Miracle-dz/UniFAR-FTUs huggingface dataset:
Examining the Influence of Solar Panel Installers on Design Innovation and Market Penetration

This work uses an agent-based model to examine how installers of photovoltaic (PV) panels influence panel design and the success of residential solar energy. It provides a novel approach to modeling intermediary stakeholder influence on product design, focusing on installer decisions instead of the typical foci of the final customer (homeowners) and the designer/manufacturer. Installers restrict homeowner choice to a subset of all panel options available, and, consequentially, determine medium-term market dynamics in terms of quantity and design specifications of panel installations. This model investigates installer profit-maximization strategies of exploring new panel designs offered by manufacturers (a risk-seeking strategy) versus exploiting market-tested technology (a risk-averse strategy). Manufacturer design decisions and homeowner purchase decisions are modeled. Realistic details provided from installer and homeowner interviews are included. For example, installers must estimate panel reliability instead of trusting manufacturer statistics, and homeowners make purchase decisions based in part on installer reputation. We find that installers pursue new and more-efficient panels over sticking-with market-tested technology under a variety of panel-reliability scenarios and two different state scenarios (California and Massachusetts). Results indicate that it does not matter if installers are predisposed to an exploration or exploitation strategy—both types choose to explore new panels that have higher efficiency.
Higher order and anisotropic hydrodynamics for Bjorken and Gubser flows

We study the evolution of hydrodynamic and nonhydrodynamic moments of the distribution function using anisotropic and third-order Chapman-Enskog hydrodynamics for systems undergoing Bjorken and Gubser flows. The hydrodynamic results are compared with the exact solution of the Boltzmann equation with a collision term in relaxation time approximation. While the evolution of the hydrodynamic moments of the distribution function (i.e., of the energy momentum tensor) can be described with high accuracy by both hydrodynamic approximation schemes, their description of the evolution of the entropy of the system is much less precise. We attribute this to large contributions from nonhydrodynamic modes coupling into the entropy evolution, which are not well captured by the hydrodynamic approximations. The differences between the exact solution and the hydrodynamic approximations are larger for the third-order Chapman-Enskog hydrodynamics than for anisotropic hydrodynamics, which effectively resums some of the dissipative effects from anisotropic expansion to all orders in the anisotropy, and are larger for Gubser flow than for Bjorken flow. Overall, anisotropic hydrodynamics provides the most precise macroscopic description for these highly anisotropically expanding systems.
Screening, Characterization and Production of Thermostable Alpha-Amylase Produced by a Novel Thermophilic Bacillus megaterium Isolated from Pediatric Intensive Care Unit

This study aimed to isolate the thermophilic Bacillus strain capable of producing a high amount of thermo-stable α-amylase.The screening and isolation of amylase producing bacteria were done on selective media. The identification of bacteria was made using routine biochemical and molecular 16s rRNA techniques. The amylase activity assay was performed by using dinitrosalicylic acid (DNS) method, and finally, the optimum temperature, Km (Michaelis constant) and maximum rate of reaction (Vmax) of the enzyme was calculated. Results: The newly thermo-stable amylolytic enzymes of Bacillus megaterium designated AGH01was isolated from pediatric intensive care unit through a selective enrichment procedure. This isolate was identified based on biochemical and morphological traits along with 16s rRNA partial sequence analysis. This isolate showed the highest amylolytic activity (19.2 U/mL after 24 h) as compared to other isolates. The optimum temperature for the enzyme activity was achieved at 90°C and pH 7.0. At this condition, Km of the enzyme for the degradation of starch was 3.36 mg/mL and (Vmax) was 0.177 mM/min. The enzyme was stable at 90°C and 80°C in 33.4% and 35.7% of its original activity at pH 7.0 after 30 minutes and 9.1% and 48% after 60 minutes of incubation, respectively. Bioinformatics analysis showed the enzyme had no disulfide bond and had a 35 amino acid signal sequence. The aliphatic index (69.14) and grand average of hydropathicity index (-0.399) calculated by ProtParam server indicated that this α-amylase may be stable for a wide range of temperature and has excellent solubility in water.Conclusions: The high-thermal stable enzyme produced by Bacillus megaterium AGH01 could be considered merit as an excellent alternative source for bioethanol production and pharmaceutical applications. Moreover, our data provides such beneficial information for a better industrial formulation with proper stability in extreme conditions.
A Survey on Twitter as a Corpus for Sentimental Analysis and Opinion Mining

Today, Microblogging is the most popular statement tool among Internet users. Every day people share their opinions on different aspects of life. Therefore, these websites have become rich sources of data for opinion mining and sentiment analysis. Because microblogging has appeared comparatively, there are few research works that were dedicated to this topic. In our paper, we focus on using Twitter, the most popular platform, for the task of sentiment analysis. It shows how we group a corpus for sentiment analysis and opinion mining which discovers phenomena of the corpus by linguistic analyzing. Using corpus, we build a sentiment classifier that is able to determine positive, negative and neutral sentiments for a document. Experimental evaluations show that our proposed techniques are efficient and perform better than previously proposed methods. In our research, we worked with English, however, the proposed technique can be used with any other language.
Likelihood Inference on Semiparametric Models: Average Derivative and Treatment Effect

In the past few decades, much progress has been made in semiparametric modeling and estimation methods for econometric analysis. This paper is concerned with inference (i.e., confidence intervals and hypothesis testing) in semiparametric models. In contrast to the conventional approach based on t-ratios, we advocate likelihood-based inference. In particular, we study two widely applied semiparametric problems, weighted average derivatives and treatment effects, and propose semiparametric empirical likelihood and jackknife empirical likelihood methods. We derive the limiting behavior of these empirical likelihood statistics and investigate their finite sample performance via Monte Carlo simulation. Furthermore, we extend the (delete-1) jackknife empirical likelihood toward the delete-d version with growing d and establish general asymptotic theory. This extension is crucial to deal with non-smooth objects, such as quantiles and quantile average derivatives or treatment effects, due to the well-known inconsistency phenomena of the jackknife under non-smoothness.
NUMERICAL AND THEORETICAL ANALYSIS OF NEURAL NETWORKS TO SOLVE DIFFERENTIAL EQUATIONS

The importance of diﬀerential equations in real world processes is indisputable because of their ubiquitous relevance in all ﬁelds of optimisation and physics. In order to solve diﬀerential equations it is important to consider advantages and disadvantages of certain methods. Methods diﬀer in quality of the solution, computational cost and amongst other things if the solution is discretised or continuous. In this work a method and the corresponding theory is presented which yields a continuous solution to diﬀerential equations, using established machine learning techniques. This work begins by stating the ﬁrst motivation, approximating real continuous functions. Following, the mathematical general form of neural networks and their theoretical properties are introduced. The universal approximating power is theoretically shown by proving that neural networks with one hidden layer and an activation function σ ∈ C k ( R ) lies dense in compacta in C k ([ a, b ]) , concluding that every continuous function could be approximated to arbitrary accuracy by a neural network. To further and more intuitively elaborate a deterministic scheme is used to approximate continuous functions and if differentiable also their derivative. In Chapter 3 algorithms used for large scale machine learning are discussed and changes to reduce computational cost using a stochastic approach, whilst retaining the accuracy needed for such tasks, are presented. In the last part of this work Chapter 4 the method is explained and the results will be discussed and compared to the popular Runge-Kutta
The Twinkling Artifact : Kidney Stone Detection with Doppler Ultrasound

Ultrasonic imaging today is a method of kidney stone detection rarely used because of its variability across users and inconsistency, as compared to Computed Tomography (CT) scans. The twinkling artifact is a relatively new solution to this problem, utilizing Doppler ultrasound to signify the presence of renal stones with color. Twinkling is a phenomenon born of abnormal events in the received Doppler signal that highlights the stones using color. The presence of color in the grayscale ultrasonic image allows the sonographer to locate the stone with ease, providing nephrolithiasis patients with an economical and radiation-free option for kidney stone diagnoses. The goal of this research is to understand what causes twinkling and how it can be stabilized in order to ultimately use this method in clinical application. This study provided some insight into factors that improve twinkling presence, and explored difficulties that sonographers may face during scans due to parameters controlled by the machine and features of the kidney stones. It was confirmed that kidney stones with smooth surfaces are less likely to produce twinkling, and there was evidence to suggest that a particular type of kidney stone, uric acid calculi, may also pose a challenge for sonographers.
Utility of Fine Needle Aspiration Cytology as a Low Cost Diagnostic Tool in the Diagnosis of Cervical Lymphadenopathy-An Institutional Based Study

DOI: 10.21276/sjams.2018. 6.11. 17 Abstract: To observe the cytomorphological patterns in patients presenting with enlarged cervical lymph nodes, diagnosed by fine needle aspiration cytology (FNAC); their distribution with respect to age and gender; and to determine the accuracy of FNAC as a diagnostic procedure in cases of cervical lymphadenopathy. Out of all patients presenting with cervical lymphadenopathy from June 2017 to June 2018, in the Department of Pathology, Index medical college, Indore, Madhya Pradesh, 100 consecutive cases were enrolled in this study, following exclusion of all cases where FNAC smears were inadequate or suboptimal for diagnostic interpretation. Histopathological examination was conducted on small excision biopsy specimens / radical neck dissection where ever possible and diagnostic efficacy of FNAC was evaluated. Overall age range of the enrolled patients, presenting with cervical lymph nodes was 1 to 72 years, mean age being 35.7 years with male-female ratio, 1.6:1. Out of 100 cases, benign and malignant lesions comprised 77 and 23 cases, respectively. Most common Benign lesion was reactive hyperplasia (29%), while squamous cell carcinoma was the most common malignant lesion. The overall sensitivity, specificity, positive predictive value, negative predictive value & the diagnostic accuracy was 100%, 91.67%, 92%, 100% and 95.7% respectively. FNAC in the diagnoses of inflammatory and neoplastic diseases serves as a reliable, low cost, rapid diagnostic tool of reasonably good accuracy that can influence patient management in terms of early diagnoses, initiation of medical intervention and prevention of unnecessary surgery in patients.
Efficient estimation in semivarying coefficient models for longitudinal/clustered data

In semivarying coefficient models for longitudinal/clustered data, usually of primary interest is usually the parametric component which involves unknown constant coefficients. First, we study semiparametric efficiency bound for estimation of the constant coefficients in a general setup. It can be achieved by spline regression provided that the within-cluster covariance matrices are all known, which is an unrealistic assumption in reality. Thus, we propose an adaptive estimator of the constant coefficients when the covariance matrices are unknown and depend only on the index random variable, such as time, and when the link function is the identity function. After preliminary estimation, based on working independence and both spline and local linear regression, we estimate the covariance matrices by applying local linear regression to the resulting residuals. Then we employ the covariance matrix estimates and spline regression to obtain our final estimators of the constant coefficients. The proposed estimator achieves the semiparametric efficiency bound under normality assumption, and it has the smallest covariance matrix among a class of estimators even when normality is violated. We also present results of numerical studies. The simulation results demonstrate that our estimator is superior to the one based on working independence. When applied to the CD4 count data, our method identifies an interesting structure that was not found by previous analyses.
The Accuracies of Smoothed Sea Surface Height Fields Constructed from Tandem Satellite Altimeter Datasets

Abstract A technique previously developed for assessing the effects of sampling errors on sea surface height (SSH) fields constructed from satellite altimeter data is extended to include measurement errors, thus providing estimates of the total mean-squared error of the SSH fields. The measurement error contribution becomes an important consideration with the greater sampling density of a coordinated tandem satellite mission. Mean-squared errors are calculated for a variety of tandem altimeter sampling patterns. The resolution capability of each sampling pattern is assessed from a subjectively chosen but consistent set of criteria for the mean value and the spatial and temporal inhomogeneity of the root-mean-squared errors computed over a representative large collection of estimation times and locations. For a mean mapping error threshold tolerance criterion of 25% of the signal standard deviation, the filter cutoff wavelength and period defining the resolution capability of SSH fields constructed from a .
A Comparison Of Discriminant Function Analysis And Logistic Regression By Categorizing The Incarcerated Mentally Ill

A COMPARISON OF DISCRIMINANT FUNCTION ANALYSIS AND LOGISTIC REGRESSION BY CATEGORIZING THE INCARCERATED MENTALLY ILL by MONA KING August 2018 Advisor: Shlomo Sawilowsky Major: Education Evaluation and Research Degree: Doctor of Education Both discriminant function analysis (DFA) and logistic regression (LR) are used to classify subjects into a category/group based upon several explanatory variables (Liong & Foo, 2013). Although the two procedures are generally related, there is no clear advice in the statistical literature on when to use DFA vs. LR, although LR appears to be preferred due to the claim that its underlying assumptions are more easily met (Liong & Foo, 2013). Although DFA and LR use different methods to accomplish their objectives, they can answer the same research questions (Antonogeorgos et al., 2009). This facilitates a practical comparison of their outcome to identify the differences and/or similarities of the two methods. Therefore, the purpose of this study is to compare the operating characteristics of DFA and LR when using dichotomous outcome, with a particular application of prison data. This analysis used the two multivariate statistical methods to evaluate if the mentally ill will be housed in prison by identifying the predictors. The outcome showed that although the assumptions normality and homogeneity of variance were violated, the results of both methods were virtually identical, and showed a disproportionate number of the mentally ill being incarcerated in
Energy Efficient Dynamic Offloading in Mobile Edge Computing for Internet of Things

With proliferation of computation-intensive Internet of Things (IoT) applications, the limited capacity of end devices can deteriorate service performance. To address this issue, computation tasks can be offloaded to the Mobile Edge Computing (MEC) for processing. However, it consumes considerable energy to transmit and process these tasks. In this paper, we study the energy efficient task offloading in MEC. Specifically, we formulate it as a stochastic optimization problem, with the objective of minimizing the energy consumption of task offloading while guaranteeing the average queue length. Solving this offloading optimization problem faces many technical challenges due to the uncertainty and dynamics of wireless channel state and task arrival process, and the large scale of solution space. To tackle these challenges, we apply stochastic optimization techniques to transform the original stochastic problem into a deterministic optimization problem, and propose an energy efficient dynamic offloading algorithm called EEDOA. EEDOA can be implemented in an online manner to make the task offloading decisions with polynomial time complexity. Theoretical analysis is provided to demonstrate that EEDOA can approximate the minimal transmission energy consumption while still bounding the queue length. Experiment results are presented which show the EEDOA’s effectiveness.
neptacarpus igarashu sp , nov . from Northern Japan ( Decapoda , Caridea , Hippolytidae )

Heptacarpus igarashii sp. nov. is described from northern Japan. It belongs to the species-group with epipods on the first three pereopods and is distinguished from the other members of the group by the combination of a short rostrum unarmed ventrally, three pairs of telson spines, three to five marginal spinules on the first antennular segment and a long outer terminal spine of antennal scale.
Changes in Soil Properties and Enzymatic Activities Following Manure Applications to a Rangeland

Abstract Manure amendments to rangelands may alter soil functions related to nutrient recycling. We investigated the influence of grazing and cattle manure on soil carbon, nitrogen, Mehlich 3 phosphorus, and activities of alkaline phosphatase and dehydrogenase. Fertilizer treatments (unamended, manure, or urea + potassium dihydrogen phosphate [KH2PO4] fertilizer) were imposed under grazed and nongrazed conditions in a short-grass native rangeland. Manure was applied at rates of ∼ 125 kg N ha−1 and ∼ 42 kg P ha−1, and urea + KH2PO4 was applied at 75 kg N ha−1 and 20 kg P ha−1, respectively. Total aboveground biomass and soil samples at 4 depths (0–200 mm) were collected throughout 2 growing seasons. A controlled environment study also evaluated fertilizer source effects on enzymatic activities at 5 P rates (0–120 mg·kg−1 P as manure or urea + KH2PO4). Amendments significantly (P < 0.05) increased extractable P following the second application for the 3 uppermost depth increments. Extractable P was greatest on manure-amended plots, increasing 44% from February 1999 to July 2000 at the surface. However, increases in P extractability as a proportion of total P applied were similar for manure and KH2PO4. Enzymatic activities were significantly (P < 0.001) influenced by sampling date and soil depth. There were no consistent grazing effects on enzyme activities. Amendments did not influence dehydrogenase activities in the field; however, in the controlled environment, activities averaged 16% greater across all rates for manure-amended soil as compared with urea + KH2PO4–amended soil (P = 0.025). Phosphatase activities increased significantly following manure applications under both field (P = 0.007) and controlled environment (P = 0.003) conditions. Elevated phosphatase activities following manure applications probably led to enhanced P mineralization and similar P extractabilities as a proportion of total P applied for manure- and KH2PO4–amended soils. Therefore, when determining applications rates, total manure P should be considered bioavailable.
Common Phobias among Egyptian Primary Schoolchildren: An Emergency Trigger for Panic Disorder due to Corona Pandemic

BACKGROUND: In the wake of the adverse situation we are currently facing globally due to the coronavirus pandemic outbreak, it is normal to feel stressed, confused, and scared but what is abnormal is to turn this to panic. Phobias are more pronounced than fears. They develop when a person has an exaggerated or unrealistic sense of danger that may be evolved to experience panic attacks. AIM: Our objective was to identify the prevalence of most common phobias as well as panic disorder (PD) due to the coronavirus pandemic among Egyptian primary schoolchildren and their determinants. METHODS: A cross-sectional study was conducted among 2015 schoolchildren from 3 to 6 grades in three governorates of Egypt. Seven types of phobias were investigated: Agoraphobia, phobias from darkness, animal, untreatable illness (mainly coronavirus), insects, height, and social phobia. The childâ€™s self-reported PD symptoms were assessed using DSM-IV with psychiatric diagnoses. RESULTS: Almost two-thirds of the surveyed primary schoolchildren have phobias of the low score (<9) versus one-third who had high phobias score (61.% vs. 35%, respectively) with highly significant difference (p < 0.001). The most prevalent phobias were from heights (66.5%) followed by darkness (60.0%). The important predictors of phobias were: Being a male child, living in an urban area, and studying at governmental school at fifth or sixth-grade residence. The prevalence of PD due to the corona epidemic is very high, it is reported by almost half of the surveyed primary schoolchildren. Fear of losing any of their family members, especially grandparents, was reported to be the highest PD symptom (97%). CONCLUSION AND RECOMMENDATION: Phobia from heights and darkness was the most common. The prevalence of PD due to the coronavirus epidemic is reported to be very high. Calming down children who might be experienced with a phobia that is triggering their PD is recommended.
Assessment of Municipal Organic Solid Waste, as a Potential Feedstock for Briquette Production in Kampala, Uganda

The current shortage of energy resources coupled with environmental degradation problems resulting from deforestation in Uganda has contributed to increased demand for renewable energy resources including municipal organic solid waste and agricultural residues. However, organic waste from Municipal Solid Waste (MSW) may contain contaminants that are harmful to public health and the environment. This study determined the heavy metal concentration in MSW in Kampala City, Uganda. Also, the physicochemical properties of briquettes produced from the MSW were compared with charcoal. The waste samples were collected from residential, institutional and market areas over a period of two weeks. They were then analyzed for the presence of heavy metals. Briquettes were made from the bio-waste and were subjected to calorific and proximate analysis. Results indicated that the mean concentrations of Cd, Cr, Cu, Fe, and Pb were 1.25 mg/kg, 2.04 mg/kg, 38.2 mg/kg, 3.97 mg/kg and 1.99 mg/kg respectively while Hg was not detected. The calorific values of briquettes ranged from 8.9 to 15.3 MJ/kg and were lower than those of charcoal. Heavy metal concentrations in bio-waste collected were below the permissible acceptable limits. These findings indicate that the sampled MSW does not pose a health hazard arising from the presence of such heavy metals and therefore could be a safe source of renewable energy.