Miracle-dz/UniFAR-FTUs huggingface dataset:
Analisis Faktor Kejadian Penyakit Skabies di Pondok Pesantren An-Nur Ciseeng Bogor 2017

Skabies adalah penyakit kulit akibat infestasi dan sensitisasi oleh tungau Sarcoptes scabiei varietas hominis. Skabies ini tidak membahayakan manusia namun adanya rasa gatal pada malam hari ini merupakan gejala utama yang mengganggu aktivitas dan produktivitas. Skabies cenderung tinggi pada anak- anak usia sekolah, remaja bahkan orang dewasa. Penelitian ini bertujuan untuk mengetahui faktor yang mempengaruhi kejadian skabies di Pondok Pesantren An-Nur Ciseeng Parung Bogor. Penelitian ini merupakan penelitian deskriptif analitik dengan menggunakan desain studi cross sectional . sampel pada penelitian ini berjumlah 75 orang yang diambil dengan metode concecutive sampling . hasil analisis didapatkan bahwa 65.3% responden mengalami kejadian skabies. Variabel personal higiene, kelembaban, ventilasi, dan kepadatan hunian menjadi faktor yang berhubungan dengan kejadian skabies di Pondok Pesantren An-Nur Ciseeng Parung Bogor.
San Josecito Cave and Its Paleoecological Contributions for Quaternary Studies in Mexico

San Josecito Cave (2250 m elevation) is located nearby Aramberri, Nuevo León, northeastern Mexico, with excavations occurring in 1935–1941 and 1990. It is a paleontological cave and the significance of its faunal data rests in the understanding of the Quaternary ecosystems of the Mexican Plateau and the Southern Plains. This significance is underpinned by a consideration of associated stratigraphic and geochronological data. The fauna is composed of mollusks, amphibians, reptiles, birds, and mammals. More than 30 extinct vertebrate species have been identified, constituting one of the most important Quaternary localities in the Americas. Radiocarbon dates and faunal correlations indicate the excavated deposits represent an interval of time between 45,000 and 11,000 14C years BP. The current synthesis demonstrates that the previous view of the assemblage as a single local fauna is erroneous and that, instead, several successive local faunas are present within a stratigraphic framework. This finding underscores the need for detailed studies of single localities in building paleoenvironmental models. As a corollary, results point to the necessity of including all vertebrate classes represented from a locality in building those models. In addition, the field and analytical methodologies demonstrate the importance of very detailed paleontological excavations, with precise spatial and temporal controls, to assess the taphonomic history of a locality, construct a stratigraphic and geochronological framework, and infer the paleoecological conditions during the time span considered based on the number of local faunas represented. The recognition of San Josecito Cave as an important Late Pleistocene vertebrate paleontological locality is enhanced with the consideration of its faunal data for paleoenvironment reconstruction and possible contribution to Quaternary paleoclimatic modeling.
Investigation of the Effects of Applying Social Sustainability Components on Residential Satisfaction

Today, decades after the emergence of the concept of sustainable development, the long-term positive effects of achieving sustainable development goals in all environmental, economic, and social dimensions on the quality of human life are undeniable. The purpose of this research is to explain the mechanism of effects of applying the social sustainability components on the level of residential satisfaction in the scale of residential complexes. The research uses a mixed (quantitative and qualitative) methodology in terms of the nature of the data and the research method is descriptive-analytical (causal and comparative). The conceptual model of the research, which includes the relationships between the variables of perceived quality of social sustainability components, residential desires associated with these components, perceived quality of housing architecture, and residential satisfaction, has been proposed based on the study of theoretical foundations through logical reasoning method. This model has been tested through obtained data from a questionnaire in a field study conducted in the Baghe Behesht residential complex in the Saadatabad neighborhood of Tehran. According to the results, the application of the components of social sustainability has a significant effect on residential satisfaction by improving the quality of housing architecture, and in conditions of qualitative deficiency in social sustainability components, residential desires associated with these components will reduce the perceived quality and the level of residential satisfaction.
Venous and arterial thromboembolism in patients with inflammatory bowel diseases

The risk of thromboembolism (TE) is increased in patients with inflammatory bowel disease (IBD), mainly due to an increased risk of venous TE (VTE). The risk of arterial TE (ATE) is less pronounced, but an increased risk of cardiovascular diseases needs to be addressed in IBD patients. IBD predisposes to arterial and venous thrombosis through similar prothrombotic mechanisms, including triggering activation of coagulation, in part mediated by impairment of the intestinal barrier and released bacterial components. VTE in IBD has clinical specificities, i.e., an earlier first episode in life, high rates during both active and remission stages, higher recurrence rates, and poor prognosis. The increased likelihood of VTE in IBD patients may be related to surgery, the use of medications such as corticosteroids or tofacitinib, whereas infliximab is antithrombotic. Long-term complications of VTE can include post-thrombotic syndrome and high recurrence rate during post-hospital discharge. A global clot lysis assay may be useful in identifying patients with IBD who are at risk for TE. Many VTEs occur in IBD outpatients; therefore, outpatient prophylaxis in high-risk patients is recommended. It is crucial to continue focusing on prevention and adequate treatment of VTE in patients with IBD.
Supplementary material to "Trends in secondary inorganic aerosol pollution in China and its responses to emission controls of precursors in wintertime"

Abstract. The Chinese government recently proposed ammonia (NH3) emissions reductions (but without a specific national target) as a strategic option to mitigate PM2.5 pollution. We combined a meta-analysis of nationwide measurements and air quality modelling to identify efficiency gains by striking a balance between controlling NH3 and acid gas (SO2 and NOx) emissions. We found that PM2.5 concentrations decreased from 2000 to 2019, but annual mean PM2.5 concentrations still exceeded 35 µg m−3 at 74 % of 1498 monitoring sites in 2015–2019. Secondary inorganic aerosols (SIA) were the dominant contributor to ambient PM2.5 concentrations. While sulfate concentrations significantly decreased over the time period, no significant change was observed for nitrate and ammonium concentrations. Model simulations indicate that the effectiveness of a 50 % NH3 emission reduction for controlling SIA concentrations decreased from 2010 to 2017 in four megacity clusters of eastern China, simulated for the month of January under fixed meteorological conditions (2010). Although the effectiveness further declined in 2020 for simulations including the natural experiment of substantial reductions in acid gas emissions during the CoVID-19 pandemic, the resulting reductions in SIA concentrations were on average 20.8 % lower than that in 2017. In addition, the reduction of SIA concentrations in 2017 was greater for 50 % acid gas reductions than for the 50 % NH3 emissions reduction. Our findings indicate that persistent secondary inorganic aerosol pollution in China is limited by acid gases emissions, while an additional control on NH3 emissions would become more important as reductions of SO2 and NOx emissions progress.
Sirtuins as Metabolic Regulators of Immune Cells Phenotype and Function

Beyond its role on the conversion of nutrients into energy and biomass, cellular metabolism is actively involved in the control of many physiological processes. Among these, it is becoming increasingly evident that specific metabolic pathways are associated with the phenotype of several immune cell types and, importantly, are crucial in controlling their differentiation, proliferation, and effector functions, thus shaping the immune response against pathogens and tumors. In this context, data generated over the last decade have uncovered mammalian sirtuins as important regulators of cellular metabolism, immune cell function, and cancer. Here, we summarize our current knowledge on the roles of this family of protein deacylases on the metabolic control of immune cells and their implications on immune-related diseases and cancer.
Changes in carbon pools and biological activities of a sandy loam soil under medium‐term conservation agriculture and diversified cropping systems

Conservation agriculture (CA) practices such as zero tillage (ZT) and permanent raised beds (PB) accelerate deposition of soil organic matter and augment associated biological properties of soil through enhanced inputs of organic carbon. However, the potential benefit of CA under intensive cereal‐based systems for key soil health indicators (such as carbon pools and biological activities) is only partially known. Therefore, we analysed the effect of three medium‐term tillage practices and four intensive crop rotations on selected soil organic carbon pools and microbial properties. The tillage practices consist of ZT, PB and conventional tillage (CT) in main plots and four crop rotations (MWMb, maize–wheat–mungbean; MCS, maize–chickpea–Sesbania; MMuMb, maize–mustard–mungbean; MMS, maize–maize–Sesbania) in subplots. The experimental design was split‐plot with three replications. After 6 years, we observed a significant positive effect of CA practices on soil organic carbon (SOC) content, labile SOC fractions, soil microbial biomass carbon (MBC) and dehydrogenase activity (DHA). The total organic carbon (TOC) was greatly affected by medium‐term tillage and diversified cropping systems; it was larger for CA and MCS and MWMb systems. The interaction effect between tillage and cropping systems for SOC content was not significant at all soil depths. Significantly larger contributions (8.5–25.5%) of labile SOC pools to TOC at various soil depths were recorded in PB and ZT. There was a significant positive effect of CA practices and diversified crop rotations on MBC and DHA at all the soil depths and sampling times, but the interaction effect between tillage and cropping systems was not significant. Thus, our medium‐term (≥ 5‐years) study showed that the combination of CA (PB and ZT) practices and appropriate choice of rotations (MCS and MWMb) appears to be the most appropriate option for restoration and improvement of the soil health of light‐textured Inceptisols through the accumulation of soil organic matter (SOM) and improvement in soil biological properties.
Assessment of Lightweight Concrete Thermal Properties at Elevated Temperatures

Structural lightweight concrete (LWC) has recently acquired research importance because of its good thermal insulation properties. However, there is a lack of knowledge about its thermal properties at elevated temperatures. The thermal properties, such as thermal conductivity and specific heat, of porous LWC vary depending on the aggregates, air voids, and moisture content of the LWC in question. To study these effects, in this paper, we measured the thermal properties of three types of structural LWCs at different temperatures, combining different characterization techniques, namely, differential scanning calorimetry (DSC), laser flash analysis (LFA), and modified transient plane source (MTPS). Bulk density and porosity were also evaluated. Specific heat is analyzed by the DSC technique from 20 to 1000 °C and the MTPS technique from 20 to 160 °C. Thermal conductivity is studied using MTPS and LFA techniques at temperatures ranging from 20 to 160 °C and 100 to 300 °C, respectively. The results indicate that the thermal properties of LWC are highly affected by moisture content, temperature, and porosity. For LWC, the current Eurocodes 2 and 4 assume a constant value of specific heat (840 J/kg°C). This research reveals variability in temperatures near 150, 450, and 850 °C due to endothermic reactions. Furthermore, for low temperatures, the higher the porosity, the higher the thermal conductivity, while, at high temperatures, the higher the porosity, the lower the thermal conductivity. Thus, Eurocodes 2 and 4 should be updated accordingly. This research contributes to a deeper understanding and more accurate prediction of LWC’s effects on thermal properties at elevated temperatures.
Prediction of the Change Points in Stock Markets Using DAE-LSTM

Since the creation of stock markets, there have been attempts to predict their movements, and new prediction methodologies have been devised. According to a recent study, when the Russell 2000 industry index starts to rise, stocks belonging to the corresponding industry in other countries also rise accordingly. Based on this empirical result, this study seeks to predict the start date of industry uptrends using the Russell 2000 industry index. The proposed model in this study predicts future stock prices using a denoising autoencoder (DAE) long short-term memory (LSTM) model and predicts the existence and timing of future change points in stock prices through Pettitt’s test. The results of the empirical analysis confirmed that this proposed model can find the change points in stock prices within 7 days prior to the start date of actual uptrends in selected industries. This study contributes to predicting a change point through a combination of statistical and deep learning models, and the methodology developed in this study could be applied to various financial time series data for various purposes.
Ensemble Models of Cutting-Edge Deep Neural Networks for Blood Glucose Prediction in Patients with Diabetes

This article proposes two ensemble neural network-based models for blood glucose prediction at three different prediction horizons—30, 60, and 120 min—and compares their performance with ten recently proposed neural networks. The twelve models’ performances are evaluated under the same OhioT1DM Dataset, preprocessing workflow, and tools at the three prediction horizons using the most common metrics in blood glucose prediction, and we rank the best-performing ones using three methods devised for the statistical comparison of the performance of multiple algorithms: scmamp, model confidence set, and superior predictive ability. Our analysis provides a comparison of the state-of-the-art neural networks for blood glucose prediction, estimating the model’s error, highlighting those with the highest probability of being the best predictors, and providing a guide for their use in clinical practice.
Reviewing the Internal Obstacles to Women’s Equal Rights Movements

The main objective of feminism movements is to make societal changes for women’s rights achieving gender neutrality, improving society into a better place to live for women while they receive numerous challenges. This paper reviews the previous various gender cases and feminism movements worldwide to specifically point out those internal oppression factors toward equal rights movements that come from the group of women. By dividing the factors into the category of consciousness and unconscious one, we find out 1) unconscious factors include women’s lack of female consciousness and the limitations in the feminist movements; 2) conscious factors consist of four parts: nonfeasance which refers to the failure to perform the law, meaningless action, individuals take advantage of equal right movements and harmful behavior. These findings imply that current feminism movements encounter obstacles that are not only from the traditional male-dominant social structures but also internal suppression. If a source of strategy could be established according to this analysis for future directions, then the efforts of feminism movements can move into a better stage.
Multitarget, Selective Compound Design Yields Picomolar Inhibitors of a Kinetoplastid Pteridine Reductase 1

The optimization of compounds with multiple targets in the drug discovery cycle is a difficult multidimensional problem. Here, we present a systematic, multidisciplinary approach to the development of selective anti-parasitic compounds. Efficient microwave-assisted synthesis of pteridines along with iterations of crystallographic structure determination were used to validate computational docking predictions and support derivation of a structure-activity relationship for multitarget inhibition. This approach yielded compounds showing picomolar inhibition of T. brucei pteridine reductase 1 (PTR1), nanomolar inhibition of L. major PTR1, along with selective submicromolar inhibition of parasitic dihydrofolate reductase (DHFR). Moreover, by combining design for polypharmacology with a property-based on-parasite optimization, we found three compounds that exhibited micromolar EC50 values against T. brucei brucei, whilst retaining their target inhibition. Our results provide a basis for the further development of pteridine-based compounds and we expect our multitarget approach to be generally applicable to the design and optimization of anti-infective agents.
Understand customer reviews with less data and in short time: pretrained language representation and active learning

In this paper, we address customer review understanding problems by using supervised machine learning approaches, in order to achieve a fully automatic review aspects categorisation and sentiment analysis. In general, such supervised learning algorithms require domain-specific expert knowledge for generating high quality labeled training data, and the cost of labeling can be very high. To achieve an in-production customer review machine learning enabled analysis tool with only a limited amount of data and within a reasonable training data collection time, we propose to use pre-trained language representation to boost model performance and active learning framework for accelerating the iterative training process. The results show that with integration of both components, the fully automatic review analysis can be achieved at a much faster pace.
Investigation of position and size for lymphedema with Infrared thermal imaging technology

The purpose is to utilize infrared-ray thermal imaging system to detect thermal temperature of the target, and the appearance of image by the digital image processing. The image of the infrared-ray thermal imager could get the related position and size of the lymphedema. The experiment measured the noncontact measurement of infrared images in the knee, calf belly and ankle of the patient's lower limb edema to observe the magnitude of its edema. The image was processed by Canny edge detection and marking method. The growing rate of knee, calf, and ankle were 4.35, 18.18, 13.64%, respectively. The position of calf had the largest growing rate than that of other positions.
2-Octyl Cyanoacrylate Glue Application on Split-Thickness Skin Graft for Lower Extremity Reconstruction

A split-thickness skin graft (STSG) is a very effective method for reconstructing skin defects in the lower extremities; however, suturing all graft margins during surgery is time-consuming. As an alternative, 2-octyl cyanoacrylate glue attaches the epidermal layer of an STSG to normal skin and functions like a suture. In this study, we assessed 2-octyl cyanoacrylate glue as a time-saving step in STSG procedures. We reviewed the charts of 87 patients who underwent STSG between May 2018 and August 2020. For the 10 patients who consented to the STSG procedure with 2-octyl cyanoacrylate glue, we evaluated the treatment site for complications and engraftment every 2 days for 14 days. The STSGs were successfully engrafted in all cases. Seroma, hematoma, wound dehiscence, or infection were not documented. No additional revision surgery was required. The use of 2-octyl cyanoacrylate glue in STSG margin fixation was safe, effective, convenient, and time-saving. This study found 2-octyl cyanoacrylate glue a good option for lower extremity skin defect reconstruction.
A Review of the Role of Machine Learning Techniques towards Brain-Computer Interface Applications

This review article provides a deep insight into the Brain–Computer Interface (BCI) and the application of Machine Learning (ML) technology in BCIs. It investigates the various types of research undertaken in this realm and discusses the role played by ML in performing different BCI tasks. It also reviews the ML methods used for mental state detection, mental task categorization, emotion classification, electroencephalogram (EEG) signal classification, event-related potential (ERP) signal classification, motor imagery categorization, and limb movement classification. This work explores the various methods employed in BCI mechanisms for feature extraction, selection, and classification and provides a comparative study of reviewed methods. This paper assists the readers to gain information regarding the developments made in BCI and ML domains and future improvements needed for improving and designing better BCI applications.
Experiments with Social Good: Feminist Critiques of Artificial Intelligence in Healthcare in India

In contemporary India, AI-enabled automated diagnostic models are beginning to control who gets access to what kind of medical care, with the most invasive systems being aimed at underserved communities. I critically question the dominant narrative of “AI for social good” that has been widely adopted by various stakeholders in the healthcare industry towards solving development challenges through the introduction of AI applications targeted towards the sick-poor. Using feminist theory, I argue that AI systems should not be seen as neutral products but complex sociotechnical processes embedded with gendered knowledge and labor. I analyze the layers of expropriation and experimentation that come into play when AI technologies become a method of using diverse bodies and medical records of the sick-poor as data to train proprietary AI algorithms at a low cost in the absence of effective state regulatory mechanisms. I posit that an overwhelming focus on “spectacular technologies” such as AI derails public efforts from solving the actual needs of populations targeted by the “AI for social good” narrative, and from the development of sustainable, responsible, situated healthcare solutions. Lastly, I offer social and policy recommendations that would enable us to envision inclusive feminist futures in which we understand and prioritize the needs of underserved populations over capitalist market logics in the development, deployment, and regulation of AI systems.
Nitrogen Transformations

Nitrogen is an important limiting element in streams (Dodds and Smith, 2016), as well as a pollutant that stimulates unwanted algal growth. Further, ammonia and nitrate can both be toxic to vertebrates, including humans, at high concentrations (Thurston et al., 1981; Carmago et al., 2005). Transport of nitrogen through streams can lead to water quality problems in coastal areas (Alexander et al., 2000), yet rivers and streams retain and process substantial amounts of nitrogen. Streams are also a globally important source of nitrous oxide (Beaulieu et al., 2011), which is one of the most potent greenhouse gasses leading to global warming. Biotic and abiotic factors influence transformations of nitrogen into different forms in the environment. The major forms of nitrogen (N) are organic N (including proteins, nucleic acids, urea, and various other compounds) and inorganic N (Dodds and Whiles, 2010). Major gaseous forms of inorganic N include N2 gas as a major component of the atmosphere, nitrous oxide (N2O), and ammonia (NH3). Dissolved inorganic ionic forms of nitrogen include ammonium (NH4 þ), nitrite (NO2 ), and nitrate (NO3 ).