Miracle-dz/UniFAR-FTUs huggingface dataset:
Limitations of econometric evaluation of nonrandomized residential energy efficiency programs: A case study of Northern California rebate programs

Abstract Residential energy efficiency programs play an important role in combating climate change. More precise quantification of the magnitude and timing of energy savings would bring large system benefits, allowing closer integration of energy efficiency into resource adequacy planning and balancing variable renewable electricity. However, it is often difficult to quantify the efficacy of an energy efficiency intervention, because doing so requires consideration of a hypothetical counterfactual case in which there was no intervention, and randomized control trials are often implausible. Although quasi-experimental econometric evaluation sometimes works well, we find that for a set of energy efficiency rebate programs in Northern California, a naïve interpretation of econometric measurement finds that rebate participation is associated with an average increase in electricity consumption of 7.2% [4.5%, 10.1%], varying in magnitude and sign depending on the type of appliance or service covered by the rebate. A subsequent household survey on appliance purchasing behavior and analysis of utility customer outreach data suggest that this regression approach is likely measuring the gross impact of buying a new appliance but fails to adequately capture a counterfactual comparison. Indeed, it is unclear whether it is even possible to construct a suitable counterfactual for econometric analyses of these rebate programs using data generally available to electric utilities. We view these results as an illustration of a limitation of econometric methods of program evaluation and the importance of weighing engineering modeling and other imperfect methods against one another when attempting to provide useful evaluations of real-world policy interventions.
Work demands, supervisor support, coping and burnout among teachers

Little is known about factors that could potentially affect burnout levels in teachers working with children diagnosed with Autism Spectrum Disorder. The aim of the study was to investigate burnout syndrome among teachers working with children with autism and to investigate how coping styles, supervisor support and workload are related to burnout. Sixty-five teachers in New South Wales Australia participated in a questionnaire study focused on their reports of burnout, work demands, supervisor support and the use of different coping strategies. Results showed that over eighty percent of teachers teaching children with autism in this study were experiencing moderate to high levels of emotional exhaustion. Results from hierarchical regression analyses indicated that the three components of burnout were associated with different factors; excessive work demands and the use of avoidance coping strategies played a central role in the prediction of increased burnout; on the other hand, the satisfactory level of supervisor support predicts the decrease in teacher burnout. Implications of this study include the importance of providing effective and appropriate supervision for staff, and for teachers to avoid the use of avoidance coping strategies, and for schools and education policy makers to improve working condition for teachers.
Optimal farm design with parabolic shape photovoltaic panels using multi-objective optimization

To acquire the maximum efficiency for solar electricity conversion, a solar panel has to absorb nearly every single photon of light emitted from the sun. The shape of the solar panel itself plays an important role in achieving this goal. Several studies have been conducted for different solar panel designs regardless of change in their internal or external shapes. In the first part of this thesis, a survey of solar photovoltaic (PV) panel shapes together with the advantages and disadvantages of the shapes is presented. The second part deals with using parabolic trough PV panels to obtain an optimal field design with two objectives, namely, maximum incident energy and minimum of the deployment cost. This design involves the relationships between the field and collector decision parameters and solar radiation data. In our proposed model, these parameters are the number of collector rows (K), the center-to-center distance between collectors (P ), dimension of collectors (H), collectors inclination angle (β), and the rim angle of the collectors (ψ). Moreover, solar collectors arranged in rows in solar fields may be subjected to shading which depends on the spacing between the collector rows, the collector height, and the inclination angle, to some extent also on the row length and on the latitude of the solar field. The proposed mathematical model of PV panels is presented in details. A multi-objective evolutionary algorithm, called Non-dominated Sorting Genetic Algorithm-II (NSGA-II), is used to achieve the optimal field design with parabolic trough panels. Finally, the
Evolution of machine learning in environmental science—A perspective

Abstract The growth of machine learning (ML) in environmental science can be divided into a slow phase lasting till the mid-2010s and a fast phase thereafter. The rapid transition was brought about by the emergence of powerful new ML methods, allowing ML to successfully tackle many problems where numerical models and statistical models have been hampered. Deep convolutional neural network models greatly advanced the use of ML on 2D or 3D data. Transfer learning has allowed ML to progress in climate science, where data records are generally short for ML. ML and physics are also merging in new areas, for example: (a) using ML for general circulation model parametrization, (b) adding physics constraints in ML models, and (c) using ML in data assimilation.
Job Satisfaction and Citizen Satisfaction with Street-level Bureaucrats: Is There a Satisfaction Mirror?

It is often argued that employees satisfied with their jobs perform better, which in turn will lead customers to be more satisfied. Private sector studies have found support for this “satisfaction mirror” hypothesis. Our study is the first to provide direct, individual-level evidence of its existence in the public sector. We conducted an original survey of village officials in small, rural Chinese villages, and local citizens interacting with them. Village officials are charged with delivering nearly all types of public services to citizens. They are typical street-level bureaucrats, directly interacting with citizens with a degree of discretion. We focus on the senior village official, known as village director. We link the responses of 949 citizens to their corresponding 96 village directors to test the connection between job satisfaction and individual citizens’ satisfaction with these village officials’ work. Using structural equation models and causal mediation modeling (all N=949), we find evidence in accordance with a “satisfaction mirror. ” To assess potential social desirability bias, we conduct a list experiment. Taking this into account and relying on an external performance measure still yields a substantively meaningful estimate of a “satisfaction mirror. ” Our study theoretically and empirically identifies the linkage between job satisfaction of street-level bureaucrats and citizen satisfaction as a key aspect of citizen-state relations.
Process Design and Economics for Biochemical Conversion of Softwood Lignocellulosic Biomass to Isoparaffinic Kerosene and Lignin Co-products

Marrs, G. , Spink, T. , & Gao, A. (2016). Process design and economics for biochemical conversion of softwood lignocellulosic biomass to isoparaffinic kerosene and lignin co-products. NARA Final Reports. Pullman, WA. Northwest Advanced Renewables Alliance (NARA).
VALIDATION OF DESIGNBUILDER SIMULATION ACCURACY USING FIELD MEASURED DATA OF INDOOR AIR TEMPERATURE IN A CLASSROOM BUILDING

Studies have shown that a very wide range of parameters affect thermal comfort of building occupants. Heat transfer through the roofs has been identified as one of the causes of overheating in building interiors. Building Performance Simulation tools can be used to determine appropriate roof design parameters with optimum thermal performance in tropical climates like Nigeria. However, ensuring the reliability of the simulation results is essential for both occupants and designers. Therefore, this study seeks to verify and validate the accuracy of simulation results of DesignBuilder as a simulation tool for the present research works on roof thermal performance. The study primarily focuses on the impact of roof design on the air temperature in the indoor learning environment. Validation is examined by comparing the measured daily indoor air temperature of Kofar Wambai Secondary School Classroom, Bauchi city data and the simulation results. Comparative analysis indicate that the % deviation of field measured and simulated results for the tested days (18th and 21st September, 2018) were 2.39% and 1.25% respectively. The R2 results indicated 99% and 75% correlation between the simulated and measured indoor temperature on the tested days.These results agreed with the recommendations of a study by (Andelkovic et al, 2016) which reported that the marginal value of R2 for measurements and simulations validation is R2 ≥ 75%. Therefore, DesignBuilder can be employed to evaluate roof thermal performance as well as predict indoor air temperature of classroom buildings.
Fractional order fuzzy PID controller for wind energy-based hybrid power system using quasi-oppositional harmony search algorithm

In this study, a novel fuzzy logic control scheme is investigated for the frequency and power control of an isolated hybrid power system (HPS) (IHPS) and HPS-based two-area power system. The studied IHPS comprises of various autonomous generation systems such as wind energy, diesel engine generator and an energy storage device (i.e. capacitor energy storage device). A novel fractional order (FO) fuzzy proportional-integral-derivative (PID) (FO-F-PID) controller scheme is deployed and the various tunable parameters of the studied model are tuned by quasi-oppositional harmony search (HS) (QOHS) algorithm for improved performance. The QOHS algorithm (based on standard HS algorithm) accelerates the convergence speed by combining the concept of quasi-opposition in the basic HS framework. This FO-F-PID controller shows better performance over the integer order-PID and F-PID controller in both linear and non-linear operating regimes. The proposed FO-F-PID controller also shows stronger robustness properties against loading mismatch and different rate constraint non-linarites than other controller structures.
Brain MRI Analysis for Alzheimer’s Disease Diagnosis Using CNN-Based Feature Extraction and Machine Learning

Alzheimer’s disease is the most common form of dementia and the fifth-leading cause of death among people over the age of 65. In addition, based on official records, cases of death from Alzheimer’s disease have increased significantly. Hence, early diagnosis of Alzheimer’s disease can increase patients’ survival rates. Machine learning methods on magnetic resonance imaging have been used in the diagnosis of Alzheimer’s disease to accelerate the diagnosis process and assist physicians. However, in conventional machine learning techniques, using handcrafted feature extraction methods on MRI images is complicated, requiring the involvement of an expert user. Therefore, implementing deep learning as an automatic feature extraction method could minimize the need for feature extraction and automate the process. In this study, we propose a pre-trained CNN deep learning model ResNet50 as an automatic feature extraction method for diagnosing Alzheimer’s disease using MRI images. Then, the performance of a CNN with conventional Softmax, SVM, and RF evaluated using different metric measures such as accuracy. The result showed that our model outperformed other state-of-the-art models by achieving the higher accuracy, with an accuracy range of 85.7% to 99% for models with MRI ADNI dataset.
Cluster Analysis for Selection of Time Series Interferometric SAR Imagery

We propose a method to select interferometric SAR pairs for time series differential SAR Interferometry (D-InSAR) by using cluster analysis.In this approach,proper decorrelation factors are selected to construct a feature space,and decorrelation factors of all possible SAR interferometric pairs are mapped to the space.Thus,interferometric pair selection is converted to a problem of feature space clustering.A cluster algorithm is developed based on the principle of interferometric pair selection.This way,selected interferometric images corresponding to the clustering result are obtained.Experimental results show that the proposed approach is simple and effective.
Iron overload, its causes and complications

Iron overload shows bioaccumulation of iron in the cells of body due to any reason or factor in the field of biology and medicine. The underlying mechanisms and causes may vary and also the physiology and pathology. The most prominent causes of iron overload are hereditary haemochromatosis (HHC) and iron overload due to blood transfusions [1]. These two causes of iron overload can result from repeated blood transfusions. Iron overload can be prevented by regulating iron absorption in most humans. People with elevated levels of iron in the body get disorders of iron overload and iron overload in the body is the risk factor for many cancers. Iron overload happens in those persons with impaired iron absorption regulation mechanisms. Iron overload caused by transfusions causes malfunctions of the liver, heart and endocrine organs. Problems may begin after 30 units of red blood cells or even earlier [2]. Human body has the regulatory mechanism to lessen the amount of iron that is absorbed by the cells through digested food. However, it doesnot completely ceases or blocks the iron transportation pathway, even in the circumstances, where the extra iron deteriorates the linings of intestine e.g. if children consume heavy dosage of iron tablets produced for adults, even this more quantity of iron may cause fatal syndrome of iron overload via bloodstream. Increased amounts of free iron in blood stream will damage the hepatic, cardiac and other cells which are involved in metabolic pathways. Iron overload also causes hepatocellular carcinoma [3]. Generally iron overload is a risk factor for cancer and this hypothesis is supported and accepted by many experiments, studies and human data. An overproduction of reactive oxygen species and free radicals could explain its oncogenic effect. Among the many factors (viral hepatitis, alcohol, tobacco etc.) which play a role in carcinogenesis, iron overload is probably an important one and therefore should be diagnosed and treated well especially at their early stages treated [4].
Research advances on antioxidation, neuroprotection, and molecular mechanisms of Lycium barbarum polysaccharides

Lycium barbarum polysaccharides (LBPs) are the major polysaccharides extracted from L. barbarum, which is used in traditional Chinese medicine (TCM) for treating diseases. Studies have shown that LBPs have important biological activities, such as antioxidation, anti-aging, neuroprotection, immune regulation. LBPs inhibit oxidative stress, improve neurodegeneration and stroke-induced neural injury, increase proliferation and differentiation of neural stem cell, and promote neural regeneration. Here we have reviewed latest advances in the biomedical activities of LBPs and improved methods for the isolation, extraction, and purification of LBPs. Then, new discoveries to decrease oxidative stress and cellular apoptosis, inhibit aging progress, and improve neural repair in neurodegeneration and ischemic brain injury have been discussed in detail through in vitro cell culture and in vivo animal studies. Importantly, the molecular mechanisms of LBPs in playing neuroprotective roles are further explored. Lastly, we discuss the perspective of LBPs as biomedical compounds in TCM and modern medicine and provide the experimental and theoretical evidence to use LBPs for the treatment of aging-related neurological diseases and stroke-induced neural injuries.
Applying Collective Intelligence in Health Recommender Systems for Smoking Cessation: A Comparison Trial

Background: Health recommender systems (HRSs) are intelligent systems that can be used to tailor digital health interventions. We compared two HRSs to assess their impact providing smoking cessation support messages. Methods: Smokers who downloaded a mobile app to support smoking abstinence were randomly assigned to two interventions. They received personalized, ratable motivational messages on the app. The first intervention had a knowledge-based HRS (n = 181): it selected random messages from a subset matching the users’ demographics and smoking habits. The second intervention had a hybrid HRS using collective intelligence (n = 190): it selected messages applying the knowledge-based filter first, and then chose the ones with higher ratings provided by other similar users in the system. Both interventions were compared on: (a) message appreciation, (b) engagement with the system, and (c) one’s own self-reported smoking cessation status, as indicated by the last seven-day point prevalence report in different time intervals during a period of six months. Results: Both interventions had similar message appreciation, number of rated messages, and abstinence results. The knowledge-based HRS achieved a significantly higher number of active days, number of abstinence reports, and better abstinence results. The hybrid algorithm led to more quitting attempts in participants who completed their user profiles.
Performance and design analysis of an improved non-isolated multiple input buck DC–DC converter

This study proposes an improved non-isolated single-inductor multiple-input buck DC–DC converter, with reduced number of components. Less component count can reduce the size, mass and cost of converter. Simultaneous or independent power transfer capability has been provided for input sources. The bidirectional power flow capability has also been provided through usage of a battery without any additional switches. This makes the topology very suitable for hybrid energy systems or hybrid electric vehicle/electric vehicle applications. Different operational modes of proposed topology have been presented. Then, a generalised relationship has been proposed for calculation of critical inductance (as an important design parameter) of proposed n-input buck topology. To make a better judgement, the proposed topology has been compared with recently presented novel topologies. Also, a simple proportional–integral based controller has been applied to regulate output voltage and assign the portion of power that each input source should provide. To confirm the validity of proposed topology and theoretical concepts, the three-input version has been modelled and simulated in PSCAD/EMTDC software and implemented experimentally. Simulation and experimental results validate proper performance of proposed topology.
A Compact, Broadband, Monopole-Like Endfire Antenna With Reconfigurable Patterns for 5G Applications

This communication presents a novel compact, broadband, monopole-like endfire antenna with reconfigurable radiation patterns for 5G applications. Based on the mirror principle, a half-size monopole-like endfire structure with a wideband response is firstly proposed and analyzed. By switching the ON/OFF states of p-i-n diodes embedded in the passive stub lines, the monopole-like endfire structure achieves omnidirectional and endfire beam switching. Since the structure has a stable and broadband frequency response, a novel flexible pattern diversity antenna with omnidirectional coverage and directional scanned radiation is easily implemented by loading multiple stub lines. To validate the working principle, a nine-mode pattern reconfigurable antenna with a compact size of $0.31\,\,\lambda _{0}\,\,\times \,\,0.31\,\,\lambda _{0}\,\,\times \,\,0.19\,\,\lambda _{0}$ ( $\lambda _{0}$ is the free space wavelength at 3.75 GHz, radiator only) is designed and fabricated. The measured overlapped −10 dB impedance bandwidth for the nine modes is from 3.30 to 4.20 GHz (>24.0%), which well covers the 5G N77/N78 band. Besides, their measured gain is greater than 4.0/5.30 dBi. The proposed antenna has the merits of a compact size, simple design, wideband, good radiation performance, and flexible multibeam switching capacity. It is a good candidate for 5G new radio and other sub-6 GHz applications.
Conditioning Factors of Preterm Birth among South America and African Countries: When Geographical Differences Become Less Important than People’s Background. The Case of Argentina & Ethiopia

Conditioning Factors of Preterm Birth among South America and African Countries: When Geographical Differences Become Less Important than People’s Background. The Case of Argentina Ethiopia. Abstract Introduction: Although preterm birth is a worldwide problem, more than 70% of these events occur in Africa, Latin America and South Asia. In order to analyze potential common factors associated to preterm labor in different scenarios, we performed a study in countries from two of the most affected continents: Africa (Ethiopia) & South America (Argentina). Methods: A hospital based, unmatched case-control study was conducted among 1222 Argentine and 192 Ethiopian women who gave birth in selected hospitals during 2019, using systematic random sampling technique. Data was collected through clinical repots, individual interviews and chart reviews. The information was analyzed in SPSS version 26,using descriptive parameters . A univariate analysis was applied to evaluate associations between independent variables and preterm birth. Result: Pre-terms birth were detected in 10.9% and 33.3% of all deliveries in Argentina and Ethiopia. The majority of preterm birth in Argentina and Ethiopia were associated to earlier ages (<25years old); first pregnancy; multiple gestation; lower education of the couple; lower socio-economic status; less health controls; hypertension; infection; bleeding or presence of congenital anomalies of newborn. Conclusion: Argentina and Ethiopia are countries with enormous differences, however regarding to preterm births it is possible to identify common factors, like socio-economic status and quality of health service related to preterm labors. Early risk detection, strengthening health care capacities, and improvement of social conditions of pregnant women are action recommended to reduce the prevalence of preterm birth and new borne morbidity/mortality in both scenarios.
PRECONCEITO NO SETOR BANCÁRIO: UMA ANÁLISE DA INSERÇÃO E ASCENSÃO DE PESSOAS COM DEFICIÊNCIA

O objetivo desta pesquisa foi compreender, a partir da percepção dos bancários de agências do Sul de Minas, como ocorre o preconceito contra Pessoas com deficiência (PCDs) no ambiente de trabalho. Para tanto, foi realizada uma pesquisa qualitativa, cujos dados foram coletados por meio de entrevistas semiestruturadas conduzidas por meio de um roteiro, analisados a partir da Análise de Conteúdo (AC). Utilizamos como aporte teórico as discussões sobre PCDs, trabalho e organizações e sobre preconceito e discriminação no ambiente de trabalho. Os resultados evidenciam que o preconceito ocorre na inserção e ascensão profissional de PCDs nos bancos, na associação entre PCDs, diferença e produtividade; e quanto ao cumprimento da lei de cotas. No caso da inserção, o preconceito faz com que, tanto gerentes quanto demais funcionários, desenvolvam medo e desconfiança relacionada ao trabalho de PCDs. A baixa produtividade e performance são associadas ao trabalho de PCDs e usadas como justificativa para a falta de oportunidades de ascensão profissional. Por fim, verificou-se o preconceito contra a lei de cotas, como se fosse uma “vantagem” para PCDs e não uma política para garantia de direito ao trabalho.