Miracle-dz/UniFAR-FTUs huggingface dataset:
Fatigue and burnout in nurses during the COVID-19 pandemic

This research was conducted to assess the fatigue and burnout levels of nurses and the factors affecting those levels during the COVID-19 pandemic. This descriptive-correlational research was carried out with 270 nurses at a university hospital in the eastern region of Turkey. To collect data, ‘Information Form, ‘The Checklist Individual Strength’ and ‘The Maslach Burnout Inventory’ were used. It was found that the mean age of the nurses who participated in this study was 27.5±5.7 years, 73.3% were female, 82.6% were bachelor graduates, 63% were single, 35.2% worked in intensive care units. It was found that the nurses obtained a total score of 94.6±20.5 on the CIS and 48.4±8.3 points on the MBI. Nurses’ fatigue levels were high and burnout levels were moderate. A positive correlation was found between fatigue and burnout (r: .288**, p=.000). The difference between gender, weekly working hours, duration of professional experience (not with fatigue) and working unit and fatigue and burnout was found to be statistically significant (p<0.05). It was found that various aspects of the COVID-19 pandemic have increased fatigue and burnout.
Design considerations for a marker-free visual-based interfacing device for telco operation

—The parts of the system in the telecommunication environment that is used by technicians are sometimes completely menu driven. The interfaces to these parts can be made much simpler. Visual-based interfacing is a relatively new field of interest with advancements being made toward marker free human input tracking. This paper investigates the issues regarding the design of one such a system. Specifically, it looks at the factors concerning the telecommunication environment as well as factors concerning the setup of the camera being used to capture the user’s input. It also investigates how shadows being cast by the user’s hand against the background, could affect the detection of user input.
Accidental opportunities? Women in family businesses taking the lead in a new market economy

Social workers have been working to empower women of diverse backgrounds across several practice settings. However, for women entrepreneurs in Ghana generally and those in family businesses specifically, some socio-cultural factors have persisted to hinder their abilities to promote and sustain their businesses. This paper describes the ways in which women in family businesses in Accra, Ghana have identified and leveraged opportunities presented by structural, cultural, socio-economic, and technological changes to ensure the success of their businesses. The study employed a qualitative research approach with purposive and snowball sampling used to identify and interview 15 women in family businesses. Findings indicate that women in family businesses in Accra, Ghana have creatively made use of the changing socio-cultural and economic landscape of the country to promote their businesses through three main trends which are significant in business growth: the advent and increasing use of social media, innovative business practices, and weakening patriarchy. We recommend that social workers apprise themselves of contexts which have unequal consequences for women and mobilize around these changes to help women entrepreneurs to take full advantage of the opportunities that are being offered in Ghana’s emerging economy.
Local Students at University: Narrative Descriptions of Lived Experiences

‘Local students’ attend college or university in the same city where they attended high school. Local students’ self-narratives suggest that their ‘identity or sense of self’ and ‘sense of belonging or connection with others’ may hinder their academic success. This study gathered data from focus groups and surveys of local and non-local students of a private, national liberal arts college concerning their lived experiences as local students at university. This data describes local students’ self-narratives about ‘identity or sense of self’ and ‘sense of belonging or connection with others. ’ The beliefs and experiences reflected by these self-narratives may suggest unique challenges that affect local students’ university outcomes. With awareness of these experiences and beliefs, institutions of higher education could tailor programs to benefit local students to improve their sense of well-being, which may improve retention and academic success.
Neurocognitive dynamics of spontaneous offline simulations: Re-conceptualizing (dream)bizarreness

ABSTRACT Although we are beginning to understand the neurocognitive processes that underlie the emergence of dreaming, what accounts for the bizarre phenomenology of dreams remains debated. I address this question by comparing dreaming with waking mind wandering and challenging previous accounts that utilize bizarreness to mark a sharp divide between conscious experiences in waking and sleeping. Instead, I propose that bizarreness is a common, non-deficient feature of spontaneous offline simulations occurring across the sleep-wake cycle and can be tied to the specific characteristics of spontaneous thought as being dynamic, unconstrained, (hyper)associative, and highly variable in content. Rather than misrepresenting waking reality, bizarreness can be employed to investigate the very building blocks of spontaneous cognition. The absence of bizarreness in thought processes is imposed by automatic and deliberate cognitive constraints. By contrast, thought and memory processes operating on their own without such constraints are inherently marked by different degrees and types of bizarreness.
Induction of compact growth in Campanula portenschlagiana plants by ethanol treatments

ABSTRACT Reducing plant height for ornamental potted plants is an important factor. Conventionally, a compact growth is obtained through the extensive use of chemical growth retardants that can be hazardous to human health or the environment. In this study, ethanol irrigation was successfully proven to produce compact growth in the economically important ornamental plant Campanula portenschlagiana. The vegetative growth decreased when ethanol concentration was increased from 0.5% to 6%. Similarly, there was also a significant reduction in root length, while higher ethanol treatments from 4% were toxic and caused injuries. Further, higher ethanol levels led to partially leave yellowing or necrosis. An increase in osmotic potential was observed, but it was not significantly different between untreated plants and those treated under moderate ethanol concentration (0.5–2%). Considering the generative parameters, ethanol from 2% delayed flower formation and the longevity of flowers treated with the higher ethanol concentration (4–6%) is also significantly reduced. Generally, ethanol at moderate concentrations of 2% has desirable effects on vegetative growth traits, while it already affects generative growth, but without other obvious damage to the plants. Thus, it has been successfully demonstrated that ethanol watering is an alternative to other growth regulating agents or applications.
Intelligent Optimization of QoS in Wireless Sensor Networks Using Multiobjective Grey Wolf Optimization Algorithm

With the advancement of technology and the emergence of new types of communication networks, new solutions have emerged to protect the environment and monitor natural resources. Wireless sensor networks (WSNs) have revolutionized environmental science and research by embedding sensors in environments where constant access and monitoring by manpower is difficult. WSNs have a variety of uses in the military, environmental monitoring, medicine, robotics, and so on. With the advent of applications in WSNs, the fundamental problem of the network has also increased. The performance of WSNs is influenced by various parameters that are varied according to the applications. In general, the performance of a WSN is typically specified through its average energy use, which determines the lifespan of the grid. A WSN should acquire the ability for controlling the overall performance of the network for ensuring the transmission of information based on quality of service (QoS) parameters in order to maximize the satisfaction of the services for the application. Therefore, we provided a multiobjective grey wolf optimization algorithm (QAMO-GWO) in order to optimize routing and improve QoS in WSNs. In the proposed method, sensor nodes receive information about the environment over regular periods of time and send it to the cluster heads in each. The selection of cluster heads in each cluster is done using a multiobjective grey wolf optimization algorithm. MO-GWO algorithm via balancing QoS parameters tries for selecting the optimal cluster heads. Finally, simulation outputs showed that the proposed method has been able to improve QoS criteria due to balancing the goals in the network.
QUANTIFYING THE REDUCTION IN COOLING ENERGY DUE TO PASSIVE COOLING TECHNIQUES FOR INDIAN CITIES

Past studies have quantified the potential of various passive design strategies to achieve thermal autonomy. Bhadra, Vaidya, & Sarraf (2017) have shown that for 60 cities in India, minimum thermal autonomy calculated by using the thermostat set point as the benchmark, is at 12% for night cooling, 75% for evaporative cooling, 28% for ground cooling and 32% for radiant cooling. This paper takes that approach further and quantifies the reduction in cooling energy due to three passive strategies, using the CIBSE method with Cooling Degree Days (CDD) for 60 Indian cities. Three passive strategies: night ventilation, comfort ventilation and evaporative cooling are considered for mixed mode institutional buildings in India. Using the CIBSE TM41 , the Balance Point Temperature (BPT) for two different versions of Indian buildings is established; 1) buildings complying with the latest energy code i.e. Energy Conservation Building Code (ECBC 2017), and 2) business-as-usual buildings in India. Using these BPT values and the latest TMY files, the residual CDD for the three passive design strategies and the CDD for buildings without passive strategies are calculated. Cooling energy consumption is then calculated for all the cases for the 60 cities. The study provides two versions of BPT for Indian buildings, which are more realistic that those published in the past. The results indicate the cooling energy reduction possible due to three common passive design strategies. The results state that the CDD calculated by using ASHRAE 55 adaptive comfort model is almost twice than that of the published values. For Ahmedabad, the value of base temperature ranges between 16-25°C for a BAU building as compared to 18.3°C constant base temperature considered by ASHRAE. Cooling energy can be reduced by upto 80% just by using passive strategies in BAU buildings and upto 86% in ECBC compliant buildings.
The Neuroprotective Effect of miR-136 on Pilocarpine-Induced Temporal Lobe Epilepsy Rats by Inhibiting Wnt/β-Catenin Signaling Pathway

Objective To explore the effect of miR-136 on temporal lobe epilepsy (Ep) and its mechanism of action. Methods 30 male rats were injected intraperitoneally with 30 mg/kg pilocarpine to construct a rat temporal lobe epilepsy model, and they were randomly divided into 5 groups (n = 6 per group): control group, Ep group, agomir NC group, miR-136 agomir group, and miR-136+LiCl group. The brain tissues of the rats were collected 7 days after the treatment. The expression of miR-136 in the hippocampus tissue was detected by qRT-PCR. H&E and Nissl staining were used to observe the histopathological changes and neuron damage in the hippocampus tissue. IL-1β, IL-6, and TNF-α levels in the hippocampus tissue were detected by ELISA. Flow cytometry was used to detect the apoptosis rate in the hippocampus tissue. Western blot was used to detect the expression levels of c-Caspase-3, Bcl-2, β-catenin, Cyclin D1, and c-myc protein in the hippocampus. Results The expression of miR-136 was significantly downregulated in the hippocampus tissue of epileptic rats. After overexpression of miR-136, the number of seizures and the duration of epilepsy in rats were significantly reduced. At the same time, hippocampal tissue damage was improved considerably, and the degree of neuronal damage decreased. Overexpression of miR-136 also significantly reduced the apoptosis rate in the hippocampus tissue and inhibited the levels of inflammatory factors. Meanwhile, miR-136 downregulates the expression of Wnt/β-catenin signaling pathway-related proteins. However, Wnt pathway activator LiCl could destroy the protective effect of miR-136. Conclusion miR-136 could exert its neuroprotective influence on temporal lobe epilepsy rats by inhibiting the Wnt/β-catenin signaling pathway.
Handling Sparse Rating Matrix for E-commerce Recommender System Using Hybrid Deep Learning Based on LSTM, SDAE and Latent Factor

: E-commerce is the most essential application for conducting business transactions. Delivering product information to customers require an essential machine called recommender system. Recommender systems have been adopted in many large e-commerce companies such as Amazon, e-Bay, Alibaba, YouTube, iTunes, and so on. Ratings have become an essential factor to calculate product information. They are users’ expressions about their satisfaction regarding a product or service. Unfortunately, the number of ratings is extremely sparse. Generating rating prediction is a major issue in the recommender system research field. The most popular model using latent factor or matrix factorization to generate rating prediction faced the problem in accuracy performance. This research aimed to develop a novel model to generate rating prediction using two deep learning variants based on Stack Denoising Auto Encoder (SDAE), Long Short Term Memory (LSTM), and combining with a latent factor model based on Probabilistic Matrix Factorization (PMF). This study considered integrated information resources, including user information and document product information. Following the experiment report involved in Movielens and Amazon Information Video dataset, our model outperformed previous works using PMF, Collaborative Deep Learning (CDL), Probabilistic Hybrid Deep Learning (PHD) and LSTM-PMF model, with more than 5% in average using Root Mean Square Error (RMSE) evaluation metrices.
Effect of Heel Height and Speed on Gait, and the Relationship Among the Factors and Gait Variables

Objective: This paper investigates gait changes according to different heel heights and speeds, and the interaction between the effects of the heel height and the speed during walking on stride parameters and joint angles. Furthermore, the relationship among heel height, speed and gait variables is investigated using linear regression. Background: Gait changes by heel height or speed have been studied respectively, but has not been reported whether there is an interaction effect between heel height and speed. It would be necessary to understand how gait changes when a person wears heels in different heights at various speeds, for example, high-heeled walking at fast speed, since it may cause unusual gait patterns and musculoskeletal disorders. Method: Ten females were asked to walk at five fixed cadences (94, 106, 118, 130 and 142 steps/min.) wearing three shoes with different heel heights (1, 5.4 and 9.8cm). Nineteen gait variables were analyzed for stride parameters and joint angles using two-way repeated measure analysis of variance and regression analysis. Results: Both heel height and speed affect movement of ankle, knee, spine and elbow joint, as well as stride length and Double/Single support time ratio. However, there is no significant interaction effect between heel height and speed. The regression result shows linear relationships of gait variables with heel height and speed. Conclusion: Heel height and speed independently affect stride parameters and joint angles without a significant interaction, so the gait variables are linearly amplified or diminished by the two factors. Application: Walking in high heels at fast speed should be careful for musculoskeletal disorders, since the amplified movement of knee and spine joint can lead to increased moment. Also, the result might give insight for animators or engineers to generate walking motion with high heels at various speeds.
DE Oxidation-Fused Industrial Wastewater Purification Fuzzy Control and Simulation

In this study, a novel microcirculation chromatography with pulsed amperometric discovery (IC/PAD) system is established for the cyanide in business sewage. For business sewage with complicated substrates, the microstrewing means is leveraged for purification and decoration, and subsequently, the IC/PAD course is utilized to psychoanalyze and accuse the cyanide in the match. Under optimum plight, cyanide exhibits some kind of linearity in the frequency of 1.0–200.0 μg/L, and the perception termination and quantification check of cyanide in business sewage are 0.15 μg/L and 0.5 0 μg/L, respectively. The scold is between 88.6% and 1 08.5%. This mode is highly caring, tenacious and awesome, and calm to manage. It offers recent discrimination for the discovery of cyanide in business sewage. In this case, the insincere-frequent uninterrupted inundate analyzer is to decide business sewage with comprehensive distinction in ammonium propellant major. The spring is compared with the mensuration rise of Nessler's test spectrophotometry. The rise has shown that when the double-stroll regularity of extended overflow analyzer is a manner to simultaneously moderate lofty and blaze concentrations of ammonia packaging gas, there is no important contention compared with the mensuration inference of Nessler's test spectrophotometry. The analysis of the regularity has whole reagents, and harmless and strong transformation. This can optimally decrease the effort intenseness of testers and is valuable of preferment. Index Terms—DE oxidation, industrial wastewater, purification, fuzzy control, simulation.
Pemanfaatan Black Soldier Fly (BSF) untuk Mengolah Sampah Organik di Kota Surabaya

Sebagai salah satu kota terbesar di Indonesia, Kota Surabaya memiliki jumlah penduduk yang sangat banyak. Banyaknya penduduk tersebut juga mempengaruhi produksi sampah di Surabaya. Berdasarkan data dari Kementerian Lingkungan Hidup dan Kehutanan, sebanyak 2.800 ton sampah dihasilkan oleh sekitar 2,9 juta penduduk Kota Surabaya tiap harinya. Dari nilai tersebut, sekitar 60% nya merupakan sampah organik. Sampah organik tersebut dapat diurai oleh Black Soldier Fly (BSF). Selain menguraikan sampah, BSF juga dapat dimanfaatkan untuk diekstrak kandungan kitin dan kitosannya. Kegiatan ini bertujuan untuk memberikan pengetahuan terkait pengolahan sampah organik dengan BSF dan nilai tambah BSF pada proses daur ulang sampah yang ada di Kedung Turi RW. 08, Kelurahan Kedungdoro, Kec. Tegalsari, Kota Surabaya dengan memanfaatkannya menjadi material kitosan. Kegiatan ini dimulai dengan survei dan wawancara dengan warga setempat. Kemudian, dilakukan uji coba penguraian sampah menggunakan fresh maggot BSF dan ekstraksi kitin di Laboratorium. Selanjutnya, kegiatan workshop dilakukan untuk memberikan pengetahuan tentang nilai tambah BSF pada warga setempat agar semakin tertarik untuk melakukan proses penguraian sampah menggunakan maggot BSF. Selain itu, saat kegiatan workshop juga diserahkan peralatan untuk penguraian sampah menggunakan BSF kepada warga setempat.
A Software Engineering Perspective on Cryptocurrencies

This work evaluates a digital cash phenomenon, called cryptocurrencies that was started in early 2009 and has created a buzz at the latest in the year 2013, by the virtual explosion of prices in currencies (e.g. prices in EUR or USD) for certain cryptocurrencies, especially for bitcoin. The evaluation is realized from the perspective of a software engineer. The central research question is derived of a claim made by the Bitcoin community: Bitcoin does create “a new kind of money”. The potential implications for existing payment systems would be tremendous if this proves to be substantial. This issue is examined by performing a requirements analysis for payment systems in general, thereby establishing certain definitions and understandings of terms, including ‘money’. An analysis and evaluation is also done for the Banking Payment System (BPS) and the Cryptocurrency Payment System (CPS) with the target to be able to draw conclusions for the CPS by comparing the two payment systems. Furthermore a detailed evaluation of Bitcoin (first letter capitalized! ) and its proposed money called ‘bitcoin’ (non-capitalized! ) is carried out by reverse engineering parts of the software project using modeling technology that is known in model driven software development.
Towards Accurate Application Characterization for Exascale (APEX)

Sandia National Laboratories has been engaged in hardware and software codesign activities for a number of years, indeed, it might be argued that prototyping of clusters as far back as the CPLANT machines and many large capability resources including ASCI Red and RedStorm were examples of codesigned solutions. As the research supporting our codesign activities has moved closer to investigating on-node runtime behavior a nature hunger has grown for detailed analysis of both hardware and algorithm performance from the perspective of low-level operations. The Application Characterization for Exascale (APEX) LDRD was a project concieved of addressing some of these concerns. Primarily the research was to intended to focus on generating accurate and reproducible low-level performance metrics using tools that could scale to production-class code bases. Along side this research was an advocacy and analysis role associated with evaluating tools for production use, working with leading industry vendors to develop and refine solutions required by our code teams and to directly engage with production code developers to form a context for the application analysis and a bridge to the research community within Sandia. On each of these accounts significant progress has been made, particularly, as this report will cover, in themore » low-level analysis of operations for important classes of algorithms. This report summarizes the development of a collection of tools under the APEX research program and leaves to other SAND and L2 milestone reports the description of codesign progress with Sandia’s production users/developers. « less
Retinal Image Lesions Assisted Diabetic Retinopathy Screening System Through Machine Learning

: This paper proposes an Automatic Screening System that identifies the symptoms of Diabetic Retinopathy from retinal fundus images and determines the severity. The entire system is of four-fold; the first phase pre-processes the retinal images and eliminates uneven illuminations. The second phase segments the major parts of images such as optic Disk and Blood Vasculature. Once the segmentation is completed, the residual part is called as lesion image and it is subjected to a hierarchical classification in the third phase. It is a two-step classification method which classifies the entire image into four regions namely Hard Exudates, Microaneursyms, Hemorrhages, cotton wool spots. For classification, three classifiers such as k-Nearest Neighbor, Gaussian Mixture Model and Support vector Machine are analyzed. Finally, the classified regions are processed through an objective function for severity grading. The novelty of this paper is a composite feature extraction method which extracts different and diverse features from lesion image. Since the lesions are very small and of different shapes, the proposed feature extraction method can provide more discriminant information for classifier. For experimental validation, a standard and publicly available DIARETDB1 dataset and the performance is measured through sensitivity, specificity, accuracy and area under curve. On an average, the proposed method gained accuracy, sensitivity, and specificity of 90.2 %, 95.2 % and 82.1 % respectively.