Miracle-dz/UniFAR-FTUs huggingface dataset:
GPPS-2018-98 Experimental and Numerical Investigations of a Low-Pressure Turbine Control Stage

Partial admission occurs in steam turbines operated under part load when one or more segments of the channel annulus are deliberately blocked and thus are not available for the working medium to flow through. This occurrence, common in control stages, is related to strongly unsteady flow conditions, which in turn have heavy repercussions on the machine’s performance and stable range of operation. Therefore, a deeper understanding of the loss generation mechanisms related to partial flow admission is necessary for a flexible and reliable machine design, stable part load operation and advanced steam turbine blades. In this study, combined numerical and experimental investigations of the flow in a low-speed axial turbine have been conducted to highlight some of the relevant features of partial flow admission. Using experimental 2D flow field traverse data, a detailed characterization of the flow field in full admission has been carried out in order to validate the experimental technique and the numerical approach. In preparation for further targeted measurements, numerical simulations of the flow in partial admission have been performed and results have been analyzed. The studies showed that the flow phenomena occurring at partial admission are characterized by increased local gradients regarding the pressure and the velocity field, causing challenges in terms of calibration, positioning and alignment of the probes during the scheduled measurements of partial admission scenarios.
Site-Specific Radio Propagation Model for Macrocell Coverage at Sub-6 GHz Frequencies

This article presents a radio propagation model for macrocell coverage predictions in built-up areas for sub-6-GHz frequency band. The proposed two-ray model is a combination of various individual propagation models available in the literature, namely the discrete mixed Fourier transform split-step parabolic-equation (DMFT-SSPE) to characterize reflection and diffraction phenomena due to the presence of buildings and terrain irregularities, the building-transmission model (BTM) to account for the signal attenuation through the nearby buildings and the International Telecommunication Union of Radio-communications (ITU-R) model for attenuation in vegetation to weight the excess attenuation caused by the surrounding vegetation. The performance of the proposed modeling approach was assessed against measurement results obtained from an extensive measurement campaign conducted in Rio de Janeiro, Brazil. The receiver (RX) antenna was assembled on an automotive van, and it was made to travel around the city recording the received signal level. Several drive tests were conducted, including different transmitter (TX) locations and scenarios, and at various frequencies foreseen for 5G systems, namely 750 MHz, 2.5 GHz, and 3.5 GHz.
Development of Building Thermal Load and Discomfort Degree Hour Prediction Models Using Data Mining Approaches

Thermal load and indoor comfort level are two important building performance indicators, rapid predictions of which can help significantly reduce the computation time during design optimization. In this paper, a three-step approach is used to develop and evaluate prediction models. Firstly, the Latin Hypercube Sampling Method (LHSM) is used to generate a representative 19-dimensional design database and DesignBuilder is then used to obtain the thermal load and discomfort degree hours through simulation. Secondly, samples from the database are used to develop and validate seven prediction models, using data mining approaches including multilinear regression (MLR), chi-square automatic interaction detector (CHAID), exhaustive CHAID (ECHAID), back-propagation neural network (BPNN), radial basis function network (RBFN), classification and regression trees (CART), and support vector machines (SVM). It is found that the MLR and BPNN models outperform the others in the prediction of thermal load with average absolute error of less than 1.19%, and the BPNN model is the best at predicting discomfort degree hour with 0.62% average absolute error. Finally, two hybrid models—MLR (MLR + BPNN) and MLR-BPNN—are developed. The MLR-BPNN models are found to be the best prediction models, with average absolute error of 0.82% in thermal load and 0.59% in discomfort degree hour.
Effects of Top-Down, Bottom-Up, and Horizontal Communication on Organizational Commitment: Evidence From Chinese Internet Firms

Background: An organization's internal communication is an important factor shaping employees’ organizational commitment. Internal communication practices can be classified into three types according to the direction of information flow: top-down, bottom-up, and horizontal communication. Research questions: 1. What are the relationships between the three types of internal communication and organizational commitment? 2. Are there any mediating routes that bridge the relationship between internal communication and organizational commitment? 3. Do the effects of internal communication on organizational commitment vary for different jobs? Literature review: Based on the social exchange theory, we uncover the underlying mechanism of the relationships between the three types of internal communication and organizational commitment. Methodology: A structural equation model using partial least squares was employed to analyze survey data from 12,817 full-time employees in the Chinese internet sector. Results and conclusions: The results suggest that top-down, bottom-up, and horizontal communication affect organizational commitment positively and significantly via the mediating routes of employees’ perceived job attractiveness or perceived customer service performance. The study also reveals a fit between employees’ job characteristics (e.g., information-processing demands) and internal communication types to improve organizational commitment. These findings generate theoretical and practical implications for professional communication management.
Biopsy Characteristics, Subtypes, and Prognostic Features in 107 Cases of Feline Presumed Immune-Mediated Polyneuropathy

Inflammatory polyradiculoneuropathy (IMPN) is one of the causes of sudden onset of neuromuscular signs such as para-/tetraparesis in young cats. Even though most cases have a favorable outcome, persistent deficits, relapses, and progressive courses are occasionally seen. As clinical presentation does not always appear to predict outcome and risk of recurrence, this study was initiated to screen for prognostic biopsy findings in a large cohort of histologically confirmed IMPN cases with clinical follow-up. In total, nerve and muscle specimens of 107 cats with biopsy diagnosis of presumed autoreactive inflammatory polyneuropathy and 22 control cases were reviewed by two blinded raters for a set of 36 histological parameters. To identify patterns and subtypes of IMPN, hierarchical k-means clustering of 33 histologic variables was performed. Then, the impact of histological parameters on IMPN outcome was evaluated via an univariate analysis to identify variables for the final multivariate model. The data on immediate outcome and follow-up were collected from submitting neurologists using a purpose-designed questionnaire. Hierarchical k-means clustering sorted the tissues into 4 main categories: cluster 1 (44/129) represents a purely inflammatory IMPN picture, whereas cluster 2 (47/129) was accompanied by demyelinating features and cluster 3 (16/129) by Wallerian degeneration. Cluster 4 (22/129) reflects normal tissues from non-neuropathic control cats. Returned questionnaires provided detailed information on outcome in 63 animals. They were categorized into recovered and non-recovered. Thereby, fiber-invasive infiltrates by mononuclear cells and mild fiber loss in intramuscular nerve branches correlated with higher probabilities of recovery. Remyelination in semithin sections, on the other hand, is correlated with a less favorable outcome. Animals grouping in cluster 1 had a tendency to a higher probability of recovery compared to other clusters. In conclusion, diagnosis of feline IMPN from nerve and muscle biopsies allowed for the identification of histologic features that were positively or negatively correlated with outcome.
Novel Therapies to Address Unmet Needs in ITP

Primary immune thrombocytopenia (ITP) is an autoimmune disorder that causes low platelet counts and subsequent bleeding risk. Although current corticosteroid-based ITP therapies are able to improve platelet counts, up to 70% of subjects with an ITP diagnosis do not achieve a sustained clinical response in the absence of treatment, thus requiring a second-line therapy option as well as additional care to prevent bleeding. Less than 40% of patients treated with thrombopoietin analogs, 60% of those treated with splenectomy, and 20% or fewer of those treated with rituximab or fostamatinib reach sustained remission in the absence of treatment. Therefore, optimizing therapeutic options for ITP management is mandatory. The pathophysiology of ITP is complex and involves several mechanisms that are apparently unrelated. These include the clearance of autoantibody-coated platelets by splenic macrophages or by the complement system, hepatic desialylated platelet destruction, and the inhibition of platelet production from megakaryocytes. The number of pathways involved may challenge treatment, but, at the same time, offer the possibility of unveiling a variety of new targets as the knowledge of the involved mechanisms progresses. The aim of this work, after revising the limitations of the current treatments, is to perform a thorough review of the mechanisms of action, pharmacokinetics/pharmacodynamics, efficacy, safety, and development stage of the novel ITP therapies under investigation. Hopefully, several of the options included herein may allow us to personalize ITP management according to the needs of each patient in the near future.
The use of mental health services among children in kinship care: an application of Andersen’s Behavioral Model for Health Services Use

ABSTRACT This study examined predisposing, enabling, and need factors associated with mental health (MH) services, the use of school-based, medical-based, and specialty MH services, and the use of ≥2 MH services among children in kinship care. We analyzed a sample of children in kinship care (N = 718) selected from wave II of the National Survey on Child and Adolescent Well-Being II. Results indicated that child’s age, gender, ethnicity (being Hispanic) and clinically significant internalizing and externalizing problems were associated with their use of MH services. Furthermore, results indicated being Hispanic, older children, living in poverty primarily with male caregivers, and having clinically significant externalizing problems were associated with higher odds of receiving school-based MH services, while being a girl and living with caregivers with better physical health were associated with lower odds of receiving medical-based MH services. Experiencing household challenges and living with caregivers with better MH were associated with the odds of receiving specialty MH services. Experiencing household challenges, being Hispanic and older children, and living in formal kinship care were associated with higher odds of receiving ≥2 MH services. These findings suggest the importance of providing accessible and diverse MH services to children in kinship care.
Adaptive Area-Based Traffic Congestion Control and Management Scheme Based on Fog Computing

How to globally construct the effective traffic congestion control and management method on large-scale urban road network is an important research challenge. Since an urban region can be divided into many areas to be managed, grid management has great potential to improve traffic management on large-scale urban road network. In this paper, we propose an adaptive area-based traffic congestion control and management scheme, which is based on fog computing-based internet of vehicles. In our proposed scheme, an urban region is divided to many traffic management areas. Based on the real-time dynamic traffic tightness degrees and the static correlation degrees between these divided management areas, we can obtain the complete real-time regional traffic correlations of these divided management areas. Further, when a traffic congestion occurs, the fog servers use the constructed measures to control the signal lights in real time and effectively guide the traffic flow through the cooperation between these divided management areas. The experimental results show our proposed scheme can effectively implement the road guidance and traffic light control to alleviate or eliminate traffic congestion when a traffic congestion occurs.
Virtual Feedback for Arm Motor Function Rehabilitation after Stroke: A Randomized Controlled Trial

A single-blind randomized controlled trial was conducted to compare whether the continuous visualization of a virtual teacher, during virtual reality rehabilitation, is more effective than the same treatment provided without a virtual teacher visualization, for the recovery of arm motor function after stroke. Teacher and no-teacher groups received the same amount of virtual reality therapy (i.e., 1 h/d, 5 dd/w, 4 ww) and an additional hour of conventional therapy. In the teacher group, specific feedback (“virtual-teacher”) showing the correct kinematic to be emulated by the patient was always displayed online during exercises. In the no-teacher group patients performed the same exercises, without the virtual-teacher assistance. The primary outcome measure was Fugl-Meyer Upper Extremity after treatment. 124 patients were enrolled and randomized, 62 per group. No differences were observed between the groups, but the same number of patients (χ2 = 0.29, p = 0.59) responded to experimental and control interventions in each group. The results confirm that the manipulation of a single instant feedback does not provide clinical advantages over multimodal feedback for arm rehabilitation after stroke, but combining 40 h conventional therapy and virtual reality provides large effect of intervention (i.e., Cohen’s d 1.14 and 0.92 for the two groups, respectively).
Feed-Forward ALINEA: A Ramp Metering Control Algorithm for Nearby and Distant Bottlenecks

This paper proposes a new ramp metering control algorithm, Feed-Foward ALINEA (FF-ALINEA), for bottlenecks located both nearby on an on-ramp and further away from it (i.e., more than just a few hundred meters). The formulation of the controller is based on a feed-forward modification of the well-known control algorithm for ramp metering, ALINEA. The feed-forward structure allows anticipating the future evolution of the bottleneck density in order to avoid or reduce traffic breakdowns. The proposed controller is tested, using the macroscopic traffic flow model METANET, for nine scenarios, and the results are compared with the ones obtained with ALINEA, PI-ALINEA, and with the optimal solution. The simulations show that the FF-ALINEA is able to approach the optimal behavior, thereby outperforming ALINEA and PI-ALINEA. Moreover, results indicate that the FF-ALINEA is quite robust in cases where different demands are considered, there are a limited number of available detectors, or there are errors in the estimation of the capacity and/or the critical density of the bottleneck.
The First Complete Mitochondrial Genome of Eucrate crenata (Decapoda: Brachyura: Goneplacidae) and Phylogenetic Relationships within Infraorder Brachyura

Characterizing the complete mitochondrial genome (mitogenome) of an organism is useful for genomic studies in taxonomy and evolution. The mitogenomic characteristics of Eucrate crenata (Decapoda: Brachyura: Goneplacidae) have never been studied. The present study decodes the first mitogenome of E. crenata by high-throughput sequencing (HTS). The length of the mitogenome is 15,597 bp, and it contains 13 protein-coding genes, 2 ribosomal RNA genes (rrnS and rrnL), and 22 transfer RNA genes. There are 14 and 23 genes observed on the heavy and light strands, respectively. E. crenata possesses a trnH-cac translocation, with the trnH-cac shifted between trnE-gaa and trnF-ttc instead of the usual location between nad5 and nad4 in decapods. Phylogenetic analyses based on the current dataset of 33 Brachyuran mitogenomes indicate that E. crenata. is closely related to Ashtoret lunaris of Matutidae. The similar codon usage and rearrangements in the two species provide evidence for their close phylogenetic relationship. Positive selection analysis showed that one residue located in cox1 was identified as a positively selected site with high BEB value (>95%), indicating that this gene was under positive selection pressure. This study is the first complete mitogenome record for the family Goneplacidae, and the results obtained may improve the understanding of the phylogeny of Goneplacidae in Brachyura.
Diagnostic monitoring of high-dimensional networked systems via a LASSO-BN formulation

ABSTRACT Quality control of multivariate processes has been extensively studied in the past decades; however, fundamental challenges still remain due to the complexity and the decision-making challenges that require not only sensitive fault detection but also identification of the truly out-of-control variables. In existing approaches, fault detection and diagnosis are considered as two separate tasks. Recent developments have revealed that selective monitoring of the potentially out-of-control variables, identified by a variable selection procedure combined with the process monitoring method, could lead to promising performances. Following this line, we propose the diagnostic monitoring that takes an additional step on from the selective monitoring idea and directs the monitoring effort on the potentially out-of-control variables. The identification of the truly out-of-control variables can be achieved by integrating the process monitoring formulation with process cascade knowledge represented by a Bayesian Network. Computationally efficient algorithms are developed for solving the optimization formulation with connection to the Least Absolute Shrinkage and Selection Operator (LASSO) problem being identified. Both theoretical analysis and extensive experiments on a simulated data set and real-world applications are conducted that show the superior performance.
BCG in Bladder Cancer Immunotherapy

Simple Summary Bacillus Calmette–Guérin (BCG), a live attenuated strain of Mycobacterium bovis, is the most successful microbial immunotherapy of cancer in current use. Intravesical treatment with BCG is recommended for most patients with high-risk non-muscle invasive bladder cancer (NMIBC). In patients with NMIBC, treatment with BCG is associated with reduced risk of bladder cancer recurrence and progression compared to transurethral resection alone. Here, we summarize current data regarding the mechanism of BCG and review indications and recommended practice for BCG treatment of bladder cancer. Abstract BCG is a live attenuated strain of Mycobacterium bovis that is primarily used as a vaccine against tuberculosis. In the past four decades, BCG has also been used for the treatment of non-muscle invasive bladder cancer (NMIBC). In patients with NMIBC, BCG reduces the risk of tumor recurrence and decreases the likelihood of progression to more invasive disease. Despite the long-term clinical experience with BCG, its mechanism of action is still being elucidated. Data from animal models and from human studies suggests that BCG activates both the innate and adaptive arms of the immune system eventually leading to tumor destruction. Herein, we review the current data regarding the mechanism of BCG and summarize the evidence for its clinical efficacy and recommended indications and clinical practice.
Estimating Irrigation Water Consumption Using Machine Learning and Remote Sensing Data in Kansas High Plains

Groundwater-based irrigation has dramatically expanded over the past decades. It has important implications for terrestrial water, energy fluxes, and food production, as well as local to regional climates. However, irrigation water use is hard to monitor at large scales due to various constraints, including the high cost of metering equipment installation and maintenance, privacy issues, and the presence of illegal or unregistered wells. This study estimates irrigation water amounts using machine learning to integrate in situ pumping records, remote sensing products, and climate data in the Kansas High Plains. We use a random forest regression to estimate the annual irrigation water amount at a reprojected spatial resolution of 6 km based on various data, including remotely sensed vegetation indices and evapotranspiration (ET), land cover, near-surface meteorological forcing, and a satellite-derived irrigation map. In addition, we assess the value of ECOSTRESS ET products for irrigation water use estimation and compare with the baseline results by using MODIS ET. The random forest regression model can capture the temporal and spatial variability of irrigation amounts with a satisfactory accuracy (R2 = 0.82). It performs reasonably well when it is calibrated on the western portion of the study area and tested on the eastern portion that receives more rain than the western one, suggesting its potential transferability to other regions. ECSOTRESS ET and MODIS ET yield a similar irrigation estimation accuracy.
Recent progress in the data-driven discovery of novel photovoltaic materials

The discovery of new photovoltaic materials can facilitate technological progress in clean energy and hence benefit overall societal development. Machine learning (ML) and deep learning (DL) technologies integrated with domain knowledge are revolutionizing the traditional trial-and-error research paradigm that is associated with high costs, inefficiency, and significant human effort. This review provides an overview of the recent progress in the data-driven discovery of novel photovoltaic materials for perovskite, dye-sensitized and organic solar cells. The integral workflow of the ML/DL training progress is briefly introduced, covering data preparation, feature engineering, model building and their applications. The cutting-edge challenges and issues in the ML/DL workflow are summarized specifically for photovoltaic materials. Real examples are emphasized to illustrate how to utilize ML/DL techniques in the discovery of novel photovoltaic materials. The prospects and future directions of the data-driven discovery of novel photovoltaic materials are also provided.