Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterisation of supersonic gas jets for different nozzle geometries for laser-plasma acceleration experiments at SPARC_LAB

Plasma-based technology promises a tremendous reduction in size of accelerators used for research, medical, and industrial applications, making it possible to develop tabletop machines accessible for a broader scientific community. The use of high-power laser pulses on gaseous targets is a promising method for the generation of accelerated electron beams at energies on the GeV scale, in extremely small sizes, typically millimetres. The gaseous target in question can be a collimated supersonic gasjet from a nozzle. In this work, a technique for optimising the so generated plasma channel is presented. In detail, a study on the influence of the nozzle throat shape in relation to the uniformity and density of the generated plasma profile is reported. These considerations are discussed first of all from a theoretical point of view, by means of a stationary one-dimensional mathematical model of the neutral gas, thus exploiting the possibility of comparing the properties of the output flow for different nozzle geometries. This is combined with an experimental approach using interferometric longitudinal density measurements of the plasma channel. The latter is generated by a high-power laser pulse focused on a helium gasjet, in the SPARC_LAB laboratories.
SBIR 2022 Phase I Solicitation Human Exploration and Operations H 3 . 08 Challenges in Carbon Dioxide Removal and Reduction : Carbon Particulate and Thermal Management

Air Revitalization Systems (ARS) are necessary for human survival during space exploration missions. Technologies to efficiently remove carbon dioxide (CO2) from the cabin atmosphere and to reduce the captured CO2 to recover oxygen are two systems that face technical challenges. Using adsorption beds to remove CO2 is a proven technology, but optimization is needed. Please see the second scope "Advanced Heaters for Solid Sorption Systems" for more information. In the area of CO2 reduction, several technologies produce solid carbon either intentionally or unintentionally. A current challenge to the development of these technologies is carbon management. Technologies and methods that will efficiently separate, remove, and store the carbon are sought. Technical solutions will allow for efficient operation of the carbon reduction process, prevent contamination of downstream hardware receiving effluent gases and avoid contamination of cabin atmosphere during carbon handling and disposal.
Development of a Multiple Sensor Based Instrumentation System for Degradation Measurement of Lubricating Oil

The degradation of Lubricating oil is not only detrimental to the machine on which it is used, but is also destructive to the environment. Prolong use of oil in a machine, will lead to its degradation. These oils needs to be checked for degradation so that it do not cause damage to the machine or harm the environment after it is discarded. This work presents a multi-sensor based instrumentation system that can measure the degradation of oil samples. Five different sensors are used to measure different parameters of oil samples. The data from the five sensors are fed to an Atmega 2560 microcontroller which takes the sensor data as ADC inputs. The ADC inputs are then are fed to a Mamdani Fuzzy inference system (FIS) programmed in the microcontroller system. The fuzzy system uses the sensor data for selecting the membership function. After defining the membership functions, the system uses the Centroid method for defuzzification. The defuzzied output gives the output in terms of degradation percentage of the oil samples which are tested in the system. The multi-sensor system is able to show the degradation percentage when tested with different oil samples and the result is displayed on an LCD.
EVALUATION OF INSULATOR STRING’S ICING STATUS BASED ON LEAKAGE CURRENT

Abstract: This paper studied the leakage current of insulator strings applied with operating stress during icing regime and evaluated the insulator string’s icing status based on leakage current characteristic. Artificial icing test were conducted on one kind of widely used ceramic insulator. Leakage current waveforms of the insulator string during the icing regime were recorded by leakage current monitoring system. The relationship between the maximum leakage current and the icing duration was studied. Power Spectral of the maximum leakage current series of every 20 minutes were analyzed. It was found that the power spectral of principal frequencies such as 50 Hz, 150 Hz and 250 Hz rise with the icing duration and relate closely to the icing thickness on insulator strings. Fitting curves of the power spectral density with the icing duration were given. It provided a good approach to evaluate the insulator strings’ icing status.
“Parameter-Specific Morphing Reveals Contributions of Timbre and Fundamental Frequency Cues to the Perception of Voice Gender and Age in Cochlear Implant Users”

With great interest, we read the article, “ParameterSpecific Morphing Reveals Contributions of Timbre and Fundamental Frequency Cues to the Perception of Voice Gender and Age in Cochlear Implant Users,” by Verena G. Skuk et al. (2020). This interesting work focuses on areas vital for cochlear implant (CI) rehabilitation, since the perception of voice gender and the identification of speakers based on voice characteristics is of great importance for everyday communication. Thus, knowledge in this area is essential for the further development of CI technology, such as coding strategies or rehabilitation measures like training programs. Skuk et al. (2020) describe two experiments dedicated to the recognition of the voice gender and vocal age. In the first experiment, brief vowel–consonant–vowel (VCV) stimuli (i.e., /aba/ and /igi/) were systematically varied with respect to the acoustic characteristics, and the resulting gender perception was assessed. Main voice characteristics are the fundamental frequency (F0) and timbre, the latter relying strongly on the position of the formant frequencies as well as the ratio of harmonic signal energy to noise. In comparison to a normal-hearing (NH) control group, whose results were based on both F0 and timbre, CI users’ gender perception almost exclusively relied on F0—a finding that confirms the results of earlier studies (e.g., Fuller et al., 2014; Meister et al., 2016). In comparison, the examination of the vocal age represents a novelty. Here, Skuk et al. varied one sentence (i.e., “Keine Antwort ist auch eine Antwort”) with respect to different acoustic characteristics, and the resulting age estimation was determined. Notably, this experiment yielded an outcome that obviously differed from the first one: At least, high-performing CI users were indeed able to use timbre cues in addition to F0. As a possible explanation, the authors suggested that “their ability to utilize acoustic parameters appears to depend on the specific social signal that defines the perceptual task” (Skuk et al., 2020, p. 3170). Based on our own work on voice recognition, we would additionally like to point to a potential general influence of stimulus type on voice perception: In Meister et al. (2016), the speaker gender perception of NH listeners and CI users
Factors influencing spatial and temporal trends in Lushoto’s agro biodiversity

Agro biodiversity is a fundamental feature of biodiversity that helps to sustain the processes that support food security. This paper explores trends and various factors that influence variations in agrobiodiversity in Lushoto District, Tanzania. Four villages namely Boheloi, Mbuzii, Yamba and Migambo were purposively selected from different altitudes. Both primary and secondary data collection methods comprising of household interviews, literature reviews, field observations and Participatory Rural Appraisals (PRAs) were applied in this study. Five percent of farming households in each village was sampled randomly for household interviews. The data obtained were analyzed using both descriptive statistics and multinomial logit model. Findings from this study indicate that over the past 30 years, indigenous agrobiodiversity in Lushoto District is gradually decreasing as most of the landraces have been replaced with modern crop varieties. This appears to have been principally influenced by both personal and socio-economic characteristics of the respondents. Notable among such characteristics are education, off-farm income and marital status. The signals of climate change that were mentioned in the area were unpredictable rainfall patterns, recurrent droughts and increasing temperatures which had been so much evident during the last 30 years. As an adaptation strategy to the changing climate, farmers are choosing crop varieties that are well-suited to the prevailing conditions. This practice had thus resulted in the loss of some indigenous key crop varies such as the local maize, beans, Irish and sweet potatoes varieties. Thus there is a need for concerted efforts to ensure the effective management of agrobiodiversity in the area. This could help the farmers better adapt to climate change and safeguard indigenous species.
Genetic Differences Revealed by Genomic-SSRs and EST-SSRs in Eucalyptus Species

Abstract: To explore the genetic differences between genomic-SSR and EST-SSR markers in different species of Eucalyptus, the genetic diversity of 6 species were analyzed using two types of SSR markers in the study reported here. Genomic-SSRs were found to be more polymorphic than EST-SSRs in all 6 Eucalyptus species, with EST-SSRsproving to be more powerful for revealing genetic relationships among different genotypes than genomic-SSRs. Moreover, cluster analyses indicated that EST-SSR markers could identify different genotypes more accurately. The genetic differences revealed in this study for genomic-SSR and EST-SSR markers will provide theoretical support for rational future use of SSR markers in studies of Eucalyptus genetic diversity, germplasm identification and other related research.
Optimizing runtime for a multi-compartment cable model of a retinal ganglion cell

— Multi-compartment cable models of retinal ganglion cells (RGCs) are important tools used to predict how the retina will respond to extracellular electric stimuli. Even with modern computing, these models are computationally expensive, particularly in simulations that require many neurons [1] . In this work, we used sensitivity analysis to identify the minimum number of compartments (spatial resolution) and maximum integration time step (temporal resolution) for an RGC cable model. Our more efficient model significantly reduces runtime with little impact on predicted activation thresholds.
Asymptotic optimality of C p-type criteria in high-dimensional multivariate linear regression models

In this supplementary materials, we show ALE and AME of GCp. Section S.1 provides four lemmas that are used for showing ALE and AME. Section S.2 gives a proof of Theorem 1, whereas a proof of Theorem 2 is obtained in Section S.3. An outline of the proofs of Theorems is based on Li (1987) and Shibata (1983) although some techniques are used to overcome the difficulties imposed by high-dimensionality.
Supplementary Materials to “Distributed Suﬃcient Dimension Reduction for Heterogeneous Massive Data”

: In the Supplement, we study three additional issues. Firstly, we propose a distributed algorithm when the central sub-spaces at the local nodes are distinctive from each other, which takes the advantages of the low-rank structure of the kernel matrices. Secondly, we demonstrate the distributed algorithm may outperform its pooled version in the presence of heterogeneity. A bootstrap procedure is also introduced to make a fair comparison. Finally, we provide technical details, such as proofs of theorems and some useful lemmas, in this Supplement.
Predicting Safety Risk of Working at Heights Using Bayesian Networks

AbstractAlthough the construction industry has shown significant improvements in safety performance over the past 30 years, falls are still a leading cause of fatalities and serious injuries. Previous studies have focused on identifying factors affecting the risk of falls, but remained silent on investigating the evidential relationships among these factors to better prevent fall accidents. This research proposes a Bayesian network (BN) based approach to diagnose the accident risk of working at heights. The proposed approach consists of a conceptual and generic model with a protocol for assessing the risk of falls and a computational module. The generic BN model was developed on the basis of an extensive review and evaluation of causal factors leading to falls. The computational module was developed on the basis of Bayes’ rule for inference to customize model input and job site characteristics. The results of the proposed approach provide probabilities associated with different states of safety risk. Addi.
05 isbn0-8157-1287-1 BTF2003 Pattillo.qxd

Since the second half of the 1990s, policymakers and citizens around the world have been increasingly concerned that high external indebtedness in many developing countries is limiting growth and development. In Pattillo, Poirson, and Ricci (PPR), we find empirical support for a nonlinear impact of debt on growth.1 At low levels debt has positive effects on growth, but above particular thresholds or turning points additional debt begins to have a negative impact on growth. This type of analysis appears very relevant for current policy debates on assessing, for example, external debt sustainability in developing countries. But there are many unanswered questions that relate to this issue. This paper’s main question is what are the channels through which debt affects growth and whether nonlinear effects are also present in the relationship of debt with the different sources of growth. Specifically, we investigate whether debt affects growth mostly through an effect on factor accumulation or on total factor productivity growth, and whether these effects are nonlinear.
Research on Employee Innovation Behavior Based on JD-R Model: Taking Well-Being as the Mediating Variable

This study explores employee innovation behavior from the perspective of job wellbeing. Based on resource conservation theory, creativity theory and positive emotion extensionconstruction theory, a theoretical model of job demand-resources, job well-being and innovation behavior was constructed. Smart PLS2.0 and SPSS23.0 were used for empirical tests. The results show that skill extension and job well-being positively affect innovation behavior. Skill extension, job autonomy and colleague support have positive effects on job well-being. Job well-being partially mediates the relationship between skill extension and innovation behavior, and fully mediates the relationship between job autonomy, colleague support and innovation behavior. Through the analysis framework of job demand-resource model, and taking job well-being as the mediating variable, the factors influencing employee innovation behavior are improved.
A five-phase 20-slot/18-pole PMSM for electric vehicles

Purpose – The purpose of this paper is to investigate a five-phase permanent-magnet synchronous machine (PMSM) that features high-power density and high-fault-tolerant capability for electric vehicles (EVs). Design/methodology/approach – The five-phase 20-slot/18-pole PMSM is designed by finite-element method. Two typical rotor structures which include Halbach array and rotor eccentricity are compared to achieve sinusoidal back electromotive force (EMF). The influence of slot dimensions on leakage inductance and short-circuit current is analyzed. The method to reduce eddy current loss of permanent magnets (PMs) is investigated. The machine performances under both healthy and fault conditions are evaluated. Finally, thermal behavior of the machine is studied by Ansys. Findings – With both no-load and load performances considered, rotor eccentricity is proposed to reduce the harmonic contents of EMF. Increasing slot leakage inductance is an effective way to limit the short-circuit current. By segmenting PMs.
Non-Invasive Assessment Of Liver Fibrosis In Patients With Non-Alcoholic Fatty Liver Disease In A Tertiary Care Hospital

Abstract Aims :To compare the NAFLD fibrosis score and FIBROSIS 4 score to fibroscan, and affirm whether the scores shall be used as a screening tool for liver fibrosis, in place of fibroscan. Methodology: It was a cross-sectional study. Patients with fatty liver on ultrasonological examination with 200 sample size. After obtaining the informed consent the following details were collected socio-demographic details, history, co-morbidities, anthropometric measurements, Laboratory investigations. Results: the ROC curve analysis of fibroscan reveals the area under curve of 0.499 and based on the cut off value of 4.50Kpas the sensitivity and specificity was found to be 85.7% and 83.5% respectively. The ROC curve analysis of fibrosis-4 reveals the area under curve of 0.495 and based on the cut off value of 0.80 the sensitivity and specificity was found to be 91.9% and 92.1% respectively. Analysis of NAFLD fibrosis score reveals the area under curve of 0.476 and based on the cut off value of -1.53 the sensitivity and specificity was found to be 93.1% and 93.9% respectively. Conclusion: Henceforth the study suggests that NAFLD fibrosis score shall be used as a non -invasive bedside assessment of liver fibrosis in high risk population and hence guiding their follow up for prevention of morbidity in resource limited settings.
LOCALLY D-OPTIMAL DESIGNS FOR HIERARCHICAL RESPONSE EXPERIMENTS

Categorical responses with a hierarchical structure are common in social sciences, public health, and marketing. The continuation ratio model is one of the most common models used to characterize such hierarchical data. Despite the wealth of research on this model, few studies have considered its design in the data collection step. Here, we study locally D-optimal designs for models with general link functions under the partial proportional odds assumption. The necessary and sufficient conditions for the positive definiteness of the Fisher information matrix are derived, which show that a feasible design may contain fewer supports than the number of parameters in the model. Based on some deduced characteristics of the D-optimal criterion, an efficient algorithm is proposed to search for optimal designs that can deal with both discrete and continuous design fields. Lastly, numerical examples illustrate the advantages of the proposed designs over some existing designs.
Deep convolutional neural network classified the PNEUMONIA and Coronavirus diseases (COVID-19) by softmax nonlinearity function

A deep learning powerful models of machine learning indicated better performance as precision and speed for images classification. The purpose of this paper is the detection of patients suspected of pneumonia and a novel coronavirus. Convolutional Neural Network (CNN) is utilized for features extract and it classifies, where CNN classify features into three classes are COVID-19, NORMAL, and PNEUMONIA. In CNN updating weights by CNN backpropagation and SGDM optimization algorithms in the training stage. The performance of CNN on the dataset is a combination between Chest X-Ray dataset (1583-NORMAL images and 4272-PNEUMONIA images) and COVID-19 dataset (126-images) for automatically anticipate whether a patient has COVID-19 or PNEUMONIA, where accuracy 94.31% and F1-Score 88.48% in case 60% training, 20% testing, and 20% validation.
Analysis of potential protective effects of caffeic acid phenethyl ester against gentamicin ototoxicity: An experimental study

Objective(s): In this study, it is aimed to investigate the potential protective effect of caffeic acid phenethyl ester (CAPE) on ototoxicity caused by gentamicin in a rat model. Materials and Methods: Thirty Wistar albino rats were divided into 3 groups. Group I was selected as the control group. Gentamicin was administered intraperitoneally in group II, gentamicin and CAPE in group III. Audiological assessment was performed by the distortion product otoacoustic emission (DPOAE) and auditory brainstem response (ABR) measurements before and after treatment of each group. At the end of the study all rats were decapitated, cochlea was removed and electron microscopic examination was performed. Results: In group II post-treatment DPOAE levels were found to be lower than pretreatment DPOAE levels (P<0.05). However, in group III, there is no significant difference between pre- and post-treatment DPOAE levels (P>0.05). Except for Group I, ABR thresholds increased after the procedure and this increase was statistically significant (P<0.0001). According to histological examination by transmission electron microscopy, CAPE has a cellular protective effect against gentamicin ototoxicity. Conclusion: CAPE may ameliorate hearing deterioration caused by gentamicin ototoxicity and protect the cochlear cells from apoptosis due to the strong antioxidant effect.
Designing the ELEXIS Parallel Sense-Annotated Dataset in 10 European Languages

Over the course of the last few years, lexicography has witnessed the burgeoning of increasingly reliable automatic approaches supporting the creation of lexicographic resources such as dictionaries, lexical knowledge bases and annotated datasets. In fact, recent achievements in the ﬁeld of Natural Language Processing and particularly in Word Sense Disambiguation have widely demonstrated their eﬀectiveness not only for the creation of lexicographic resources, but also for enabling a deeper analysis of lexical-semantic data both within and across languages. Nevertheless, we argue that the potential derived from the connections between the two ﬁelds is far from exhausted. In this work, we address a serious limitation aﬀecting both lexicography and Word Sense Disambiguation, i.e. the lack of high-quality sense-annotated data and describe our eﬀorts aimed at constructing a novel entirely manually annotated parallel dataset in 10 European languages. For the purposes of the present paper, we concentrate on the annotation of morpho-syntactic features. Finally, unlike many of the currently available sense-annotated datasets, we will annotate semantically by using senses derived from high-quality lexicographic repositories.
NEXUS BETWEEN OUTREACH AND PRODUCTIVITY OF MICROFINANCE IN LATIN AMERICA

Our work aims to consider the effect of outreach on the productivity of staff working in microfinance institutions (MFIs). The study considers the panel data of Latin American countries for the period of 2006-2020. For this study, we use several techniques, including Ordinary least square (OLS), Random effect model (RE) and generalized method of momentum(GMM). Accessing poor customers is more dangerous to MFIs and harmful for staff efficiency and productivity. So, we disclosed that the influence of depth of outreach on staff productivity is negative. Whilethe breadth of outreach is found to impact staff productivity, serving better off clients positively is not risky for MFIs and improves the institution's productivity. Better off clients provide a cushion against credit risk, so MFIs are more focused on extending the loan to these clients, increasing staff productivity. GMM also present the same results. MFIs prefer to provide loan facilities to all those customers who work for, extending the staff productivity, and this technique also reduces the credit risk. Our study on the impact of MFIs outreach on its productivity is comparatively new in the microfinance field instead of others studies like impact on efficiency, sustainability and profitability.