The conclusion of this research is that the literary exegesis approach not only enriches the understanding of the text but also offers practical solutions to the challenges faced by Muslims in fasting practices. Recommendations for future research include conducting field studies to collect more empirical data and exploring the integration of literary exegesis with other interpretive approaches for broader application in Muslims' daily lives.

L’utilité du facteur d’épissage SRSF1 dans le grading des tumeurs gliales: Une méta-analyse

The grading of glial tumors is based on morphological and sometimes on molecular features. Many markers have been assessed in order to grade the glial tumours without a real consensus. Some authors reported that SRSF1, a spiling factor, presents an expression correlated to the tumours grades. In this study, we aimed to assess the utility of the SRSF1 into the grading of gliomas based on its immunohistochemical expression. The authors conducted a meta-analysis under the PRISMA guidelines during a 10-year-period (2013-2023). The Meta-Disc software 5.4 (free version) was used. Q test and I2 statistics were carried out to explore the heterogeneity among studies. Meta-regression was performed in case of significant heterogeneity. Publication bias was assessed using the funnel plot test and the Egger's test (free version JASP). According to the inclusion criteria, 4 studies from 193 articles were included. The pooled SEN, SPE and DOR accounted respectively for 0.592, 0.565 and 1.852. The AUC was estimated to 0.558 suggesting a bad diagnostic accuracy. The heterogeneity in the pooled SEN and SPE was statistically significant. The meta-regression analysis focusing on the technique used, the clones, the dilution, the interpretation technique revealed no covariate factors (P>0.05). Even if this meta-analysis highlighted the absence of a real diagnostic utility of the SRSF1 in grading the glial tumours, the heterogeneity revealed reinforces the need for more prospective studies performed according to the quality assessment criteria.

Sexual Life Experiences of Infertile Individuals: A Meta-Synthesis Study

This study aims to systematically interpret and synthesize the evidence obtained from qualitative research conducted on the sexual experiences of infertile individuals. This study is a meta-synthesis study. A thorough literature review was conducted between February and March 2024 across five electronic databases: PubMed, Web of Science, CINAHL, EBSCO, and Science Direct Embase. This article includes 12 studies published in English since 1990 that have examined the sexual experiences of individuals who have been diagnosed with infertility. This research adhered to the Enhancing Transparency in Reporting the Synthesis of Qualitative Research (ENTREQ) guidelines. The Critical Appraisal Skills Programme tool for qualitative research was used to evaluate the quality of all included articles. To synthesize the data, deductive and interpretation techniques according to Sandelowski and Barroso’s approach have been used. The sexual experiences of individuals who have been diagnosed with infertility were summarized under four main themes: “Reconceptualising Sexuality,” “Change,” “Coping with Sexual Problems” and “Unmet Need for Information on Sexuality.” It has been revealed that individuals diagnosed with infertility need more information and support about sexual health. Health professionals might help these individuals cope with sexual health problems by providing counseling services.

Forward Simulation and Complex Signal Analysis of Concrete Crack Depth Detection Using Tracer Electromagnetic Method

Cracks are the most typical faults of concrete structures, and their extension can lead to structural fracture. However, when cracks develop inside a structure, the most important depth information is invisible and difficult to measure. The tracer electromagnetic method is an effective technique for detecting the depth of concrete cracks, but since concrete is a multiphase stochastic composite material, its complex internal structure often interferes with the radar detection results, making the conventional radar interpretation technique difficult. In this study, the detection results for concrete crack depth detection based on the tracer electromagnetic method were comprehensively analyzed by combining the complex signal analysis technique, using transient information such as amplitude, phase, and frequency in order to improve the precision and accuracy of radar signal interpretation. In this study, a numerical model was established to determine whether typical cracks such as vertical cracks and diagonal cracks contain indicators or not, and the ground-penetrating radar forward simulation software was used to perform forward simulation of the numerical model and analyze the forward results. The complex signal analysis technique was used to obtain the response characteristics of typical cracks when they did and did not contain the indicator, and the complex signal was finally analyzed by combining it with the actual crack depth detection data. The results show that the tracer electromagnetic method can significantly improve the crack bottom’s reflection ability for radar signals, and when the crack bottom contains an indicator, the amplitude of the reflected signal at the bottom of the crack is enhanced, the phase is reversed, and the frequency is reduced. The distribution of the crack morphology and the location of the crack bottom can be analyzed more conveniently by using the complex signal analysis technique.

Interpretation techniques to explain the output of a spatial land subsidence hazard model in an area with a diverted tributary

Due to the nature of black-box machine learning (ML) models used in the spatial modelling field of environmental and natural hazards, the interpretation of predictive model outputs is necessary. For this purpose, we applied four interpretation techniques consisting of interaction plot, permutation feature importance (PFI) measure, shapley additive explanation (SHAP) decision plot, and accumulated local effects (ALE) plot to explain and interpret the output of an ML model applied to map land subsidence (LS) in the Nazdasht plain, Hormozgan province, southern Iran. We applied a stepwise regression (SR) algorithm and five ML models (Cforest (as a conditional random forest), generalized linear model (GLM), multivariate linear regression (MLR), partial least squares (PLS) and extreme gradient boosting (XGBoost)) to select important features and to map the LS hazard, respectively. Thereafter, several interpretation techniques were used to explain the spatial ML hazard model output. Our findings revealed that a GLM model was the most accurate approach to map LS in our study area, and that 24.3% of the total study area had a very high susceptibility to the LS hazard. According to the interpretation techniques, land use, elevation, groundwater level and vegetation were the most important variables controlling the LS hazard and also the most important variables contributing to the model’s output. Overall, human activities, especially the diversion of the route of one of the main tributaries feeding the plain and the recharging of groundwater five decades ago, intensified the current LS occurrence. Therefore, management activities such as water spreading projects upstream of the plain can be useful to mitigate LS occurrence in the plain.

An interpretable machine learning method for risk stratification of patients with acute coronary syndrome

Risk stratification for major adverse cardiovascular events (MACE) within one year in patients with acute coronary syndrome (ACS) undergoing percutaneous coronary intervention (PCI) remains a challenge. Although several predictive models based on machine learning have emerged, they are difficult to understand. This study aimed to develop a machine learning prediction model that is easy to understand and trustworthy by lay people to assess the risk of MACE in ACS patients undergoing PCI within one year of the procedure. This retrospective cohort study used medical data from 1105 patients to construct a machine-learning model. To ensure thoroughness and multidimensionality of model parsing, Shapley Additive explanations (SHAP) analysis and Local interpretable model-agnostic explanations (LIME) interpretation techniques were used to systematically and deeply interpret the constructed models from a global to a detailed level. The study assessed 12 machine learning methods' prediction models and found that the Random Forest model was the most effective in predicting the risk of MACE in ACS patients after undergoing PCI. The model achieved an AUC value of 0.807 in the validation set, with an accuracy of 0.82, and a stable F1 score of 0.51. SHAP analysis ranked eight key feature variables, such as LVEF, in global importance. The weights of each feature range in the prediction model were revealed using LIME analysis. The machine learning prediction model we developed is capable of accurately predicting the likelihood of patients with ACS experiencing a MACE within one year of surgery.

Intelligent Crop Management Optimization using Machine Learning Algorithms: A Linear Analytical Approach

This research paper explores Utilizing machine learning techniques in practice, to enhance crop management recommendations. It leverages historical and real-time data, encompassing weather conditions, soil characteristics, growth stages, and past information. A rigorous data prepossessing procedure is employed to generate a well-structured 1.31 lacs data set. Various methodologies are utilized to construct predictive. These models' effectiveness is evaluated using common assessment metrics, such as Recall, accuracy, precision, and F1-score. To enhance the reliability and transparency of the recommendations, ensemble methods such as Naive Bayes, Support vector machines, decision trees, gradient boosting, and random forests, Linear Regression, and k-Nearest Neighbors}, along with interpretative techniques, are incorporated. The results provide insights into different algorithms and predict crucial agricultural activities such as planting, irrigation, fertilization, and pest control. Among all the machine learning models considered, Naive Bayes emerges as the best-performing model, achieving perfect scores of 1.0 in accuracy, precision, recall, and F1-scores. The second-best machine learning model is Random Forests, which follows closely behind with Achieving remarkable results reaching 0.99. These prognostic parameters have a big impact on agricultural productivity and sustainability. In order to improve crop management productivity and resource efficiency, this study promotes the use of data-driven decision-making in agriculture.

Application of Analytical Hierarchy Process in evaluation of neotectonic variability on drainage basin systems in the Edea – Eseka region (SW Cameroon Atlantic Coast)

Complex geology and tectonics of the SW Cameroon Atlantic Coast are responsible for the present-day landscapes of the Edea – Eseka Region (EER). The EER is tectonically active and shows differential tectonic uplift, contrasting relief, variations in erosion rates, in river incision, and in channel gradient. Drainage system divides are dynamic features of a landscape that migrate over time during the development of river networks. To study the neotectonic variability in this region, we investigate the drainage river system and the topographic expression of active regional tectonics across the EER. From the Digital elevation model (DEM, 30 m pixel resolution) using Geographic Information System (GIS) interpretive techniques, we extracted eleven morphometric parameters of thirty-two main drainage basins which were combined with Analytical Hierarchy Process (AHP) method. The inter-relationships between these indices grouped into four categories such as watershed geometry, relief characteristics, drainage texture analysis and regional tilting/uplift; can determine the influence of regional tectonic activity in the shape development of drainage basins. The results of the landscape analysis reveal significant variations of the morphometric parameters within the study area indicate a strong tectonic control. From the AHP values, the area is subdivided into high (∼25 %), moderate (∼43.75 %), and low (∼31.25 %) regional tectonic classes. The spatial distribution of different AHP classes shows a gradational pattern from coast to hinterland, suggesting a gradual decrease (from west to east) in the neotectonic activity in the EER drainage basin systems. The moderate-to-high neotectonic activity (∼68.75 %), demonstrates the complexity of the EER unstable tectonic character due to lithospheric mantle dynamism/asthenospheric upwelling as well as the Cameroon Volcanic Line (CVL) activity.

An assessment of global land susceptibility to wind erosion based on deep-active learning modelling and interpretation techniques

Spatial accurate mapping of land susceptibility to wind erosion is necessary to mitigate its destructive consequences. In this research, for the first time, we developed a novel methodology based on deep learning (DL) and active learning (AL) models, their combination (e.g., recurrent neural network (RNN), RNN-AL, gated recurrent units (GRU), and GRU-AL) and three interpretation techniques (e.g., synergy matrix, SHapley Additive exPlanations (SHAP) decision plot, and accumulated local effects (ALE) plot) to map global land susceptibility to wind erosion. In this respect, 13 variables were explored as controlling factors to wind erosion, and eight of them (e.g., wind speed, topsoil carbon content, topsoil clay content, elevation, topsoil gravel fragment, precipitation, topsoil sand content and soil moisture) were selected as important factors via the Harris Hawk Optimization (HHO) feature selection algorithm. The four models were applied to map land susceptibility to wind erosion, and their performance was assessed by three measures consisting of area under of receiver operating characteristic (AUROC) curve, cumulative gain and Kolmogorov Smirnov (KS) statistic plots. The results revealed that GRU-AL model was considered as the most accurate, revealing that 38.5%, 12.6%, 10.3%, 12.5% and 26.1% of the global lands are grouped at very low, low, moderate, high and very high susceptibility classes to wind erosion hazard, respectively. Interpretation techniques were applied to interpret the contribution and impact of the eight input variables on the model’s output. Synergy plot revealed that the soil carbon content exhibited high synergy with DEM and soil moisture on the model’s predictions. ALE plot showed that soil carbon content and precipitation had negative feedback on the prediction of land susceptibility to wind erosion. Based on SHAP decision plot, soil moisture and DEM presented the highest contribution on the model’s output. Results highlighted new regions at high latitudes (southern Greenland coast, hotspots in Alaska and Siberia), which exhibited high and very high land susceptibility to wind erosion.

Achieving Housing Affordability in the U.S. through Sustained Use of AI and Robotic Process Automation for Prefabricated Modular Construction

The rising housing issues occasioned by high costs in the United States (US) are traceable to the high costs of building materials and construction. This study proposes the combined leveraging of AI and Robotic Process Automation (RPA) for prefabricated modular construction as a strategic means of reducing high costs of housing and increasing efficiency in construction. It argues that doing so would pave way for housing affordability in the US. Deploying descriptive survey and qualitative method alongside the applicable interpretive and descriptive techniques, the study demonstrates in the course of its analysis that AI and RPA are viable means of reducing costs of construction and housing, and increasing efficiency. It submits that amidst the established constraints to the ideal extent of leveraging AI for construction activities, the leveraging of AI and RPA for prefabricated modular construction yields huge positive results among which are reduced costs of construction and housing and increased efficiency in construction. Government and construction organizations are charged to play enabling roles in the adoption and sustainability of AI and RPA in construction activities, including prefabricated modular construction, so as to achieve reduced costs of housing and increase efficiency in construction.

A study of "left against medical advice" emergency department patients: an optimized explainable artificial intelligence framework.

The issue of left against medical advice (LAMA) patients is common in today's emergency departments (EDs). This issue represents a medico-legal risk and may result in potential readmission, mortality, or revenue loss. Thus, understanding the factors that cause patients to "leave against medical advice" is vital to mitigate and potentially eliminate these adverse outcomes. This paper proposes a framework for studying the factors that affect LAMA in EDs. The framework integrates machine learning, metaheuristic optimization, and model interpretation techniques. Metaheuristic optimization is used for hyperparameter optimization-one of the main challenges of machine learning model development. Adaptive tabu simulated annealing (ATSA) metaheuristic algorithm is utilized for optimizing the parameters of extreme gradient boosting (XGB). The optimized XGB models are used to predict the LAMA outcomes for patients under treatment in ED. The designed algorithms are trained and tested using four data groups which are created using feature selection. The model with the best predictive performance is then interpreted using the SHaply Additive exPlanations (SHAP) method. The results show that best model has an area under the curve (AUC) and sensitivity of 76% and 82%, respectively. The best model was explained using SHAP method.

A Structural equation modeling analysis of generative AI chatbots adoption among students and educators in higher education

In an era where artificial intelligence (AI) is reshaping educational paradigms, this study explores AI-based chatbot adoption in higher education among students and educators. Employing a Structural Equation Modeling (SEM) approach, the research focuses on developing and validating a comprehensive model to understand the multifaceted factors impacting the acceptance and use of these chatbots. The methodology integrates an extensive literature review, construction of a theoretical model, administration of a detailed questionnaire to a representative sample from the higher education sector, coupled with advanced SEM techniques for data analysis and interpretation. The SEM analysis validates the model's robustness and highlights the relationships between several key factors affecting users' perspectives and chatbots adoption. Results reveal a predominantly positive perception towards AI-chatbots among both students and educators, underscoring the potential to substantially enrich their educational journey. However, it also uncovers critical concerns pertaining to trust, privacy, response bias, and information accuracy. Moreover, the study offers valuable insights into how moderators such as technological proficiency, user roles, and gender influence the adoption model relationships. This emphasizes the need for customizing AI-chatbots deployment to meet the diverse needs of users effectively. Contributing a robust framework for understanding users' perceptions towards AI-chatbots and their adoption patterns, this study offers actionable insights for educational leaders, policymakers, and technology developers. It also lays the groundwork for future research, including longitudinal studies to evaluate the long-term impact of these technologies, investigations into their effect on learning outcomes, and explorations of the ethical and privacy considerations involved.