Context Of Models Research Articles

Evaluasi Program Bantuan Sosial Lansia Non Potensial di Kota Madiun dengan Model Context Input Process Product (CIPP)

The non-potential elderly social assistance program is Madiun City's flagship program for handling helpless elderly which is implemented on an ongoing basis, but has never been evaluated for its effectiveness. The aim of the research is to describe the results of the match between program achievements and the initial design and to determine the driving and inhibiting factors of the program. The research method is descriptive qualitative, with data collection techniques using observation, interviews and documentation. Evaluation uses the Context, Input, Process, and Product (CIPP) model. The results of the Context evaluation explain that the program objectives and target priorities are in line with community needs. Input shows that the instruments for implementing the program are incomplete. Process describes program implementation as less effective. The product shows that the program objectives have been achieved but have not been maximized. Meanwhile, factors driving the success of the program include the commitment of the Mayor of Madiun, fairly complete regulations, funding support, and community participation in implementing the program. Inhibiting factors include the limited number of implementing human resources, the lack of synergy between various parties, and the lack of an integrated system. The conclusion is that this program is still needed for the treatment of non-potential elderly, but program improvements need to be implemented immediately so that the goals are achieved more optimally.

Equitable outreach: leveraging a university–community partnership for fall prevention training with older adults in rural Texas

ABSTRACT Older Texans living in rural areas may face challenges in accessing resources and services not felt by their urban counterparts, especially related to fall risk reduction. In a state the size of Texas, entities wishing to serve these individuals encounter barriers due to the vast geography that must be covered to reach rural communities. This paper explores a novel partnership between a university and a community organization to work toward rural equity in fall risk reduction education in Texas. We apply the Interactive and Contextual Model of Community–University Collaboration to describe efforts to gain entry into rural communities, develop and sustain the partnership, recognize outcomes, and resolve threats. Through shared decision-making and co-creation, we show how university–community partnerships can be a powerful tool for promoting rural equity and ensuring older, rural Texans have access to fall risk reduction education.

Remote physiological signal recovery with efficient spatio-temporal modeling.

Contactless physiological signal measurement has great applications in various fields, such as affective computing and health monitoring. Physiological measurements based on remote photoplethysmography (rPPG) are realized by capturing the weak periodic color changes. The changes are caused by the variation in the light absorption of skin surface during systole and diastole stages of a functioning heart. This measurement mode has advantages of contactless measurement, simple operation, low cost, etc. In recent years, several deep learning-based rPPG measurement methods have been proposed. However, the features learned by deep learning models are vulnerable to motion and illumination artefacts, and are unable to fully exploit the intrinsic temporal characteristics of the rPPG. This paper presents an efficient spatiotemporal modeling-based rPPG recovery method for physiological signal measurements. First, two modules are utilized in the rPPG task: 1) 3D central difference convolution for temporal context modeling with enhanced representation and generalization capacity, and 2) Huber loss for robust intensity-level rPPG recovery. Second, a dual branch structure for both motion and appearance modeling and a soft attention mask are adapted to take full advantage of the central difference convolution. Third, a multi-task setting for joint cardiac and respiratory signals measurements is introduced to benefit from the internal relevance between two physiological signals. Last, extensive experiments performed on three public databases show that the proposed method outperforms prior state-of-the-art methods with the Pearson's correlation coefficient higher than 0.96 on all three datasets. The generalization ability of the proposed method is also evaluated by cross-database and video compression experiments. The effectiveness and necessity of each module are confirmed by ablation studies.

Early Cervical Cancer Diagnosis with SWIN-Transformer and Convolutional Neural Networks.

Introduction: Early diagnosis of cervical cancer at the precancerous stage is critical for effective treatment and improved patient outcomes. Objective: This study aims to explore the use of SWIN Transformer and Convolutional Neural Network (CNN) hybrid models combined with transfer learning to classify precancerous colposcopy images. Methods: Out of 913 images from 200 cases obtained from the Colposcopy Image Bank of the International Agency for Research on Cancer, 898 met quality standards and were classified as normal, precancerous, or cancerous based on colposcopy and histopathological findings. The cases corresponding to the 360 precancerous images, along with an equal number of normal cases, were divided into a 70/30 train-test split. The SWIN Transformer and CNN hybrid model combines the advantages of local feature extraction by CNNs with the global context modeling by SWIN Transformers, resulting in superior classification performance and a more automated process. The hybrid model approach involves enhancing image quality through preprocessing, extracting local features with CNNs, capturing the global context with the SWIN Transformer, integrating these features for classification, and refining the training process by tuning hyperparameters. Results: The trained model achieved the following classification performances on fivefold cross-validation data: a 94% Area Under the Curve (AUC), an 88% F1 score, and 87% accuracy. On two completely independent test sets, which were never seen by the model during training, the model achieved an 80% AUC, a 75% F1 score, and 75% accuracy on the first test set (precancerous vs. normal) and an 82% AUC, a 78% F1 score, and 75% accuracy on the second test set (cancer vs. normal). Conclusions: These high-performance metrics demonstrate the models' effectiveness in distinguishing precancerous from normal colposcopy images, even with modest datasets, limited data augmentation, and the smaller effect size of precancerous images compared to malignant lesions. The findings suggest that these techniques can significantly aid in the early detection of cervical cancer at the precancerous stage.

Analisis Perbandingan Pembelajaran Individu dengan Pembelajaran Berkelompok Melalui Model Pembelajaran Kontekstual Mata Pelajaran Ekonomi terhadap Hasil Belajar di SMPN 1 Borbor

The problem in this study is to find out how individual learning compares with group learning through the contextual learning model of economics subjects on learning outcomes at SMPN 1 Borbor on various materials of human needs and fulfillment tools with a sample of 60 students. The research method in this study is used with a quantitative method. The students' scores in individual learning through contextual learning have an average score of 78.7586 with a standard deviation of 6.836 and group learning through the contextual learning model has an average score of 88.6667 with, so it can be concluded that there is a significant difference between the variables X1 (individual learning through the contextual learning model) X2 (group learning through contextual learning). As a study conducted by Suhprihtin Siti, et al, the influence of economic learning with a contextual teaching and learning (CTL) approach on economic learning outcomes is reviewed from the initial ability of students

Non-stationary mechanical sound source separation: An all-neural beamforming network driven by time–frequency convolution and self-attention

To tackle the challenging task of extracting target signal features amidst significant interference from complex, time-varying non-stationary noise in industrial scenarios, we propose a novel time-domain all-neural beamforming network tailored for non-stationary mechanical sound source separation, named TFANBNet. Leveraging time–frequency convolution and self-attention, TFANBNet promised robust performance in challenging acoustic environments. First, the proposed time–frequency convolution module employs parameterized complex-valued convolutional layers to simulate time–frequency transformations, thereby implicitly extracting time–frequency features. Then, the proposed adaptive attention transformer network enhances local attention interaction by integrating intermediate features, effectively enhancing long-range context modeling. Third, the beamforming module utilizes deep neural networks for beamforming weight estimation, replacing conventional noise covariance matrix computations, which have inherent performance limitations. Finally, the experimental results on the multi-channel spatial reverberation dataset synthesized from the MIMII dataset show that the proposed TFANBNet yields superior separation performance and generalization capabilities compared to the considered competitive methods.

Comments on wormholes and factorization

In AdS/CFT partition functions of decoupled copies of the CFT factorize. In bulk computations of such quantities contributions from spacetime wormholes which link separate asymptotic boundaries threaten to spoil this property, leading to a “factorization puzzle.” Certain simple models like JT gravity have wormholes, but bulk computations in them correspond to averages over an ensemble of boundary systems. These averages need not factorize. We can formulate a toy version of the factorization puzzle in such models by focusing on a specific member of the ensemble where partition functions will again factorize.As Coleman and Giddings-Strominger pointed out in the 1980s, fixed members of ensembles are described in the bulk by “α-states” in a many-universe Hilbert space. In this paper we analyze in detail the bulk mechanism for factorization in such α-states in the topological model introduced by Marolf and Maxfield (the “MM model”) and in JT gravity. In these models geometric calculations in α states are poorly controlled. We circumvent this complication by working in approximate α states where bulk calculations just involve the simplest topologies: disks and cylinders.One of our main results is an effective description of the factorization mechanism. In this effective description the many-universe contributions from the full α state are replaced by a small number of effective boundaries. Our motivation in constructing this effective description, and more generally in studying these simple ensemble models, is that the lessons learned might have wider applicability. In fact the effective description lines up with a recent discussion of the SYK model with fixed couplings [1]. We conclude with some discussion about the possible applicability of this effective model in more general contexts.

Enhancing Practical Skills in Computer Networking: Evaluating the Unique Impact of Simulation Tools, Particularly Cisco Packet Tracer, in Resource-Constrained Higher Education Settings

This study examines the effectiveness of networking simulation tools, particularly Cisco Packet Tracer, in enhancing the learning experiences of exit-level students at a higher education institution based in the Eastern Cape of South Africa. Utilizing the Context, Input, Process, and Product (CIPP) evaluation model, the research assesses these tools’ impact, effectiveness, and sustainability in a resource-constrained, rural-based higher education context. The findings indicate that simulation tools significantly improve students’ practical skills, understanding of theoretical concepts, and preparedness for professional work in computer networking. Despite challenges such as software crashes and compatibility issues, the benefits of using simulation tools, including cost-effectiveness and convenience, are evident. The study concludes that, while simulation tools are valuable, continuous improvements and support are necessary to maximize their educational potential.

Implementasi Pojok Literasi Sebagai Strategi Penigkatan Literasi Siswa di Lingkungan SDN 1 Tongka Kalimantan Tengah

This community service program was implemented to address the low literacy levels among students in remote areas of Central Kalimantan, specifically at SDN 1 Tongka, Gunung Timang District, North Barito Regency. The main objective of the program was to implement a literacy corner as a strategy to improve student literacy and create a sustainable literacy ecosystem in the school environment. The method used included participatory and adaptive approaches, applying the concept of a "mobile literacy corner" adapted to the limitations of local space and resources. The program was carried out over 45 days, covering preparation, implementation, and evaluation phases. The results showed a significant increase in students' reading interest, with a 68% rise in the frequency of visits to the literacy corner. Pre-test and post-test evaluations indicated an average increase in students' literacy skills of 27%, particularly in reading comprehension and vocabulary mastery. Participatory observation revealed the formation of a stronger literacy culture in the school environment, characterized by student-initiated literacy discussions and increased use of references in academic tasks. The program also successfully developed an adaptive and contextual model for implementing literacy corners, which has the potential to be replicated in other remote areas of Indonesia.

Penerapan Model Pembelajaran Contextual Teaching And Learning Untuk Meningkatkan Aktivitas Dan Hasil Belajar Administrasi Pajak Di SMKN 1 Sidikalang

This study overcomes the problem of low activity and learning outcomes of students in class XI AKL 1 SMK N 1 Sidikalang, consisting of 36 students. This study aims to improve activity and learning outcomes by using the Contextual Teaching and Learning model. Data were collected through activity observation sheets and learning outcomes tests, then analyzed quantitatively and qualitatively. 1) In cycle I, the first meeting showed an average of 27.77% active activities, then in cycle II, it increased with an average of 77.77% active activities. 2) The average pre-test score was 72.77% with 17 students (47.22%) complete, post-test cycle I 75% with 19 students (52.77%) complete, and post-test cycle II 85.27% with 32 students (88.88%) complete, meeting the success indicator ≥85%. In conclusion, the application of the Contextual Teaching and Learning model succeeded in improving student learning activities and outcomes.

Ethical empowerment: Examining the impact of business education in Indian universities through the lens of international standards and CIPP model

This study assessed the effectiveness of Indian postgraduate business education in addressing ethical challenges, integrating International Education Standards (IES) and the Context, Input, Process, and Product (CIPP) model. Using a positivist approach, a cross-sectional survey was conducted, focusing on 233 postgraduate business students through various sampling methods. Data analysis employed descriptive (frequency, percentage, mean, and standard deviation) and inferential (multiple linear regression) statistics. Findings revealed a significant 75% alignment with IES 4 in surveyed universities' business programs. Participative teaching methods were found to be used often in ethics instruction, particularly in accounting courses. Students perceived the program as highly effective in meeting ethical requirements, influenced by age, religiosity, and to a lesser extent, gender. Future research should explore components affecting ethical preparedness, considering content, teaching methods, case studies, long-term impacts, and cultural variations. The study, which was constrained by self-reported data and student perspectives, highlights the need for diverse viewpoints and methods in future cross-cultural research.

Absenteeism and Achievement in Early Elementary Grades: A Multilevel Organizational Analysis

This paper presents empirical analyses illustrating the organizational relationship between chronic absenteeism and student achievement as estimated via a contextual model of school-level and student-level relationships. We conduct a series of descriptive analyses and multilevel hierarchical models (HLM) in which absenteeism is used to predict variation in student achievement for nearly 50,000 individual students in grades K-3 in 109 elementary schools in Delaware during the 2014–2015 through 2017–2019 school years. Results demonstrate that chronic absenteeism tends to be clustered in a subset of schools, and that the adverse contextual effects of absenteeism at the school level are many times larger than the negative effects at the individual level. Implications for education policy and interventions are discussed.

Traumatic Brain Injury Structure Detection Using Advanced Wavelet Transformation Fusion Algorithm with Proposed CNN-ViT

Detecting Traumatic Brain Injuries (TBI) through imaging remains challenging due to limited sensitivity in current methods. This study addresses the gap by proposing a novel approach integrating deep-learning algorithms and advanced image-fusion techniques to enhance detection accuracy. The method combines contextual and visual models to effectively assess injury status. Using a dataset of repeat mild TBI (mTBI) cases, we compared various image-fusion algorithms: PCA (89.5%), SWT (89.69%), DCT (89.08%), HIS (83.3%), and averaging (80.99%). Our proposed hybrid model achieved a significantly higher accuracy of 98.78%, demonstrating superior performance. Metrics including Dice coefficient (98%), sensitivity (97%), and specificity (98%) verified that the strategy is efficient in improving image quality and feature extraction. Additional validations with “entropy”, “average pixel intensity”, “standard deviation”, “correlation coefficient”, and “edge similarity measure” confirmed the robustness of the fused images. The hybrid CNN-ViT model, integrating curvelet transform features, was trained and validated on a comprehensive dataset of 24 types of brain injuries. The overall accuracy was 99.8%, with precision, recall, and F1-score of 99.8%. The “average PSNR” was 39.0 dB, “SSIM” was 0.99, and MI was 1.0. Cross-validation across five folds proved the model’s “dependability” and “generalizability”. In conclusion, this study introduces a promising method for TBI detection, leveraging advanced image-fusion and deep-learning techniques, significantly enhancing medical imaging and diagnostic capabilities for brain injuries.

Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application

The mining industry is experiencing a transformative shift with the integration of automation, particularly through autonomous haul truck systems, and further advancements are anticipated with the application of swarm robotics. This study evaluates the performance of four swarm robot models, namely baseline, ant, firefly, and honeybee, in optimizing key mining operations such as ore detection, extraction, and transportation. Simulations replicating real-world mining environments were conducted to assess improvements in operational efficiency, scalability, reliability, selectivity, and energy consumption. The results demonstrate that these models can significantly enhance the precision and productivity of mining activities, especially in complex and dynamic settings. A case study of the Pilbara iron ore mine in Australia is presented to illustrate the practical applicability of these models in an actual mining context. The study also highlights specific enhancements in each model, including role specialization in the ant model, advanced communication in the firefly model, and improved localization combined with hybrid control in the honeybee model. While the honeybee model showed superior performance in high-precision tasks, its reliability was limited under high-error conditions, and it faced a computational resources bottleneck in large-scale operations, highlighting the need for further development. By evaluating these models against performance criteria, the study identifies the most suitable swarm models for various mining conditions, offering insights into achieving more sustainable, scalable, and efficient mining operations.

Leveraging Temporal Trends for Training Contextual Word Embeddings to Address Bias in Biomedical Applications: Development Study.

Women have been underrepresented in clinical trials for many years. Machine-learning models trained on clinical trial abstracts may capture and amplify biases in the data. Specifically, word embeddings are models that enable representing words as vectors and are the building block of most natural language processing systems. If word embeddings are trained on clinical trial abstracts, predictive models that use the embeddings will exhibit gender performance gaps. We aim to capture temporal trends in clinical trials through temporal distribution matching on contextual word embeddings (specifically, BERT) and explore its effect on the bias manifested in downstream tasks. We present TeDi-BERT, a method to harness the temporal trend of increasing women's inclusion in clinical trials to train contextual word embeddings. We implement temporal distribution matching through an adversarial classifier, trying to distinguish old from new clinical trial abstracts based on their embeddings. The temporal distribution matching acts as a form of domain adaptation from older to more recent clinical trials. We evaluate our model on 2 clinical tasks: prediction of unplanned readmission to the intensive care unit and hospital length of stay prediction. We also conduct an algorithmic analysis of the proposed method. In readmission prediction, TeDi-BERT achieved area under the receiver operating characteristic curve of 0.64 for female patients versus the baseline of 0.62 (P<.001), and 0.66 for male patients versus the baseline of 0.64 (P<.001). In the length of stay regression, TeDi-BERT achieved a mean absolute error of 4.56 (95% CI 4.44-4.68) for female patients versus 4.62 (95% CI 4.50-4.74, P<.001) and 4.54 (95% CI 4.44-4.65) for male patients versus 4.6 (95% CI 4.50-4.71, P<.001). In both clinical tasks, TeDi-BERT improved performance for female patients, as expected; but it also improved performance for male patients. Our results show that accuracy for one gender does not need to be exchanged for bias reduction, but rather that good science improves clinical results for all. Contextual word embedding models trained to capture temporal trends can help mitigate the effects of bias that changes over time in the training data.

IPECAD Modeling Workshop 2023 Cross-Comparison Challenge on Cost-Effectiveness Models in Alzheimer’s Disease

ObjectivesDecision-analytic models assessing the value of emerging Alzheimer’s disease (AD) treatments are challenged by limited evidence on short-term trial outcomes and uncertainty in extrapolating long-term patient-relevant outcomes. To improve understanding and foster transparency and credibility in modeling methods, we cross-compared AD decision models in a hypothetical context of disease-modifying treatment for mild cognitive impairment (MCI) due to AD. MethodsA benchmark scenario (US setting) was used with target population MCI due to AD and a set of synthetically generated hypothetical trial efficacy estimates. Treatment costs were excluded. Model predictions (10-year horizon) were assessed and discussed during a 2-day workshop. ResultsNine modeling groups provided model predictions. Implementation of treatment effectiveness varied across models based on trial efficacy outcome selection (clinical dementia rating – sum of boxes, clinical dementia rating – global, mini-mental state examination, functional activities questionnaire) and analysis method (observed severity transitions, change from baseline, progression hazard ratio, or calibration to these). Predicted mean time in MCI ranged from 2.6 to 5.2 years for control strategy and from 0.1 to 1.0 years for difference between intervention and control strategies. Predicted quality-adjusted life-year gains ranged from 0.0 to 0.6 and incremental costs (excluding treatment costs) from −US$66 897 to US$11 896. ConclusionsTrial data can be implemented in different ways across health-economic models leading to large variation in model predictions. We recommend (1) addressing the choice of outcome measure and treatment effectiveness assumptions in sensitivity analysis, (2) a standardized reporting table for model predictions, and (3) exploring the use of registries for future AD treatments measuring long-term disease progression to reduce uncertainty of extrapolating short-term trial results by health-economic models.

Multi-Sensory Marketing and Impulsive Buying Behavior: The Role of Impulsive Buying Traits

The rapid growth of the retail market, coupled with increased consumer income, has led to a surge in impulsive and unplanned shopping. This study aims to examine the relationships between multi-sensory marketing, customers' moods, impulsive buying traits, and impulsive buying behavior, using the Stimulus-Organism-Response (S-O-R) model. Customers' mood acts as a mediator and impulsive buying traits as a moderator. The researchers surveyed 450 consumers in Vietnam using the method of non-probability sampling and analyzed the data using SPSS and Smart PLS 4.0. The results show that customers' moods and impulsive buying traits positively influence impulsive buying behavior, with impulsive buying traits also significantly moderating the relationship between mood and impulsive buying. Additionally, customers' moods mediate the relationship between multi-sensory marketing and impulsive buying behavior. This research fills gaps in previous research by incorporating multi-sensory marketing and impulsive buying traits as variables and provides managerial implications for businesses and retailers to develop effective marketing strategies to encourage impulsive purchases and increase sales. Academically, the research reaffirms the relevance of the S-O-R model in the retail context and paves the way for future investigations into consumers' impulsive buying behavior. Doi: 10.28991/ESJ-2024-08-05-05 Full Text: PDF

Analysis of co-operative irrigation farming and household food security in Africa: A PRISMA model approach

This study provides a comprehensive bibliometric analysis of the relationship between co-operative irrigation farming and household food security in Africa. The research aims to identify key studies, authors, and thematic clusters, analyse the geographical distribution of research efforts, and evaluate the impact of co-operative irrigation farming on food security indicators. Utilizing the PRISMA model, data were systematically gathered from peer-reviewed publications indexed in the Dimensions database, focusing on materials published between 2019 and 2023. The study employed both bibliometric and descriptive-quantitative methodologies to analyse the data. The results reveal a significant positive impact of co-operative irrigation farming on improving household food security across various regions in Africa. The findings highlight the need for targeted policy interventions and sustainable agricultural practices that support co-operative irrigation efforts. A growing scholarly attention toward co-operative irrigation farming and household food security in recent years reveals a potentiality in finding a sustainable solution to food insecurity challenge. These insights are essential for shaping future research and informing policies aimed at enhancing food security through co-operative farming models in the African context.

Enhancing few-shot lifelong learning through fusion of cross-domain knowledge

Humans can continually solve new problems with a few examples and enhance their learned knowledge by incorporating new ones. Few-shot lifelong learning (FSLL) has been presented to mimic human learning ability. However, they overlook the significance of cross-domain knowledge and little effort has been made to investigate it. In this paper, we explore the effects of cross-domain knowledge in FSLL and propose a new framework to enhance the model’s ability by fusing cross-domain knowledge into the learning process. Moreover, we investigate the impact of both debiased and non-debiased models in the FSLL context for the first time. Compared with previous works, our setting presents a unique challenge: the model should continually learn new knowledge from cross-domain few-shot data and update its existing knowledge by fusing new knowledge throughout its lifelong learning process. To address this challenge, the proposed framework focuses on learning and updating while migrating the well-known issues of forgetting and overfitting. The framework comprises three key components designed for learning cross-domain knowledge: the Debiased Base Learning strategy, Knowledge Acquisition, and Knowledge Update. The superiority of the framework is validated on mini-ImageNet, CIFAR-100, OfficeHome, and Meta-Dataset. Experiments show that the proposed framework exhibits the capability to perform in cross-domain situations and also achieves state-of-the-art performance in the non-cross-domain situation.

A method for evaluating deep generative models of images for hallucinations in high-order spatial context

Deep generative models (DGMs) have the potential to revolutionize diagnostic imaging. Generative adversarial networks (GANs) are one kind of DGM which are widely employed. The overarching problem with deploying any sort of DGM in mission-critical applications is a lack of adequate and/or automatic means of assessing the domain-specific quality of generated images. In this work, we demonstrate several objective and human-interpretable tests of images output by two popular DGMs. These tests serve two goals: (i) ruling out DGMs for downstream, domain-specific applications, and (ii) quantifying hallucinations in the expected spatial context in DGM-generated images. The designed datasets are made public and the proposed tests could also serve as benchmarks and aid the prototyping of emerging DGMs. Although these tests are demonstrated on GANs, they can be employed as a benchmark for evaluating any DGM. Specifically, we designed several stochastic context models (SCMs) of distinct image features that can be recovered after generation by a trained DGM. Together, these SCMs encode features as per-image constraints in prevalence, position, intensity, and/or texture. Several of these features are high-order, algorithmic pixel-arrangement rules which are not readily expressed in covariance matrices. We designed and validated statistical classifiers to detect specific effects of the known arrangement rules. We then tested the rates at which two different DGMs correctly reproduced the feature context under a variety of training scenarios, and degrees of feature-class similarity. We found that ensembles of generated images can appear largely accurate visually, and show high accuracy in ensemble measures, while not exhibiting the known spatial arrangements. The main conclusion is that SCMs can be engineered, and serve as benchmarks, to quantify numerous per image errors, i.e., hallucinations, that may not be captured in ensemble statistics but plausibly can affect subsequent use of the DGM-generated images.