Miracle-dz/UniFAR-FTUs huggingface dataset:
Knowledge, Attitude & Practices towards Safe Water, Sanitation and Hygiene

Affordable and sustainable access to water, sanitation and hygiene (WASH) is a key public health issue and focus of Sustainable Development Goals. Literature showed that households having prior knowledge and an acceptable attitude towards WASH practices have less number of diseases. The main objective of the study was to explore the level of respondents' knowledge, attitude and practices towards safe Water, Sanitation and Hygiene Conditions in South Punjab, Pakistan. The study was mixed-method research. SPSS also applied, and results demonstrate that there was very lack of knowledge about safe WASH practices; the majority of respondents have a traditional attitude. Whereas only 27.3% of respondents have always access to safe drinking water, 96% of respondents were not using any domestic water treatment method, 22.9% were defecating in the open, and the percentage of always handwashing with soap was found to only 29.6%. Social Mobilization programs along with government action to ensure safe WASH conditions are recommended.
Role of Database Marketing in Improving Quality of Service in Jordanian Private Hospitals

Current study aims to determine the role of database marketing in improving quality of service in Jordanian private hospitals. The study's population is consisted of marketing managers only or their deputies in the Jordanian private hospitals which contains (69) hospitals. Online questionnaire was distributed over a random sample amounting (59) managers, (51) questionnaires were collected, that is (86.44%) of the total sample. The study results indicated that Jordanian private hospitals use database marketing in improving quality of Service in Jordanian Private Hospitals. There is no difference in the role of database marketing in improving quality of service in Jordanian private hospitals according to the managers’ educational level. There is a difference in the role of database marketing in improving quality of service in Jordanian private hospitals according to the managers’ experience.
Body deformation study in a Formula One race car crashing into a rigid barrier at different crash angles

This paper presents the dynamic behaviour of a race car body impacting a rigid barrier. The amount of deformation in the front bumper area and its effect on the driver's seat are investigated. The car crash is studied at different impact angles of 0°, 10°, 30°, 45°, 60° and 80°. The generated elastic and plastic stress and strain fields, specifically at the front bumper and the frontal area of the driver's seat, are compared accurately. Comparing the amount of deformation in different situations shows that an increase in the angle of impact results in more plastic deformation in the bumper but less on the driver's seat instead. As the bumper neutralises the effect of the largest part of kinetic energy and inertia forces, the frontal part of car body behind the bumper is safer.
Sliding mode control for direct current motor system with multi-channels and external disturbances

To solve the deficiencies of speed-tracking control for DC motor systems with multiple transmission channels and external disturbances in recent remote-control systems, a second-order super twisting sliding mode control method is proposed. First, the model of a DC motor with multiple channels and external disturbances is considered. Then, an observer in the form of the super twisting algorithm is presented to estimate the states of the system. Furthermore, a second-order super twisting sliding mode control algorithm based on the super twisting observer is designed to track the speed of the DC motor. In this, a nonlinear term is constructed to restrain the external disturbances and jitters while switching among the channels. Also, the proposed method is testified to be stable. Finally, both simulations and practical experiments are conducted to demonstrate the availability of the methodology.
Impacts of Long-Term Composted Manure and Straw Amendments on Rice-Associated Weeds in a Rice–Wheat Rotation System

Abstract As part of a long-term experiment to determine the impacts of composted manure and straw amendments (replacing 50% of chemical fertilizer with composted pig manure, wheat straw return combined with chemical fertilizer, and setting no fertilizer and chemical fertilizer-only as controls) on rice-associated weeds in a rice (Oryza sativa L.)–wheat (Triticum aestivum L.) rotation system, species richness, abundance, density, and biomass of weeds were assessed during years 8 and 9. Fertilization decreased the species richness and total density of rice-associated weeds but increased their total biomass. The species richness and densities of broadleaf and sedge weeds decreased with fertilization, while species richness of grass weeds increased only with straw return and density was not significantly affected. The shoot biomass per square meter of grass and broadleaf weeds was significantly higher with fertilization treatments than with the no-fertilizer control, while that of sedge weeds declined with fertilizer application. With fertilization, the densities of monarch redstem (Ammannia baccifera L.) and smallflower umbrella sedge (Cyperus difformis L.) decreased, that of Chinese sprangletop [Leptochloa chinensis (L.) Nees] increased, and those of barnyardgrass [Echinochloa crus-galli (L.) P. Beauv. ] and monochoria [Monochoria vaginalis (Burm. f.) C. Presl ex Kunth] were not significantly affected. Ammannia baccifera was the most abundant weed species in all treatments. Whereas composted pig manure plus fertilizer resulted in higher density of A. baccifera and lower shoot biomass per plant than chemical fertilizer only, wheat straw return plus chemical fertilizer caused lower density and shoot biomass of A. baccifera. Therefore, it may be possible that fertilization strategies that suppress specific weeds could be used as improved weed management program components in rice production systems.
Detecting Web Attacks Based on Clustering Algorithm and Multi-branch CNN

Abstract—This paper proposes and develops a web attack detection model that combines a clustering algorithm and a multi-branch convolutional neural network (CNN). The original feature set was clustered into clusters of similar features. Each cluster of similar features was generalized in a convolutional structure of a branch of the CNN. The component feature vectors are assembled into a synthetic feature vector and included in a fully connected layer for classification. Using K-fold cross-validation, the accuracy of the proposed method 98.8%, F1-score is 98.9% and the improvement rate of accuracy is 1.479%. Tóm tắt—Bài báo đề xuất và phát triển mô hình phát hiện tấn công Web dựa trên kết hợp thuật toán phân cụm và mạng nơ-ron tích chập (CNN) đa nhánh. Tập đặc trưng ban đầu được phân cụm thành các nhóm đặc trưng tương ứng. Mỗi nhóm đặc trưng được khái quát hoá trong một nhánh của mạng CNN đa nhánh để tạo thành một vector đặc trưng thành phần. Các vector đặc trưng thành phần được ghép lại thành một vector đặc trưng tổng hợp và đưa vào lớp liên kết đầy đủ để phân lớp. Sử dụng phương pháp kiểm thử chéo trên mô hình đề xuất, độ chính xác đạt 98,8%, F1-score đạt 98,8% và tỉ lệ cải tiến độ chính xác là 1,479%.
Comprehensive literature review on COVID-19 vaccines and role of SARS-CoV-2 variants in the pandemic

Since the outbreak of the COVID-19 pandemic, there has been a rapid expansion in vaccine research focusing on exploiting the novel discoveries on the pathophysiology, genomics, and molecular biology of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Although the current preventive measures are primarily socially distancing by maintaining a 1 m distance, it is supplemented using facial masks and other personal hygiene measures. However, the induction of vaccines as primary prevention is crucial to eradicating the disease to attempt restoration to normalcy. This literature review aims to describe the physiology of the vaccines and how the spike protein is used as a target to elicit an antibody-dependent immune response in humans. Furthermore, the overview, dosing strategies, efficacy, and side effects will be discussed for the notable vaccines: BioNTech/Pfizer, Moderna, AstraZeneca, Janssen, Gamaleya, and SinoVac. In addition, the development of other prominent COVID-19 vaccines will be highlighted alongside the sustainability of the vaccine-mediated immune response and current contraindications. As the research is rapidly expanding, we have looked at the association between pregnancy and COVID-19 vaccinations, in addition to the current reviews on the mixing of vaccines. Finally, the prominent emerging variants of concern are described, and the efficacy of the notable vaccines toward these variants has been summarized.
Metabolomics Identifies Biomarker Signatures to Differentiate Pancreatic Cancer from Type 2 Diabetes Mellitus in Early Diagnosis

Methods Plasma metabolic profiles in 26 PC patients, 27 DM patients, and 23 healthy volunteers were examined using an ultraperformance liquid chromatography coupled with tandem mass spectrometry platform. Differential metabolite ions were then identified using the principal component analysis (PCA) model and the orthogonal partial least-squares discrimination analysis (OPLS-DA) model. The diagnosis performance of metabolite biomarkers was validated by logistic regression models. Results We established a PCA model (R2X = 23.5%, Q2 = 8.21%) and an OPLS-DA model (R2X = 70.0%, R2Y = 84.9%, Q2 = 69.7%). LysoPC (16 : 0), catelaidic acid, cerebronic acid, nonadecanetriol, and asparaginyl-histidine were found to identify PC, with a sensitivity of 89% and a specificity of 91%. Besides, lysoPC (16 : 0), lysoPC (16 : 1), lysoPC (22 : 6), and lysoPC (20 : 3) were found to differentiate PC from DM, with higher accuracy (68% versus 55%) and higher AUC values (72% versus 63%) than those of CA19-9. The diagnostic performance of metabolite biomarkers was finally validated by logistic regression models. Conclusion We succeeded in screening differential metabolite ions among PC and DM patients and healthy individuals, thus providing a preliminary basis for screening the biomarkers for the early diagnosis of PC.
Research on Ride Comfort of Half-Rotating Walking Mechanism

Bionic walking mechanism is an important part of bionic motion machinery. According to the characteristics of biped walking, a new biped-imitating walking mechanism called Half-Rotating Walking Mechanism (HRWM) was developed using Half-Rotating Mechanism (HRM) which can generate asymmetrical motion. The drive way and walking principle of HRWM were firstly introduced. On the basis of deducing kinematical equations of HRWM, some main factors of ride comfort were presented, which include a new factor named relative amplitude that could be used to valuate the ride comfort of various walking mechanism. The optimization model of HRWM structure was then proposed, which took parameter relationship as design variable. The optimized result could be applicable to different size HRWM. Finally, the calculating result shows that HRWM with optimized parameter relationships has good performance on ride comfort in the process of walking.
Quadratic variation, models, applications and lessons

Time changes of Brownian motion impose restrictive jump structures in the motion of asset prices. Quadratic variations also depart from time changes. Quadratic variation options are observed to have a nonlinear exposure to risk neutral skewness. Joint Laplace Fourier transforms for quadratic variation and the stock are developed. They are used to study the multiple of the cap strike over the variance swap quote attaining a given percentage price reduction for the capped variance swap. Market prices for out-of-the-money options on variance are observed to be above those delivered by the calibrated models. Bootstrapped data and simulated paths spaces are used to study the multiple of the dynamic hedge return desired by a quadratic variation contract. It is observed that the optimized hedge multiple in the bootstrapped data is near unity. Furthermore, more generally, it is exposures to negative cubic variations in path spaces that raise variance swap prices, lower hedge multiples, increase residual risk charges and gaps to the log contract hedge. A case can be made for both, the physical process being closer to a continuous time change of Brownian motion, while simultaneously risk neutrally this may not be the case. It is recognized that in the context of discrete time there are no issues related to equivalence of probabilities.
CMOS translinear circuits for minimum supply voltage

Three basic translinear circuit topologies are modified for use at low supply voltage. They are: (1) the balanced translinear loop; (2) the alternating translinear loop; and (3) the instantaneous-companding integrator. The key to this work is the constructive use of nonsaturated MOS transistors operating in weak inversion. This provides two improvements. First, circuit operation is extended to low supply voltage. Second, accurate realization is enabled since the circuits become immune to MOS body-effect. The proposed techniques are suitable for static and dynamic analog signal processing circuits in mixed-signal chips fabricated in digital CMOS technology and operating at the minimum possible supply voltage.
Soft-Decision Viterbi Decoding Scheme and a New Reliability Metric for 4/6 Modulation Code in Holographic Data Storage

A soft-decision Viterbi decoding for a 4/6 modulation code is proposed in a holographic data storage system. Since the previous proposed soft-decision Viterbi decoding does not support a higher rate modulation codes than 1/2 rate modulation codes, we propose a new reliability metric to support the 4/6 modulation code. The reliability metric is used to calculate the branch metric on the trellis. For a better coding gain, we find a symbol mapping method for the modulation code which provides a large free distance on the trellis to have the enhanced error correcting capability. Simulation results show that the proposed scheme has about 2 dB coding gains than the conventional soft-decision Viterbi decoding scheme.
Subway tunnel guided electromagnetic wave propagation at mobile communications frequencies

A measurement campaign has been carried out in the Berlin subway to characterize electromagnetic wave propagation in underground railroad tunnels. The received power levels at 945 and 1853.4 MHz are used to evaluate the attenuation and the fading characteristics in a curved arched-shaped tunnel. The measurements are compared to ray-optical modeling results, which are based on ray density normalization. It is shown that the geometry of a tunnel, especially the cross-sectional shape and the course, is of major impact on the propagation behavior and thus on the accuracy of the modeling, while the material parameters of the building materials have less impact.
Refining the content and design of an alcohol reduction app, Drink Less, to improve its usability and effectiveness: a mixed methods approach

Background: Digital interventions have the potential to reduce alcohol consumption, although evidence on the effectiveness of apps is lacking. Drink Less is a popular, evidence-informed app with good usability, putting it in a strong position to be improved upon prior to conducting a confirmatory evaluation. This paper describes the process of refining Drink Less to improve its usability and likely effectiveness. Methods: The refinement consisted of three phases and involved qualitative and quantitative (mixed) methods: i) identifying changes to app content, based on findings from an initial evaluation of Drink Less, an updated review of digital alcohol interventions and a content analysis of user feedback; ii) designing new app modules with public input and a consultation with app developers and researchers; and iii) improving the app’s usability through user testing. Results: As a result of the updated review of digital alcohol interventions and user feedback analysis in Phase 1, three new modules: ‘Behaviour Substitution’, ‘Information about Antecedents’ and ‘Insights’, were added to the app. One existing module – ‘Identity Change’ – was removed based on the initial evaluation of Drink Less. Phases 2 and 3 resulted in changes to existing features, such as improving the navigational structure and onboarding process, and clarifying how to edit drinks and goals. Conclusions: A mixed methods approach was used to refine the content and design of Drink Less, providing insights into how to improve its usability and likely effectiveness. Drink Less is now ready for a confirmatory evaluation.
Artificial intelligence-assisted detection and classification of colorectal polyps under colonoscopy: a systematic review and meta-analysis

Background Artificial intelligence (AI) is used to solve the problem of missed diagnosis of polyps in colonoscopy, which has been proved to improve the detection rate of adenomas. The aim of this review was to evaluate the diagnostic performance of AI-assisted detection and classification of polyps in colonoscopy. Methods The literature search was undertaken on 4 electronic databases (PubMed, Web of Science, Embase, and Cochrane Library). The inclusion criteria were as follows: studies reporting AI-assisted detection and classification of polyps; studies containing patients, images, or videos receiving AI-assisted diagnosis; studies which included AI-assisted diagnosis and reported classification based on histopathology; and studies providing accurate diagnostic data. Non-English language studies, case-reports, reviews, meeting abstracts and so on were excluded. The Quality Assessment of Diagnostic Accuracy Studies-2 scale was used to evaluate the quality of literature and the Stata 13.0 software was used to perform meta-analysis. Results Twenty-six articles were included with all of medium quality. Meta-analysis showed none of literature had any obvious publication bias. The application of AI in detection of colorectal polyps achieved a sensitivity of 0.95 [95% confidence interval (CI): 0.89–0.98] and an area under the curve (AUC) of 0.79 (95% CI: 0.79–0.82). In the AI-assisted classification, the sensitivity was 0.92 (95% CI: 0.88–0.95) with a specificity of 0.82 (95% CI: 0.71–0.89) and an AUC of 0.94 (95% CI: 0.92–0.96). For the classification of diminutive polyps, the AI-assisted technique yielded a sensitivity of 0.95 (95% CI: 0.94–0.97), a specificity of 0.88 (95% CI: 0.74–0.95), and an AUC of 0.97 (95% CI: 0.95–0.98). For AI-assisted classification under magnifying endoscopy, the sensitivity was 0.954 (95% CI: 0.92–0.96) with a specificity of 0.95 (95% CI: 0.80–0.99) and an AUC of 0.97 (95% CI: 0.95–0.98). Discussion The AI-assisted technique demonstrates impressive accuracy for the detection and characterization of colorectal polyps and can be expected to be a novel auxiliary diagnosis method. Our study has inevitable limitations including heterogeneity due to different AI systems and the inability to further analyze the specificity and sensitivity of AI for different types of endoscopes.
Social Security Video Surveillance Systems

the reform and open policy has brought us Chinese people rich and colorful. However, the disparity between the rich and poor gap are increasing due to the uneven development in Chinese economic so that state of social security become more and more terrible than the pass. Thus, it is very meaningful to establish a social security video surveillance system to achieve security linkage management by this way that monitors the important insecure point by installing one or more camera to capturing the situation real-time. When there is something happen, the monitoring center will cause an alert signal to administer for further processing. In this paper, we present a scheme to achieve the social security requirement.
Análise epidemiológica das fichas de notificação de mulheres vítimas de violência no município de Foz do Iguaçu

Objetivo: Analisar fichas de notificação compulsória de mulheres vítimas de violência do município de Foz do Iguaçu-PR. Métodos: Trata-se de uma pesquisa descritiva e exploratória utilizando-se de fichas de notificação compulsória de casos de violência contra a mulher registrados em 2020. Foram levantados dados de caracterização das participantes, os distritos sanitários com maiores índices de violência, os tipos de violência e o vínculo de parentesco do agressor. Resultados: Foram notificados 330 casos violência contra a mulher no município de Foz do Iguaçu, com idade predominante de 18 a 34 anos, solteiras, que residem em regiões de grandes vulnerabilidades sociais. A agressão física foi o principal tipo de violência, ocorridos no período da noite e na residência. Conclusão: Por meio do estudo, foi possível traçar o perfil dos casos de violência contra a mulher registradas no município. Faz-se necessário a aplicação de busca ativa dessa população de risco, pois ficou evidente que essas mulheres estão em grande vulnerabilidade.
Infrared unmanned aerial vehicle detection based on generative adversarial network data augmentation

As the application of UAVs in military and civilian fields becomes more and more widespread, the detection of UAVs in the low-altitude range has also become an important research direction. Compared with radar and visible light detection, infrared technology has become the major UAV detection method with its advantages of all-weather and long range. Most of the current infrared target detection methods are based on convolutional neural networks (CNN), which achieve target detection through feature extraction and feature classification. The performance of all such detection algorithms is highly dependent on their training set. A data set with a large number of samples and wide coverage tends to train a more robust and accurate detector. So, to obtain better detection effects, we perform data augmentation on the infrared UAV dataset by adversarial generative network (GAN). First, we extract the targets from the training set and train a GAN network, using its generator to obtain many new targets which are different from the training set samples, then we randomly extend these targets to the original dataset, and finally we retrain the detectors using the new dataset to achieve better detection. We created an infrared UAV image dataset for our experiments, with only a single target on each image. After data augmentation, multiple UAV targets are randomly generated. The experiments demonstrate that the new dataset trains the model with better detection results. And the GAN data augmentation can be combined with many advanced detectors to make a large improvement in detection.