Miracle-dz/UniFAR-FTUs huggingface dataset:
Teacher and caregiver perceptions of family engagement in teacher-led task-shifted child mental health care in a low-and-middle-income country

ABSTRACT The mental health needs of children in low-and-middle income countries (LMICs) often go unmet due to a lack of qualified mental health professionals. Task-shifting the provision of mental health services to teachers may facilitate access to care. Family engagement in task-shifting may support mental health outcomes but is understudied in this context. The current study explored teacher and caregiver perceptions of family engagement within a teacher-led, task-shifted mental health intervention in an LMIC. Primary school teachers from five schools in Darjeeling, India delivered evidence-based, indicated mental health care to children with mental health needs throughout the school day. We conducted semi-structured interviews (SSIs) with teachers (n=17) and caregivers (n=21). SSIs were coded for themes related to family engagement. Teachers and caregivers were compared based on perceived levels of engagement. Participants reported three patterns of engagement: families who fully engaged; families who felt positively about teachers but displayed little engagement; and families with limited engagement. Barriers included logistical challenges and misconceptions about the programme. Many teachers implicated family engagement as a facilitator of the programme, suggesting that family involvement may support intervention outcomes. Future work could involve the development of an intervention component to better facilitate engagement in this context. Trial registration: Clinical Trials Registry India identifier: CTRI/2018/01/011471.
Improving process improvement: executing the analyze and improve phases of DMAIC better

Purpose – This paper aims to highlight that a define, measure, analyze, improve, and control (DMAIC) project should be carried out keeping the broader business goal of achieving continuous improvement in mind and that a design of experiment (DOE) based improvement approach should be preferred to achieve this goal.Design/methodology/approach – “Ease of control” of the improved process and “gain in process knowledge” from a DMAIC study are identified as two measures for judging the contribution of a DMAIC project towards continuous improvement. Various improvement approaches are classified into seven groups and the likely impact of each of these seven approaches on the above two quality measures are discussed.Findings – The improvement approach adopted during the improve phase is partially determined by the nature of the root cause(s) – type X or type Y. The type Y root cause leads to the adoption of the “innovation‐prioritization” approach, which is very popular but has many limitations. Accordingly, an “a.
Application of bootstrap-based neural networks for monthly rainfall forecasting in Western Jilin Province, China

Rainfall forecasting is an important pre-requisite for effectively managing and planning water resources. This study developed a generalized regression neural network (GRNN) combined with a bootstrap approach for rainfall forecasting, and the forecasting results were compared with the autoregressive model and single GRNN model. The test was performed in western Jilin Province, China with a 53-year (1957–2010) monthly rainfall time series. To obtain the good performance of GRNN model, the number of input neurons was decided by the analysis of Bayesian information criterion, and the appropriate spread was selected considering the performance of the training and testing phases. mean absolute error, root mean square error, coefﬁcient of efﬁciency and R 2 are employed to evaluate the performances of the forecasting models. The results showed that the bootstrap-based GRNN model performed better than single GRNN and AR models in forecasting monthly rainfall and the proposed method can improve the prediction accuracy of monthly rainfall time series, while generating uncertainty estimates of the rainfall forecasting.
Building Use and Safety: An Overview with Emphasis on Current Research Applications to Codes and Standards

This is, in part, a preview of the program for the International Conference on Building Use and Safety Technology to be held November 7–9, 1984 in Washington, D.C. Background information is also provided on current developments in the major U.S. model codes and safety standards organizations where there are discussions on applications of research, with a human factors orientation, especially in relation to circulation safety and egress concepts. Relative to what was the case only a few years ago such discussions are now fairly intensive. They are leading to revisions which can be regarded as revolutionary. New research agendas, opportunities and sponsoring arrangements are being considered in the same contexts.
Extracellular Vesicles in Pulmonary Fibrosis Models and Biological Fluids of Interstitial Lung Disease Patients: A Scoping Review

Introduction: Interstitial lung diseases (ILDs) are a heterogeneous group of diffuse parenchymal lung disorders characterized by the pathogenetic involvement of interstitium. Therefore, an elucidation of the etiology and pathogenesis as well as the identification of diagnostic and prognostic biomarkers of such diseases is more compelling than ever. It is of note that there is increasing evidence of the involvement of extracellular vesicles (EVs) in the pathogenesis of lung diseases including lung cancer, chronic obstructive pulmonary disease and pulmonary fibrosis. It has been speculated that EVs play a pivotal role as mediators of intercellular communication, as well as the highlighting of the role of EVs as co-operators in the development of lung diseases such as IPF. Methods: The present study aimed to carry out a systematic exploratory search of the literature (through the scoping review approach) to identify and systematize the main results of the pathogenetic role of EVs in pulmonary fibrosis models and biological fluids from ILD patients, including plasma, bronchoalveolar lavage (BAL) and sputum. Conclusion: Fibroblast-to-mesenchymal differentiation, collagen and extracellular matrix deposition are key mechanisms in the development and progression of IPF. EV-coupled miRNA are important modulators of biological processes in terms of intercellular communication as shown in pulmonary fibrosis models as well as biofluids. The helpfulness of EVs as diagnostic and theranostic markers is worth further investigation. The evolving potential of EVs to translate effective EV-based therapies into clinical practice is of growing interest, due to the urgent need for novel therapeutic strategies for IPF patients.
Risk Factors for Poor Outcomes Among Patients with Extensively Drug-Resistant Tuberculosis (XDR-TB): A Scoping Review

Abstract In recent years, there has been an upsurge in cases of drug-resistant TB, and strains of TB resistant to all forms of treatment have begun to emerge; the highest level of resistance is classified as extensively drug-resistant tuberculosis (XDR-TB). There is an urgent need to prevent poor outcomes (death/default/failed treatment) of XDR-TB, and knowing the risk factors can inform such efforts. The objective of this scoping review was to therefore identify risk factors for poor outcomes among XDR-TB patients. We searched three scientific databases, PubMed, Scopus, and ProQuest, and identified 25 articles that examined relevant risk factors. Across the included studies, the proportion of patients with poor outcomes ranged from 8.6 to 88.7%. We found that the most commonly reported risk factor for patients with XDR-TB developing poor outcomes was having a history of TB. Other risk factors were human immunodeficiency virus (HIV), a history of incarceration, low body mass, being a smoker, alcohol use, unemployment, being male, and being middle-aged. Knowledge and understanding of the risk factors associated with poor outcomes of XDR-TB can help policy makers and organizations in the process of designing and implementing effective programs.
Characterization of n-type beta -SiC as a piezoresistor

SiC is currently being investigated for device applications involving high temperatures. The properties of n-type beta -SiC relevant to piezoresistive devices, namely the gauge factor (GF) and temperature coefficient of resistivity (TCR), are characterized for several doping levels. The maximum gauge factor observed was -31.8 for unintentionally doped (10/sup 16/-10/sup 17//cm/sup 3/) material. This gauge factor decreases with temperature to approximately half its room-temperature value at 450 degrees C. Unintentionally doped SiC has a roughly constant TCR of 0.72%/ degrees C over the range 25-800 degrees C and exhibits full impurity ionization at room temperature. Degenerately doped gauges (N/sub d/=10/sup 20//cm/sup 3/) exhibited a lower gauge factor (-12.7), with a more constant temperature behavior and a lower TCR (0.04%/ degrees C). The mechanisms of the piezoresistive effect and TCR in n-SiC are discussed, as well as their application towards sensors. >
Changes in the Local Conformational States Caused by Simple Na+ and K+ Ions in Polyelectrolyte Simulations: Comparison of Seven Force Fields with and without NBFIX and ECC Corrections

Electrostatic interactions have a determining role in the conformational and dynamic behavior of polyelectrolyte molecules. In this study, anionic polyelectrolyte molecules, poly(glutamic acid) (PGA) and poly(aspartic acid) (PASA), in a water solution with the most commonly used K+ or Na+ counterions, were investigated using atomistic molecular dynamics (MD) simulations. We performed a comparison of seven popular force fields, namely AMBER99SB-ILDN, AMBER14SB, AMBER-FB15, CHARMM22*, CHARMM27, CHARMM36m and OPLS-AA/L, both with their native parameters and using two common corrections for overbinding of ions, the non-bonded fix (NBFIX), and electronic continuum corrections (ECC). These corrections were originally introduced to correct for the often-reported problem concerning the overbinding of ions to the charged groups of polyelectrolytes. In this work, a comparison of the simulation results with existing experimental data revealed several differences between the investigated force fields. The data from these simulations and comparisons with previous experimental data were then used to determine the limitations and strengths of these force fields in the context of the structural and dynamic properties of anionic polyamino acids. Physical properties, such as molecular sizes, local structure, and dynamics, were studied using two types of common counterions, namely potassium and sodium. The results show that, in some cases, both the macroion size and dynamics depend strongly on the models (parameters) for the counterions due to strong overbinding of the ions and charged side chain groups. The local structures and dynamics are more sensitive to dihedral angle parameterization, resulting in a preference for defined monomer conformations and the type of correction used. We also provide recommendations based on the results.
A conceptual framework for Jatropha seed yield estimation using adaptive neuro-fuzzy inference system (ANFIS) modelling

Jatropha seed yield prediction is one of the most important influencing factors for developing a supply chain modelling of Jatropha seed. The oil from this Jatropha seed is used to blend with diesel to obtain biofuel (Hiromi, Yamamoto, Junichi Fujino, and Kenji Yamaji. 2001. “Evaluation of Bioenergy Potential with a Multi-regional Global-Land-Use-and-Energy Model.” Biomass and Bioenergy 21: 185–203). The Jatropha plant is easy to cultivate and produces high yield if properly maintained. The main focus of this study is to utilize uncultivable wastelands for cultivating Jatropha seeds. As suggested by Liu et al. (Liu, Xiaohong, David B. Grant, Alan C. McKinnon, and Yuanhua Feng. 2010. “An Empirical Examination of the Contribution of Capabilities to the Competitiveness of Logistics Service Providers: A Perspective from China.” International Journal of Physical Distribution & Logistics Management 40 (10): 847–866), the effectiveness of prediction affects the functional characteristics of a supply chain network design. The yield prediction of Jatropha seeds has two important roles which include (i) the identification of external parameters that affects the yield and (ii) the detection of internal attributes that changes the growth characteristics of the Jatropha plant. The development of the fuzzy inference system is characterized by a large number of input variables (Dobrila Petrovic. 2001. International Journal of Production Economics 71: 429–438). A Matlab programming software was used to integrate an adaptive neuro-fuzzy inference system. This approach gave the numerical as well as graphical output that was used to interpret the final result. The root mean square error values were identified for the given inputs which were then compared with the trained input variables to select the best input among the given alternative variables.
Multi-wavelength 128 Gbit s−1 λ −1 PAM4 optical transmission enabled by a 100 GHz quantum dot mode-locked optical frequency comb

Semiconductor mode-locked lasers (MLLs) with extremely high repetition rates are promising optical frequency comb (OFC) sources for their usage as compact, high-efficiency, and low-cost light sources in high-speed dense wavelength-division multiplexing transmissions. The fully exploited conventional C- and L- bands require the research on O-band to fulfil the transmission capacity of the current photonic networks. In this work, we present a passive two-section InAs/InGaAs quantum-dot (QD) MLL-based OFC with a fundamental repetition rate of ∼100 GHz operating at O-band wavelength range. The specially designed device favours the generation of nearly Fourier-transform-limited pulses in the entire test range by only pumping the gain section while with the absorber unbiased. The typical integrated relative intensity noise of the whole spectrum and a single tone are −152 and −137 dB Hz−1 in the range of 100 MHz–10 GHz, respectively. Back-to-back data transmissions for seven selected tones have been realised by employing a 64 Gbaud four-level pulse amplitude modulation format. The demonstrated performance shows the feasibility of the InAs QD MLLs as a simple structure, easy operation, and low power consumption OFC sources for high-speed fibre-optic communications.
Modelling the capture of gasoline engine exhaust particulate matter in three-way catalytic converters

The aim of this work was to numerically model the effect of a three-way catalytic converter on nano-scale particulate matter from a gasoline direct injection engine. The work used a deep bed filtration model to simulate and validate experimental findings. A 1.6L, gasoline direct injection, spark ignition, turbocharged and intercooled Euro IV engine was used for experimentation. The capture efficiency was experimentally determined by measuring pre and post-catalyst particulate matter numbers using a DMS-500 differential mobility spectrometer at different dilution ratio settings. Despite variable experimental results at lower engine speeds and loads, the model was capable of predicting catalytic converter particle capture efficiency in the majority of cases. This indicates that deep bed filtration theory can indeed be used to explain nano-scale particle capture within three-way catalytic converters. The results also suggest that there is no significant agglomeration of particles within the catalytic converter. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Stratified microbial communities are highly sensitive towards multiple combined global change factors, revealing antagonistic and synergistic effects

Microbial communities in many ecosystems are facing a broad range of global change scenarios, resulting in microbial changes and possibly regime shifts with unknown ecological consequences. While the influence of single stressors is already described in numerous studies, the effects of multiple stressors working simultaneously are still poorly understood. In this study, we used 240 highly replicable oxic/anoxic aquatic lab micro-ecosystems to understand the influence of four stressors (fertilizer, glyphosate, metal pollution, antibiotics) in all possible combinations at three different temperatures (20 °C, 24 °C, and 28 °C) to shed light into consequences of multiple stressors on different levels of organization, ranging from species abundance to community and ecosystem parameters. Our data reveal that (i) combination of specific stressors can change the biological consequence and direction compared to single stressors in all levels of organisation (ii), effects of stressor combinations are modified by temperature, and (iii) that the number of stressors applied also lead to significant changes. In sum, our study confirmed the need of investigating multiple stressors working simultaneously across different ecological levels of organisation.
COMBINE HARVEST AREA DETERMINATION BY VECTOR PROCESSING OF GPS POSITION DATA

The measurement of actual harvested area per unit time is an important component in the creation of accurate crop yield maps. For row crops, such as corn, these measurements can be made manually on most conventional yield monitors. However, in drilled or broadcast crops a more accurate and automated method is required. In this study, a vector method was developed to determine actual combine harvest area at each time step of the harvest process from a global positioning system (GPS) trajectory. The algorithm was coded into a geographic information system (GIS), and modifications were made to increase computational efficiency. The method was compared with a previously reported raster method of harvest area determination on data collected during the 1997 drilled soybean harvest, using high accuracy real time kinematic GPS data. The vector method improved harvest area estimates by an average of 11% over the assumption of a constant, full swath width and provided a number of distinct advantages over the raster method.
Relationship between pressure and thermal pain threshold, pain intensity, catastrophizing, disability, and skin temperature over myofascial trigger point in individuals with neck pain.

OBJECTIVE The objective of the study was to correlate the thermal pain threshold (heat and cold) on myofascial trigger points with measurements of pain and skin temperature in patients with chronic neck pain. METHODS This is a cross-sectional study. We included participants of both genders, aged between 18-45 years, with chronic neck pain (>90 days), and with active bilateral myofascial trigger point centrally located in the upper trapezius muscle. Neck Disability Index, Numerical Rating Scale, Pain-Related Catastrophizing Thoughts Scale, algometry, infrared thermography, and quantitative sensory testing were used for the evaluation. RESULTS A significant, weak, and negative association was observed between pain intensity and heat pain threshold on the myofascial trigger point to the right (rho -0.381, p=0.022) and to the left (rho -0.334, p=0.049), and a significant, weak, and positive association was observed between pain intensity and cold pain threshold on the myofascial trigger point to the right (rho 0.471, p=0.004) and to the left (rho 0.339, p=0.043). CONCLUSION Thermal pain threshold (heat and cold) on myofascial trigger points is associated with pain intensity in individuals with chronic neck pain.
Characterizing Malignant Melanoma Clinically Resembling Seborrheic Keratosis Using Deep Knowledge Transfer

Simple Summary Malignant melanomas (MMs) with aypical clinical presentation constitute a diagnostic pitfall, and false negatives carry the risk of a diagnostic delay and improper disease management. Among the most common, challenging presentation forms of MMs are those that clinically resemble seborrheic keratosis (SK). On the other hand, SK may mimic melanoma, producing ‘false positive overdiagnosis’ and leading to needless excisions. The evolving efficiency of deep learning algorithms in image recognition and the availability of large image databases have accelerated the development of advanced computer-aided systems for melanoma detection. In the present study, we used image data from the International Skin Image Collaboration archive to explore the capacity of deep knowledge transfer in the challenging diagnostic task of the atypical skin tumors of MM and SK. Abstract Malignant melanomas resembling seborrheic keratosis (SK-like MMs) are atypical, challenging to diagnose melanoma cases that carry the risk of delayed diagnosis and inadequate treatment. On the other hand, SK may mimic melanoma, producing a ‘false positive’ with unnecessary lesion excisions. The present study proposes a computer-based approach using dermoscopy images for the characterization of SΚ-like MMs. Dermoscopic images were retrieved from the International Skin Imaging Collaboration archive. Exploiting image embeddings from pretrained convolutional network VGG16, we trained a support vector machine (SVM) classification model on a data set of 667 images. SVM optimal hyperparameter selection was carried out using the Bayesian optimization method. The classifier was tested on an independent data set of 311 images with atypical appearance: MMs had an absence of pigmented network and had an existence of milia-like cysts. SK lacked milia-like cysts and had a pigmented network. Atypical MMs were characterized with a sensitivity and specificity of 78.6% and 84.5%, respectively. The advent of deep learning in image recognition has attracted the interest of computer science towards improved skin lesion diagnosis. Open-source, public access archives of skin images empower further the implementation and validation of computer-based systems that might contribute significantly to complex clinical diagnostic problems such as the characterization of SK-like MMs.