Miracle-dz/UniFAR-FTUs huggingface dataset:
Correlation between Overconfidence and Learning Motivation in Postgraduate Infection Prevention and Control Training

Introduction: Training in hand hygiene for health care workers is essential to reduce hospital-acquired infections. Unfortunately, training in this competency may be perceived as tedious, time-consuming, and expendable. In preceding studies, our working group detected overconfidence effects in the self-assessment of hand hygiene competencies. Overconfidence is the belief of being better than others (overplacement) or being better than tests reveal (overestimation). The belief that members of their profession are better than other professionals is attributable to the clinical tribalism phenomenon. The study aimed to assess the correlation of overconfidence effects on hand hygiene and their association with four motivational dimensions (intrinsic, identified, external, and amotivation) to attend hand hygiene training. Methods: We conducted an open online convenience sampling survey with 103 health care professionals (physicians, nurses, and paramedics) in German, combining previously validated questionnaires for (a) overconfidence in hand hygiene and (b) learning motivation assessments. Statistics included parametric, nonparametric, and cluster analyses. Results: We detected a quadratic, u-shaped correlation between learning motivation and the assessments of one’s own and others’ competencies. The results of the quadratic regressions with overplacement and its quadratic term as predictors indicated that the model explained 7% of the variance of amotivation (R2 = 0.07; F(2, 100) = 3.94; p = 0.02). Similarly, the quadratic model of clinical tribalism for nurses in comparison to physicians and its quadratic term explained 18% of the variance of amotivation (R2 = 0.18; F(2, 48) = 5.30; p = 0.01). Cluster analysis revealed three distinct groups of participants: (1) “experts” (n1 = 43) with excellent knowledge and justifiable confidence in their proficiencies but still motivated for ongoing training, and (2) “recruitables” (n2 = 43) who are less competent with mild overconfidence and higher motivation to attend training, and (3) “unawares” (n3 = 17) being highly overconfident, incompetent (especially in assessing risks for incorrect and omitted hand hygiene), and lacking motivation for training. Discussion: We were able to show that a highly rated self-assessment, which was justified (confident) or unjustified (overconfident), does not necessarily correlate with a low motivation to learn. However, the expert’s learning motivation stayed high. Overconfident persons could be divided into two groups: motivated for training (recruitable) or not (unaware). These findings are consistent with prior studies on overconfidence in medical and non-medical contexts. Regarding the study’s limitations (sample size and convenience sampling), our findings indicate a need for further research in the closed populations of health care providers on training motivation in hand hygiene.
Technological Impact of Placer Gold Mine on Water Quality: Case of Tuul River Valley in the Zaamar Goldfield, Mongolia

The paper finds that placer gold mining operation has significant impact on the surface water quality, which varies from mine to mine depending on the types of gold recovery technologies applied. Only primitive gold recovery technologies are dominating in the Zaamar goldfield. We assume that the mines with foreign ownership have cleaner technologies with less impact on the water quality than the local ones. But results from the field study show that mines with joint ownerships of Russian or Chinese companies in Zaamar area have more polluting technologies than the local smaller companies. However the results from the empirical analysis support our assumption and show that domestic owned mines have more impact on the water quality than the mines jointly owned with Russian or Chinese companies since they dominantly uses primitive technologies to recover the gold.
Privacy-preservation and the Use of Data for Research: A COVID-19 Use Case in Randomly Generated Healthcare Records

The provision of clinical data for research purposes has become central to monitoring and understanding the COVID-19 outbreak. In such a pandemic scenario, obtaining new research results is an imperative and urgent requirement. However, nowadays, personal data are protected by different legal regulations, to which all these data must comply, especially those related to the health of individuals. Then, a tough challenge arises in the academic sphere: how to provide a large amount of detailed clinical data for research and, simultaneously, guarantee the privacy of the individuals involved? Thus, this article discusses how the biomedical community may face this challenge and it presents the main ongoing initiatives and available emergent technologies that are useful to meet such urgent demand. Moreover, it also shows, through a use case, how it is possible to deal with this challenge, presenting the applicability of privacy-preserving techniques over a randomly generated typical dataset of COVID-19 health records. Copyright © 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.
Combined Spatial-Spectral Hyperspectral Image Classification Based on Adaptive Guided Filtering

Hyperspectral image classification has a low accuracy in the face of a small training set. To solve the problem, this paper proposes a combined spatial-spectral hyperspectral image classification approach based on adaptive guided filtering. From coarse to fine classification, the local binary pattern (LBP) histogram features were improved, the spatial contrast description was enhanced, and enhanced spatial-spectral features were prepared through Gabor transform of different scales and directions, combined with super pixel blocks. Then, the pre-classification was completed by the support vector machine (SVM) classifier. To reduce noise interference, the pre-classification results were filtered again by a guided filter based on the adaptive regularization factor. To verify its effectiveness, the proposed approach was compared with the state-of-the-arts approaches through repeated experiments. The comparison shows that our approach achieved a high classification accuracy, while suppressing noise interference. This research provides a new tool for hyperspectral image classification with a small training set.
Antitumor Efficacy of Doxorubicin-Loaded Electrospun Attapulgite–Poly(lactic-co-glycolic acid) Composite Nanofibers

Currently, cancer chemotherapeutic drugs still have the defects of high toxicity and low bioavailability, so it is critical to design novel drug release systems for cancer chemotherapy. Here, we report a method to fabricate electrospun drug-loaded organic/inorganic hybrid nanofibrous system for antitumor therapy applications. In this work, rod-like attapulgite (ATT) was utilized to load a model anticancer drug doxorubicin (DOX), and mixed with poly(lactic-co-glycolic acid) (PLGA) to form electrospun hybrid nanofibers. The ATT/DOX/PLGA composite nanofibers were characterized through various techniques. It is feasible to load DOX onto ATT surfaces, and the ATT/DOX/PLGA nanofibers show a smooth and uniform morphology with improved mechanical durability. Under neutral and acidic pH conditions, the loaded DOX was released from ATT/DOX/PLGA nanofibers in a sustained manner. In addition, the released DOX from the nanofibers could significantly inhibit the growth of tumor cells. Owing to the significantly reduced burst release profile and increased mechanical durability of the ATT/DOX/PLGA nanofibers, the designed organic–inorganic hybrid nanofibers may hold great promise as a nanoplatform to encapsulate different drugs for enhanced local tumor therapy applications.
Modeling di (2-ethylhexyl) Phthalate (DEHP) and Its Metabolism in a Body’s Organs and Tissues through Different Intake Pathways into Human Body

Phthalate esters (PAEs) are ubiquitous in indoor environments as plasticizers in indoor products. Residences are often exposed to indoor PAEs in the form of gas, particles, settled dust, and surface phases. To reveal the mechanism behind the accumulation of PAEs in different tissues or organs such as the liver and the lungs when a person exposed to indoor PAEs with different phases, a whole-body physiologically based pharmacokinetic model for PAEs is employed to characterize the dynamic process of phthalates by different intake pathways, including oral digestion, dermal adsorption, and inhalation. Among three different intake pathways, dermal penetration distributed the greatest accumulation of DEHP in most of the organs, while the accumulative concentration through oral ingestion was an order of magnitude lower than the other two doses. Based on the estimated parameters, the variation of di-ethylhexyl phthalate (DEHP) and mono (2-ethylhexyl) phthalate (MEHP) concentration in the venous blood, urine, the liver, the thymus, the pancreas, the spleen, the lungs, the brain, the heart, and the kidney for different intake scenarios was simulated. The simulated results showed a different accumulation profile of DEHP and MEHP in different organs and tissues and demonstrated that the different intake pathways will result in different accumulation distributions of DEHP and MEHP in organs and tissues and may lead to different detrimental health outcomes.
A Literature Review on Methods for Learning to Rank

: The increasing number of indexed documents makes manual retrieval almost impossible when they are retrieved or stored automatically. The solution to this problem consists of using information retrieval systems, which seek to present the most relevant data items to the user in order of relevance. Therefore, this work aims to conduct a theoretical survey of the most used algorithms in the Information Retrieval ﬁeld using Learning to Rank methods. We also provide an analysis regarding the datasets used as benchmarks in the literature. We observed that RankSVM and LETOR collection are the most frequent method and datasets employed in the analyzed works.
Taxi Booking Mobile App Order Demand Prediction Based on Short-Term Traffic Forecasting

The Didi Dache app is China’s biggest taxi booking mobile app and is popular in cities. Unsurprisingly, short-term traffic demand forecasting is critical to enabling Didi Dache to maximize use by drivers and ensure that riders can always find a car whenever and wherever they may need a ride. In this paper, a short-term traffic demand forecasting model, Wave SVM, is proposed. It combines the complementary advantages of Daubechies5 wavelets analysis and least squares support vector machine (LS-SVM) models while it overcomes their respective shortcomings. This method includes four stages: in the first stage, original data are preprocessed; in the second stage, these data are decomposed into high-frequency and low-frequency series by wavelet; in the third stage, the prediction stage, the LS-SVM method is applied to train and predict the corresponding high-frequency and low-frequency series; in the last stage, the diverse predicted sequences are reconstructed by wavelet. The real taxi-hailing orders data are applied to evaluate the model’s performance and practicality, and the results are encouraging. The Wave SVM model, compared with the prediction error of state-of-the-art models, not only has the best prediction performance but also appears to be the most capable of capturing the nonstationary characteristics of the short-term traffic dynamic systems.
The Value of Epistemic Justice

The notion of epistemic injustice has become an important topic of inquiry in recent times. It refers to the injustice committed to a person when her claim to knowledge is not given due consideration. This article argues that there are two major sources of epistemic injustice: One is the dominating tendencies present in us, and the other is susceptibility to cognitive biases and distortions. When societies become more complex, injustice increases and one can see countless instances of epistemic injustice in everyday life. To reduce epistemic injustice, one has to tackle both sources. Increasing cooperative behaviour is the key in this regard which, in turn, may require revisiting the way the self is automatically understood.
Egg production, economic indices, and external and internal egg quality parameters of laying Japanese quails (Coturnix japonica) fed palm (Elaeis guineensis) kernel cake

The feeding trial aimed to evaluate the egg production, quality parameters and economic benefits of using palm kernel cake, a byproduct of palm oil production in the diets of laying Japanese quails. Two hundred (200) nine-week-old laying Japanese quails were randomized into 5 groups replicated 4 times of 10 birds each in a six weeks trial. Palm kernel cake (PKC) was incorporated at 0 %, 10 %, 20 %, 30 % and 40 % in formulating feed for the quails. The form of oil removal affected the PKC such that it had 91.51% dry matter, 15.70 % crude protein, 21.43 % crude fibre, 12.30 % ether extract, 1.44 % ash, 40.78 % nitrogen-free extract and 12.67MJkg-1 metabolizable energy. All egg production parameters were significantly different (P ≤ 0.05). The results in this study showed that feed cost/ kg, feed cost per crate, profit per crate and egg-feed-price ratio were significantly influenced (P ≤ 0.05) as well as the shell percentage, yolk-albumen weight, albumen weight, yolk height, colour and yolk index. In conclusion, laying Japanese quails can permit up to 40 % dietary PKC without negative effect on laying performance, better cost-benefit, improved shell percentage, albumen weight and percentage, and yolk index, however, the yolk colour becomes lighter with increased PKC inclusion.
APPLYING MACHINE LEARNING IN HEALTHCARE TO FIGHT THE COVID-19: A CASE STUDY OF FORECASTING THE OUTBREAK IN SAUDI ARABIA

During this evolved worldwide health crisis, the medical industry is searching for new technologies to monitor and control the Coronavirus pandemic's spread. Artificial intelligence is a technology that can easily trace the reach of this virus, classify high-risk patients, and maintain this infection in real-time. It can also foretell mortality risk by analyzing the previously obtained data of the patients. It can help oppose this virus by population screening, medical help, notification, and infection control suggestions. Machine Learning (ML) techniques help improve detection, tracing, prognosis, and prevention. This paper will explore how the healthcare sector benefits from utilizing ML models to fight against the COVID-19. It introduces the techniques that enhanced health care delivery efficiency, accessibility, and cost-effectiveness during the pandemic. This work presents a case study of forecasting the COVID-19 numbers in Saudi Arabia using the ARIMA model to provide further insights and practically support health care management.
Microsoft Word-83079995-file00

Laboratory of Respiratory Biology (J.P.G., M.L.E., J.A.B., A.G., J.C., D.C.Z.), Laboratory of Structural Biology (F.B.L., K.B.T.), and the Cellular and Molecular Pathology Branch (T.A.M, N.P.C), National Toxicology Program Laboratory, Division of the National Toxicology Program (W.Q.), National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina; and Charles River Laboratories – Pathology Associates, Durham, North Carolina (J.P.M.) DMD Fast Forward. Published on January 11, 2013 as doi:10.1124/dmd.112. 049429
Non-Quantum Teleportation in a Rotating Space With a Strong Electromagnetic Field

In 1991 we derived the physical conditions opening the gate to a fully degenerate space-time, where from the point of view of a regular observer the observable spatial and time intervals are equal to zero. The one of the conditions, under which the observable interval of time is zero, enables instant displacement (non-quantum teleportation) of physical bodies at any distance. In this article, we derive the teleportation condition for Schwarzschild’s mass-point metric, Schwarzschild’s metric inside a sphere ﬁlled with an incompressible liquid and de Sitter’s metric of a space ﬁlled with the physical vacuum. We also introduce the modiﬁcations of the above three metrics, which contain rotation due to the space-time non-holonomity (non-orthogonality of the time lines to the three-dimensional spatial section) and derive the teleportation condition in each of these spaces. The obtained teleportation condition requires either a near-light-speed rotation or a super-strong gravitational ﬁeld (depending on the particular space metric), which is very problematic if not impossible in a regular laboratory. On the other hand, the non-orthogonality of the time lines to the three-dimensional section can be implemented not only by a mechanical rotation of the laboratory space, but also using other physical factors. Thus, we are looking for how to do it using a strong electromagnetic ﬁeld (the latter is not a problem for modern technologies). We introduce a space-time metric, which rotates due to its non-holonomity, and the gravitational ﬁeld is neglected. Then, substituting the components of the obtained metric into Einstein’s ﬁeld equations with the electromagnetic energy-momentum tensor on the right hand side, we obtain the conditions under which the equations vanish and, therefore, the metric space is Riemannian and contains an electromagnetic ﬁeld. As a result, we obtain how the electromagnetic ﬁeld parameters can replace the rotation of space in the teleportation condition. The obtained result shows how to teleport physical bodies from an earth-bound laboratory to any remote point in the Universe using a super-strong electromagnetic ﬁeld. Creating such devices is a very interesting task for engineers in the near future.
Intelligent Text Input Methods and Metrics

Text input serves as a fundamental interactive part of almost any human-computer system, enabling expression, communication, and information capture. Since the cuneiform was invented as a writing system back in 3500 BCE, text as the medium of information has been created, recorded and communicated throughout the human civilization. The way we enter text significantly affects the efficiency we communicate with the device. Traditional desktop settings employ hardware keyboards for text input, which worked well for skilled typists. However, the devices in the current era are becoming mobile and ubiquitous, and have various form factors: from watch-sized wearable devices to handheld smartphones and wall-size large displays, most of which do not offer a physical keyboard. As a result, we need new text input interactions to communicate with those new devices. On the other hand, devices are becoming “intelligent”, with powerful hardware processors to run advanced algorithms. Text input can take advantage of these capabilities but only if new algorithms and interaction techniques are developed. To address the need, my thesis aims to develop intelligent text input systems utilizing the state-of-the-art machine learning techniques, in order to support a comprehensive spectrum of text interactions, including entry, editing, and entry of special symbols such as emojis. To encourage fast typing and unleash the power of the auto-correction, I design PhraseFlow which decodes the keystrokes on phrase-level and uses future input sequences to correct previous text. To improve the text editing process on touch-screen devices, I design Gedit , a set of on-keyboard gestures for mobile text editing. I also invent a novel concept, Type, Then Correct , together with three accompanying interaction techniques to improve the text correction on touch screens. People are not only communicating in text: they use non-textual symbols such as emojis to enrich the expression. Another part of my work includes understanding how people use lexical and semantic emoji suggestion systems, and designing a voice-based interaction Voicemoji to facilitate blind and low vision users in entering and exploring emojis. As advanced algorithms enabled more intelligent text input interactions, we need new metrics and evaluation methods to quantify their performance. I present a method-independent model, transcription sequences , for intelligent text input evaluations. Furthermore, to combine the speed and accuracy into a unified performance metric, I derive
Effector Function in the Target Organ Blockade Prevents T Cell Retention and Autoimmune Intervention by CD154

The CD40-CD154 interaction is an attractive target for therapeutic intervention in many autoimmune disorders, including multiple sclerosis. Previously, we showed that CD154 blockade both inhibited the onset of experimental autoimmune encephalomyelitis and blocked clinical disease progression (relapses) in mice with established disease. The mechanism of this protection is poorly understood. Because CD154 plays a role in Th1 development, its blockade has been thought to promote anti-inﬂammatory Th2 responses. However, these conclusions have primarily been based on extrapolated data from in vitro experiments, which may not accurately reﬂect the more complex events occurring in vivo. In this paper we determine how the immune response develops under the inﬂuence of therapeutic CD154 blockade in vivo. We demonstrate that anti-CD154 treatment does not alter the early expansion of Ag-speciﬁc T cells in secondary lymphoid organs or result in deviation to a Th2-dominant response. Interestingly, the late expansion and retention of Th1 cells in the lymph nodes were markedly reduced following immunization of Ab-treated mice, and this coincided with a recompartmentalization of these cells to the spleen. Most importantly, anti-CD154 treatment eliminated the retention/expansion of encephalitogenic Th1 cells, but not their entry into the CNS. These data indicate that a major mechanism by which CD154 blockade protects against autoimmune disease is by controlling the amplitude of acute phase Th1 responses in the draining lymph nodes and by preventing the sustained expansion of effector cells within the target organ. The Journal of Immunology, 2001, 166: 1547–1553.
Monitoring the variability of petrochemical ZA fertilizer production process based on subgroup observations

The development in technology and requirement to control the process quality characteristics simultaneously have leads to the use of multivariate control chart. This chart considered the correlations between quality characteristics, hence improve the performance of that statistical chart. This study deals with the Shewhart-type control chart of multivariate process of ZA fertilizer production in petrochemical industry. The main objective of this study is to monitor the process variability. In order to perform the monitoring process, Phase I and Phase II need to be done. In Phase I, the historical data set (HDS) with m = 36 observations were used to construct the control limits in order to detect any outlier observations, and then removed them to calculate the new control limits with the remaining observations. This step was repeated eight times before the incontrol process was obtained together with the estimated parameter, which is the average of sample covariance matrices. Thus, a clean HDS with m = 20 observations were used to calculate the control limits for monitoring multivariate process variability of new observation in Phase II operation. Here, the study concluded whether the process is stable or not. In purpose of the study, generalized variance (GV) chart is presented as well as vector variance (VV) chart to perform the operations in both phases. Based on GV chart, no outlier is detected and the process is in-control. However, VV chart had detected outliers and concluded that the process is out-of-control. This illustrated that the VV chart is more effective in detecting process variability rather than GV chart.