Miracle-dz/UniFAR-FTUs huggingface dataset:
Coaching tools in the development of nursing leadership in primary health care* Ferramentas do coaching no desenvolvimento da liderança da enfermagem na atenção primária à saúde Herramientas de coaching para desenvolvimiento de lideranza de enfermeria en atención primaria de salud

Objective: To describe the use of coaching tools in the leadership development process of nurses working in Primary Health Care (PHC). Method: A descriptive study with a qualitative approach of the action-research type. Data from the application of the Wheel of Life and the E.S. P.E. R.T. A Goal tools for nine nurses were organized in the WebQDA software and analyzed by content categorization. Results: The participants experienced three main actions: self-knowledge, goal setting and consolidation of changes. The changes happened in delegation of responsibilities, emotional balance, conflict management, and communication. Final considerations: the tools favored the identification of advantages, limitations and directed the process of change in attitude and behavior by setting goals. The study presented an innovative characteristic, combining experimentation and objectification, leading nurses to incorporate changes in daily work and PHC leadership.
On Competitive Strategy from OEM to OBM - A Case Study on a Sealing Parts Company in Taiwan

As the market becomes more globalized and the international situation continues to undergo complex changes, the income and profits of the OEM business are getting thinner and smaller. In such circumstances, many companies have initiated OBM business, causing fierce brand competition in the market. This study took a sealing parts company in Taiwan as the subject to explore how the company operates smoothly to maximize profits with both OEM and OBM business, determines the proper business strategy according to different customer groups and market segments, distributes customer orders and develops business strategy when OEM business conflicts with OBM business, so as to avoid customer complaints and reduce the risk of losing orders. This study first discussed the brand positioning of the subject company and brand competitions in the industry. Next, through qualitative interviews, it analyzed the market segmentation under the competition among international brands and the impacts of COVID-19 on the development of company strategy.
Benchmark of deep learning algorithms for the automatic screening in electrocardiograms transmitted by implantable cardiac devices

The objective of this work was to benchmark different deep learning architectures for noise detection against cardiac arrhythmia episodes recorded by pacemakers and implantable cardioverter-defibrillators (PM/ICDs) and transmitted for remote monitoring. Up to now, most signal processing from ICD data has been based on classical hand-crafted algorithms, not AI or DL-based ones. The database consist of PM/ICD data from 805 patients representing a total of 10471 recordings from three different channels: the right ventricular (RV), the right atria (RA), and the shock channel. Four deep learning approaches were trained and optimized to classify PM/ICDs' records as actual ventricular signal vs noise episodes. We evaluated the performance of the different models using the F2 score. Results show that the use of 2D representations of 1D signals led to better performances than the direct use of 1D signals, suggesting that the detection of noise takes advantage of a spectral decomposition of the signal, which remains to be confirmed in other contexts. This study proposes deep learning approaches for the analysis of remote monitoring recordings from PM/ICDs. The detection of noise allows efficient management of this large daily flow of data.
Machine Learning Enabled Design Automation and Multi-Objective Optimization for Electric Transportation Power Systems

This article presents an automated design and optimization framework for electric transportation power systems (ETPS) enabled by machine learning (ML). The use of physical models, simulations, and optimization methods can greatly aid the engineering design process. However, when considering the optimal co-design of multiple interdependent subsystems that span multiple physical domains, such model-based simulations can be computationally expensive, and traditional metaheuristic optimization methods can be unreliable. Bayesian optimization (BO), an ML framework, paves one feasible pathway to realize an efficient design process practically. However, current state-of-the-art BO algorithms are non-compatible or perform poorly when applied to system-level ETPS design with multiple objectives and constraints. This article proposes a novel BO algorithm referred to as max-value entropy search for multiobjective optimization with constraints (MESMOC) to solve multiobjective optimization (MOO) problems with black-box constraints that can only be evaluated through design simulations. After a full presentation of the algorithm, MESMOC is applied to a realistic ETPS design case using a heavy-duty electric vertical-takeoff-landing (eVTOL) urban aerial vehicle (UAV) power system. Two MOO experimental trials show a drastic reduction in the number of design simulations to discover a high-quality Pareto front. In Trial 1, MESMOC uncovered the entire Pareto front while only requiring to explore ~4% of the design space. With expanded design parameters and larger design space in Trial 2, a near complete but high-quality Pareto front was uncovered. Both trials compared MESMOC to the popular genetic algorithm NSGA-II and another BO algorithm predictive entropy search for multi-objective Bayesian optimization with constraints (PESMOC), showing superior performance.
An Optimal Scheme to Recharge Communication Drones

The adoption and integration of drones in commu-nication networks is becoming reality thanks to the deployment of advanced solutions for IoT and cellular communication relay schemes. However, using drones introduces new energy con-straints and scheduling issues in the dynamic management of the network topology, due to the need to call back and recharge, or substitute, drones that run out of energy. In this paper, we describe the design of a drone recharging scheme for realisti-cally limited flight time of drones, and leverage the presence of recharging stations. Indeed, drones need to be recharged periodically, and maximizing the operational time of drones is paramount to minimize the size of the fleet of drones to be devoted to a drone mission, hence its cost. We design Homogeneous Rotating Recharge (HRR), an optimal drone recharging scheduling that extends the coverage of a cellular network. HRR minimizes the number of back-up drones needed to guarantee a fixed number of operational drones, so as to support the operation of an underlying cellular network. Results show that operating a network of drones with our scheme provides reliable and stable performance over time.
Electronic structure, effect of metal electrodes and transport properties of 2,5-diphenylthiophene molecular nanowire under applied electric field

theoretical electronic structure and transport properties of Au and thiol substituted 2,5-diphenylthiophene have been calculated from high level Density functional theory (DFT) using B3LYP method with LANL2DZ basis set. The molecular geometric parameters predicted by DFT method are in agreement with the reported results. Variation in MPA and NPA atomic charges and the electric dipole moment of the molecules of various levels of applied electric fields have been analyzed. The decrease of HOMO-LUMO gap from 2.12 eV to 0.94 eV determined from density of states spectrum for the applied field (0 - 0.21 VA-1) shows that thiol linked 2,5-diphenylthiophene molecule can act as efficient molecular nanowire for Au electrodes.
Emissivity Retrievals with FORUM's End-to-end Simulator: Challenges and Recommendations

Abstract. Spectral emissivity is a key property of the Earth surface of which only very few measurements exist so far in the far-infrared (FIR) spectral region, even though recent work has shown its FIR contribution is important for accurate modelling of global climate. The European Space Agency's 9th Earth Explorer, FORUM (Far-infrared Outgoing Radiation Understanding and Monitoring) will provide the first global spectrally resolved measurements of the Earth's top-of-the-atmosphere (TOA) spectrum in the FIR. In clear-sky conditions with low water vapour content, these measurements will provide a unique opportunity to retrieve spectrally resolved FIR surface emissivity. In preparation for the FORUM mission with an expected launch in 2026, this study takes the first steps towards the development of an operational emissivity retrieval for FORUM by investigating the sensitivity of the emissivity product of a full spectrum optimal estimation retrieval method to different physical and operational parameters. The tool used for the sensitivity tests is the FORUM mission's end-to-end simulator. These tests show that spectral emissivity of most surface types can be retrieved for dry scenes in the 350–600 cm−1 region with an uncertainty ranging from 0.005 to 0.01. In addition, the quality of retrieval is quantified with respect to the precipitable water vapour content of the scene, and the uncertainty caused by the correlation of emissivity with surface temperature is investigated. Two main recommendations are made based on these investigations: (1) As the extent of TOA sensitivity to the surface in the FIR depends on the atmospheric state, the spectral region of the emissivity product should be decided using a so-called information quantifier, calculated from the ratio of the retrieval uncertainty to the a-priori uncertainty. (2) Depending on retrieval input parameters, the correlation of emissivity with surface temperature allows for retrieved emissivities within a small range around the true emissivity. Thus the impact of this correlation on the uncertainty estimates of the product should be quantified in detail during further development of the operational retrieval.
Chitosan Biosynthesis and Virulence in the Human Fungal Pathogen <named-content content-type='genus-species'>Cryptococcus gattii</named-content>

Cryptococcus gattii R265 is a hypervirulent fungal strain responsible for the recent outbreak of cryptococcosis in Vancouver Island of British Columbia in Canada. It differs significantly from Cryptococcus neoformans in its natural environment, its preferred site in the mammalian host, and its pathogenesis. Our previous studies of C. neoformans have shown that the presence of chitosan, the deacetylated form of chitin, in the cell wall attenuates inflammatory responses in the host, while its absence induces robust immune responses, which in turn facilitate clearance of the fungus and induces a protective response. The results of the present investigation reveal that the cell wall of C. gattii R265 contains a twoto threefold larger amount of chitosan than that of C. neoformans. The genes responsible for the biosynthesis of chitosan are highly conserved in the R265 genome; the roles of the three chitin deacetylases (CDAs) have, however, been modified. To deduce their roles, single and double CDA deletion strains and a triple CDA deletion strain were constructed in a R265 background and were subjected to mammalian infection studies. Unlike C. neoformans where Cda1 has a discernible role in fungal pathogenesis, in strain R265, Cda3 is critical for virulence. Deletion of either CDA3 alone or in combination with another CDA (cda1 3Δ or cda2 3 ) or both (cda1 2 3 ) rendered the fungus avirulent and cleared from the infected host. Moreover, the cda1Δ2Δ3Δ strain of R265 induced a protective response to a subsequent infection with R265. These studies begin to illuminate the regulation of chitosan biosynthesis of C. gattii and its subsequent effect on fungal virulence. IMPORTANCE The fungal cell wall is an essential organelle whose components provide the first line of defense against host-induced antifungal activity. Chitosan is one of the carbohydrate polymers in the cell wall that significantly affects the outcome of host-pathogen interaction. Chitosan-deficient strains are avirulent, implicating chitosan as a critical virulence factor. C. gattii R265 is an important fungal pathogen of concern due to its ability to cause infections in individuals with no apparent immune dysfunction and an increasing geographical distribution. Characterization of the fungal cell wall and understanding the contribution of individual molecules of the cell wall matrix to fungal pathogenesis offer new therapeutic avenues for intervention. In this report, we show that the C. gattii R265 strain has evolved alternate regulation of chitosan biosynthesis under both laboratory growth conditions and during mammalian infection compared to that of C. neoformans.
Pogonip: Scheduling Asynchronous Applications on the Edge

The microservice architectural style is changing the design of modern applications. Orchestration tools, such as Kubernetes, deploy them on computing nodes assuming that resources are interconnected through fast communication links. However, running microservices in the emerging edge computing environments requires considering the heterogeneity and nonnegligible network delays among edge resources. In this context, although the problem of scheduling synchronous microservice-based applications has been widely explored, scheduling asynchronous applications, where microservices interact using a queue system, has only recently started to be investigated. In this paper, we present Pogonip, an edge-aware scheduler for Kubernetes, designed for asynchronous microservices. We formulate an optimization problem and a heuristic for determining the placement of microservices, which is tailored for edge environments. We integrate them in Kubernetes by building custom scheduler plugins. Using a benchmark application, we show the advantages of the proposed network-aware solutions over other state-of-the-art solutions.
PROMOTECH: A UNIVERSAL TOOL FOR PROMOTER DETECTION IN BACTERIAL GENOMES

A promoter is a genomic sequence where the transcription machinery binds to start copying a gene into an RNA molecule. Finding the location of bacterial promoter sequences is essential for microbiology since promoters play a central role in regulating gene expression. There are several tools to recognize promoters in bacterial genomes; however, most of them were trained on data from a single bacterium or a specific set of sigma factors. Promotech was developed to overcome this limitation, offering a machine-learning-based classifier trained to generate a model that generalizes and detects promoters in a wide range of bacterial species. During the study, two model architectures were tested, Random Forest and Recurrent Networks. The Random Forest model, trained with promoter sequences with a binary encoded representation of each nucleotide, achieved the highest performance across nine different bacteria and was able to work with short 40bp sequences and entire bacterial genomes using a sliding window. The selected model was evaluated on a validation set of four bacteria not used during training, having 50% positive and 50% negative promoter sequences resulting in an average AUPRC of 0.73±0.13 and an AUROC of 0.71±0.13. The Random Forest model achieved an average AUPRC and AUROC across the validation set’s entire genomes of 0.14±0.1 and 0.71±0.17, but increased its performance to 0.75±0.18 AUPRC and 0.90±0.06 AUROC when it was configured to detect promoter clusters. Promotech was compared against state-of-the-art bacterial promoter detection programs using the balanced data set and outperformed these methods.
Transfer Function Gain Between Heart Period and QT Variabilities Increases During Sympathetic Activation Induced by Head-up Tilt

The variability of the duration of the cardiac electrical activity, approximated by the onset of the Q-wave to the end of the T-wave (QT), computed in the low frequency (LF, from 0.04 to 0.15 Hz) band, is utilized as a marker of sympathetic modulation directed to the ventricles. However, the magnitude of QT variability might be affected by heart period (HP) variability as a consequence of the QT-HP relation. We hypothesize that the gain of the relationship between HP and QT in the LF band could be better suited than QT variability to infer the cardiac sympathetic control as a consequence of its intrinsic normalization by the magnitude of HP changes. In this study we assessed transfer function gain (TFG) from HP to QT as well as spectral QT and HP markers in 23 healthy young subjects (age $26\pm 6$ years, 11 males) undergoing head-up tilt test at 45° (T45) and 90° (T90). Results showed that QT variability in the LF band increased during T90. TFG increased as well but the raise was evident also at a lower tilt table inclination, namely during T45. H, the gain of the TFG in the LF band could be a more sensitive marker of the increased sympathetic drive than the LF power of QT variability.
The Role of Anxiety in Executive Functions of Children with Attention Deficit Hyperactivity Disorder and Mathematical Learning Disability Comorbidity

Executive dysfunction is common symptom among patients with attention deficit hyperactivity disorder (ADHD) and/ or Mathematical Learning disability (MLD). Current research evidences indicate that anxiety can lead to numerous cognitive deficits. Therefore, through comparing executive functioning in children with ADHD/MLD, who have high and low levels of anxiety, the present study examined the probable role of anxiety in intensifying their problems in executive functions, especially verbal and visuospatial working memory. In a retrospective quasi-experimental study, 8-12 years old children, who were diagnosed for ADHD and MLD comorbidity were selected using purposive convenience sampling (n=85). They completed the multidimensional anxiety scale for children. Then, due to their scores on this scale, 20 children with high anxiety (1.5 standard deviations and more above the mean) and 20 children with low anxiety (1.5 standard deviations and more below the mean) were selected and placed in two groups. Then, executive functions assessment tasks, including Tower of London test, Wechsler Memory Scale (WMS), and the Benton’s Visual Retention Test, were carried out. Collected data were analyzed using independent t test. Findings showed that anxiety can be considered as an intensifying factor in executive functioning of children with ADHD and MLD. Therefore, executive functions can be improved by balancing the levels of anxiety and preventing further impairment of executive function in these children.
Program Targeting with Machine Learning and Mobile Phone Data: Evidence from an Anti-Poverty Intervention in Afghanistan

Can mobile phone data improve program targeting? By combining rich survey data from a"big push"anti-poverty program in Afghanistan with detailed mobile phone logs from program beneficiaries, we study the extent to which machine learning methods can accurately differentiate ultra-poor households eligible for program benefits from ineligible households. We show that machine learning methods leveraging mobile phone data can identify ultra-poor households nearly as accurately as survey-based measures of consumption and wealth; and that combining survey-based measures with mobile phone data produces classifications more accurate than those based on a single data source.
Designed Ankyrin Repeat Protein (DARPin) Neutralizers of TcdB from <named-content content-type='genus-species'>Clostridium difficile</named-content> Ribotype 027

Clostridium difficile infection (CDI) is a leading cause of hospital-acquired diarrhea. In recent decades, the emergence of the “hypervirulent” BI/NAP1/027 strains of C. difficile significantly increased the morbidity and mortality of CDI. The pathogenesis of CDI is primarily mediated by the action of two toxins, TcdA and TcdB, with TcdB being the major virulent factor in humans. In this report, we describe the engineering of a panel of designed ankyrin repeat proteins (DARPins) that potently neutralize TcdB from the BI/NAP1/027 strains (e.g., TcdBUK1). The most effective DARPin, D16, inhibits TcdBUK1 with a 50% effective concentration (EC50) of 0.5 nM, which is 66-fold lower than that of the FDA-approved anti-TcdB antibody bezlotoxumab (EC50, 33 nM). Competitive enzyme-linked immunosorbent assays (ELISAs) showed that D16 blocks interactions between TcdB and its receptor, chondroitin sulfate proteoglycan 4 (CSPG4). The dimeric DARPin U3D16, which pairs D16 with DARPin U3, a disrupter of the interaction of TcdB with Frizzled 1/2/7 receptor, exhibits 10-fold-to-20-fold-enhanced neutralization potency against TcdB from C. difficile strains VPI 10463 (laboratory strain) and M68 (CF/NAP9/017) but identical activity against TcdBUK1 relative to D16. Subsequent ELISAs revealed that TcdBUK1 did not significantly interact with Frizzled 1/2/7. Computation modeling revealed 4 key differences at the Frizzled 1/2/7 binding interface which are likely responsible for the significantly reduced binding affinity. IMPORTANCE We report the engineering and characterization of designed ankyrin proteins as potent neutralizers of TcdB toxin secreted by a hypervirulent ribotype 027 strain of Clostridium difficile. We further show that although TcdB toxins from both ribotype 027 and VPI 10461 interact efficiently with TcdB receptors CSPG4 and Pvrl3, TcdB027 lacks significant ability to bind the only known physiologically relevant TcdB receptor, Frizzled 1/2/7.
A Branched Deep Neural Network for End-to-end Classification from ECGs with Varying Dimensions

Objective: Computerized electrocardiogram (ECG) interpretation plays a critical role in the clinical ECG workflow. For 12-lead ECGs, deep neural networks (DNNs) could match state-of-the-art algorithms when trained in openly available datasets (e.g., 2020 PhysioNet/CinC Challenge data). However, there is limited evidence to demonstrate the utility of reduced-lead ECGs for capturing a wide range of diagnostic information. Thus, in this work, we aimed to develop a deep learning-based algorithm to identify clinical diagnoses from twelve-lead, six-lead, four-lead, three-lead, and two-lead ECG recordings. Methods: We have employed a branched DNN that utilizes two CNNs (similar to the residual network, but adapted to signals with varying dimensions) with different filter sizes. This architecture can effectively capture abnormal patterns of diseases and suppress noise interference by introducing double branch and skip connections. Moreover, the Squeeze-and-Excitation block is added to every branch of the architecture, which allows the DNNs to learn more effective features from input signals. Results: Our code failed during the official phase and was not able to process the training data to complete the training process and therefore was not scored. Our (CQUPT_Dontwant) method is evaluated using the twelve-lead, six-lead, four-lead, three-lead, and two-lead of the datasets, and we received a 5 -fold cross-validation Challenge metric score of 0.639, 0.610, 0.638, 0.619, and 0.625, respectively. However, the cross validated results on the training data are overly-optimistic, given the results reported by other teams on the test data.