Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulation and practice: a repeated measurements perspective

La simulación puede brindar oportunidades poderosas para el desarrollo de habilidades esenciales en un entorno seguro que no tiene consecuencias para los pacientes. Sin embargo, para comprender los efectos de la simulación en la práctica clínica, necesitamos un diseño que nos permita medir sistemáticamente una o más variables de interés repetidamente durante y después de la simulación y, cuando sea posible, también antes de la simulación. Aunque este tipo de investigación a menudo se asocia con muestras más grandes de participantes, a través de un ejemplo simulado este artículo demuestra que este tipo de diseño se puede utilizar incluso en entornos donde solo hay un participante. Un uso más frecuente de este tipo de diseño puede ayudarnos a comprender los efectos de la simulación en la práctica a corto y largo plazo y cómo estos efectos dependen del contexto en el que se lleva a cabo la simulación. Simulation can provide powerful opportunities for the development of essential skills in a safe environment that has no consequences for patients. However, to understand the effects of simulation on clinical practice, we need a design that allows us to systematically measure one or more variables of interest repeatedly during and after the simulation and where possible also before the simulation. Although this kind of research is often associated with larger samples of participants, through a simulated example this article demonstrates that this type of design can be used even in settings where there is only one participant. An increased use of this kind of design may help us to understand the effects of simulation on practice on the short and long term and how these effects may depend on the context in which simulation takes place.
Seven Switching Techniques for the Ladder Resonant Switched-Capacitor Converters With Full-Range Voltage Regulation

Ladder resonant switched-capacitor converters (RSCCs) provide high step-up voltage gain, but the continuous regulation range is typically limited with an extremely wide switching frequency band. This article introduces a new full-range regulation method for step-up ladder RSCCs along with seven switching techniques. Each switching technique offers multiple possibilities for implementation. Furthermore, ladder RSCCs with the proposed switching techniques feature the merits of simple control, soft switching, and high efficiency. A comprehensive analysis of operation principle, voltage gain curves, device stress, active switch utilization, zero voltage switching range, output voltage ripple, and input current ripple is given for the proposed seven switching techniques. A 3X step-up Ladder RSCC prototype was built and tested to validate the theoretical results.
Response of Quinoa plant grown under Drought Stress to Foliar Application with Salicylic Acid, Paclobutrazol and Algae Extract

The aim of this study was to evaluate the growth and productivity of quinoa grown under drought stress through using growth regulator, retardant and bio stimulant. The experiment was carried out at Agricultural Research and Experimental Station, Faculty of Agriculture, Cairo University, Giza, Egypt during two winter successive growing seasons of 2019 and 2020.The horizontal plots were devoted to two irrigation regimes [well water (80% filed capacity) and drought stress (40% filed capacity)]. The vertical plots were allocated to drought alleviating materials (salicylic acid, paclobutrazol and algae extract) in addition to control treatment (tap water). The results revealed that exogenous application of drought alleviating materials significantly enhanced growth parameters, inflorescence length, No. inflorescences plant -1 , No. seeds plant -1 , 1000-seed weight and seed yield ha -1 compared with untreated treatment. In addition, spraying plants with salicylic acid, paclobutrazol and algae extract at 40% field capacity (FC) exhibited a significant increase in growth and chemical characters compared with untreated plants. Foliar spray with 100 ppm salicylic acid caused positive changes in growth, yield and anatomical structure of stem and leaves of quinoa. Also, spraying plants with the aforementioned materials increased almost all macro and microelements, besides total carbohydrate percentage and crude protein in seeds. Analysis of amino acids in quinoa seeds using Amino Acid Analyzer discovered higher concentration in phenylalanine, leucine and lysine. As well as fatty acids was analysis detected higher percentages in linoleic.
Kelly Criterion for Optimal Credit Allocation

The purpose of this study is to address the critical issue of optimal credit allocation. Predicting a borrower’s probability of default is a key requirement of any credit allocation system but turning it into labeled classes leads to problems in performance measurement. In this paper the connection between the probability of default and optimal credit allocation is established through a conceptual construct called the Kelly criterion. Conflicting performance measures in dichotomous classification are replaced with coherent criteria for judging the performance of credit allocation decisions. Extensive testing on peer-to-peer lending data shows that the Kelly strategy enables consistent outperformance and efficiency in processing information relative to alternative credit allocation approaches.
Factors Influencing the Level of Local Participation in Planning and Management of the Planned Salzburger Lungau & Kärntner Nockberge Biosphere Reserve in Austria

The participation of the local population in the planning and management of biosphere reserves is one of the preconditions for success. While numerous studies underpin its importance, few studies have addressed to what extent participation is desired by local residents and which factors determine the level of participation. A postal survey among local residents (n = 449) explored factors influencing their willingness to participate in the planning and management of the Salzburger Lungau & Kärntner Nockberge Biosphere Reserve in Austria before it was officially recognised by UNESCO. By applying the “Theory of Planned Behaviour”, the study found a high willingness to be involved among the local population, but a considerable variance as to what extent. Regression models showed a strong influence of perceived behavioural control and the social environment, whereas the factors identified in previous studies were less relevant. The results show that the readiness to become active seems to be higher than expected by local bodies and more linked to the design of the participatory process or other barriers. The results support the biosphere reserve management in developing appropriate participatory approaches to maximise satisfaction with participation and management success.
Novel ZnO–MnO2–Cu2O triple nanocomposite: Facial synthesis, characterization, antibacterial activity and visible light photocatalytic performance for dyes degradation-A comparative study

Novel ZnO–MnO2–Cu2O (ZMC) triple nanocomposite (TNC) was synthesized via the facial synthesis method. For comparison, ZnO–MnO2 (ZM) and ZnO–CuO (ZC) nanocomposites (NCs) were prepared. The prepared samples were characterized by UV–vis, FTIR, XRD, SEM, and EDS techniques. The optical activity of the samples was studied via UV–vis analysis. The FTIR spectra confirmed the presence of metal-O bands in samples. The crystalline structure of the samples was studied via XRD pattern. The morphology of the prepared compounds was analyzed via SEM images. The presence of Zn, Mn, and Cu in the prepared samples was confirmed via EDS test. The photocatalytic activity of ZM NC and ZMC TNC was examined by degradation of Rhodamine B and Direct Red dyes in visible light. In comparison, ZMC TNC degrades dyes considerably faster than ZM NC. The antimicrobial activity of ZMC TNC, ZM NC, and ZC NC displayed a high activity against the common pathogens Escherichia coli (E. coli, ATCC 9637) and Staphylococcus aureus (S. aureus, ATCC 12600) especially in the case of ZMC TNC. As an important achievement, the present researchers showed that the simple synthesis of TNCs is possible, and also TNCs have high capabilities due to their new, consolidated, triple and combined properties.
A Novel Multi-Dimensional Clinical Response Index Dedicated to Improving Global Assessment of Pain in Patients with Persistent Spinal Pain Syndrome after Spinal Surgery, Based on a Real-Life Prospective Multicentric Study (PREDIBACK) and Machine Learning Techniques

The multidimensionality of chronic pain forces us to look beyond isolated assessment such as pain intensity, which does not consider multiple key parameters, particularly in post-operative Persistent Spinal Pain Syndrome (PSPS-T2) patients. Our ambition was to produce a novel Multi-dimensional Clinical Response Index (MCRI), including not only pain intensity but also functional capacity, anxiety-depression, quality of life and quantitative pain mapping, the objective being to achieve instantaneous assessment using machine learning techniques. Two hundred PSPS-T2 patients were enrolled in the real-life observational prospective PREDIBACK study with 12-month follow-up and received various treatments. From a multitude of questionnaires/scores, specific items were combined, as exploratory factor analyses helped to create a single composite MCRI; using pairwise correlations between measurements, it appeared to more accurately represent all pain dimensions than any previous classical score. It represented the best compromise among all existing indexes, showing the highest sensitivity/specificity related to Patient Global Impression of Change (PGIC). Novel composite indexes could help to refine pain assessment by informing the physician’s perception of patient condition on the basis of objective and holistic metrics, and also by providing new insights regarding therapy efficacy/patient outcome assessments, before ultimately being adapted to other pathologies.
FEMuS-Platform: a numerical platform for multiscale and multiphysics code coupling

Nowadays, many open-source numerical codes are available to solve physical problems in structural mechanics, ﬂuid ﬂow, heat transfer, and neutron diﬀusion. However, even if these codes are often highly specialized in the numerical simulation of a particular type of physics, none of them allows simulating complex systems involving all the physical problems mentioned above. In this work we present a numerical framework, based on the SALOME platform, developed to perform multiscale and multiphysics simulations involving all the mentioned physical problems. In particular, the developed numerical platform includes the multigrid ﬁnite element in-house code FEMuS for heat transfer, ﬂuid ﬂow, turbulence and ﬂuid-structure modeling; the open-source ﬁnite volume CFD software OpenFOAM; the multiscale neutronic code DONJON-DRAGON; and a system-scale code used for thermal-hydraulic simulations. Eﬃcient data exchange among these codes is performed within computer memory by using the MED libraries, provided by the SALOME platform.
Whisper Speech Enhancement Using Joint Variational Autoencoder for Improved Speech Recognition

Whispering is the natural choice of communication when one wants to interact quietly and privately. Due to vast differences in acoustic characteristics of whisper and natural speech, there is drastic degradation in the performance of whisper speech when decoded by the Automatic Speech Recognition (ASR) system trained on neutral speech. Recently, to handle this mis-matched train and test scenario Denoising Autoencoders (DA) are used which gives some improvement. To improve over DA performance we propose another method to map speech from whisper domain to neutral speech domain via Joint Variational Auto-Encoder (JVAE). The proposed method requires time-aligned parallel data which is not available, so we developed an algorithm to convert parallel data to time-aligned parallel data. JVAE jointly learns the characteristics of whisper and neutral speech in a common latent space which signiﬁ-cantly improves whisper recognition accuracy and outperforms traditional autoencoder based techniques. We benchmarked our method against two baselines, ﬁrst being ASR trained on neutral speech and tested on whisper dataset and second being whis-per test set mapped using DA and tested on same neutral ASR. We achieved an absolute improvement of 22.31% in Word Error Rate (WER) over the ﬁrst baseline and an absolute 5.52% improvement over DA.
Personalized Chronic Disease Follow‐Up Appointments: Risk‐Stratified Care Through Big Data

Managing patients with chronic conditions is challenging. It requires timely care adjustments based on the patient's health status. We leverage big data to optimize patient monitoring frequencies and improve treatment. Our research is motivated by the need to improve patient care at the Veterans Affairs (VA) hospitals. We propose an integrated model to better serve patients with chronic kidney disease (CKD). CKD is prevalent, complex, and costly. The demand for kidney care has steadily increased; however, there is a decline in the availability of nephrologists. We propose a finite‐horizon Markov decision process (MDP) model, which utilizes evidence‐based and data‐driven approach to identify the best follow‐up appointment schedule for patients. The MDP model helps attain an optimal dynamic treatment plan to enhance patient's quality of life. It is parameterized by data from 11 US Department of Veterans Affairs hospitals, containing 68,513 CKD patients (mostly males between 60 and 90 years old) geographically dispersed throughout the United States between January 1, 2009 and February 21, 2016. Through various estimates and assumptions, we propose an optimal monitoring policy. We find that CKD severity, comorbidities, age, and distance to nephrologist all play roles in shaping patients’ needs of care. Through the VA clinical data, we have numerically validated our recommendation and shown that it considerably outperforms the current kidney care guidelines adopted by the VA.
Visible-Light-Mediated Methane Activation for Steam Methane Reforming under Mild Conditions: A Case Study of Rh/TiO2 Catalysts

Hot-carrier-induced molecular activation over photoexcited metal nanostructures is an important research field in solar-to-chemical energy conversion. Here, we report that visible light-illuminated TiO2-supported Rh nanoparticles could significantly enhance methane (CH4) activation in steam methane reforming at mild operating temperature (below 300 °C) with an ∼50% decrease in apparent activation energy compared to that of the pure thermal process. Femtosecond time-resolved infrared spectroscopic measurement and density functional theory calculations show an ultrafast separation of hot carriers at the Rh-TiO2 interface, resulting in the formation of an electron-deficient state of Rhδ+ at the surface for successive CH4 activation at low temperatures. Wavelength-dependent activities and kinetic isotope experiments validate that the photoexcited hot carriers in the Rh nanoparticles play a critical role in facilitating the rate-determining steps, i.e., the cleavage of the C–H bond in CH4. This study opens a p.
Identifying current driver rehabilitation practices for clients with physical impairments

Introduction Driving is an important occupation that influences a person’s ability to access community resources and participate in meaningful activities. Occupational therapists and other clinicians who are trained as driver rehabilitation specialists provide interventions to improve function and independence in driving. The objective of this study was to examine consistency of adaptive driving equipment prescription and driver retraining recommendations for clients with physical impairment and identify factors that influence driver rehabilitation recommendations. Methods Clinicians practicing in Canada and the United States were surveyed electronically. Data were analyzed using descriptive statistics and content analysis. Results Out of 36 respondents, the majority (97.2%) were occupational therapists. Completion of in-clinic (97.0%) and on-road (84.4%) evaluations were consistent among the majority of respondents, yet components comprising evaluations varied. Fewer respondents were in agreement in equipment recommendations for secondary controls. Respondents emphasized the importance of clinical reasoning skills when making equipment and intervention recommendations. Conclusion Further development of clinical guidelines and advanced training in driver rehabilitation may be beneficial to facilitate clinical reasoning and improve consistency of practice.
Turkish EFL teachers’ attitudes towards online instruction throughout the Covid-19 outbreak

The purpose of the present study is to shed light on the attitudes of Turkish EFL teachers towards online instruction (OI), the impact of training on their attitudes, the obstacles they encounter, and the strategies to deliver OI more successfully throughout the Covid-19 outbreak. The participants were 70 EFL teachers (52 female, 18 male). The data was collected through a questionnaire and semi-structured interviews. The data were analyzed by using SPSS 26. Descriptive statistics of each item was calculated and an independent sample t-test was conducted to answer the research questions. For the analysis of the interview data, the responses of the participants were transcribed and the recurring themes were identified. The results revealed that the attitudes of Turkish EFL teachers, who experienced online instruction during the Covid-19 outbreak, are not as positive as expected regarding some of the results of the related research in the literature. The interview findings revealed that EFL teachers do not feel capable of delivering online instruction effectively since they do not have adequate knowledge and necessary skills to do that. In view of the findings, it’s suggested that online instruction methodology should be integrated into teacher training programs and teachers should receive an in-service training about the related subject.
MACA: A Residual Network with Multi-Attention and Core Attributes for Code Search (S)

—Code search technique has gradually become a key skill to accelerate software development. However, the current deep learning methods only use the encoded results and ignores the original content of the code. Besides, the feature expression of the code is too single, which makes the model’s understanding insufﬁcient. And the last problem is the lack of separate processing of core attributes, which will cause the model to lack differentiated learning of the attributes with different importance. Therefore, we propose a residual network based on Multi-Attention, so that the model can not only retain the original content of the code but also allow the code to perform a large number of combined learning in different aspects to obtain differentiated features. Then we treat three core attributes and speciﬁc implementation of the code differently so that the model can pay extra attention to the core attributes. We use 158,201 Java code-comment pairs for training. In our experimental results, our model is 9.5% higher than the existing method on the indicator of MRR and 12% higher on the SuccessRate@1.
The Intangible Gender Gap: An Asset Channel of Inequality

We propose an "asset channel of inequality" that contributes to gender inequities. We establish that industries with low (high) gender pay gaps have high (low) shares of tangible assets. Because asset tangibility determines firms' ability to collateralize assets and borrow, credit conditions affect industries differently. We show that credit expansions further reduce the pay gap in low-pay-gap industries while leaving it unaffected in high-pay-gap industries, making low-pay-gap industries more appealing for women. Consequently, gender sorting across industries increases, which then cements gender roles and accentuates workplace gender bias. Ultimately, credit expansions help women "swim upstream" but also reinforce glass ceilings.
Kinetic study on the thermal decomposition of layered double hydroxides

The thermal decomposition of layered double hydroxides (LDH) influences the preparation of composite oxides by calcination of LDH precursors. The thermal decomposition of LDH with different Mg/Al ratios was measured using thermalgravimetric analysis and differential temperature analysis. The kinetic data for the thermal decomposition was also calculated. The initial Mg2Al-LDH and the calcination products at various temperatures were characterized by X-ray diffraction and IR spectroscopy. The results show the evolution of carbonates in the interlayer between 250 ℃ and 700 ℃, with the main evolution at 330～400 ℃. New materials were produced by decarbonation and dehydroxylation of OH bound with the sheet. The LDH decomposed to mixed oxides upon heating to 500～700 ℃.
Lean Construction and Organizational Knowledge Creation

It is essential for the construction industry to continuously create new knowledge, aiming at innovation and maintaining competitiveness. As for Lean Construction in addition to improving construction processes, the characteristic inherent in its implementation is that of creating collaborative, interdisciplinary moments with a high level of information sharing, which shows the great potential of the methodology for the creation of knowledge. This article sets out to analyze how people participating in Lean Construction implementation projects see the potential of Lean Construction for creating knowledge. To do so, in addition to a thorough review of the literature on the subject, the results of a form-based survey conducted with six Brazilian construction companies, partners of a consultancy company, are presented. The results show that everyone surveyed agrees that Lean Construction increases the sharing of information between people, the creation of improvement actions for projects, and finally, knowledge creation for the company. This perception is sharper in people who occupy management positions than in those in operational positions. Also, the present study concludes that the most effective way to generate organizational knowledge in Lean Construction implementation projects is to combine tools, methods and training that make use of both tacit and explicit knowledge.
Multi-UAV Navigation for Partially Observable Communication Coverage by Graph Reinforcement Learning

In this paper, we aim to design a deep reinforcement learning (DRL) based control solution to navigating a swarm of unmanned aerial vehicles (UAVs) to fly around an unexplored target area under partial observation, which serves as Mobile Base Stations (MBSs) providing optimal communication coverage for the ground mobile users. To handle the information loss caused by the partial observability, we introduce a novel network architecture named Deep Recurrent Graph Network (DRGN), which could obtain extra spatial information through graph-convolution based inter-UAV communication, and utilize historical features with a recurrent unit. Based on DRGN and maximum-entropy learning, we propose a stochastic DRL policy named Soft Deep Recurrent Graph Network (SDRGN). In SDRGN, a heuristic reward function is elaborated, which is based on the local information of each UAV instead of the global information; thus, SDRGN reduces the training cost and enables distributed online learning. We conducted extensive experiments to design the structure of DRGN and examine the performance of SDRGN. The simulation results show that the proposed model outperforms four state-of-the-art DRL-based approaches and three heuristic baselines, and demonstrate the scalability, transferability, robustness, and interpretability of SDRGN.