Miracle-dz/UniFAR-FTUs huggingface dataset:
Do Rape Cases Sit in a Moral Blindspot? The dual process theory of moral judgment and rape

Empirical research has distinguished moral judgments that focus on an act and the actor’s intention or mental states, and those that focus on results of an action and then seek a causal actor. Studies indicate these two types of judgments may result from a “dual - process system” of moral judgment (Cushman 2008, Kneer and Machery 2019). Results-oriented judgements may be subject to the problem of resultant moral luck because different results can arise from the same action and intention. While some argue luck should not bear on persons’ culpability, Victor Kumar has argued that the tendency to hold unlucky agents responsible for harm is justified by consequentialist aims of punishment (Kumar 2019). In contrast, judgments that focus on acts and intentions may be primarily retributive. Kumar claims that judgments focused on results track external, public harm because this increases the reliability of punishment and better achieves instrumental aims, including deterrent effect. In this chapter I examine rape cases using Kumar’s theory of punishment . Rape involves outcomes that are not publicly available. If judgments of punishment depend on outcomes, then we would expect such judgments to be less stable for those instances of wrongdoing that lack public outcomes such as rape, because such judgments would rely instead on often biased and unreliable inferential processes to establish the presence of mental states, which are essentially private. In this way Kumar’s theory actually predicts the way in which w e see criminal justice institutions fail with regard to arrest, prosecution, and punishment related to rape; and we might expect similar failures for other crimes that lack publicly available results. In sum, a fundamental problem with institutionalized punishment centered upon results may be that some crimes sit within a moral blindspot.
DMSO_A_349467 933..941

Purpose: To investigate the prevalence, clinical and metabolic characteristics of atherosclerosis (AS) in newly diagnosed patients with ketosis-prone type 2 diabetes (KPT2D) or non-ketotic type 2 diabetes (NKPT2D). Patients and Methods: About 1072 subjects with non-autoimmune new-onset diabetes were included in the cross-sectional study. Patients were classified as non-ketotic type 2 diabetes (NKPT2D, n = 662) or ketosis-prone type 2 diabetes (KPT2D, n = 410). Blood samples were collected to determine the levels of glucose, HbA1c, insulin and C-peptide. Routine liver and kidney function tests were also performed. AS was determined by vascular ultrasonography. Results: The levels of fasting blood glucose and HbA1c were significant higher in the KPT2D group when compared to the NKPT2D group (P<0.001). The levels of fasting C-peptide, 2 h C-peptide and HOMA-β were lower in the KPT2D group than those in NKPT2D group (P<0.001). However, no significant difference was observed for HOMA-IR between the two groups. The onset age of the patients with KPT2D was significantly lower compared to NKPT2D patients (38±13 vs 49±14, P<0.001). After adjusting age of the two groups, the KPT2D patients had a higher prevalence of AS compared to the NKPT2D patients (31.4% vs 21.1%, P=0.005). In both groups, age and gender were independent risk factors for AS, whereas estimated glomerular filtration rate (eGFR) was an independent risk factor in the NKPT2D patients and 2-h postprandial plasma glucose (2h-PPG) was an independent risk factor in the KPT2D patients. Conclusion: AS was more prevalent in KPT2D patients compared to the NKPT2D cohort, which was independent of age and gender. These data suggest that KPT2D patients may have a higher risk of macrovascular complications compared to NKPT2D of the same age.
A Bayesian non‐linear state space copula model for air pollution in Beijing

Air pollution is a serious issue that currently affects many industrial cities in the world and can cause severe illness to the population. In particular, it has been proven that extreme high levels of airborne contaminants have dangerous short‐term effects on human health, in terms of increased hospital admissions for cardiovascular and respiratory diseases and increased mortality risk. For these reasons, an accurate estimation of airborne pollutant concentrations is crucial. In this paper, we propose a flexible novel approach to model hourly measurements of fine particulate matter and meteorological data collected in Beijing in 2014. We show that the standard state space model, based on Gaussian assumptions, does not correctly capture the time dynamics of the observations. Therefore, we propose a non‐linear non‐Gaussian state space model where both the observation and the state equations are defined by copula specifications, and we perform Bayesian inference using the Hamiltonian Monte Carlo method. The proposed copula state space approach is very flexible, since it allows us to separately model the marginal distributions and to accommodate a wide variety of dependence structures in the data dynamics. We show that the proposed approach allows us not only to accurately estimate particulate matter measurements, but also to capture unusual high levels of air pollution, which were not detected by measured effects.
Apodization Optimization of FBG Strain Sensor for Quasi-Distributed Sensing Measurement Applications

A novel optimized apodization of Fiber Bragg Grating Sensor (FBGS) for quasi-distributed strain sensing applications is developed and introduced in this paper. The main objective of the proposed optimization is to obtain a reflectivity level higher than 90% and a side lobe level around −40 dB, which is suitable for use in quasi-distributed strain sensing application. For this purpose, different design parameters as apodization profile, grating length, and refractive index have been investigated to enhance and optimize the FBGS design. The performance of the proposed apodization has then been compared in terms of reflectivity, side lobe level (SLL), and full width at half maximum (FWHM) with apodization profiles proposed by other authors. The optimized sensor is integrated on quasi-distributed sensing system of 8 sensors demonstrating high reliability. Wide strain sensitivity range for each channel has also been achieved in the quasi-distributed system. Results prove the efficiency of the proposed optimization which can be further implemented for any quasi-distributed sensing application.
Public Hospitals Performance Measurement through a Three-Staged Data Envelopment Analysis Approach: Evidence from an Emerging Economy

Abstract This study proposes a three-staged approach to data envelopment analysis (DEA) modeling for hospital efficiency. The approach aims to overcome the constraint on the number of inputs/outputs relative to the number of DMUs. Initially, the principal components of all inputs and outputs are determined using principal component analysis (PCA). Next, these principal components enter a factor analysis (FA) process to construct a two-level hierarchy of inputs/outputs and to establish a weighting scheme based on explained variances of components. Finally, a two-level DEA (TLDEA) method is applied to the resultant framework to determine the relative efficiency of hospitals using data from the healthcare context of Iran as an emerging economy. The outcomes of applying the proposed PCA-FA-TLDEA approach are argued to offer a substantial increase in the discriminatory power of classical DEA methods and could incorporate a relatively large set of inputs/outputs already existing in the hospital efficiency literature. As demonstrated in the evaluated hospitals, the PCA-FA-TLDEA methodology improved the discrimination from 0% in the original DEA to 45%. The paper proposes a novel three-stage DEA model by using PCA to extract the principal components from the inputs and outputs; therefore, reducing the number of inputs and outputs and their inter-correlations. Secondly, a hierarchy of inputs and outputs by applying FA to the principal components is constructed. Finally, the TLDEA method to the hierarchy of inputs and outputs is applied to evaluate the performance of public hospitals.
Research on the resonance frequency splitting mechanism and novel modal characteristics of a rotating tire acoustic cavity

Tire acoustic cavity resonance (TACR) noise contributes significantly to interior noise for lower powertrain noise passenger cars and electric cars, which affects the ride comfort obviously. To design sound absorption structures effectively, it is crucial to clarify the evolution mechanism and influence factors of the resonance frequencies and acoustic modal shapes with the running speed. Aiming at these problems, in this paper, a theoretical model of sound wave propagation in a tire acoustic cavity is constructed based on the superposition principle of traveling waves, and the sound field distributions under different rotating speeds are investigated. The formation conditions of TACR are summarized from the perspective of wavenumber. Especially for a rotating tire acoustic cavity, some novel modal characteristics, such as the novel deflective modal shapes and the continuously changing phase, are found. And the theoretical calculation results are verified by the experiment and simulation. The significance of this work is that the evolution mechanisms of TACR frequency and modal shape with the tire rotating speed are theoretically clarified and revealed, which are helpful to obtain effective solutions to suppress TACR noise.
A notable Gamma‐Lindley first‐order autoregressive process: An application to hydrological data

A new Gamma‐Lindley (GaL) first‐order autoregressive process (AR) is introduced, called GaL‐AR(1). The distribution for the GaL‐AR(1) innovation process and some of its structural properties are derived, such as the Laplace transform for its bivariate distribution, the conditional variance and expected value, and the spectral and autocorrelation functions. We propose two different estimation procedures whose asymptotic properties are studied by Monte Carlo experiments. An application to actual data from hydrology is performed. Results point out that our process outperforms other six non‐Gaussian AR(1) models that are well defined in the time series literature.
Assessment of Climate Change Impact on Water Resources of Lesser Zab, Kurdistan, Iraq Using SWAT Model

Kurdistan in northern Iraq, a semi-arid region, predominantly a pastureland, is nourished by Lesser Zab, which is the second major tributary of Tigris River. The discharge in the tributary, in recent times, has been experiencing increasing variability contributing to more severe droughts and floods supposedly due to climate change. For a proper appreciation, SWAT model has been used to assess the impact of climate change on its hydrological components for a half-centennial lead time to 2046-2064 and a centennial lead time to 2080-2100. The suitability of the model was first evaluated, and then, outputs from six GCMs were incorporated to evaluate the impacts of climate change on water resources under three emission scenarios: A1B, A2 and B1. The results showed worsening water resources regime.
Speed sensorless model predictive current control with ability to start a free running induction motor

In this study, a speed-sensorless finite control set-model predictive current control method is proposed based on an adaptive full order observer. The control system features simplicity and low cost because of no requirement for speed measurement, modulator and tuning of weighting factors. In most sensorless based schemes, the motor is assumed to be started from standstill. There is limited research considering starting a free running motor with unknown rotational direction and speed. To start a free-running induction motor (IM), the feedback gain matrix is designed to guarantee the convergence of estimated speed to actual speed even with incorrect initial value. To improve the efficiency, amplitude of flux is adjusted along with load condition. The presented results show that the proposed method is able to smoothly start an IM with unknown initial speed and work well over a wide speed range. The effectiveness of the proposed method is verified by both simulation and experimental tests on a two-level inverter fed IM drive platform.
Instruments and Meters

This chapter discusses the concepts of accuracy and precision and talks about industry standard color coding of thermocouple extension cables. While the distributed control system (DCS) receives information on the status of equipment in the facility and makes control decisions to operate this equipment, the instrumentation forms the nervous system of the power generation facility to feed valid data back to the control system. The instrumentation system monitors various physical parameters in the power plant and provides either local or remote indication of these parameters to the operational staff to assist in the reliable and safe operation of the power station. There are many sensors in use in the power generation facility to monitor temperature. Temperatures on bearings, motor windings, gas streams, steam streams, hotwell fluid, and the like are all monitored. Potential transformers (PT) are used to monitor voltages in the power plant.
Synergistic Influence of Sulphur and Boron Fertilization on Enhancing the Productivity of Rapeseed (Brassica napus L.) and Nutrient Status in Subtropical Acidic Soil of Assam, India

ABSTRACT Rapeseed has an immense requirement for S and B, largely for the biosynthesis of glucosinolates and S amino acids. The present investigation was conducted for three consecutive rabi seasons of the year 2014–17 in rapeseed (cv.TS-38) to assess the impact of S and B fertilization on crop yield, soil chemical properties and nutrient status. Results of three years of investigations indicated that among the various soil chemical properties, the combined appliance of S and B level of S30B1.5 (30 kg S ha−1 + 1.5 kg B ha−1) significantly augmented the SOC, while soil pH, Fe2O3 and Al2O3 content in soil were found to be non-affected significantly. Interaction effect between S and B significantly affected available primary nutrients as fertilization with S30B1.5 registered maximum available N (298.7 kg ha−1), P (31.0 kg ha−1) and K (126.2 kg ha−1), respectively. Application of the same levels of S30B1.5 enhanced the status of exchangeable Ca and Mg, available S and B up to 0.90 cmol(p+) kg−1, 0.50 cmol(p+) kg−1, 13.67 mg kg−1 and 0.65 mg kg−1, respectively. The NUE in soil were found to improve on scaling both the levels of S and B. Yield attributing characters were not significantly affected by graded levels of S and B, however, there was a significant improvement in seed yield, stover yield and biological yield of rapeseed. Yield gap analysis exhibited that the combined application of S30B1.0 resulted in the lowest yield gap of 275 kg ha−1 while the control plot recorded the highest yield gap of 647 kg ha−1. Correlation study indicated the existence of a highly significant correlation among most of the soil parameters under investigation. Path analysis revealed that available P was the most dominant soil nutrient implicated in improving the BYR. The inclusive results indicated that higher levels of both S and B could improve the soil fertility status and yield of rapeseed.
Attachment D: Submission to DFAT with respect to patents and data protection

Patents were originally designed to encourage technological innovation, which would not otherwise occur, and which create spillover benefits. Careful design is needed to ensure patents do not provide windfall benefits to inventions which would take place absent patents. Further, for the grant of a patent to be economically rational the patented invention must have a reasonable probability of providing spillover (dynamic growth) benefits that exceed monopoly (static inefficiency) losses. This paper draws on the substantial empirical research on industrial innovation and how patent systems work in practice to develop a first-best set of policy parameters for a balanced (parsimonious) patent system. That is, it attempts to design a set of parameters which maximise dynamic growth benefits while minimising static efficiency losses, thus complying with TRIPS Article 7. These parameters are compared with TRIPS and with the TRIPS-Plus elements which the USA is seeking from bi-lateral and regional trade treaties. The resulting schema allows a clearer view of the cost of patent policy provisions in "trade" treaties.
Role and Function of Regulatory T Cell in Chronic Rhinosinusitis with Nasal Polyposis

Chronic rhinosinusitis with nasal polyps (CRSwNP) is a subtype of chronic rhinosinusitis characterized by high edema in the stroma, albumin deposition, and formation of pseudocysts. The pathogenesis of CRSwNP is not yet fully understood. Regulatory T (Treg) cells are a subset of CD4+ T cells that play a suppressive immunoregulatory role in the process of CRSwNP. Recent studies have found that there was a significant reduction in Treg cells in polyp tissues, which leads to the onset of CRSwNP. An imbalance between Th17 and Treg cells can also aggravate inflammation toward the Th2 type. This review focuses on our understanding of the function and role of Treg cells and their regulatory factors and clinical significance in CRSwNP. We also summarize the current drug treatments for CRSwNP with Tregs as the potential therapeutic target, which will provide new ideas for the treatment of CRSwNP in the future.
The Role of Achievement, Gender, SES, Location, and Policy in Explaining the Indigenous Gap in High-School Completion

Internationally there is a gap in high school completion rates for Indigenous and non-Indigenous students. In Australia, gap estimates are commonly based on lag indicators, precluding examination of underlying mechanisms. Using two longitudinal and representative samples of Australian youth, we explored differences in high school completion between Australian Indigenous and non-Indigenous rates and whether the gap varies for students of similar academic ability. Using an intersectional approach, we show the Indigenous gap varies by SES and location. Specifically, high SES and living in urban settings are protective factors for non-Indigenous students, but not so for Indigenous students. Results also show the Indigenous gap declined in response to government policy that increased the compulsory school leaving age.
ELECTRICITY CONSUMPTION FORECASTING SYSTEM OF INDIA: A REVIEW

The paper provides a comprehensive overview of research related to machine learning models which are used in predictive analysis. A manual search of published articles in the previous 13 years (2007 to 2020) for electricity consumption forecasting utilizing the classical methods and novel neural network models was used to conduct the systematic review. In the study, it was found that alone classical model can't forecast properly. The accuracy in forecast can be improved by using a hybrid approach. The widely used method for predictive analysis is artificial neural network. ANN has been discovered to be a highly innovative and effective model when it comes to problem-solving and machine learning. Hence, for time series forecasting LSTM has proved to be best model to predict electricity consumption in India.
ANALYSIS OF WAVE INTERACTION WITH DOUBLE VERTICAL SLOTTED WALLS USING MBEM

In this paper, a multi-domain boundary element method (MBEM) is formulated and applied to study wave interaction with double vertical slotted walls, which are modeled as thin or non-thickness structures. Two-dimensional motion with wave crests parallel to the vertical slotted walls and linearized irrotational flow are assumed. The accuracy of the solution obtained using the numerical technique is demonstrated by comparing the numerical values with those obtained from experiments and from other analytical solutions. A comparison of the hydrodynamic performance of the breakwater with identical or different double vertical slotted walls is conducted. In addition, the numerical results of the wave reflection, transmission and energy dissipation for different relative permeable depths, chamber widths, and porosities are presented and discussed. Double vertical slotted walls with a longer rear wall are recommended because they more effectively suppressed wave energy at deeper submergence. The double vertical slotted walls also very effectively dissipate the incident wave energy. Our numerical results indicate that when the permeable middle part of the seaward (first) wall (dm_1/h = 0.6) and the permeable middle part of the leeward (second) wall (dm_2/h = 0.2) have different porosities of e_1 = 0.5 and e_2 = 0.3, respectively, the breakwater has a high reflection coefficient, a low transmission coefficient and the maximum energy dissipation coefficient. The maximum energy dissipation coefficient of 0.963 occurs at kh = 1.635.
Socially Responsible Investment During the COVID-19 Pandemic: Evidence from Morocco, Egypt and Turkey

Socially responsible investing (SRI) seeks to combine financial returns with social and environmental performance. In the context of the Covid-19 pandemic, SRI is seen as an alternative way to maintain sustainable returns. This article attempts to assess the impact of COVID-19 on the performance of socially responsible stocks. In other words, we test the resilience of ESG (Environmental, Social and Governance) oriented companies’ stock prices to the global crisis, and compare it with the performance of selected non-ESG stocks. To do so, we focus on companies listed on the Moroccan, the Egyptian and the Turkish stock exchanges. We use the event study methodology, which relies mainly on calculating the daily abnormal returns of each company and aggregating them over an event window to test their statistical significance. The results reveal that all the companies listed on these three stock exchanges suffered from the COVID-19 crisis, posting negative abnormal returns. However, the ESG oriented companies listed on the Turkish stock exchange were more resilient compared to non-ESG companies. Sustainable investing underperformed non-ESG investing in Morocco and Egypt, as ESG oriented companies posted more pronounced negative abnormal returns, compare to non-ESG companies. So, unlike Turkey, ESG oriented companies were less portfolio protective alternative during the crisis in Morocco and Egypt.