Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis and simulation of efficiency optimized IPM drives in constant torque region with reduced computational burden

Moving from internal combustion engine based towards electric based transportation is crucial for wide societies as they facilitate the use of green energy technologies such as wind and solar. Interior mounted permanent magnet (IPM) machines, also known as salient brushless alternating current (AC) machines, are commonly employed in traction applications as they have superior features, such as high efficiency operation, high torque, and power densities. The efficiency optimization in IPM drives is achieved by obtaining and operating at accurate and unique current angle for a certain electromagnetic torque demand. In conventional drives, the optimum current angle is obtained by online utilization of iterative techniques. However, these techniques result in increased burden on the processor. In addition, accuracy of the generated current commands, and hence, the drive efficiency may significantly reduce if the number of iterations is not adequate. More importantly, online zero divisions issue may result in instability of the drive system at certain operating points in real life experiments. This paper proposes an efficiency optimized IPM drive with smooth output torque production addressing the aforementioned issues. The optimum operating points as a function of electromagnetic torque demand are computed offline by Newton–Raphson approximation technique in the paper, and they are stored in the controller as look-up tables, which are then employed to achieve optimum efficiency operation at a wide range. The superiority of the proposed drive is validated through extensive simulation of a 4.1 kW prototype IPM machine designed and manufactured for traction applications and the results are discussed in detail. The paper also presents detailed high efficiency control analysis of permanent magnet machines and hence it would be quite insightful for new researchers on the topic.
Education In Physiotherapy Department In The University Of Zawia In Libya

Physiotherapy is a specialist branch of medicine that helps remediate impairments in movement and promote patients' quality of life. Furthermore, Physiotherapy plays a crucial role in helping people who have lost their mobility or suffer from movement disorders. Educational criteria for physiotherapy providers vary from state to state and from country to country, and among various levels of professional responsibility. The purpose of the study to assess the impact and effectiveness of the educational process in physiotherapy in Libya, and to provide an overview of education and training at the University of Zawia in Libya including the graduate students, staff members, and demonstrators from 2002-2012. The data was conducted from the department documents and from the staff in the Physiotherapy Department at the University of Zawia. As a result, during 2002-2009, the mean number of graduate students was 5.43. While from 2009-2012 a visible increase in the number of graduate students was reported and the mean reached 18, owing to the rise in the knowledge of physiotherapy importance and the raise of awareness that has been shown that between the society and association of students. On the other hand, the percentage of the national and foreign teachers' members was reported 46% and 36% respectively, while the assistant staff members were 18%. In addition, during 2002-2010, the demonstrators were 5 while in 2010-2012 there were 12, this increase was associated with the rising number of graduate students.
Incident Linking: Assigning Tweets to Entries in a Disaster Database

Twitter o ers outstanding potential for collecting information about disaster events. However, these disaster events usually are recorded in incident databases. Consequently, we want to extract the structured data from tweets to supplement the entries in the traditionally acquired incident databases if some of the metadata (impact, location, description, time) is missing and create new entries, if necessary. To accomplish this, there is a need for a linking method that allows analyzing the relation between tweet texts and incident metadata. Unfortunately, established algorithms for event detection and entity linking cannot solve this problem. Event detection algorithms require a large volume of tweets for each event, which is often unavailable, and they do not attempt to connect tweets and incident databases. Entity linking algorithms require named entities in the databases, which are often missing in event databases. A new Incident Linking Framework (ILF) is proposed and evaluated in this thesis to accomplish this task automatically. This method is a two-step approach that includes individual components like candidate generation and candidate ranking. The candidate generation step returns the possible incidents that match a tweet. The candidate ranking ranks these candidates individually using scoring metrics and produces the bestt pair of tweets and incidents. There are two di erent experiments conducted to evaluate this approach. Results for ILF shows that tweets can automatically get assigned to the incident database only if it matches the metadata. In addition, the candidate generation shows promising results compared to the candidate ranking.
Blockchains for Pay-per-use Business Models of Industrial Equipment: Insights From a Design Research Study

A blockchain features an immutable, encrypted, and distributed ledger that enables transactions even if trust and trusted intermediaries are absent. Despite research on their technological properties, few papers are on record to demonstrate (a) what business scenarios blockchains can enable and (b) under which circumstances they outperform rival technologies. This shortfall of design knowledge obscures blockchains’ value proposition, its conceptual limitations, and its positioning towards rival classes of IT artifacts. We set out to design a blockchain-based IS that enables pay-per-use business models for industrial equipment—a business model that suffers from high transaction costs and complex agency dilemma caused by asymmetric information. We demonstrate how smart contracts deployed in a blockchain can level these information asymmetries. However, we also find that blockchains will only outperform rival technologies if the business scenario fulfils a set of specific properties, narrowing down the scope of application scenarios for applying blockchains substantially.
Exploring Time Series Data Properties in SAS ®

Box and Jenkins popularized graphical methods for studying time series properties of time series data. Dickey-and Fuller did the same for unit-root tests. Both methods seek to understand the non-stationary properties of data and SAS® is a popular software used by applied researchers. The purpose of this paper is to provide a series of steps using SAS MACRO Language, PROC SGPANEL, ARIMA, AUTOREG, and %DFTEST to diagnose non-stationary properties of data. A comparison among three competing SAS procedures is presented with SAS capabilities highlighted using simulated time series.
Incentives, Self-Selection, and Coordination of Motivated Agents for the Production of Social Goods

We study, theoretically and empirically, the effects of incentives on the self-selection and coordination of motivated agents to produce a social good. Agents join teams where they allocate effort to either generate individual monetary rewards (selfish effort) or contribute to the production of a social good with positive effort complementarities (social effort). Agents differ in their motivation to exert social effort. Our model predicts that lowering incentives for selfish effort in one team increases social good production by selectively attracting and coordinating motivated agents. We test this prediction in a lab experiment allowing us to cleanly separate the selection effect from other effects of low incentives. Results show that social good production more than doubles in the low- incentive team, but only if self-selection is possible. Our analysis highlights the important role of incentives in the matching of motivated agents engaged in social good production.
Popularity Shocks, Self-Selection and Electoral Performance: Evidence from City Council Dismissals in Italy

This paper studies the effects of municipal government dissolutions on electoral accountability. It uses a novel data set assembled from episodes of city council dismissals in Italy, where the central government may replace locally elected officials if they break the law or if the normal functioning of the city council is compromised. The analysis compares the electoral performance of incumbent mayors that experienced the negative popularity shock resulting from a municipal government dissolution with that of incumbent mayors who completed the term. The results indicate that voters significantly reduce their support for mayors accountable for a city council dismissal. Using the individual options outside politics as a source of exogenous variation in the likelihood of running for re-election, I show that this effect is more pronounced when I account for the positive self-selection of incumbents into elections. The findings suggest that voters hold politicians accountable for their actions even more than what highlighted by the previous literature.
Financial Inclusion, Banking Stability, and Digital Technology Development in ASEAN

The purpose of this study is to examine the effect of financial inclusion, supported by digital technology development on income inequality, poverty, and banking stability in ASEAN’s emerging countries. This study employs the Generalized Method of Moment (GMM) and Generalized Least Square (GLS) methodology, using annual data for ten years from 2007 to 2016. The empirical results support the argument. First, digital technology development (usage of the mobile phone) can improve financial inclusion because technology makes it easier to access financial services to people who are difficult to reach. Second, financial inclusion decreases income inequality, but it has no significant effect on reducing poverty. This finding indicates that formal financial services seem to be unable to reach the poor. Finally, the empirical results show that the increasing use of banking services through financial inclusion contributes positively to banking stability. Results of this study could encourage the presence of better policies to reform the financial sector by showing that the expansion in the use of financial services has a direct impact on financial/economic distribution. In addition, the paper provides implication for the banking regulator that the usage of banking and formal financial services still dominated by middle- and high-income society. Furthermore, the synergies between promoting financial inclusion and financial stability can also exist if using the right tools.
Social Media Management: Evidence on Seasoned Equity Offerings

We find that firms undergoing Seasoned Equity Offerings (SEOs) receive more favorable messages on financial social media platform, StockTwits, from 62 to 10 trading days prior their offerings and from 10 trading days following offerings to the expiration of lockup agreements, compared to matched non-SEO control firms. On average, SEO firms ranking in the top quartile of abnormal pre-SEO social media bullishness reduce their underpricing by 1.12%. After lockup expirations, stocks of these firms exhibit long-run return reversal and underperformance. Moreover, insiders of these firms exhibit higher net sales in the 90 days following lockup expirations. Overall, our findings suggest that SEO firms have strong incentives to actively manage their social media coverage to reduce underpricing and increase SEO proceeds.
Deep Reinforcement Learning for Dialogue Systems with Dynamic User Goals

Dialogue systems have recently become a widely used system across the world. Some of the functionality offered includes application user interfacing, social conversation, data interaction, and task completion. Most recently, dialogue systems have been developed to autonomously and intelligently interact with users to complete complex tasks in diverse operational spaces. This kind of dialogue system can interact with users to complete tasks such as making a phone call, ordering items online, searching the internet for a question, and more. These systems are typically created by training a machine learning model with example conversational data. One of the existing problems with training these systems is that they require large amounts of realistic user data, which can be challenging to collect and label in large quantities. Our research focuses on modifications to user simulators that "change their mind" mid-episode with the goal of training more robust dialogue agents. We do this by taking an existing dialogue system, modifying its user simulator, and observing quantitative and qualitative effects against a set of goals. With these results we demonstrate benefits, drawbacks, and tangential effects of using various rules and algorithms while recreating goal changing behavior. iii DocuSign Envelope ID: B42117EB-F447-4716-BE0D-BAB96FB7CEEF
Hybrid photonic system integration using thin glass platform technology

Abstract. Demand for high integration of optoelectronic and micro-optical components into micro-electronic systems for communication, computing, medical, and sensing applications is increasing. Advanced hybrid packaging technologies are used to enhance glass-based substrates featuring electrical, thermal, and optical functionalities with laser diodes, modulators, isolators, photonic integrated circuits (PIC), beam-splitting components, and micro-lenses. Such glass-based substrates can be thin glass layers on large panels or more mini-bench-like boards that can be embedded into organic printed circuit boards (PCBs). Optical fiber interconnects, connectors, and electrical–optical integration platforms are used for higher level system integration and need to be miniaturized on module and board level to fulfill decreasing channel pitch requirements. We provide background on and discuss thin glass as a suitable base material for ion exchanged waveguide panels and interposers, precise glass structuring for posts and holders, the related high precision assembly techniques, and advanced fiber interconnects. Some examples of PCB photonic integration, micro-bench optical sub-assemblies, including PIC, and 3D optical resonator packages that combine most of these approaches will be shown.
Deep Neural Ranking Models for Argument Retrieval

Conversational argument retrieval is the problem of ranking argumentative texts in a collection of focused arguments, ordered based on their quality. This study investigates eight different deep neural ranking models proposed in the literature with respect to their suitability to this task. In order to incorporate the insights from multiple models into an argument ranking, we further investigate a simple linear aggregation strategy. The Touché @ CLEF shared task on Conversational Argument Retrieval (Task 1) targets a retrieval scenario in a focused argument collection composed of roughly 387740 arguments to support argumentative conversation. We approach the problem of argument retrieval as finding relevant premises to the input query. Considering the lack of annotated data for the purpose of ranking, we take a distant supervision approach. Based on this approach, the conclusion (claim) of the arguments play the role of queries and the corresponding premises of the argument are the related documents to that queries in an ad-hoc information retrieval scenario. Using a fuzzy similarity measure we have assigned unrelated premises to each conclusion and formed all the information provided by a qrel file in a typical ranking task. To learn the relation of the queries and the related and unrelated documents we have utilized deep neural ranking models. We first use a Siamese network to generate similarity score. DRMM, KNRM, , and CKNRM are the deep neural ranking models used for retrieving arguments. We use the contextualized embedding known as BERT in three networks including vanilla-BERT, DRMM and KNRM. We also exploited an end-to-end neural ranking model called SNRM which does not require any candidate document in the inference phase. Finally, we aggregate the document scores for the test queries achieved by different networks. Before the aggregation, we analyzed how diverse the result of the networks are from each other. A linear regression is performed on the validation set to learn how to estimate the final document scores for the test queries. Test results suggest that focusing on the models whose main concern is to map the interaction between the input pairs would contribute to the better results. The poor test scores of the models highlight the fact that argument retrieval in the sense of retrieving arguments meeting different argument quality dimensions is not a trivial problem.
To Predict the Risk of Lower Limb Amputation in Diabetic Foot Ulcer Patients by Using the DIAFORA Score (Diabetic Foot Risk Assessment Score)

Background: A recently introduced modality is DIAFORA score, for assessing the risk of lower extremity amputation in diabetic foot ulcer patients, which is more accurate as compared to other existing classification systems. Aims and Objective: To predict the risk of lower limb amputation in diabetic foot ulcer patients by using the DIAFORA score (Diabetic foot risk assessment score). Materials and Methods: The study was conducted from 2018 to 2020 on a total of 50 patients with DFUs from OPD, medicine and surgery wards of the MMIMSR, Mullana. Subjects were examined and relevant investigations were sent. The DIAFORA score [3] was calculated for each patient and was categorized into one of the 3 risk groups (low, medium and high). Patients with a score less than 15 were categorized into low risk group, patients with score between 15 and 25 were categorized into medium risk group and patients with score more than 25 were categorized into high risk group for LEA and each subject was followed upto 3 months (91 days) or until LEA/death/recovery of the subject. Result: Mean age of the patients was 55.82 ± 14.52 years. Distribution of study subjects according to the DIAFORA score into high, medium and low risk groups were 17 (34.0%), 10 (20.0%), 23 (46.0%) respectively. Mean DIAFORA score of the low risk group, medium risk group, high risk group was 7.52 ± 3.189, 22.20 ± 3.120, 33.76 ± 3.945 respectively, statistically significant (p < 0.001). DIAFORA score had Area under the receiver operating characteristic (ROC) curve 0.992 (99%) with a p value of 0.0001 (highly significant). DIAFORA score of 23 taken as cut off value for amputation had sensitivity of 91.3%, specificity of 100%, positive predictive value of 100.0% and negative predictive value of 93.1%. Amputation was done in 23 (46.0%) patients whereas rest 27 (54.0%) were recovered. In the amputation group, Peripheral neuropathy, foot deformities, Peripheral Arterial Disease, bone involvement and gangrene were present in 20 (87%),14 (60.9%), 20 (87%), 18 (78.3%), 23 (100%). Conclusion: We conclude that the recently introduced DIAFORA score model is a reliable predictor of lower extremity amputation amongst patients with Diabetic Foot Ulcer. It has high sensitivity and specificity to predict amputation.
Quantum Gravity, and the calculation of maximum SOL mass

Here Temporal Mechanics shall derive the mass of the electron using the same system of logic that derived the mass of the neutrino in paper 35 (Temporal mechanics (E): Time-Space Logistics) to then derive new equations for gravity via a process of equating time to space, and thus in theory, providing a theory for quantum gravity. There, an equation for gravity will be proposed that calculates the minimum and maximum limits of mass for a gravitational field, thence deriving the maximum mass of a sun from the mass of the neutrino, subsequently presenting a key insight to cosmology theory. From here, an equation proposing the maximum and minimum limits of space shall be derived in supporting the proposed time-equation and the temporal event of time not passing at the speed of light c, successfully deriving the Planck length, putting into question the current one-dimensional mathematical formalisms for time.
Anomaly detection in order to improve the quality of customer forecast data

In order to evaluate the quality of customer forecast data, anomaly detection applications are widely used. This thesis designs the most effective anomaly detection model in order to evaluate the data quality at a leading semiconductor manufacturer. First, we conducted a literature review to identify effective anomaly detection methods for time series. Additionally, evaluation measures were identified in this literature review. Secondly, two different forecasting based detection methods are compared. Our evaluation shows that anomalies can be detected with a high accuracy by both forecasting methods. The ARIMA method achieved a F1 score of 0.74, in comparison with a F1 score of 0.63 for Double Exponential Smoothing. However, Double Exponential Smoothing outperforms Autoregressive Integrated Moving Average, based on computational efficiency. Finally, the feasibility of the method at the semiconductor manufacturer is evaluated by conducting a feasibility study. This study showed three key findings: first, the method showed similar error scores on a test dataset which supports the generalizability of the method for different customer industries. Secondly, implications were identified about the total anomaly volume flagged on a weekly basis. To account for these implications, minor anomalies were excluded, which resulted in a more approachable anomaly volume. Additionally, This exclusion increased the classification performance for both forecasting methods to a F1 score of 0.75. Thirdly, the relevance of the method could be increased by taking into account the demand volumes of flagged anomalies. Based on the performance of the model and the findings from the feasibility study, we can conclude that the introduced model would be effective for Nexperia.