Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparison of the Capital Costs of a Vanadium Redox-Flow Battery and a Regenerative Hydrogen-Vanadium Fuel Cell

The capital costs of a Regenerative Hydrogen-Vanadium Fuel Cell and a Vanadium Redox-Flow Battery are compared for grid level energy storage. The bulk of the capital costs for a Vanadium Redox-Flow Battery lie in the costs of the vanadium electrolyte, while the Regenerative Hydrogen-Vanadium Fuel Cell presents a potential for savings by eliminating the need for half of the vanadium electrolyte required by a Vanadium Redox-Flow Battery. It was found that the Regenerative Hydrogen-Vanadium Fuel Cell would cost $57 less per kWh than the Vanadium Redox-Flow Battery, with savings garnered from the elimination of half of the electrolyte somewhat mitigated by the costs of the catalyst and air compressor required. If the capital costs are annualized through straight line depreciation, and the operation costs are included, the Vanadium Redox-Flow Battery is $5 per kWh less per year than the Regenerative Hydrogen-Vanadium Fuel Cell.
An observational study to find association between Hypertension and severe and fatal COVID-19 infection in COVID dedicated hospital, Mumbai

Introduction: World Health Organization (WHO) has declared COVID-19 outbreak as a “public health emergency of international concern” on January 31, 2020. After that the epidemic spread rapidly around the world. Chronic non-communicable diseases were identified as risk factors for SARS-CoV-2. Hypertension (HTN) is one of the most common cardiovascular diseases with a prevalence ranging from 10% to 20% among adult population. After COVID-19 outbreak, identification of risk factors for severe or fatal disease is necessary to allocate limited resources. For primary care physicians, this is of vital importance to know the association between hypertension and severe and fatal COVID-19 infection as hypertension is affecting millions of people all over the world and primary health care is of immense importance in a country like India. Duration of stay in hospital which will be taken as proxy measure of duration of illness, symptomatic status, need of ICU care and death will be taken as indicators of severe and fatal COVID-19 infection. There are few researches to find an association between Hypertension and severe and fatal COVID-19 infection. There is a need to assess the same. Objectives: To find an association of hypertension and severe and fatal COVID-19 infection. Methodology: A hospital-based cross-sectional study was carried out at a COVID dedicated hospital, Mumbai in 9 months from 1 April 2020 to 31 December 2020. All patients admitted in hospital were included in the study by universal sampling. Ethical Approval: The permission to conduct the study was taken by Institutional Ethics Committee. Results: There was a significant association between hypertension and severe and fatal COVID-19 infection defined by more duration of stay in hospital, admission to ICU and mortality. Conclusions: It is concluded from this study that hypertension is associated with severe and fatal COVID-19 infection. It gives a clear idea for primary care physicians to give more focus towards patients with hypertension with COVID-19 infection. Blood pressure monitoring should be strictly practiced during COVID-like illnesses. Special attention is to be given as far as treatment and early referral to higher centre with ICU is concerned for better outcome. Hypertensive patients should be protected more by adopting COVID appropriate behaviour.
Assessing logistic regression applied to respondent-driven sampling studies: a simulation study with an application to empirical data

ABSTRACT The aim of this study is to investigate the impact of different logistic regression estimators applied to RDS studies via simulation and the analysis of empirical data. Four simulated populations were created with different connectivity characteristics. Each simulated individual received two attributes, one of them associated to the infection process. RDS samples with different sizes were obtained. The observed coverage of three logistic regression estimators were applied to assess the association between the attributes and the infection status. In simulated datasets, unweighted logistic regression estimators emerged as the best option, although all estimators showed a fairly good performance. In the empirical dataset, the performance of weighted estimators presented an unexpected behavior, making them a risky option. The unweighted logistic regression estimator is a reliable option to be applied to RDS samples, with a performance roughly similar to random samples and, therefore, should be the preferred option.
A comparative study on psychiatric morbidity among elderly population residing in old-age homes and community

Aims and Objectives: The objective of this study is to study, assess and compare the socio-demographic profile, psychiatric morbidity, cognitive functioning, quality of life and social support of senior citizens in care homes as well as in the communities. Material and Methods: Hundred subjects (50 were from old-age home and the rest were from the community) were assessed using intake pro forma details and an interview questionnaire. The Geriatric Mental Status questionnaire, Mini-Mental Status Examination, Quality of Life Support, and Multidimensional Scale of Perceived Social Support scales were then performed on each subject from the two groups in that order, and they were rated on all scales. Results: Individuals with lack of spouse and lack of children were higher and common attitude was death in old-age homes. Past history of psychiatric illnesses was more in the inmates of old-age homes. Memory disturbances, concentration problems, hypochondriasis, somatic dysfunction, error behaviour, thought disturbances, perceptual disturbances, persecution, expressed happiness and satisfaction were greater in the inmates of old-age care homes. The quality of life was poor in community group than in old-age home group. Conclusion: There is a need to increase community and health-care professional awareness of mental health problems in later life, as well as improve access to appropriate geriatric and mental healthcare.
FEMALE REPRESENTATION, CORPORATE GOVERNANCE, AND EARNING MANAGEMENT IN INDONESIAN NON-FINANCIAL FIRMS

This study analyzes the effect of female directors, managerial ownership, female audit committee, audit committee size, independent commissioners, board size on earnings management in Indonesian Non-Financial Firms. The sample of this study consist of 291 non-financial firms over the period 2015-2017. This study measures earnings management using discretionary accruals. The data were analyzed using multiple regression. This study found that audit committee size negatively affects earnings management. This study also found that female directors, managerial ownership, female audit committee, independent commissioners, and board size do not affect earnings management. This study contributes to the important of audit committee as corporate governance mechanisms to reduce
Motivations and Limitations of Prefabricated Building: An Overview

Based on previous studies, prefabricated building usage motivations in the construction sector differ over time and vary from country to country. To adopt this technology, awareness on current trends and the latest innovations should be increased by reviewing previous studies. The objective of this study is to conduct a revision of common motivations and limitations of prefabricated building in the construction industry taking into consideration the experience gained and reported by several countries. Results show that the adaptation of prefabricated building mainly depends on factors such as labor shortage, labor cost, housing demand, building process efficiency, weather, as well as reduction of waste material and energy consumption. Prefabricated building has a relatively low uptake in construction industries worldwide despite its inherent economic, environmental, and social benefits.
Building green supply chain management in pharmaceutical companies in Indonesia

This study aims to analyze the contribution of Green Manufacturing and Green Distribution on improving the performance of Green Supply Chain Management (GSCM) through Reverse Logistics. The development of industry and increasing consumer concern for the environment as well as issues regarding the concept of an environmentally sound industry have forced industries to adjust in line with the GSCM concept. To make the program a success, Green Manufacturing, Green Distribution, and Reverse Logistics are assumed to be supporting the implementation process. This study uses quantitative methods, with the number of samples taken randomly as many as 70 people. The analysis was carried out using the Path Analysis method. Hypothesis testing was carried out in two stages, namely Structural Model-1 and Structural Model-2 testing to obtain each path coefficient number. The results of the study conclude that there is the contribution of Green Manufacturing, Green Distribution, and Reverse Logistics on the success of GSM implementation so that companies must always pay attention to the facilities and related policies to improve the performance of those variables.
Musical expertise shapes visual-melodic memory integration

Music can act as a powerful mnemonic device that can elicit vivid episodic memories. However, how musical information is integrated with non-musical information is largely unknown. Here, we investigated whether and how musical expertise modulates binding of melodies and visual information into integrated memory representations. We reasoned that the significant mnemonic demands of musicianship might alter the underlying integration process and reveal mechanisms by which music promotes retrieval of non-musical memories. Professional musicians and musical laypersons learned overlapping pairs of everyday objects and melodies (AB- and BC-pairs, object-melody and melody-object pairs). Participants were then tested for memory of studied pairs (direct trials) and for inferential AC-decisions (indirect trials). Although musicians showed a higher overall performance than non-musicians, both groups performed well above chance level in both trial types. Non-musicians reacted faster in indirect compared to direct trials, whereas the reverse pattern was found in musicians. Differential correlations of trial type performance between groups further suggested that non-musicians efficiently formed integrated ABC-triplets already during the encoding phase of the task, while musicians separately memorized AB- and BC-pairs and recombined them at retrieval for AC-decisions. Our results suggest that integrative encoding is a default mechanism for integration of musical and non-musical stimuli that works with great efficacy even in musically untrained subjects and may contribute to the everyday experience of music-evoked episodic memories. By contrast, recombination at retrieval seems to be an advanced strategy for memory integration that critically depends on an expert ability to maintain and discriminate musical stimuli across extended memory delays.
Classification of EMG signals to control a prosthetic hand using time-frequesncy representations and Support Vector Machines

Myoelectric signals (MES) are viable control signals for externally-powered prosthetic devices. They may improve both the functionality and the cosmetic appearance of these devices. Conventional controllers, based on the signal's amplitude features in the control strategy, lack a large number of controllable states because signals from independent muscles are required for each degree of freedom (DoF) of the device. Myoelectric pattern recognition systems can overcome this problem by discriminating different residual muscle movements instead of contraction levels of individual muscles. However, the lack of long-term robustness in these systems and the design of counterintuitive control/command interfaces have resulted in low clinical acceptance levels. As a result, the development of robust, easy to use myoelectric pattern recognition-based control systems is the main challenge in the field of prosthetic control. This dissertation addresses the need to improve the controller's robustness by designing a pattern recognition-based control system that classifies the user's intention to actuate the prosthesis. This system is part of a cost-effective prosthetic hand prototype developed to achieve an acceptable level of functional dexterity using a simple to use interface. A Support Vector Machine (SVM) classifier implemented as a directed acyclic graph (DAG) was created. It used wavelet features from multiple surface EMG channels strategically placed over five forearm muscles. The classifiers were evaluated across seven subjects. They were able to discriminate five wrist motions with an accuracy of
A prospective observational cohort study of traumatic brain injury in the northern region of Sweden

ABSTRACT Objective Generally, to map epidemiological and demographic features of patients with traumatic brain injury (TBI) in Umeå county, Sweden. Specifically, to evaluate the subjects needing a computed tomography (CT) of the head after suffering from TBI and frequencies of 1) intracranial lesions detected with CT, 2) need for neurosurgical intervention and 3) admission to hospital. Methods Patients with a suspected TBI, undergoing CT within 24 hours of arrival to hospital, were included in a database for evaluation. Results Out of 302 patients (63% male), 83% were GCS 13–15, 7% were GCS 9–12 and 10% were GCS <9. The frequency of abnormal CT findings was 23% in GCS 13–15, 67% in GCS 9–12 and 97% in GCS <9. Neurosurgical intervention was needed by 4% of those with GCS 13–15, 52% of those with GCS 9–12 and by 76% of those with GCS <9. Conclusions Subjects with GCS 13–15 had higher frequencies of abnormal CT findings, need for neurosurgical intervention and hospital admission than previously reported. A similar trend was observed for patients with GCS 9–12, which can be of serious nature, with a higher frequency of need for neurosurgical intervention than previously described.
A study to predict fracture risk using bone mineral density and FRAX score in patients on chronic maintenance haemodialysis

Introduction: Disturbances in mineral and bone metabolism are prevalent in chronic kidney disease (CKD) and are important causes of morbidity also their diagnosis often delayed and require a spectrum of investigations. The current study attempts to predict and correlate, the fracture risk using simple tools like BMD and FRAX (Fracture Risk Assessment Tool) score in CKD patients. Methods: A cross-sectional study among 50 CKD patients age more that 40 years attending OPD (Out Patient Department) at a tertiary care Hospital in north India. Results: There is a negative correlation between BMD (NOF) and FRAX score for hip fracture risk and major osteoporotic fracture risk. Conclusion: The 10-year fracture risk in these patients, as predicted by FRAX score using FRAX (Indian) calculator, was significantly higher in CKD patients. Recommendation: FRAX can be useful tool for early screening of fracture risk in such situations for timely interventions.
Projects as temporary organisations: insights from requirement volatility management practices

Projects represent a form of temporary organisation having specific contexts. The nature of the project environment is thus expected to influence selection of project management strategies and project outcome. Using a combination of interviews and survey in two phases, we investigate the phenomena by focusing on usage of software project management approaches under requirement volatility. Analysis of survey results based on responses from 82 software professionals belonging to India identifies 13 different approaches to managing software projects under requirement volatility. The usage of these approaches was found to be contingent on the project environment, thus refuting suggestions pertaining to adoption of best practices in similar situations. The results are expected to benefit practitioners, and promote further discussions in the area.
Reality and Fiction: Scandinavian Tour Operators’ Image of Italy through Photographs

ABSTRACT The fact that traditional information sources are neglected by tour operators and the statement arguing that digital information sources, contrarily to traditional paper ones, allow the projection of modern and innovative destination images, served as a starting point for this study. The aim of this research is to determine what destination images of Italy are projected by traditional and digital information sources from 15 Scandinavian tour operators. A qualitative method based on secondary data (1306 photographs) and a thematic analysis has been employed. Images from catalogs mainly illustrate Italy with hidden mass tourism, famous Italy, and culture, while images from social media accounts mainly represent Italy with mass tourism, unfamiliar Italy, and culture. Further, each Scandinavian country focuses on specific features of Italy. This research has implications for Scandinavian tour operators, DMOs (Destination Management Organizations) in Italian cities and for Scandinavian tourists to Italy, suggesting how displaying images more consistent with reality would better match the customers’ need for information through photographs and more accurately form and shape their pre-travel expectations.
The Emergence of New Industries at the Regional Level in S pain: A Proximity Approach Based on Product Relatedness

abstract How do regions diversify over time? Inspired by recent studies, we argue that regions diversify into industries that make use of capabilities in which regions are specialized. Since the spread of capabilities occurs through mechanisms that have a strong regional bias, we expect that capabilities that are available at the regional level play a larger role than do capabilities that are available at the country level for the development of new industries. To test this hypothesis, we analyze the emergence of new industries in 50 S panish regions at the NUTS 3 level in the period 1988–2008. We calculate the capability-distance between new export products and existing export products in S panish regions and provide econometric evidence that regions tend to diversify into new industries that use similar capabilities as existing industries in these regions. We show that proximity to the regional industrial structure plays a much larger role in the emergence of new industries in regions than does proximity to the national industrial structure. This finding suggests that capabilities at the regional level enable the development of new industries.
Less Constrained Practice Tests Enhance the Testing Effect for Item Memory but Not Context Memory

Research shows that retrieval practice, compared with restudying, improves memory for previously learned information (i.e., testing effect); however, less work has examined testing effects for context memory (i.e., memory for associated details encountered at study). The present investigation examines the extent to which giving less information (i.e., fewer constraints) on a practice test improves item and context (source and font color) memory on a final test. Participants studied highly associated word pairs in red or green font. Participants were then shown the word pairs in a lower-constraint practice test (e.g., “brief-____”), higher-constraint practice test (e.g., “open-coels”), or restudy condition (e.g., “blaze-fire”). Results showed better item memory in the lower-constraint test than in both the higher-constraint test and restudy, but there was no difference for context memory across conditions. These findings demonstrate that using a less constrained practice test can increase the effectiveness of this learning strategy, but benefits may not transfer to contextual details.
A novel hidden Markov approach to studying dynamic functional connectivity states in human neuroimaging

How can we better understand the underlying neural mechanisms of brain dynamics? Many groups have examined brain states as a means of identifying recurring patterns of activity or connectivity. Although there is an abundance of methods of establishing and interpreting brain states, hidden Markov models are becoming an increasingly popular choice to extract recurring patterns of intensity or connectivity in neuroimaging data. These models not only recognize spatial patterns of brain states, but also ascertain their temporal progression. An assortment of hidden Markov model instantiations has arisen for diverse purposes, and these have been applied to a variety of neuroimaging datasets. However, most of these instantiations have focused on intensity-based states, i.e. states defined by the activity levels of one or more nodes, rather than connectivity-based states, i.e. states defined by patterns of functional connectivity between nodes. The intensity-based approach is problematic if we want to understand connectivity dynamics, since there is no reason to believe that the resultant states can provide any useful information about dynamic connectivity patterns. Here we aimed to remedy this methodological challenge by introducing a new hidden Markov model approach based on identifying states defined as full functional connectivity profiles among brain regions, and applying this approach to directly extract connectivity-based states in functional magnetic resonance imaging (fMRI) data. We then empirically explore the behavior of this new model in comparison to existing approaches based on intensity-based states and summed functional connectivity states, utilizing the widely-available HCP unrelated 100 functional magnetic resonance imaging “resting state” dataset. Results show that our newly-introduced ‘full functional connectivity’ model discovered connectivity states with more distinguishable patterns than those derived from previously-employed approaches, and demonstrated clear superiority in recovering simulated connectivity-based states. These findings suggest that if our goal is to extract and interpret connectivity states in neuroimaging data, our new model can reveal more insights than previous methods, and intensity-based or summed functional connectivity-based approaches miss crucial information about the evolution of functional connectivity in the brain. Highlights • Hidden Markov models can be used to investigate brain states noninvasively • Previous models “recover” connectivity from intensity-based hidden states • Other previous models “recover” connectivity from connectivity ‘summed’ across nodes • Only full connectivity-based models can reveal true connectivity hidden states