Miracle-dz/UniFAR-FTUs huggingface dataset:
Reliability in Time: Evaluating the Web Sources of Information on COVID-19 in Wikipedia across Various Language Editions from the Beginning of the Pandemic

There are over a billion websites on the Internet that can potentially serve as sources of information on various topics. One of the most popular examples of such an online source is Wikipedia. This public knowledge base is co-edited by millions of users from all over the world. Information in each language version of Wikipedia can be created and edited independently. Therefore, we can observe certain inconsistencies in the statements and facts described therein - depending on language and topic. In accordance with the Wikipedia content authoring guidelines, information in Wikipedia articles should be based on reliable, published sources. So, based on data from such a collaboratively edited encyclopedia, we should also be able to find important sources on specific topics. This effect can be potentially useful for people and organizations. The reliability of a source in Wikipedia articles depends on the context. So the same source (website) may have various degrees of reliability in Wikipedia depending on topic and language version. Moreover, reliability of the same source can change over the time. The purpose of this study is to identify reliable sources on a specific topic - the COVID-19 pandemic. Such an analysis was carried out on real data from Wikipedia within selected language versions and within a selected time period.
Average Redundancy of Variable-Length Balancing Schemes à la Knuth

We study and propose schemes that map messages onto constant-weight codewords using variable-length prefixes. We provide polynomial-time computable formulas that estimate the average number of redundant bits incurred by our schemes. In addition to the exact formulas, we also perform an asymptotic analysis and demonstrate that our scheme uses $\frac12 \log n+O(1)$ redundant bits to encode messages into length-$n$ words with weight $(n/2)+{\sf q}$ for constant ${\sf q}$. We also propose schemes that map messages into balanced codebooks with error-correcting capabilities. For such schemes, we provide methods to enumerate the average number of redundant bits.
Stand-alone PV System Simulation for DG Applications, Part II: DC-DC Converter feeding Maximum Power to Resistive Load

This paper acts as a second part for stand-alone photovoltaic power system simulation to be used on site for distributed generation (DG) applications. It proposes a PV module when coupled to a load through DC-DC Converter to supply this resistive load with the maximum power from the PV module. Then some of DC-DC converters topologies are discussed in brief with concentration on Cuk and SEPIC Converters operations. After that, the mechanism of load matching is described to give the required converter duty cycle at maximum power point (MPP). Relations in 3D figures are introduced for the most probable situations for irradiance and temperature with the corresponding PV voltage and current. Also, 3D figures for the desired duty cycle, output voltage and current of DC-DC converter to gain the maximum power to the resistive load at various irradiance and temperature values. Finally, Artificial Neural Network (ANN) is used to implement a neural model with its algebraic function to take the probable irradiance and temperature values and outs the proposed converter duty cycle to give maximum power for the load. This is done with suitable number of layers, number of neurons for each and suitable performance goals.
Lowpass to Bandpass and Bandstop Transformation: Relations of Coefficients and Poles

Lowpass to bandpass or bandstop transformation: relations of coefficients and poles This work deals with algebraic formulae that relate coefficients of bandpass (bandstop) functions with those of the prototype lowpass function under frequency transformation, and also the algebraic formulae relating the poles (and zeros) of the original and transformed functions. These formulae are easy to use with programmable calculators. Examples are given.
Exploring Factors Influencing Medication Compliance in Saudi Rheumatoid Arthritis Patients: A Nationwide Cross-Sectional Survey – Results from the COPARA Study

Purpose Compliance is essential to achieve treatment goals in rheumatoid arthritis (RA) patients. The current study evaluated compliance and related factors in a large and diverse population. Patients and Methods Patients with RA who received active treatment were invited to participate in an online survey. The Arabic versions of the 5-Item Compliance Questionnaire for Rheumatology (ACQR-5) and the RA Impact of Disease (RAID) were used to measure compliance and disability, respectively. The patients were sub-grouped based on background disease-modifying anti-rheumatic drugs (DMARDs). Variables associated with high compliance were selected for the logistic regression analysis. Results A total of 1241 patients completed the survey and were included in the final analysis. Of those, 1055 (85%) were females with a mean (±SD) age and disease duration of 47.14 ± 13.71 and 8.77 ± 7.43 years, respectively. The mean RAID was 4.4±2.58, with 980 (79%) having an unacceptable level state. Patients with an unacceptable RAID level had a lower compliance rate (78.8% vs 85.8%, p = 0.001). Demographics associated with high compliance were female sex and increased age, with reported odds ratios of 1.018 (95% CI: 1.007–1.028) and 1.464 (95% CI: 1.016–2.108), respectively. Compliance was similar between patients on Janus kinase inhibitors or biological DMARDs (88.14% vs 80.83%, p = 0.17), between monotherapy, double therapy, or triple therapy recipients (80% vs 82.23% vs 81.32%, p = 0.665), and between patients receiving injectable and oral therapy (77.32% vs 81.14%, p = 0.246). Conclusion A high compliance level was observed in this population, with patient demographics influencing compliance rather than the medication type or route of administration. Interventional studies should focus on the of high-risk patients identified in this study.
Comparison Between Statistical Model and Machine Learning Methods for Predicting the Risk of Renal Function Decline Using Routine Clinical Data in Health Screening

Purpose Using machine learning method to predict and judge unknown data offers opportunity to improve accuracy by exploring complex interactions between risk factors. Therefore, we evaluate the performance of machine learning (ML) algorithms and to compare them with logistic regression for predicting the risk of renal function decline (RFD) using routine clinical data. Patients and Methods This retrospective cohort study includes datasets from 2166 subjects, aged 35–74 years old, provided by an adult health screening follow-up program between 2010 and 2020. Seven different ML models were considered – random forest, gradient boosting, multilayer perceptron, support vector machine, K-nearest neighbors, adaptive boosting, and decision tree - and were compared with standard logistic regression. There were 24 independent variables, and the baseline estimate glomerular filtration rate (eGFR) was used as the predictive variable. Results A total of 2166 participants (mean age 49.2±11.2 years old, 63.3% males) were enrolled and randomly divided into a training set (n=1732) and a test set (n=434). The area under receiver operating characteristic curve (AUROC) for detecting RFD corresponding to the different models were above 0.85 during the training phase. The gradient boosting algorithms exhibited the best average prediction accuracy (AUROC: 0.914) among all algorithms validated in this study. Based on AUROC, the ML algorithms improved the RFD prediction performance, compared to logistic regression model (AUROC:0.882), except the K-nearest neighbors and decision tree algorithms (AUROC:0.854 and 0.824, respectively). However, the improvement differences with logistic regression were small (less than 4%) and nonsignificant. Conclusion Our results indicate that the proposed health screening dataset-based RFD prediction model using ML algorithms is readily applicable, produces validated results. But logistic regression yields as good performance as ML models to predict the risk of RFD with simple clinical predictors.
Design Of Automation System For Ceramic Surface Quality Control Using Fuzzy Logic Method At Balai Besar Keramik (bbk)

Indonesia is the world's top 10 ceramic users. To obtain best quality need to consider process of quality control, in SNI ISO 13006: 2010 there are six variables characteristic requirements related to quality of ceramic tiles. Currently the process of ceramic quality inspection in Indonesia, particularly in the Balai Besar Keramik is still done manually by human vision. Therefore, it is necessary to design a visual inspection system for digital image processing of ceramic automation using Fuzzy Logic method. Fuzzy model is one method that can be used to determine ceramic surface quality control. This study aims to apply fuzzy model in design of automation system for ceramic surface quality control and describes its accuracy rate and automatic database. To perform feature extraction using GLCM extraction method to obtain autocorrelation, sum of square (variance), and number of object. The information is used as input for data processing using fuzzy model in the identification of quality of ceramic surface defect. This research uses 13 real time test data which can produce the accuracy of automatic ceramic quality identification 92.31%. Keywords : Automated Ceramic Surface Quality Control, Ceramic Defect Detection, Image Processing, Fuzzy Logic Model
Lariciresinol displays anti-diabetic activity through inhibition of α-glucosidase and activation and enhancement of insulin signaling.

SCOPE The aim of this study was to investigate the antidiabetic effect of lariciresinol (LSR) in C2C12 myotubes and STZ-induced diabetic mice. METHODS AND RESULTS To investigate antidiabetic potential of LSR, α-glucosidase inhibitory assay, molecular docking, glucose uptake assay, western blot assay on antidiabetic biomarkers were performed. STZ-induced diabetic model was used for in vivo study by calculating oral glucose tolerance test, histochemical examination, and glycogen assay. LSR inhibited α-glucosidase activity with an IC50 value of 6.97 ± 0.37 μM and acts as a competitive inhibitor with an inhibitory constant (Ki) value of 0.046 μM. In C2C12 cells, LSR activated insulin signaling leading to glucose transporter 4 (GLUT4) translocation and augmented glucose uptake. Furthermore, in Streptozotocin (STZ)-treated diabetic mice, three weeks of oral LSR administration (10 mg kg-1 ) considerably decreased blood glucose levels, while increasing insulin levels in an oral glucose tolerance test, improved pancreatic islet size, increased GLUT4 expression, and significantly enhanced insulin signaling in skeletal muscle. LSR treatment also activated GSK-3β resulting in improved glycogen content. CONCLUSION Our findings indicate a potential usefulness for oral LSR in the management and prevention of diabetes by enhancing glucose homeostasis. This article is protected by copyright. All rights reserved.
Pelvic exenteration: a review of current issues/controversies

Management of advanced or recurrent pelvic cancer has evolved dramatically over the past few decades. Patients who were previously considered inoperable are now candidates for potentially curative surgery and avoid suffering with intractable symptoms. Up to 10% of primary rectal cancers present with isolated advanced local disease and between 10% and 15% of patients develop localized recurrence following proctectomy. Advances in surgical technique, reconstruction and multidisciplinary involvement have led to a reduction in mortality and morbidity and culminated in higher R0 resection rates with superior longer‐term survival outcomes. Recent studies boast over 50% 5‐year survival for rectal with an R0 resection. Exenteration has cemented itself as an important treatment option for advanced primary/recurrent pelvic tumours, however, there are still a few controversies. This review will discuss some of these issues, including: limitations of resection and the approach to high/wide tumours; the role of acute exenteration; re‐exenteration; exenteration in the setting of metastatic disease and palliation; the role of radiotherapy (including intra‐operative and re‐irradiation); management of the empty pelvis; and the impact on quality of life and function.
Reappraisal of the Medical Research Council Antiepileptic Drug Withdrawal Study: Contamination‐adjusted and dose‐response re‐analysis

The 1991 Medical Research Council (MRC) Study compared seizure relapse for seizure‐free patients randomized to withdraw vs continue of antiseizure medications (ASMs). We re‐analyzed this trial to account for crossover between arms using contamination‐adjusted intention to treat (CA ITT) methods, to explore dose‐response curves, and to validate predictions against external data. ITT assesses the effect of being randomized to withdraw, as‐treated analysis assesses the confounded effect of withdrawing, but CA ITT assesses the unconfounded effect of actually withdrawing.
Handling and Presenting Harmful Text

Textual data can pose a risk of serious harm. These harms can be categorised along three axes: (1) the harm type (e.g. misinformation, hate speech or racial stereotypes) (2) whether it is elicited as a feature of the research de-sign from directly studying harmful content (e.g. training a hate speech classiﬁer or auditing unﬁltered large-scale datasets) versus spuriously invoked from working on unrelated problems (e.g. language generation or part of speech tagging) but with datasets that nonethe-less contain harmful content, and (3) who it affects, from the humans (mis)represented in the data to those handling or labelling the data to readers and reviewers of publications produced from the data. It is an unsolved problem in NLP as to how textual harms should be handled, presented, and discussed; but, stop-ping work on content which poses a risk of harm is untenable. Accordingly, we provide practical advice and introduce H ARM C HECK , a resource for reﬂecting on research into textual harms. We hope our work encourages ethical, responsible, and respectful research in the NLP community.
Breaking the Linear Error Barrier in Differentially Private Graph Distance Release

Releasing all pairwise shortest path (APSP) distances between vertices on general graphs under weight Differential Privacy (DP) is known as a challenging task. In the previous attempt of (Sealfon 2016}, by adding Laplace noise to each edge weight or to each output distance, to achieve DP with some fixed budget, with high probability the maximal absolute error among all published pairwise distances is roughly $O(n)$ where $n$ is the number of nodes. It was shown that this error could be reduced for some special graphs, which, however, is hard for general graphs. Therefore, whether the approximation error can be reduced to sublinear in $n$ is posted as an interesting open problem. We break the linear barrier on the distance approximation error of previous result, by proposing an algorithm that releases a constructed synthetic graph privately. Computing all pairwise distances on the constructed graph only introduces $\tilde O(n^{1/2})$ error in answering all pairwise shortest path distances for fixed privacy parameter. Our method is based on a novel graph diameter (link length) augmentation via constructing"shortcuts"for the paths. By adding a set of shortcut edges to the original graph, we show that any node pair has a shortest path with link length $\tilde O(n^{1/2})$. Then by adding noises with some positive mean to the edge weights, we show that the new graph is differentially private and can be published to answer all pairwise shortest path distances with $\tilde O(n^{1/2})$ approximation error using standard APSP computation. Additionally, we consider the graph with small feedback vertex set number. A feedback vertex set (FVS) of a graph is a set of vertices whose removal leaves a graph without cycles, and the feedback vertex set number of a graph, $k$, is the size of a smallest feedback vertex set. We propose a DP algorithm with error rate $\tilde O(k)$.
Lifting Height as the Dominant Risk Factor for Low-Back Pain and Loading During Manual Materials Handling: A Scoping Review

OCCUPATIONAL APPLICATIONS There is an increased risk of developing low-back pain when lifting objects closer to the floor when compared to lifting at waist height. These results support the key message of “store-it-off-the-floor,” which small businesses can easily implement to identify and control lifting hazards in their workplaces. Since the majority of the workforce is comprised of small businesses, ergonomic solutions (e.g., store-it-off-the-floor) should be designed to take into account the special challenges small businesses often face when trying to implement musculoskeletal disorder prevention activities, such as limits on time, knowledge, and budget. TECHNICAL ABSTRACT Background: Manual materials handling is a common hazard for workplace injuries such as low-back pain. Although the risk factor of lifting height has been shown in the literature to be a large contributor to low-back injury incidence, currently there have been no attempts to consolidate this information into a form suitable for knowledge dissemination. Purpose: The purpose of this study was to summarize the effects of lifting height on low-back loading and the risk of developing low-back pain. Methods: Three electronic databases (PubMed, Scopus, and Ergonomic Abstracts) were searched to find studies that could document the effect of lifting at various heights on low-back loading. Results: In total, 35 studies had outcome measures that were included in the analysis and interpretation for this article. Lifting from the floor was reported to produce up to twice the amount of spinal loading as lifting from a more desirable location, such as elbow height. This evidence supports a focus on avoiding lifting from low heights: below the knee and especially from the floor. Lifting from the floor also magnified the deleterious effects of manual materials handling on workers with low-back pain symptoms, older workers, workers with knee osteoarthritis, and workers with a high body mass index. Conclusions: The results of this review support a knowledge dissemination campaign targeting lifting from the floor as both a method for hazard identification and as the basis of controls in workplaces with little ergonomics knowledge, or in small business.
Potential effects of nectar microbes on pollinator health

Floral nectar is prone to colonization by nectar-adapted yeasts and bacteria via air-, rain-, and animal-mediated dispersal. Upon colonization, microbes can modify nectar chemical constituents that are plant-provisioned or impart their own through secretion of metabolic by-products or antibiotics into the nectar environment. Such modifications can have consequences for pollinator perception of nectar quality, as microbial metabolism can leave a distinct imprint on olfactory and gustatory cues that inform foraging decisions. Furthermore, direct interactions between pollinators and nectar microbes, as well as consumption of modified nectar, have the potential to affect pollinator health both positively and negatively. Here, we discuss and integrate recent findings from research on plant–microbe–pollinator interactions and their consequences for pollinator health. We then explore future avenues of research that could shed light on the myriad ways in which nectar microbes can affect pollinator health, including the taxonomic diversity of vertebrate and invertebrate pollinators that rely on this reward. This article is part of the theme issue ‘Natural processes influencing pollinator health: from chemistry to landscapes’.
Do amino and fatty acid profiles of pollen provisions correlate with bacterial microbiomes in the mason bee Osmia bicornis?

Bee performance and well-being strongly depend on access to sufficient and appropriate resources, in particular pollen and nectar of flowers, which constitute the major basis of bee nutrition. Pollen-derived microbes appear to play an important but still little explored role in the plant pollen–bee interaction dynamics, e.g. through affecting quantities and ratios of important nutrients. To better understand how microbes in pollen collected by bees may affect larval health through nutrition, we investigated correlations between the floral, bacterial and nutritional composition of larval provisions and the gut bacterial communities of the solitary megachilid bee Osmia bicornis. Our study reveals correlations between the nutritional quality of pollen provisions and the complete bacterial community as well as individual members of both pollen provisions and bee guts. In particular pollen fatty acid profiles appear to interact with specific members of the pollen bacterial community, indicating that pollen-derived bacteria may play an important role in fatty acid provisioning. As increasing evidence suggests a strong effect of dietary fatty acids on bee performance, future work should address how the observed interactions between specific fatty acids and the bacterial community in larval provisions relate to health in O. bicornis. This article is part of the theme issue ‘Natural processes influencing pollinator health: from chemistry to landscapes’.
Framework for Assessing the Feasibility of Carbon Dioxide Removal Options Within the National Context of Germany

Removal of carbon dioxide from the atmosphere will be required over the next decades to achieve the Paris Agreement goal of limiting global warming to well below 2°C aiming at not exceeding 1.5°C. Technological and ecosystem-based options are considered for generating negative emissions through carbon dioxide removal (CDR) and several nations have already included these in their Long-Term Low Greenhouse Gas Emission Development Strategies. However, strategies for development, implementation, and upscaling of CDR options often remain vague. Considering the scale at which CDR deployment is envisioned in emission pathways for limiting global warming to 1.5°C, significant environmental, social, and institutional implications are to be expected and need to be included in national feasibility assessments of CDR options. Following a multi-disciplinary and comprehensive approach, we created a framework that considers the environmental, technological, economic, social, institutional, and systemic implications of upscaling CDR options. We propose the framework as a tool to help guide decision-relevant feasibility assessments of CDR options, as well as identify challenges and opportunities within the national context. As such, the framework can serve as a means to inform and support decision makers and stakeholders in the iterative science-policy process of determining the role of CDR options in national strategies of achieving net-zero carbon emissions.