Miracle-dz/UniFAR-FTUs huggingface dataset:
A Smartphone Food Record App Developed for the Dutch National Food Consumption Survey: Relative Validity Study

Background In the Dutch National Food Consumption Survey, dietary intake has been assessed since 2003 through 24-hour dietary recalls using the GloboDiet software. A new self-administered smartphone food record app called DitEetIk! was developed for potential use in future surveys. Objective This study aims to evaluate the data collected using the DitEetIk! app and its relative validity for food group, energy, and nutrient intake compared with the previous dietary assessment method (GloboDiet 24-hour dietary recalls). Methods A total of 300 participants aged 18 to 79 years were recruited from a consumer panel. Participants were asked to keep a record of their consumption using the DitEetIk! app on 3 nonconsecutive days. Trained dietitians conducted a 24-hour dietary recall interview by telephone using the GloboDiet software (International Agency for Research on Cancer) regarding 1 of 3 DitEetIk! recording days. Nutrient intake was calculated using the NEVO database (version 2021/7.0). Relative validity was studied by comparing data from GloboDiet 24-hour dietary recalls and the DitEetIk app for the same day. Participants with implausible records, defined as days with energy intake of <0.6 or >3.0 basal metabolic rate, were excluded from the analyses. For 19 food groups and 29 nutrients, differences in median intake were assessed using the Wilcoxon signed rank test, and Spearman correlation coefficients were calculated. Bland-Altman plots with mean differences and 95% limits of agreement were created for energy intake and the contribution to energy intake from fat, carbohydrates, and protein. Results A total of 227 participants completed a combination of a DitEetIk! app recording day and a 24-hour dietary recall interview for the same day. Of this group, 211 participants (n=104, 49.3% men and n=107, 50.7% women) had plausible recording days. Of all recorded food items, 12.8% (114/894) were entered via food barcode scanning, and 18.9% (169/894) were searched at the brand level. For 31% (5/16) of the food groups, the median intake assessed using the DitEetIk! app was >10% lower than that assessed using 24-hour dietary recalls; this was the case for fruit (P=.005), added fats (P=.001), milk and milk products (P=.02), cereal products (P=.01), and sauces (P<.001). This was also the case for 14% (4/29) of the nutrients (all P<.001). Regarding mean intake, differences were generally smaller. Regarding energy intake, the mean difference and 95% limits of agreement were 14 kcal (−1096 to 1124). Spearman correlation coefficients between intake assessed using the DitEetIk! app and 24-hour dietary recalls ranged from 0.48 to 0.88 (median 0.78) for food groups and from 0.58 to 0.90 (median 0.72) for nutrients. Conclusions Compared with GloboDiet 24-hour dietary recalls, the DitEetIk! app assessed similar mean energy intake levels but somewhat lower median intake levels for several food groups and nutrients.
Physical activity levels among undergraduate students at the College of Applied Medical Sciences, King Saud University, Riyadh: A prevalence study

Physical inactivity and sedentary behavior pose significant health concerns, particularly among young adults like college students. However, little research exists on the attitudes towards physical activity among Saudi Arabian healthcare students. This study aimed to assess the prevalence of physical activity and determine the reason for being physically inactive among students and interns across all programs in the College of Applied Medical Sciences (CAMS) at King Saud University (KSU), Riyadh, Saudi Arabia. A cross-sectional online survey was conducted between March and May 2023. CAMS students aged 18 and above, fluent in Arabic or English, and willing to participate were included. The Global Physical Activity Questionnaire (GPAQ) was used to measure physical activity. Descriptive statistics were used for data analysis. Out of 433 respondents, 377 met the inclusion criteria (response rate: 89.3%). Physical activity prevalence was 54.60%, with walking being the most common activity (42.70%). Lack of time was the primary reason for inactivity (51.40%). On average, students engaged in 1.5 hours/day of vigorous-intensity activities and 1.3 hours/day of vigorous-intensity sports and fitness activities at work. Sedentary behavior, characterized by continuous sitting, averaged 5.37 hours per day. Approximately half of CAMS students are physically active, while 20% rely solely on short walks due to time constraints, limited activity hours, or sedentary habits. A lack of time was cited as the primary reason for inactivity by most students. Future research should focus on strategies to enhance physical activity among healthcare students. In addition, academicians and policymakers should propose time-efficient initiatives that accommodate the demanding and busy schedules of healthcare students.
Simulating complex snow distributions in windy environments using SnowTran-3D

Abstract We present a generalized version of SnowTran-3D (version 2.0), that simulates wind-related snow distributions over the range of topographic and climatic environments found globally. This version includes three primary enhancements to the original Liston and Sturm (1998) model: (1) an improved wind sub-model, (2) a two-layer sub-model describing the spatial and temporal evolution of friction velocity that must be exceeded to transport snow (the threshold friction velocity) and (3) implementation of a three-dimensional, equilibrium-drift profile sub-model that forces SnowTran-3D snow accumulations to duplicate observed drift profiles. These three sub-models allow SnowTran-3D to simulate snow-transport processes in variable topography and different snow climates. In addition, SnowTran-3D has been coupled to a high-resolution, spatially distributed meteorological model (MicroMet) to provide more realistic atmospheric forcing data. MicroMet distributes data (precipitation, wind speed and direction, air temperature and relative humidity) obtained from meteorological stations and/or atmospheric models located within or near the simulation domain. SnowTran-3D has also been coupled to a spatially distributed energy- and mass-balance snow-evolution modeling system (SnowModel) designed for application in any landscape and climate where snow is found. SnowTran-3D is typically run using temporal increments ranging from 1 hour to 1 day, horizontal grid increments ranging from 1 to 100 m and time-spans ranging from individual storms to entire snow seasons.
Path Planning and Trajectory Optimization Based on Improved APF and Multi-Target

Aiming at the shortcomings of the path generated by the artificial potential field (APF) method, such as local minimum, target unreachability, and low path smoothness, an improved artificial potential field method is proposed. First, to reduce the collision risk and planning difficulty, based on known environmental information such as the location of obstacles and targets, the area with fewer obstacles is selected as the priority area for path planning. Second, to improve the path smoothness and reduce the computation amount, an adaptive step-size adjustment method based on the distance and angle relationship with obstacles within the prediction range is proposed. Third, in view of the effect on each other between obstacle, local minimum, and unsmooth path, a multi-target model considering the size and influence range of obstacles and an improved potential field function are proposed on the basis of the identified planning priority area. Finally, in order that the path is smooth enough to be tracked by autonomous mobile robots, a safe driving corridor without collision with obstacles is constructed on the planned path, and a trajectory fully constrained to the safe driving corridor is generated using the quadratic programming method. The simulation comparison experiments are carried out on matlab simulation software and the smoothness of IAPF is improved by an average of 97.3% as compared to traditional APF and 45.19% as compared to DWA. The sum of the proposed IAPF path planning and optimization time is improved by 45.1% on average compared to DWA path planning time.
Backtesting Quantum Computing Algorithms for Portfolio Optimization

In portfolio theory, the investment portfolio optimization problem is one of those problems whose complexity grows exponentially with the number of assets. By backtesting classical and quantum computing algorithms, we can get a sense of how these algorithms might perform in the real world. This work establishes a methodology for backtesting classical and quantum algorithms in equivalent conditions, and uses it to explore four quantum and three classical computing algorithms for portfolio optimization and compares the results. Running 10 000 experiments on equivalent conditions we find that quantum can match or slightly outperform classical results, showing a better escalability trend. To the best of our knowledge, this is the first work that performs a systematic backtesting comparison of classical and quantum portfolio optimization algorithms. In this work, we also analyze in more detail the variational quantum eigensolver algorithm, applied to solve the portfolio optimization problem, running on simulators and real quantum computers from IBM. The benefits and drawbacks of backtesting are discussed, as well as some of the challenges involved in using real quantum computers of more than 100 qubits. Results show quantum algorithms can be competitive with classical ones, with the advantage of being able to handle a large number of assets in a reasonable time on a future larger quantum computer.
Do Political Connections Affect Tax Avoidance in Indonesia? A Systematic Review and Meta-Analysis

Previous correlational studies have shown a significant effect between political connections and tax avoidance. However, this also cannot deny other empirical facts which state that there is no significant influence between political connections and tax avoidance. This study aims to generalize the results of previous studies that discuss the influence of political connections and tax avoidance in Indonesia. Quantitative methods were chosen to analyze this; the researchers used Systematic Review and Meta-Analysis. We searched for eligible studies from the databases Garba Rujukan Digital (GARUDA), Ebsco, and Google Scholar until 17 September 2022. Meta-analysis was carried out using STATA software with the help of Microsoft Excel to calculate the effect size of each study data. Fourteen studies were successfully netted for analysis, of which 14 developed into 29 data using the data available in each study. We find that the relationship between political connections and tax avoidance in Indonesia has an insignificant direction; this is indicated by the value of z = -1.23 and p-value of 0.22 or above 0.05. It is the latest study in Indonesia to investigate the relationship between political connections and tax avoidance. Furthermore, the test results indicate that differences in tax avoidance proxies affect the test results for political connections. In contrast, there was no significant effect on the political connection proxy. The relevant tax authorities can consider significant tax avoidance proxies for political connections as one of the detection indicators in the Compliance Risk Management of Taxpayer Supervision.
A feasibility study of borehole radar as a permanent downhole sensor

Permanent downhole sensors provide the eyes and ears to the reservoir and enable monitoring the reservoir conditions on a real‐time basis. In particular, the use of sensors and remotely controlled valves in wells and on the surface, in combination with reservoir flow models provide enormous benefits to reservoir management and oil production. We suggest borehole radar measurements as a promising technique capable to monitor the arrival of undesired fluids in the proximity of production wells.
Improved iterative Wishart classification algorithm based on polarimetric scattering characteristics preservation

To overcome the deficiencies of traditional iterative Wishart classification algorithm based on polarimetric scattering characteristics preservation,especially its inadaptability in urban area and insufficiency for classifying mixed scattering pixels,a novel improved approach was proposed in this paper.This improved classification algorithm could be implemented through three following steps.First,all pixels were divided into four fundamental scattering categories and a mixed scattering category using the four-component decomposition algorithm.Then,pixels in fundamental scattering categories were clustered adaptively in terms of the average merging measurement.Finally,Wishart classification operation which can preserve polarimetric scattering characteristics was utilized iteratively on all pixels.Experimental results showed that the proposed method could perform better in adaptability and classification than traditional one.
Semantic Food Segmentation Using Convolutional Deconvolutional Pyramid Network for Health Monitoring

—This paper presents semantic food segmentation to detect individual food items in an image in the context of Food Recognition (FoodRec) project. FoodRec aims to study and develop an automatic framework to track and monitor the dietary habits of people, during their smoke quitting protocol. Studies have shown a strong correlation between dietary habits’ changes of individuals and smoking cessation process. Abstinence from smoking is associated with several negative effects such as gain of weight, eating disorders, mood changes, and irritability during the initial period of smoke quitting. In this contribution, a novel Convolutional Deconvolutional Pyramid Network (CDPN) is proposed for food segmentation to understand the semantic information of an image at a pixel level. This network employs convolution and deconvolution layers to build a feature pyramid and achieves high-level semantic feature map representation. As a consequence, the novel semantic segmentation network generates a dense and precise segmentation map of the input food image. Furthermore, the proposed method achieved competitive results with 91.77% mean Intersection over Union (IOU) on TrayDataset and 77% mean IOU on MyFood dataset when compared to the state-of-the-art techniques.
Implementasi Natural Language Processing (NLP) Dalam Pengembangan Aplikasi Chatbot Pada SMK YPE Nusantara Slawi

Perkembangan teknologi informasi memberikan berbagai perubahan dalam berbagai bidang kehidupan, salah satunya adalah dalam dunia pendidikan. Perkembangan teknologi tersebut ditandai dengan munculnya teknologi berbasis Artificial Intelligence (AI) yang sudah mulai digunakan. Salah satunya adalah aplikasi chatbot. Aplikasi chatbot merupakan suatu aplikasi yang dibangun dengan mengadopsi pengetahuan manusia yang dapat memberikan respon secara cepat dan tepat. SMK YPE Nusantara Slawi sudah memiliki aplikasi chatbot akan tetapi aplikasi tersebut hanya digunakan untuk pembelajaran materi di salah satu prodi saja. Pihak sekolah mengalami kesulitan dalam promosi dan juga penyampaian informasi kepada masyarakat. Banyak masyarakat yang ketika membutuhkan informasi harus datang ke sekolah. Bahkan seringkali pertanyaan yang ditanyakan adalah sama antar satu dengan yang lainnya, sehingga terjadi ketidak efisiensi. Oleh karenanya dibutuhkan suatu aplikasi interaktif yang dapat memberikan informasi tentang sekolah tersebut. Aplikasi chatbot yang dibuat berisi informasi secara menyeluruh seputar sekolah yang terdiri dari jurusan, guru maupun data lainnya yang berkaitan dengan sekolah. Penelitian dilakuakn dalam beberapa tahapan mulai dari pengambilan data, analisis data, pembuatan aplikasi pengujian sampai ke implementasi sistem. Aplikasi dikembangkan dengan menggunakan Natural Language Procesing (NPL) yang akan dikembangkan berbasis website. Luaran penelitian yang ditargetkan adalah menghasilkan suatu aplikasi chatbot informasi sekolah dan hasilnya dapat dipublikasikan dalam jurnal nasional terakreditasi.
Community engagement in population-level research in the context of Covid-19 in rural Bangladesh

Globally, researchers have been struggling with the ethical and practical questions of implementing community-based population-level research interventions during the Covid-19 pandemic. The safety of researchers and participants while implementing the interventions, and the need to avoid overburdening a health system already under pressure, are priorities, but it is also important to understand community perspectives on research. This article analyses community opinions about restarting a population-level community-based intervention for the prevention and control of type 2 diabetes in rural Bangladesh in a population of 120,000. We used qualitative phone interviews, discussions with key stakeholders and consultation with a trial steering committee. Community members were able and interested to attend intervention group meetings, but Covid-19 preventative measures were not being implemented routinely. People with diabetes did not perceive themselves to be at particular risk from Covid-19. Community members were meeting in groups and felt safe among friends and neighbours, but they did not feel safe among ‘outsiders’. As a result of our community engagement, we restarted the intervention but controlled the size of the groups and screened group members and group facilitators for Covid-19 symptoms. Community engagement enabled community members to contribute to decisions about how to restart the intervention.
Application of NDVI for predicting fuel curing at landscape scales in northern Australia: can remotely sensed data help schedule fire management operations?

The success of early dry season burning programs in tropical savannas of northern Australia could be improved with timely information on curing state of fuel loads. Variable characteristics of each wet season, the onset of the dry season, and variations of fuel loads within major landscape types affect the annual cycle of curing. Significant relationships were derived between ground-based visual estimates of curing, and estimates of relative greenness derived from NDVI images from NOAA AVHRR and SPOT Vegetation satellite sensors. There were distinct differences between soil types (red v. black) and seasons (1999 v. 2000). The next stage is to test if relationships are robust enough to be used operationally to schedule aerial control burning operations in remote, inaccessible and sparsely unpopulated areas.
Building nonenhanced CT based radiomics model in discriminating arteriovenous malformation related hematomas from hypertensive intracerebral hematomas

Objective To develop and validate radiomics models on non-enhanced CT for discrimination of arteriovenous malformation (AVM) related hematomas from hypertensive intracerebral hematomas. Materials and methods A total of 571 patients with acute intraparenchymal hematomas and baseline non-enhanced CT scans were retrospectively analyzed, including 297 cases of AVM related hematomas and 274 cases of hypertensive intracerebral hematomas. The patients were divided into training and validation cohorts in a 7:3 ratio with a random seed. A total of 1,688 radiomics features of hematomas were extracted from non-enhanced CT. Then, the least absolute shrinkage and selection operator (LASSO) regression was applied to select features and construct the radiomics models. In this study, a radiomics-based model was constructed that based on the radiomics features only. Furthermore, a combined model was constructed using radiomics features, clinical characteristics and radiological signs by radiologists’ evaluation. In addition, we compared predictive performance of the two models for discrimination of AVM related hematomas from hypertensive intracerebral hematomas. Results A total of 67 radiomics features were selected to establish radiomics signature via LASSO regression. The radiomics-based model was constructed with 2 classifiers, support vector machine (SVM) and logistic regression (LR). AUCs of the radiomics-based model in the training set were 0.894 and 0.904, in validation set were 0.774 and 0.782 in SVM classifier and LR classifier, respectively. AUCs of the combined model (combined with radiomics, age and calcification) in the training set were 0.976 and 0.981, in validation set were 0.896 and 0.907 in SVM classifier and LR classifier, respectively. The combined model showed greater AUCs than radiomics-based model in both training set and validation set. Conclusion The combined model using radiomics, age and calcification showed a satisfactory predictive performance for discrimination of AVM related hematomas from hypertensive intracerebral hematomas and hold great potential for personalized clinical decision.
The silver lining of supply chain complexity: building supply chain resilience and robustness through exploitation and exploration

Purpose This study aims to analyse whether the presence of supply chain complexity (SCC) influences firms to improve their supply chain (SC) resilience and SC robustness capability. This study also examines an important paradox: whether investing in both exploitation and exploration practices is conflicting or complementary to enabling SC resilience and robustness in the presence of SCC. Design/methodology/approach The authors used a survey-based approach to collect 242 useful responses from SC professionals of Pakistani firms, an important emerging economy context. The data were analysed with covariance-based structural equation modelling to statistically validate the model. Findings The analysis reveals several key findings: the presence of SCC has a direct, positive influence on SC resilience and SC robustness; while exploitation practices only partially mediate the nexus between SCC and SC resilience, they fully mediate the relationship between SCC and SC robustness; while exploration practices partially mediate the nexus between SCC and SC resilience, they do not mediate the relationship between SCC and SC robustness and SCC has a significant influence on SC resilience and SC robustness sequentially through exploitation and exploration (i.e. one after the other). Practical implications These findings help to reconcile the exploitation versus exploration paradox in cultivating SC resilience and SC robustness in the presence of SCC. The findings assist SC managers in determining how to deploy their limited resources most effectively to enhance SC resilience and SC robustness while facing SCC. Originality/value The authors devise and empirically validate a unique framework that demonstrates how the presence of SCC works as a stimulus to build SC resilience and SC robustness.
The Worldwide Expansion of Evaluation: a World of Possibilities for Rural

organizations and public policies at a regional, national and local level, as well as by the exponential growth of evaluation societies around the world. Focusing on rural development, this expansion is due to the utility of evaluation in providing evidence to help in the decision-making and improve interventions, as well as being a value system that establishes criteria and explicit standards by which to judge these interventions. This paper aims to provide clarity on these aspects through a comprehensive analysis of the evolution and the current situation of the evaluation field. The literature reviewed and our evaluation experiences confirm the evaluation’s ability to provide a scientific basis to the decisionmaking in rural development programmes.