Context Of Models Research Articles

Readiness to change in Islamic banking: the significance of adaptability and Islamic work ethics

Purpose This paper aims to investigate Islamic work ethics and adaptability towards change readiness of Islamic Bank employees in Indonesia with commitment as a mediating variable. This study focuses on the Sharia banking industry, which is facing transformation from Sharia business units to Sharia commercial banks by preparing its human resources, thus minimizing resistance. Design/methodology/approach This study is based on a survey of 135 Bank Syariah Indonesia employees via an online questionnaire. PLS-SEM is used to analyze data and test hypotheses using SMART-PLS. Findings The results of the study show that adaptability does not have a positive and significant effect on readiness for change in Indonesian Sharia banks and organizational commitment. Meanwhile, Islamic work ethics have a positive and significant effect on the change readiness of Islamic bank employees in Indonesia as well as organizational commitment. This study also found that organizational commitment mediates the impact of Islamic work ethics on the change readiness of Islamic bank employees in Indonesia. Research limitations/implications This research uses a sample of employees at Sharia commercial banks (BUS) in Indonesia, so it is still limited to certain types of banks. Future research needs to test the model in different contexts, such as banks that still have Sharia business unit (UUS) status. In addition, further research needs to use other variables such as leader support for transformational change readiness. Future research also needs to control more variables and use a more complex scale than a binary scale (e.g. percentage of share ownership or area within a province or district/city). This study recommends that employee change readiness is very necessary to avoid resistance and resistance to the transformation and acceleration of Sharia banking in Indonesia. Originality/value This research looks at employee readiness for change in Indonesian Sharia banks through the lens of adaptability Islamic work ethics and organizational commitment.

Моделирование перехода частных пертинентностей в коллективную релевантность в интернет-коммуникации

The following study describes the core modeling principles of correspondence of private pertinence to collective relevance, analyzes the mechanisms for creating generalized content, and the formation of common significance areas in the value space of network communication. The methodological principle of the individual and collective transgression is the concept of contextual models with focal detalization. The formation of the pertinence field is described as internal relevance characterizing polylogue delayed interaction within internet communication. The inclusion of consituational formants in the contextual model as a part of prognostication of modificational potencies of pertinences in the pentatomic field “speaker – moderator – text – recipient – moderator” allows for a description of communication process in the “downtime” situation as a substantiation of personal pertinence fields into a single relevance field, but also for the algorithmization of deriving generalized content in any indirect delayed discoursive practices. Based on a number of different property classifications, the authors derive a list of criteria features of pertinence and relevance transgression mechanisms. The authors conclude that the transaction of individual components and their introduction into the general space of significance should be accompanied by compliance with certain criteria: interpretation of the horizontal context in a wide field, transmodality, continuality of communicative action, creation of boundary models within the contextualization framework, referential eventfulness, variability of models and their permanent contextual modification, correspondence of models to the horizontal context, complexity of structure and simplification of formal composition, the presence of intersection areas of cognitive assets, the fundamental possibility of significance awareness, context hierarchy, preliminary conventionalization, forecasting the productivity of the applied model, unity of representation patterns, discursive conditionality of meanings, individual and collective alternations of meanings, variability of situations, coherence of discursive moves, mandatory verbalization of consituative components, role hierarchy of particular meanings functionality.

Exploring the Nature of Diversity Dishonesty within Predominantly White Schools of Medicine, Pharmacy, and Public Health at the Most Highly Selective and Highly Ranked U.S. Universities

The concept of “diversity dishonesty” has emerged as a pressing concern within highly selective and highly ranked schools of medicine, pharmacy, and public health at elite and highly ranked U.S. universities, particularly in the context of diversity, equity, and inclusion (DEI) efforts. This phenomenon, defined as a lack of sincerity and genuineness in an organization’s commitment to genuine and measurable change regarding diversity, raises significant questions about the authenticity of their endeavors. Organizations often engage in surface-level or performative actions related to diversity, creating the illusion of progress and dedication while failing to enact substantive and meaningful advancements in promoting diversity and inclusivity. This applied research inquiry uses a review of literature, research theories, and research frameworks to delve into the nuanced dynamics of diversity dishonesty, exploring how organizations demonstrate a commitment in form but not in substance. The practice of tokenism, where diverse individuals are hired and prominently featured in organizational materials but are not genuinely valued, emerges as a defining characteristic of diversity dishonesty. Moreover, when questions regarding the authenticity of their commitment arise, organizations may resort to gaslighting minorities, further exacerbating the issue. Recognizing the critical need to address diversity dishonesty, this article comprehensively explores frameworks to understand and combat this phenomenon. It seeks to engage with viable theories, problem-solving approaches, and contextual models that can illuminate the complex interplay of factors contributing to diversity dishonesty. By shedding light on the mechanisms through which elite and highly ranked predominantly White schools of medicine, pharmacy, and public health engage in performative acts without enacting transformative cultural change, this research aims to pave the way for more genuine and impactful DEI efforts and future research in this area.

Discrete-time graph neural networks for transaction prediction in Web3 social platforms

In Web3 social platforms, i.e. social web applications that rely on blockchain technology to support their functionalities, interactions among users are usually multimodal, from common social interactions such as following, liking, or posting, to specific relations given by crypto-token transfers facilitated by the blockchain. In this dynamic and intertwined networked context, modeled as a financial network, our main goals are (i) to predict whether a pair of users will be involved in a financial transaction, i.e. the transaction prediction task, even using textual information produced by users, and (ii) to verify whether performances may be enhanced by textual content. To address the above issues, we compared current snapshot-based temporal graph learning methods and developed T3GNN, a solution based on state-of-the-art temporal graph neural networks’ design, which integrates fine-tuned sentence embeddings and a simple yet effective graph-augmentation strategy for representing content, and historical negative sampling. We evaluated models in a Web3 context by leveraging a novel high-resolution temporal dataset, collected from one of the most used Web3 social platforms, which spans more than one year of financial interactions as well as published textual content. The experimental evaluation has shown that T3GNN consistently achieved the best performance over time and for most of the snapshots. Furthermore, through an extensive analysis of the performance of our model, we show that, despite the graph structure being crucial for making predictions, textual content contains useful information for forecasting transactions, highlighting an interplay between users’ interests and economic relationships in Web3 platforms. Finally, the evaluation has also highlighted the importance of adopting sampling methods alternative to random negative sampling when dealing with prediction tasks on temporal networks.

Wrinkling of a film/substrate bilayer with periodic material properties: An assessment of the Winkler foundation model

The Winkler foundation model is often used to analyze the wrinkling of a film/substrate bilayer under compression, and it can be rigorously justified when both the film and substrate are homogeneous and the film is much stiffer than the substrate. We assess the validity of this model when the substrate is still homogeneous but the film has periodic material properties in the direction parallel to the interface. More precisely, we assume that each unit cell is piecewise homogeneous, and each piece can be described by the Euler–Bernoulli beam theory. We provide analytical results for the critical compression when the substrate is viewed as a Winkler foundation with stiffness modeled either approximately (as in some previous studies) or exactly (using the Floquet theory). The analytical results are then compared with those from Abaqus simulations based on the three-dimensional nonlinear elasticity theory in order to assess the validity of the Euler–Bernoulli beam theory and the Winkler foundation model in the current context.

Factors affecting the use of e-banking services among customers of Algerian banks: A qualitative study

This study aims to identify the factors influencing the use of e-banking services among clients of Algerian banks. To this end, a theoretical model was proposed based on the technology acceptance model (TAM) and the concept of electronic word of mouth (eWOM). To validate the variables of the proposed model in the Algerian context, we conducted a qualitative study using semi-structured interviews, which were carried out with 17 clients from various cities across Algeria, including those in the east, west, north, and south of the country. The data collected were subjected to thematic content analysis using a constructivist research approach. The results revealed significant effects of perceived usefulness, perceived ease of use, perceived trust, and electronic word-of-mouth (eWOM) on the use of e-banking services among clients of Algerian banks. The results also uncovered other variables that are expected to have a significant effect on e-banking use in Algeria, namely perceived costs and perceived service quality.

Application of Contextual Strategy with Audio Visual Media to Increase Interest in Learning Indonesian Language in Students

In learning Indonesian, the study of practical problems regarding language in everyday life is very useful for a child or student in living and living now and in the future. With the hope that these students will later be able to realize and understand that they cannot live without the contribution of language in every action, but living in a large environment is very difficult when the language used is not appropriate to the context, if you want to live a decent life then language is one of the tools to be able to connect good communication with others. The next problem is related to the choice and learning approach, which has not generated much student activity and creativity. This is because they still often use conventional approaches, where learning is centered on the teacher, so that students become passive. If the learning can activate students, it will be interesting and the large amount of material can be quickly absorbed by students. Because they are enthusiastic, want to learn seriously. Based on the problem above, we are interested in implementing a contextual learning model which is seen as being able to help students be active in learning and teaching activities. The aim of this research is to find out the influence of contextual learning on increasing students' interest in learning Indonesian. To find out whether there is an increase in students' interest in learning Indonesian. To determine the simultaneous influence of contextual learning and student interest in learning on the learning achievement of class IX students at SMP Negeri 3 Bawolato. The method used in this research is a quantitative method by applying an experimental approach to class IX students at SMP Negeri 3 Bawolato, data collection using research instruments, (1) attention to learning, (2) participation in learning activities, (3) effort in completing assignments, (4) interest in learning material. The results of the research show that there is a simultaneous influence of contextual learning and student interest in learning on the learning achievement of class IX students at SMP Negeri 3 Bawolato. Students' perceptions of the contextual learning model have a greater effective contribution to increasing students' interest in learning Indonesian, although not yet to its full potential.

Developing a Contextual Learning Model in Islamic Education to Improve Applicable Knowledge and Foster Knowledge-Based Virtues

This study addresses the prevalent reliance on theoretical-abstract learning in Islamic religious education, which often emphasizes theories, symbols, textual materials, and memorized facts. Recognizing the need for a more practical approach, our research aims to develop a contextual learning design specifically focused on prayer in Islamic religious education. The goal is to enhance both knowledge acquisition and the cultivation of charitable virtues. Utilizing a combined quantitative and qualitative research approach, we employed research and development (R&D) methods. Data collection involved participatory observation, documentation studies, and questionnaires. The findings reveal that the contextual learning model significantly improves students' theoretical understanding and practical application of prayer. Results show enhanced academic achievements and the integration of prayer values into students' daily lives. The contextual learning model presents a more realistic, concrete, engaging, and meaningful approach to prayer instruction, fostering active student participation and the application of learned material in real-life scenarios. This study underscores the potential of contextual learning in transforming Islamic religious education, making it more effective and relevant for students.

Long-term resilience in wastewater management: Optimizing treated wastewater allocation with a dynamic multi-agent approach

Water scarcity poses a significant challenge to sustainable development, necessitating innovative approaches to manage limited resources efficiently. Effective water resource management involves not just the conservation and distribution of freshwater supplies but also the strategic reuse of treated wastewater (TWW). This study proposes a novel approach for the optimal allocation of treated wastewater among three key sectors (user agents): agriculture, industry, and urban green space. Recognizing the intricate interplays among these sectors, System Dynamics (SD) and Agent-Based Modeling (ABM) were integrated in a Complex Adaptive System (CAS) to capture the interactions and feedback mechanisms inherent within treated wastewater allocation systems. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) serves as the optimization tool, enabling the identification of optimal allocation strategies across various management scenarios over a 25-year simulation period. Our research navigates the complexities of long-term resource management, accounting for each sector's evolving its objectives and guidelines along the whole system objectives and strategies. The outcomes demonstrate how treated wastewater can be effectively distributed to support economic and social equity –as the system objectives-while supporting agricultural and industrial growth and enhancing efficiency and social well-being –reflecting individual agent objectives-within the CAS framework. The research explores four distinct management scenarios, each prioritizing different sectors to address water resource management challenges. Notably, all four scenarios align with the strategies required by the ruler (government), providing strategic guidance to water resource managers for decision-making. The simulation results reveal a scenario where all sectors' demands are met, with Scenario 4 emerging as the most effective. Scenario 4 aligned with the objectives and guidelines of each sector, demonstrating significant improvements in the CY (Agriculture agent index; increased from 0.2 to 0.68), IGI (Industry agent index; increased from 1 to 1.63), and GAI (Urban Green Space agent index; increased from 1 to 1.23) indices over the 25-year simulation period. By providing a strategic blueprint for policymakers and stakeholders, this study contributes significantly to the discourse on sustainable water resource management, presenting a replicable model for similar contexts globally, where judicious allocation of treated wastewater is paramount for achieving harmony between human activity and ecological preservation.

Evaluating the Regional Contracted Teachers Appointment Program at the Junior High School Level in Tambrauw Regency, Southwest Papua Province : Context of Education in Remote Areas

This study aims to assess the achievements and impacts of the program regarding the appointment of contract teachers at the junior high school (SMP) level in Tambrauw Regency, Southwest Papua Province. This research used an evaluation method with the Context, Input, Process, and Product (CIPP) Evaluation Model developed by Stufflebeam. Respondents consist of representatives from the Department of Education, School Principals, and Teachers. Data collection methods include interviews, observations, and document studies. The collected data are then analyzed qualitatively and presented verbally. The utilization of triangulation and qualitative analysis methods in this study enhances the credibility of the data and the validity of the resulting findings. The analysis results indicate that: Firstly, in terms of context, the implementation of this program still aligns with the existing needs. Secondly, in terms of input, the program can proceed due to the availability of adequate financial support and resources. Thirdly, during the implementation process, external interventions influence the achievement of program objectives. Lastly, in terms of product, this program successfully achieves its predetermined goals and positively impacts the improvement of educational quality. In conclusion, the program regarding the appointment of contract teachers in junior high schools in Tambrauw Regency has a significant impact on enhancing the quality of education. This assessment is crucial for refining and optimizing existing programs. This study provides a valuable contribution to the program evaluation literature, particularly in the context of education in remote areas.

MDC-RHT: Multi-Modal Medical Image Fusion via Multi-Dimensional Dynamic Convolution and Residual Hybrid Transformer.

The fusion of multi-modal medical images has great significance for comprehensive diagnosis and treatment. However, the large differences between the various modalities of medical images make multi-modal medical image fusion a great challenge. This paper proposes a novel multi-scale fusion network based on multi-dimensional dynamic convolution and residual hybrid transformer, which has better capability for feature extraction and context modeling and improves the fusion performance. Specifically, the proposed network exploits multi-dimensional dynamic convolution that introduces four attention mechanisms corresponding to four different dimensions of the convolutional kernel to extract more detailed information. Meanwhile, a residual hybrid transformer is designed, which activates more pixels to participate in the fusion process by channel attention, window attention, and overlapping cross attention, thereby strengthening the long-range dependence between different modes and enhancing the connection of global context information. A loss function, including perceptual loss and structural similarity loss, is designed, where the former enhances the visual reality and perceptual details of the fused image, and the latter enables the model to learn structural textures. The whole network adopts a multi-scale architecture and uses an unsupervised end-to-end method to realize multi-modal image fusion. Finally, our method is tested qualitatively and quantitatively on mainstream datasets. The fusion results indicate that our method achieves high scores in most quantitative indicators and satisfactory performance in visual qualitative analysis.

Automated crack localization for road safety using contextual u-net with spatial-channel feature integration

Accurate and timely crack localization is crucial for road safety and maintenance, but image processing and hand-crafted feature engineering methods, often fail to distinguish cracks from background noise under diverse lighting and surface conditions. This paper proposes a framework utilizing contextual U-Net deep learning model to automatically localize cracks in road images. The framework design considers crack localization as a task of pixel-level segmenting, and analyzing each pixel in a road image to determine if it belongs to a crack or not. The proposed U-Net model uses a robust EfficientNet encoder to capture crucial details (spatial features) and overall patterns (channel-wise features) within the road image. This balanced approach helps the model learn effectively from both individual elements and the context of the images, leading to improved crack detection. A customized hierarchical attention mechanism is designed to make U-Net model contextually adaptive to focus on relevant features at different scales and resolutions for accurately localizing road cracks that can vary widely in size and shape. The model's effectiveness is demonstrated through extensive evaluations on the benchmarked and custom-made datasets.

A Contextual Model for Visual Information Processing

Despite significant achievements in the artificial narrow intelligence sphere, the mechanisms of human-like (general) intelligence are still undeveloped. There is a theory stating that the human brain extracts the meaning of information rather than recognizes the features of a phenomenon. Extracting the meaning is finding a set of transformation rules (context) and applying them to the incoming information, producing an interpretation. Then, the interpretation is compared to something already seen and is stored in memory. Information can have different meanings in different contexts. A mathematical model of a context processor and a differential contextual space which can perform the interpretation is discussed and developed in this paper. This study examines whether the basic principles of differential contextual spaces work in practice. The model is developed with Rust programming language and trained on black and white images which are rotated and shifted both horizontally and vertically according to the saccades and torsion movements of a human eye. Then, a picture that has never been seen in the particular transformation, but has been seen in another one, is exposed to the model. The model considers the image in all known contexts and extracts the meaning. The results show that the program can successfully process black and white images which are transformed by shifts and rotations. This research prepares the grounding for further investigations of the contextual model principles with which general intelligence might operate.

Modelling and analysis of production management system using vacation queueing theoretic approach

This paper explores a queueing model in a production management context, featuring periods of working vacations and Bernoulli vacation. When there are no pending orders, the manufacturing unit transits into a maintenance phase, also termed as working vacation, during which production continues, albeit at a slower rate. This diminished productivity could result in longer lead times and potential customer dissatisfaction. Depending on the influx of demand, the maintenance phase could be interrupted, propelling the unit back to full operational capacity. Conversely, if no orders are received during the maintenance phase, the unit has the choice of switching to standby mode in anticipation of incoming orders, or initiating an extended break. We utilize mathematical techniques such as continued fractions, Modified Bessel functions, and Laplace transforms to precisely compute the transient state probabilities of this model. To further illustrate the impact of these operational dynamics on production management, we present corroborative numerical examples.

Comparison of Classification Algorithm and Language Model in Accounting Financial Transaction Record: A Natural Language Processing Approach

The problem of financial recording not following the principles of accounting science has the potential to cause unnecessary problems. However, micro, small, and medium enterprises with their distinctive characteristics, though not all, still face many obstacles in writing financial reports. Even though there is already much financial software available, our study aims to investigate opportunities for implementing automation of accounting financial transaction records using the NLP approach, to interpret financial transactions based on text written on the transaction form into accounting journals (debits and credits). Experiments were carried out by comparing the performance of three classification algorithms, namely SVM, K-Nearest Neighbor, and Random Forest, with traditional (TF-IDF and BOW) and contextual (Word2Vec) Language Models. There are 200 financial transaction datasets consisting of ten classes. The data is divided into two parts, namely, the balance dataset and the imbalance dataset. The pair SVM and Word2Vec in the balanced dataset gave the highest accuracy (92.5%), precision (92.5%), recall/sensitivity (93.33%), and F1 score (92%). However, compared with the results of related semantic research (the average performance reaches 95%), the results obtained in this study are still lower. One point that may have a significant effect is the amount of data in the corpus, which is still lacking. Researchers suggest increasing the number of datasets and using a combination of other language models such as Glove, Bert etc. This study can also be used as a model for more complex financial transaction cases in future research.

Ethical Tensions in Municipality Management

This study investigates ethical tension situations that municipal managers face in their everyday working life by identifying the occurrence and nature of such situations, their contextual and organizational dependence and what resources are available to handle and/or solve them. The study is based on in-depth qualitative interviews with 21 municipal managers from various sectors. In total, 222 ethical tension situations were revealed. The main finding of the study is the identification of the importance of contextual and organizational dependencies, regarding not only managers’ experience of ethical tensions but also the resolution of such tensions. In the paper a contextual model of managerial tensions is presented that gives account to the different logics, value systems, norms, regulations, and ethics, that make up the complex world in which municipal managers find themselves and need to handle.

NSBR-Net: A Novel Noise Suppression and Boundary Refinement Network for Breast Tumor Segmentation in Ultrasound Images

Breast tumor segmentation of ultrasound images provides valuable tumor information for early detection and diagnosis. However, speckle noise and blurred boundaries in breast ultrasound images present challenges for tumor segmentation, especially for malignant tumors with irregular shapes. Recent vision transformers have shown promising performance in handling the variation through global context modeling. Nevertheless, they are often dominated by features of large patterns and lack the ability to recognize negative information in ultrasound images, which leads to the loss of breast tumor details (e.g., boundaries and small objects). In this paper, we propose a novel noise suppression and boundary refinement network, NSBR-Net, to simultaneously alleviate speckle noise interference and blurred boundary problems of breast tumor segmentation. Specifically, we propose two innovative designs, namely, the Noise Suppression Module (NSM) and the Boundary Refinement Module (BRM). The NSM filters noise information from the coarse-grained feature maps, while the BRM progressively refines the boundaries of significant lesion objects. Our method demonstrates superior accuracy over state-of-the-art deep learning models, achieving significant improvements of 3.67% on Dataset B and 2.30% on the BUSI dataset in mDice for testing malignant tumors.

Gauging the Technology Acceptance of Manufacturing Employees: A New Measure for Pre-Implementation

Recent technological advances are bringing about the digitalization of manufacturing, enabled by introducing and integrating new and improved technologies into existing processes and activities. Integrating advanced technologies into the workplace can have a positive effect on manufacturing efficiency and competitiveness, as well as sustainability and environmental impact. Employee acceptance of these new technologies is critical for manufacturing organizations to achieve these goals. Unfortunately, a notable deficiency of tools to assess the readiness of an employee work group or organization to accept a new technology exists. The objective of this study was to develop and validate a new tool for gauging employee technology acceptance in a pre-implementation decision context known as the Technology Acceptance in a Manufacturing Environment (TAME). Statistical validation measures were conducted on survey responses from 823 respondents across seven locations of one large organization. The results indicate that TAME is appropriate for assessing readiness for technology acceptance among manufacturing workers with little to no training or knowledge of the technology being considered for implementation (R2 = 86%). TAME can facilitate the organizational assessment of employee perception of new technologies before implementation, increasing the chances of a successful launch. This research results in the first known application of technology acceptance models in a pre-implementation context in a manufacturing environment.

Consumption of Values

Consumption decisions are partly influenced by values and ideologies. Consumers care about global warming, child labor, fair trade, etc. We develop an axiomatic model of intrinsic values—those that are carriers of meaning in and of themselves—and argue that they often introduce discontinuities near zero. For example, a vegetarian’s preferences would be discontinuous near zero amount of animal meat. We distinguish intrinsic values from instrumental ones, which are means rather than ends and serve as proxies for intrinsic values. We illustrate the relevance of our value-based model in different contexts, including equity concerns and prosocial behavior. This paper was accepted by Manel Baucells, behavioral economics and decision analysis. Funding: The authors acknowledge support from the Agence Nationale de la Recherche [Grants ANR-11-IDEX-0003 and 11-LABX-0047], the Israel Science Foundation [Grant 1443/20], the AXA Research Fund [Chair for Decision Sciences], the Foerder Institute at Tel-Aviv University, and the Sapir Center for Economic Development (Gilboa). Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.01632 .

Comparative study of construction land development suitability assessment: Insights from multiple models and driving forces

Construction land development suitability (CLDS) assessment holds paramount importance in contemporary urban planning and management. Previous CLDS studies have faced challenges such as insufficient evaluation indicators, model complexity, and limited interpretability. To address these gaps, this study applies three simplistic yet mature models from the field of geological hazard assessment - Weight of Evidence (WOE), Information Value (IV), and Frequency Ratio (FR) - to evaluate the CLDS in Wuhan, China, and examine their feasibility. In parallel, a comparative analysis with the Patch-generating Land Use Simulation (PLUS) model was conducted. The WOE, IV, and FR models demonstrate a gradual decrease in CLDS from the city center and the Yangtze and Han rivers towards the periphery. Conversely, the PLUS model indicates the highest suitability around the city's development boundaries, followed by the city center, with suburbs displaying lower suitability. In terms of accuracy, the PLUS model's CARS module outperforms others, particularly for micro-level urban expansion simulations. The IV and WOE models follow closely, manifesting a preference for macro-level urban planning. Additionally, this study pinpoints key drivers influencing CLDS, notably population density, distance to social service Points of Interest (POI) and proximity to road networks. The cross-validation of the WOE, IV, and FR models underscores their reliability, with their straightforward principles positioning them as valuable tools in urban development planning. This comparative study validates the application of geological hazard models in urban contexts and diversifies CLDS assessment methodologies.