Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual-Stator Five-Phase Permanent Magnet Synchronous Machine with Hybrid Spoke-Vernier Type Rotor for Electric Vehicles

This paper proposes a dual-stator five-phase permanent magnet synchronous motor with hybrid spoke-vernier type rotor for electric vehicles (EVs) traction applications. The proposed machine can achieve high torque density, high-power factor, and wide speed range operation with a hybrid spoke-vernier type PM rotor. The first design (Initial Design) consists of an outer stator having twenty slots that carry a four-pole, five-phase concentrated winding with an inner spoke-type PM rotor supported by a magnetic hub. The latter is utilized in the second design (2nd Design) to house a set of flux barriers and additional pieces of PMs arranged in a vernier type configuration, to obtain a hybrid rotor structure. From the 2nd Design, the proposed machine (Proposed Design) is obtained. The proposed design is equipped with a second inner stator core that also houses a four-pole, five-phase concentrated winding. The three machines were modelled for ac magnetic transient analysis using the 2-D Finite Element Method (FEM). The influence of different electromagnetic parameters on the traction performance of the proposed machine is evaluated. The Finite Element Analysis (FEA) results demonstrate the effectiveness of the proposed machine, which has positioned itself to be a strong candidate in EVs traction applications.
Implementation of Balanced Scorecard in Improving Private Universities’ Performance

The role of private universities in improving quality must accommodate all aspects of the performance of the tri dharma of higher education. The rapid development of higher education institutions must be ready to change. Changes in the global business environment also trigger the increasing intensity of competition between higher education service providers so that each higher education service provider will try to offer high-performance higher education services. This research aims to see how far the implementation of BSC is in improving the performance of private universities. Specifically, this study describes four perspectives, including (1) financial perspective, (2) customer perspective, (3) internal business process perspective (4) learning and growth perspective. They were using the theory of Robert Kaplan and David Norton. This study uses a descriptive method with a qualitative approach and data collection by conducting observations, interviews, and documentation studies. The research results indicate that the BSC implementation has been applied in a systematic, measurable, and planned manner so that the Vision, Mission, and objectives are achieved. In an implementation, there is a balance between financial and non-financial.
Prognosis of Epileptic Seizure Event Onsets Using Random Survival Forests

Abstract This article introduces a machine learning approach, based on a nonparametric, decision-tree-based random survival forest (RSF) model, for a continuous prognosis of epileptic seizure events using electroencephalogram (EEG) data. While earlier seizure prediction methods forecast seizure occurrences only at a specified future time, the RSF model allows estimation of the probability of seizure onset, in terms of a hazard function, over the entire prediction horizon. These estimates are crucial for developing individualized quantitative risk measures and management plans for epilepsy patients. Additionally, RSF can identify the key risk factors by capturing the interdependencies among the features extracted from EEG data. The performance of RSF was evaluated for prognosing seizure onsets of the rat and mice specimens in an 80 small animals cohort at the Texas A&M Department of Neuroscience and Experimental Therapeutics. The results suggest that RSF outperforms other contemporary survival models, including the popular Cox proportional hazard, with 87.5% lower integrated Brier Score (IBS) errors, and 17.5% higher concordance index (C-index). Further, a continuous seizure prediction sensitivity of 83% and specificity of 87% were obtained even over a 5-min prediction horizon (the average time between successive seizure onsets was 5 min long). These results suggest that the RSF model can be used to effectively quantify the likelihood of seizure onsets over time to the patients and caregivers, promoting informed decision making.
Physiological Justification of Udder Emptying in Highly Productive Cows

This study encompasses to measure frequency of physiologically motivated emptying the udder by Holstein cows during lactation. With the recording of the hours of the day, the experiment shows that the first-born cows emptied the udder, on average, almost three times a day, the cows having second lactation emptied 2.5 times a day, and cows having 3rd and 4th lactations emptied udder three or more times a day. Analysis of the daily attending a robotic milking machine during lactation by first-born cows showed that the visitation frequency was relatively even, except for the maximum peaks from 11.00 to 12.00 o’clock and from 21.00 to 23.00 o’clock. Similarly, among the cows of the 2nd lactation there were two maximum peaks of milking or udder emptying – at night from 22.00 to 1.00 o'clock and in the day from 12.00 to 17.00 o'clock. For cows having 3rd lactation, there were two maximum peaks of udder emptying - one from 21.00 to 1.00 o'clock at night, and the second from 12.00 to 17.00 o'clock in the afternoon. Cows under 4th lactation had three relatively high maxima: the first from 1.00 to 4.00 o'clock at night, the second from 10.00 to 12.00 o'clock in the afternoon, and the third from 18.00 to 22.00 o'clock at night. This study concludes that high milk productivity of cows needs emptying (milking) of the udder at least three times a day. Since almost a third of udder emptying occurs at night, at that time it is necessary to provide animals with adequate water and feed on the feed table.
A Study in the Variation of <i>G</i>

There are indications that the Newtonian gravitational constant may not be a constant but may vary with respect to some other physical parameter. Various possible characterizations of Newton’s gravitational parameter as a function of the cosmic scale parameter are proposed and studied within the frame-work of classical Newtonian cosmology. A number of toy cosmologies with varying Newtonian gravitational parameters are developed and analyzed. The numerical solutions to the temporal evolution of the universe from the Friedmann equation are examined and discussed as well as kinematic observables. Finally, other avenues of research are addressed.
A Recurrent Adaptive Network: Balanced Learning for Road Crack Segmentation with High-Resolution Images

Road crack segmentation based on high-resolution images is an important task in road service maintenance. The undamaged road surface area is much larger than the damaged area on a highway. This imbalanced situation yields poor road crack segmentation performance for convolutional neural networks. In this paper, we first evaluate the mainstream convolutional neural network structure in the road crack segmentation task. Second, inspired by the second law of thermodynamics, an improved method called a recurrent adaptive network for a pixelwise road crack segmentation task is proposed to solve the extreme imbalance between positive and negative samples. We achieved a flow between precision and recall, similar to the conduction of temperature repetition. During the training process, the recurrent adaptive network (1) dynamically evaluates the degree of imbalance, (2) determines the positive and negative sampling rates, and (3) adjusts the loss weights of positive and negative features. By following these steps, we established a channel between precision and recall and kept them balanced as they flow to each other. A dataset of high-resolution road crack images with annotations (named HRRC) was built from a real road inspection scene. The images in HRRC were collected on a mobile vehicle measurement platform by high-resolution industrial cameras and were carefully labeled at the pixel level. Therefore, this dataset has sufficient data complexity to objectively evaluate the real performance of convolutional neural networks in highway patrol scenes. Our main contribution is a new method of solving the data imbalance problem, and the method of guiding model training by analyzing precision and recall is experimentally demonstrated to be effective. The recurrent adaptive network achieves state-of-the-art performance on this dataset.
TLBO Tuned a Novel Robust Fuzzy Control Structure for LFC of a Hybrid Power System with Photovoltaic Source

: This study proposes a new fuzzy logic control (FLC) design-based I controller plus a Fuzzy Cascade FOPI-FOPD (I + F C FOPI-FOPD) for load frequency control (LFC) in power systems. The structure of this design offers a satisfactory level of reliability as well as excellent robustness performance. The proposed fuzzy design is employed in a hybrid dual area power system based on a photovoltaic renewable energy plant in area one and a thermal generation unit in area two. In order to achieve the best possible dynamic performance of the proposed structure, the teaching learning-based optimization (TLBO) algorithm is suggested to optimally tune the scaling factor gains of the proposed fuzzy conﬁguration. The superiority of the suggested fuzzy control design is investigated by conducting a comparative study between this design and a previously applied PI-based ﬁreﬂy algorithm. Simulation results revealed that the fuzzy logic controller introduced in this study is reliable and superior, and appropriately handled the problem of frequency variation.
A New Ocean State After Nuclear War

Nuclear war would produce dire global consequences for humans and our environment. We simulated climate impacts of US‐Russia and India‐Pakistan nuclear wars in an Earth System Model, here, we report on the ocean impacts. Like volcanic eruptions and large forest fires, firestorms from nuclear war would transport light‐blocking aerosols to the stratosphere, resulting in global cooling. The ocean responds over two timescales: a rapid cooling event and a long recovery, indicating a hysteresis response of the ocean to global cooling. Surface cooling drives sea ice expansion, enhanced meridional overturning, and intensified ocean vertical mixing that is expanded, deeper, and longer lasting. Phytoplankton production and community structure are highly modified by perturbations to light, temperature, and nutrients, resulting in initial decimation of production, especially at high latitudes. A new physical and biogeochemical ocean state results, characterized by shallower pycnoclines, thermoclines, and nutriclines, ventilated deep water masses, and thicker Arctic sea ice. Persistent changes in nutrient limitation drive a shift in phytoplankton community structure, resulting in increased diatom populations, which in turn increase iron scavenging and iron limitation, especially at high latitudes. In the largest US‐Russia scenario (150 Tg), ocean recovery is likely on the order of decades at the surface and hundreds of years at depth, while changes to Arctic sea‐ice will likely last thousands of years, effectively a “Nuclear Little Ice Age. ” Marine ecosystems would be highly disrupted by both the initial perturbation and in the new ocean state, resulting in long‐term, global impacts to ecosystem services such as fisheries.
Molecular Characterization and Antimicrobial Resistance of Pathogenic Escherichia coli Strains in Children from Wolaita Sodo, Southern Ethiopia

Introduction Pathogenic Escherichia coli strains cause diarrheal infection in children due to their virulence factors. A nonanalytical observational study followed by a purposive sampling technique was conducted from October 2017 to June 2018, to determine the antimicrobial susceptibility patterns and molecularly detect pathogenic Escherichia coli strains in under-five children at Wolaita Sodo town using molecular and the Kirby-Bauer disc diffusion method. Result In the current investigation, out of 110 stool samples, Escherichia coli was isolated in 68 (61.8%) (95% CI: 52.1–70.9%). Out of 68 Escherichia coli isolates, 61.9% of Escherichia coli isolates were resistant, 9.4% were intermediately resistant, and 28.7% were susceptible. Among the antimicrobial agents, 91.2% of Escherichia coli isolates were highly sensitive to ciprofloxacin followed by norfloxacin (86.8%). Virulence genes were detected in 55.9% (38/68) (95% CI 52.1%–70.9%) of isolates. The following genes were detected: stx1 (6 (8.8%)), stx2 (4 (5.9%)), eaeA (5 (7.4%)), eaat (14 (20.5%)), and St (4 (5.9%)). Out of 68 Escherichia coli isolates, 43 (63.2%) isolates had shown multidrug resistance patterns. Furthermore, 11.8% of Escherichia coli isolates had shown resistance to eight different drugs. The multidrug resistance index value of Diarrheagenic Escherichia coli pathotypes was greater than or equal to 0.4, which indicates the high risk of resistance. Conclusion This study demonstrated important pathogenic Escherichia coli strains and multidrug resistance in isolates containing virulence genes. Wise use of antimicrobials and improving the hygienic practices amongst parents of children reduce its occurrence. Therefore, appropriate usage of antimicrobial agents should also be highly practiced in hospitals.
Privatisation of Prisons in India: Possibilities and Challenges

Privatisation of prisons refers to the contracting out by the government the operations of prisons to private companies. ‘Private prisons’ are not private sector prisons but (state) prisons run on contract for the responsible government department by a private sector company (Ramsbotham, 1995). Public vs. private debate on prison institution in India is gaining much attention among scholars, policymakers, civil-society organisations and peoples’ representatives as well. Advocates of privatisation argue that private suppliers deliver public services at a lower cost than public employees do (Logan, 1990; Savas, 1982). On the other hand, critics of government contracting quibble that quality of public service provided by private contractors is inferior in comparison to the service delivered by the government employees (Hart et al., 1997; Shichor 1995). Privatisation of prisons has triggered heated discussion over the cost and quality in the incarceration process (Donahue, 1988). Quality inside the prisons covers many aspects, for instance, living conditions (physical health conditions, mental and emotional conditions of prisoners, availability of medical facilities to prisoners, accessibility to educational facilities, accessibility to recreation facilities and conditions of prison labour, etc.), prisoners’ accessibility to legal aid facility, implementation of speedy trial, sanction of parole facility to prisoners, sanction of bail to prisoners, prisoners’ access to judgment, grievance redressal mechanism and functioning of prison panchayat, and so on. Prisoners’ right against violence includes (prison offense and punishment, judicious use of handcuff and security inside the prison, etc.). The major objection against privatisation of prisons is that private contractors will engage untrained and unqualified prison guards and low quality goods to save cost and thereby jeopardise prison safety, security and health issues of prisoners. Hence, through this Note, the author tries to highlight the problems and prospects of privatisation of prison in India. While discussing about privatisation of prison, the most pertinent question is to what extent government can entrust the private companies to deliver services? Not all, but some essential facilities in prisons can be privatised or handed over Note
PowerShell Malware Analysis Using a Novel Malware Rating System

Recent high-profile cyberattacks highlight an increased use of social engineering attacks and ransomware by hackers worldwide. These attacks target human operators directly, bypassing many of the cyber-safeguards developed through years of malware analysis. In response to these challenges, many organizations have turned to white-hat hackers and penetration testing to identify potential weaknesses and reinforce cyber-safety protocols. To assist in the threat evaluation process, malware rating systems are often used to highlight the danger and potential damage malware may cause. Current malware rating systems focus on assigning a danger score for malware based on its ability to move throughout the network, damage system resources, and evade detection. Due to the increased reliance on social engineering, a new malware rating system is proposed that incorporates malware deceitfulness as a means to trick human operators. The novel rating system will score malware based on its Stealth, Ease of Creation, Deceitfulness, Versatility, Instantaneousness, and Persistence. This system provides operators with insight into each key characteristics as opposed to a single value. To showcase the malware evaluation process, different PowerShell Reverse Bind Shell malwares are rated based on the proposed criteria.
Integration of Thermal and RGB Data Obtained by Means of a Drone for Interdisciplinary Inventory

Thermal infrared imagery is very much gaining in importance in the diagnosis of energy losses in cultural heritage through non-destructive measurement methods. Hence, owing to the fact that it is a very innovative and, above all, safe solution, it is possible to determine the condition of the building, locate places exposed to thermal escape, and plan actions to improve the condition of the facility. The presented work is devoted to the technology of creating a dense point cloud and a 3D model, based on data obtained from UAV. It has been shown that it is possible to build a 3D point model based on thermograms with the specified accuracy by using thermal measurement marks and the dense matching method. The results achieved in this way were compared and, as the result of this work, the model obtained from color photos was integrated with the point cloud created on the basis of the thermal images. The discussed approach exploits measurement data obtained with three independent devices (tools/appliances): a Matrice 300 RTK drone (courtesy of NaviGate); a Phantom 4 PRO drone; and a KT-165 thermal imaging camera. A stone church located in the southern part of Poland was chosen as the measuring object.
Research on the Effect of Narcissistic Leadership on Employee Job Embeddedness

Narcissistic leadership is the synthesis of narcissistic personality traits and leadership behaviors that are motivated mainly by self-interest needs and arrogant beliefs. Such leadership style has multiple effects on organizations and employees. The amplifying influence of narcissistic leadership on their subordinates has become a hot topic in the field of organizational behavior. Based on the social exchange theory and the resource conservation theory, the current study constructs a chain mediation model of narcissistic leadership affecting employees’ job embeddedness with 405 corporate employees as survey respondents. The results of data analysis show that narcissistic leadership is significantly and negatively related to employees’ job embeddedness; Leader-member exchange (LMX) and perceived insider status not only play a mediating role between narcissistic leadership and job embeddedness but also play a chain mediating roles in the relationship between narcissistic leadership and job embeddedness. Our findings deepen the theoretical exploration of narcissistic leadership and help all types of organizations to improve their leadership practices.
Diffusion capacity for carbon monoxide in patients recovered from COVID-19: A useful parameter to assess pulmonary function

Diffusion capacity of the lungs for carbon monoxide (DL CO ), in combination with spirometry and lung volumes, is indicated to evaluate the parenchymal and non‑parenchymal lung diseases. The preoperative DL CO is not routinely measured in patients in most cardiac surgery units. DL CO is used to assess the severity of obstructive and restrictive lung diseases, pulmonary vascular disease, and preoperative risk. The patients surviving coronavirus disease‑2019 (COVID‑19) are frequently reported to have pulmonary sequelae. [1] We recently encountered five patients admitted for cardiac surgery, who had contracted COVID‑19 about 6–8 months ago. Preoperative assessment of the pulmonary function included chest X‑ray, high‑resolution computed tomography of the chest, arterial blood gas analysis at room air, and spirometry with DL CO measurement. The DL CO was measured by SPIRO AIR ® volumetric PFT (pulmonary function test) System (Medisoft, Sorinnes, Belgium) using a single‑breath technique. The patients’ details and PFT values are shown in Table 1. There was severe impairment of DL CO and alveolar volume (Va) values (37% and 45% of predicted, respectively) in patient number one. The patient was referred for pulmonary rehabilitation and optimization of medical therapy, and was asked for a follow‑up at 3 months. The remaining four patients with normal DL CO values underwent cardiac surgery uneventfully.
Biochemical and Kinetic Characterization of the Glucose-6-Phosphate Dehydrogenase from Helicobacter pylori Strain 29CaP

Helicobacter pylori (H. pylori) has been proposed as the foremost risk factor for the development of gastric cancer. We found that H. pylori express the enzyme glucose-6-phosphate dehydrogenase (HpG6PD), which participates in glucose metabolism via the pentose phosphate pathway. Thus, we hypothesized that if the biochemical and physicochemical characteristics of HpG6PD contrast with the host G6PD (human G6PD, HsG6PD), HpG6PD becomes a potential target for novel drugs against H. pylori. In this work, we characterized the biochemical properties of the HpG6PD from the H. pylori strain 29CaP and expressed the active recombinant protein, to analyze its steady-state kinetics, thermostability, and biophysical aspects. In addition, we analyzed the HpG6PD in silico structural properties to compare them with those of the HsG6PD. The optimal pH for enzyme activity was 7.5, with a T1/2 of 46.6 °C, at an optimum stability temperature of 37 °C. The apparent Km values calculated for G6P and NADP+ were 75.0 and 12.8 µM, respectively. G6P does not protect HpG6PD from trypsin digestion, but NADP+ does protect the enzyme from trypsin and guanidine hydrochloride (Gdn-HCl). The biochemical characterization of HpG6PD contributes to knowledge regarding H. pylori metabolism and opens up the possibility of using this enzyme as a potential target for specific and efficient treatment against this pathogen; structural alignment indicates that the three-dimensional (3D) homodimer model of the G6PD protein from H. pylori is different from the 3D G6PD of Homo sapiens.
Prognostic Value of Association of Copy Number Alterations and Cell-Surface Expression Markers in Newly Diagnosed Multiple Myeloma Patients

Multiple myeloma results from the clonal proliferation of abnormal plasma cells (PCs) in the bone marrow (BM). In this study, the cell surface expression markers (CD) on atypical PCs (detected by multiparametric flow cytometry (MFC)) were correlated with copy number alterations (CNAs) in the genome (detected by multiplex ligation-dependent probe amplification (MLPA)) to assess their impact on prognosis in newly diagnosed MM patients. Statistically significant results were obtained when different stages of PC maturation (classified based on CD19 and CD81 expression) were associated with CD117 expression and identified CNAs. In the intermediately differentiated PC group (CD19(−) CD81(+)), patients who didn’t express CD117 had a lower median progression free survival (PFS) (p = 0.024). Moreover, within this group, patients with less than three adverse CNAs, which harbor CD117, had a better outcome with a PFS of more than 48 months compared with 19 months (p = 0.008). Considering all the results, our study suggested the need to integrate both the CD markers and copy number alterations to evaluate the prognosis of newly diagnosed multiple myeloma patients.