Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital Gamification Tools to Enhance Vaccine Uptake: Scoping Review

Background Gamification has been used successfully to promote various desired health behaviors. Previous studies have used gamification to achieve desired health behaviors or facilitate their learning about health. Objective In this scoping review, we aimed to describe digital gamified tools that have been implemented or evaluated across various populations to encourage vaccination, as well as any reported effects of identified tools. Methods We searched Medline, Embase, CINAHL, the Web of Science Core Collection, the Cochrane Database of Systematic Reviews, the Cochrane Central Register of Controlled Trials, Academic Search Premier, PsycInfo, Global Health, and ERIC for peer-reviewed papers describing digital gamified tools with or without evaluations. We also conducted web searches with Google to identify digital gamified tools lacking associated publications. We consulted 12 experts in the field of gamification and health behavior to identify any papers or tools we might have missed. We extracted data about the target population of the tools, the interventions themselves (eg, type of digital gamified tool platform, type of disease/vaccine, type and design of study), and any effects of evaluated tools, and we synthesized data narratively. Results Of 1402 records, we included 28 (2%) peer-reviewed papers and 10 digital gamified tools lacking associated publications. The experts added 1 digital gamified tool that met the inclusion criteria. Our final data set therefore included 28 peer-reviewed papers and 11 digital gamified tools. Of the 28 peer-reviewed papers, 7 (25%) explained the development of the tool, 16 (57%) described evaluation, and 2 (7%) reported both development and evaluation of the tool. The 28 peer-reviewed papers reported on 25 different tools. Of these 25 digital gamified tools, 11 (44%) were web-based tools, 8 (32%) mobile (native mobile or mobile-enabled web) apps, and 6 (24%) virtual reality tools. Overall, tools that were evaluated showed increases in knowledge and intentions to receive vaccines, mixed effects on attitudes, and positive effects on beliefs. We did not observe discernible advantages of one type of digital gamified tool (web based, mobile, virtual reality) over the others. However, a few studies were randomized controlled trials, and publication bias may have led to such positive effects having a higher likelihood of appearing in the peer-reviewed literature. Conclusions Digital gamified tools appear to have potential for improving vaccine uptake by fostering positive beliefs and increasing vaccine-related knowledge and intentions. Encouraging comparative studies of different features or different types of digital gamified tools could advance the field by identifying features or types of tools that yield more positive effects across populations and contexts. Further work in this area should seek to inform the implementation of gamification for vaccine acceptance and promote effective health communication, thus yielding meaningful health and social impacts.
Evidence-Based Monitoring of International Migration Flows in Europe

Abstract In Europe, the monitoring and management of migration flows are high on the political agenda. Evidence-based monitoring calls for adequate data, which do not exist. The sources of data on international migration differ significantly between countries in Europe and the initiatives to improve data collection and produce comparable data, including new legislation, did not yield the expected outcome. Scientists have developed statistical models that combine quantitative and qualitative data from different sources to derive at estimates of migration flows that account for differences in definition, undercoverage, undercount and other measurement problems. Official statisticians are reluctant to substitute estimates for measurements. This article reviews the progress made over the last decades and the challenges that remain. It concludes with several recommendations for better international migration data/estimates. They range from improved cooperation between actors to innovation in data collection and modelling.
The effect of innovation on sustainable competitive advantage through local wisdom in Padang restaurants in Pekanbaru

Business competition due to a dynamic business environment that makes Padang restaurant businesses must have the ability to have a sustainable competitive advantage. This study aims to analyze the effect of innovation on sustainable competitive advantage through local wisdom as a mediating variable. A quantitative explanatory approach was used in this study with 100 respondents. It uses the accidental sampling technique of the Padang restaurant business in Pekanbaru. The analytical tool used is PLS, with research showing that innovation affects competitive advantage through local wisdom as a mediating variable. Local wisdom as a mediating variable shows the novelty of this research as a form of cultural integration to achieve sustainable competitive advantage. The unique value contained in the product is a symbol of a strong identity so that it can appear different from competitors. The value of the authenticity and uniqueness of Padang food as a culinary and cultural heritage passed down from generation to generation is different from competitors. So, every innovation made by Padang restaurants in Pekanbaru based on the values of local wisdom will increase sustainable competitive advantage.
Comparison of CNN Architecture of Image Classification Using CIFAR10 Datasets

This paper demonstrates image classification using deep learning. Deep learning has the inherent ability to automatically discover and extract meaningful features for specific applications. Among the popular techniques in deep learning, the convolutional neural network (CNN) stands out. CNN consists of an input layer, hidden layers, and an output layer, where meaningful features are automatically extracted from input images. This paper presents the performance and identifies the most effective CNN architectures for accurately classifying images in the CIFAR-10 dataset. Five CNN architectures were implemented, namely [Architecture 1], [Architecture 2], [Architecture 3], [Architecture 4], and [Architecture 5] using the CIFAR-10 dataset. The architectures were selected based on the need to explore variations in convolutional filter sizes, dense layers, and batch normalization to assess their impact on CIFAR-10 image classification performance. Each architecture was trained on a standard training set and evaluated on a validation set. We used specific details on data preprocessing and training settings for a consistent and fair comparison. After training and evaluation, we have obtained the following results for each architecture. Architecture 1 has a training accuracy of 74.7% and validation accuracy of 76.6%, Architecture 2 has 96.09% and 86.08%, Architecture 3 has 77% and 78.1%, Architecture 4 has 67.91% and 69.54%, and Architecture 5 has 94.64% and 87.34% of training accuracy and validation accuracy respectively. After conducting a comparative analysis, we found that Architecture 5 has achieved the highest validation accuracy in classifying images in the CIFAR-10 dataset. These findings suggest that Architecture 5 is a promising choice for image classification tasks involving the CIFAR-10 dataset.
Enhancing Wheat Crop Resilience to Drought Stress through Cellulolytic Microbe-Enriched Cow Dung Vermicompost

Background: Wheat, an important cereal crop, is commonly cultivated in arid and semiarid areas, and therefore, it often experiences water deficit conditions. The consequences of induced stress on wheat can be mitigated through vermicompost amendments. To address drought stress on wheat seedlings, a pot experiment was conducted in the wire-house in which two contrasting wheat cultivars, Faisalabad-08 (drought-tolerant) and Galaxy-13 (drought-sensitive), were exposed to three water level conditions: well-watered [D0, 70% of field capacity (FC)], moderate drought (D1, 45% FC), and severe drought (D2, 30% FC). Four rates of vermicompost, derived from cow dung enriched with cellulolytic microbes, were applied (VT0, control; VT1, 4 t ha–1; VT2, 6 t ha–1; and VT3, 8 t ha–1) to the experiment. Data on various physiological, biochemical, and enzymatic antioxidants were recorded. Results: Our results demonstrated that the drought treatments significantly reduced nutrient accumulation, chlorophyll and SPAD values, and carotenoid content in both cultivars where the maximum reduction was recorded for severe drought stress. Nonetheless, the application of vermicompost significantly improved these traits, and statistically maximum chlorophyll contents, SPAD value, and total carotenoid contents were observed for VT1 in both cultivars under drought treatments. While the lowest chlorophyll and carotenoid contents were recorded for untreated replicated pots. Among the cultivars, Faisalabad-08 exhibited greater resistance to drought, as evidenced by higher values of the aforementioned traits compared to Galaxy-13. Soil-applied vermicompost also showed a positive influence on antioxidant enzyme activities in both wheat cultivars grown under well-watered as well as water-scarce conditions. Conclusions: The findings of this study revealed that drought conditions substantially decreased the enzymatic antioxidants and physiological and biochemical attributes of the wheat crop. However, soil-applied vermicompost, particularly at an optimum rate, had a positive impact on the wheat seedlings under drought conditions. Moving forward, exploring the potential of utilizing cellulolytic microbe-enriched cow dung vermicompost stands as a promising avenue to mitigate the detrimental effects of water stress on wheat. Further research in this direction could offer substantial insights into enhancing wheat resilience and productivity under water stress conditions.
Detection and Characterization of Fracture Zones in Bedrock - Possibilities and Limitations

In Norway, resistivity measurements have already been tested in marine environments in order to detect subsea fracture zones. However, most of these data have been processed without taking into account the special conditions the presence of seawater creates. More recent studies worldwide have also applied ERT in marine conditions, but under more favorable conditions nevertheless since they dealt with brackish water of considerably higher resistivity than pure seawater. This study summarizes our efforts to establish basic rules when considering whether or not pure sea water ERT can satisfactorily detect weak zones inside resistive bedrock, a problem engineers in Norway usually come up against in tunnel construction sites. The scope for this study is related to the construction of a sub-sea tunnels and the potential application of ERT to detect fractured zones as part of the geotechnical study. Our results indicate that ERT surveys for fracture zone detection in Norwegian marine environments can be promising under certain conditions but at the same time ambiguous since they suffer from reduced resolution and major artificial effects. Based on the modeling results, we were able to improve interpretations of ERT measurements made across the straits at Kvitsoy and plan further investigations in southern Norway.
Recommendation Generation on Extracted Web Data Using Cosine Similarity

A online databases can be queried and/or updated through World Wide Web (WWW). Online databases are also called as web databases. Online databases generate result pages in the form of response to queries posted by users. Such Result pages are called as Resulted Query pages (QRPs). Nowadays, many analytical applications necessitate the automatic extraction of data from these query result pages. The extracted result from query result pages is very important for many web applications. This is because the QRP is a consolidation of related data which is in fields of database. While trying to extract QRPs initially the webpage data which is in the HyperText Markup Language (HTML) format is converted to Extensible Markup Language (XML) format using web harvest tool. The data is in XML format is semi-structured or non-structured data. The alignment of this non-structured data into structured format is done using cosine similarity approach. Data records which are aligned are used for text mining purposes. Keywords— Cosine Similarity, Data Extraction, Recommendation.
Polyphenol-Standardized Aphanamixis polystachya Leaf Extract Ameliorates Diabetes, Oxidative Stress, Inflammation, and Fibrosis in Streptozotocin-Induced Diabetic Rats

Background. Diabetes is a rising disorder that affects millions of people annually. It also creates more complications, such as neuropathy, oxidative stress, and hepatic and kidney impairment. Aphanamixis polystachya plant, which possesses multiple medicinal values, is used in this study to explore its potential in treating diabetes. Methods. A single dose (65 mg/kg) of intraperitoneal streptozotocin injection was utilized to mediate diabetes in Sprague-Dawley rats. Diabetic animals were treated orally with 250 or 500 mg/kg of standardized leaves’ extract of A. polystachya (AP) for 28 days to evaluate the antidiabetic and organ-protective effects of the plant. Different biochemical and histological markers are measured according to the established protocol. Results. Our results demonstrated a significant decrease in blood glucose (p <0.001) and HbA1c (p <0.05) levels in the diabetic animal after administering AP (250 and 500 mg/kg doses) compared to the control groups. AP can also regularize lipids, glycogen, alanine aminotransferase, and aspartate aminotransferase. Furthermore, serum urea nitrogen and creatinine decreased after treatment with AP in diabetic rats. AP also reduced oxidative stress markers and showed a substantial elevation in antioxidant enzymes in diabetic animals. Overall, AP at 500 mg/kg revealed comparable results against the standard antidiabetic drug glyburide. Besides, the histological investigation showed the AP’s efficacy in attenuating kidney tissue inflammation and damage. HPLC data elucidated that the AP leaf extract contains polyphenols with potential antioxidant, antidiabetic, and organ protective agents: gallic acid, rutin hydrate, and quercetin hydrate. Conclusion. Taken together, AP may be one of the potential sources of antidiabetic agents.
From Roots to Leaves: Understanding Consumer Acceptance in Implementing Climate-Resilient Strategies in Viticulture

This study investigates consumer perceptions and acceptance of innovative strategies implemented to counter the effects of climate change in the wine industry. The acceptance of wines derived from grapes grown using four different vineyard management practices—kaolin application, use of plant growth regulators, introduction of resilient rootstocks, and fungus-resistant grape varieties (PIWI)—is analyzed. Utilizing a latent profile analysis, the study identifies five distinct consumer profiles, each displaying unique sensitivities and perceptions towards climate change threats. The findings reveal notable variations in consumers’ willingness to accept these innovations. Additionally, the study offers insights into how these preferences influence the marketability of wines produced using these innovative techniques. The overall results depict heterogeneous acceptance of these practices. Consumers seem more inclined to choose wines derived from vines with innovative rootstocks and those treated with kaolin. The acceptance for PIWI varieties and PGR is comparatively marginal. The outcomes of this study provide valuable insights for winegrowers, policymakers, and other industry stakeholders on effectively implementing and communicating these solutions. Overall, the research findings contribute significantly to understanding consumer behavior within the context of climate change in the wine industry, presenting substantial implications for sustainable viticulture practices and wine marketing strategies.
Sponges from North Borneo and their bioactivity against human colorectal cancer cells

Invertebrates are a major reservoir for numerous cytotoxic compounds that are used to defence themselves against prey and adaptation towards the environment. Throughout the years, numerous studies discovered from sponge extracts were effective against a wide range of cancer cells. In this study, 23 sponges comprising of some 19 species were collected from Northeast Borneo. Sponges were treated and extracted using modified Folch extraction method, followed by cytotoxicity assay to determine their effectiveness against different colorectal cancer cells. From the results, Monanchora clathrate, Dysidea sp. , and Jaspis sp. were found to possess different degrees of cytotoxicity against wide range of human colorectal cancer cell. Monanchora clathrate (KDT07), Dysidea sp. (KDT09), and Jaspis sp. (KDT18) are among the demosponges which possess significant cytotoxicity against colorectal cancer cell lines, including HCT116, LoVo, SW480, and SW620. Besides, we found that environmental factors tend to alter chemical yield from the different species under the same genera. Among them, KDT08 and KDT21 which fall under same genus Dysidea, yet possess insignificant cytotoxicity against colorectal cancer cells. This research article provides a preliminary test for cytotoxicity activity of a wide range of marine sponges. Throughout this study, acquired results could provide useful information to determine the worthiness to further isolate nor purify the nature product from these sponges.
Investigations on extraction of buildings from RS imagery using deep learning models

ABSTRACT Building roofs are crucial elements that must be extracted from satellite images for use in services like updating geodatabases, risk analysis and rescue maps. The diversity and complexity of the building structures present challenges for accurate and efficient building rooftop extraction from very high resolution (VHR) images. The significance of building extraction from satellite imagery in various applications has attracted the attention of the research community in this domain. In this paper, the progression of the research in building extraction is presented based on two streams, such as Traditional building extraction techniques and Evolving DL-based building extraction techniques. Furthermore, the categorization of traditional techniques into transform-based techniques, indices-based methodologies and machine learning-based methodologies are summarized. An enhanced comprehension of the challenges associated with extracting buildings from aerial images using DL models is given. A performance analysis of the popular Deep Learning models used for building extraction, such as UNet, SegNet, ResUNet, UNet++, ENRUNet and Dilated ResUNet, in terms of Accuracy, IoU and Dice Loss is presented. We hope that this paper will improve researchers’ understanding of the problems associated with extraction of buildings from images and ultimately to develop new models with better performance.
Anthropometric measurements of severe malnourished children in Elnohoud educational hospital in West Kordofan State, Sudan.

The present work was conducted in Elnohoud educational hospital, West Kordofan State, Sudan. The aim of the study was to study anthropometric measurements of Protein Energy Malnutrition (PEM) among children less than five years of age. Study included 100 children diagnosed with protein energy malnutrition. The children were given the ready to use formula diets, F 75 and F 100, and ReSoMal. The anthropometric measurements i.e. weight (kg), height (cm) and MUAC (Mid-Upper Arm Circumference) and bilateral odema and appetite test were measured in order to assess nutritional status of the malnourished children according to WHO standards. Results indicated that the prevalence of marasmus was 84% among the studied group (75% age group from 13 to 24 month, 19% in >24 months and 6% in age group 6-12 months), pursued by Kwashiorkor 16% (62% in age group 13-24 months and 38% in group of >24 months). Statistical analysis showed that the average weight on admission significantly (p ≤ 0.05) different compared to average weight on discharge. Additionally, 69% of odema (+ve) patients were fully recovered after two weeks prior to admission, while 31% with odema (++ve) showed gradually improvement till discharge after four weeks prior to admission. Apparently, children with malaria only 5 patients at admission three of them recovered after two weeks of admission, while 10 patients with diarrhea, completely restored health at discharge. The 97% of children restored their normal appetite after one week of treatments, majority of children at discharge demonstrated excellent behavioral conditions, but three of them died during the treatments, the study found that the Body Measurement Index (BMI) for investigated group of Elnohoud educational hospital were ranged between 13.61-15.42 kg/M2 at admission and between 14.79- 16.66 kg/M2 . The BMI for this investigation categorized the studied group in malnourished status either at admission or at discharge according to FAO standards.
Predictive Modeling of Proteins Encoded by a Plant Virus Sheds a New Light on Their Structure and Inherent Multifunctionality

Plant virus genomes encode proteins that are involved in replication, encapsidation, cell-to-cell, and long-distance movement, avoidance of host detection, counter-defense, and transmission from host to host, among other functions. Even though the multifunctionality of plant viral proteins is well documented, contemporary functional repertoires of individual proteins are incomplete. However, these can be enhanced by modeling tools. Here, predictive modeling of proteins encoded by the two genomic RNAs, i.e., RNA1 and RNA2, of grapevine fanleaf virus (GFLV) and their satellite RNAs by a suite of protein prediction software confirmed not only previously validated functions (suppressor of RNA silencing [VSR], viral genome-linked protein [VPg], protease [Pro], symptom determinant [Sd], homing protein [HP], movement protein [MP], coat protein [CP], and transmission determinant [Td]) and previously identified putative functions (helicase [Hel] and RNA-dependent RNA polymerase [Pol]), but also predicted novel functions with varying levels of confidence. These include a T3/T7-like RNA polymerase domain for protein 1AVSR, a short-chain reductase for protein 1BHel/VSR, a parathyroid hormone family domain for protein 1EPol/Sd, overlapping domains of unknown function and an ABC transporter domain for protein 2BMP, and DNA topoisomerase domains, transcription factor FBXO25 domain, or DNA Pol subunit cdc27 domain for the satellite RNA protein. Structural predictions for proteins 2AHP/Sd, 2BMP, and 3A? had low confidence, while predictions for proteins 1AVSR, 1BHel*/VSR, 1CVPg, 1DPro, 1EPol*/Sd, and 2CCP/Td retained higher confidence in at least one prediction. This research provided new insights into the structure and functions of GFLV proteins and their satellite protein. Future work is needed to validate these findings.