Miracle-dz/UniFAR-FTUs huggingface dataset:
On Undecidability of Finite Subsets Theory for Torsion Abelian Groups

Let M be a commutative cancellative monoid with an element of infinite order. The binary operation can be extended to all finite subsets of M by the pointwise definition. So, we can consider the theory of finite subsets of M. Earlier, we have proved the following result: in the theory of finite subsets of M elementary arithmetic can be interpreted. In particular, this theory is undecidable. For example, the free monoid (the sets of all words with concatenation) has this property, the corresponding algebra of finite subsets is the theory of all finite languages with concatenation. Another example is an arbitrary Abelian group that is not a torsion group. But the method of proof significantly used an element of infinite order, hence, it can’t be immediately generalized to torsion groups. In this paper we prove the given theorem for Abelian torsion groups that have elements of unbounded order: for such group, the theory of finite subsets allows interpreting the elementary arithmetic.
Faculty Behavioral Intentions in Hospitality Education: Effect of Service Quality, Service Value, Sacrifice, and Satisfaction

ABSTRACT In evaluating the service quality of hospitality education, few studies have considered the perspectives of faculty. This study measured how service quality, service value, sacrifice, and satisfaction affected faculty’s behavioral intentions. A survey was conducted with a sample of 134 faculty members from top institutes of hotel management in India. Results showed that service quality had a significant positive indirect impact and total impact on the behavioral intentions of the faculty in institutes of hotel management; that satisfaction had a significant positive direct impact on behavioral intentions, as well as a significant mediating impact on the relationship between service quality and behavioral intentions; and that service value had a significant mediating impact on the relationship between service quality and behavioral intentions. Theoretical and practical implications are discussed.
Big Data and Start-up Businesses

Start-up companies live in a dynamic and competitive environment. Many start-up companies through their formation work to solve a problem where the solution is not obvious and success of this venture is not guaranteed. The start-up company is constrained by factors such as capital, commitment, support, competition and demand. Technology has been one tool that start-ups use to add value to their offerings. Technology has many benefits such as simplifying task and generating data. The data generated may be line of business data which is transactional data or customer behavioral data. These data hold intelligence about the business and its elements which can be leveraged for many benefits. The kind of data generated depends on what technology is implemented as part of the business model (BM). The BM reflects how the organisation obtains value. It dictates the information and technology assets needed for operation. The research is a single case study of an online ecommerce store as a start-up. The start-up has six departments which are the units of analysis; the units of observation are the employees. Data was collected through face to face interviews with purposefully selected employees, the collected data were analysed to reveal findings. The findings showed that the employees had a narrowed definition of big. This is true for employees who were aware of big data as a concept; this cannot be said of other employees who were unaware of BD. The disparity paves the way to argue that the organisation does not have a big data strategy for integration or application of BD within the production environment. The study concludes by proposing a hybrid data approach which integrates a more modern approach to data management which include the use of non-relational approaches to augment data management.
Quantized Tensor Neural Network

Tensor network as an effective computing framework for efficient processing and analysis of high-dimensional data has been successfully applied in many fields. However, the performance of traditional tensor networks still cannot match the strong fitting ability of neural networks, so some data processing algorithms based on tensor networks cannot achieve the same excellent performance as deep learning models. To further improve the learning ability of tensor network, we propose a quantized tensor neural network in this article (QTNN), which integrates the advantages of neural networks and tensor networks, namely, the powerful learning ability of neural networks and the simplicity of tensor networks. The QTNN model can be further regarded as a generalized multilayer nonlinear tensor network, which can efficiently extract low-dimensional features of the data while maintaining the original structure information. In addition, to more effectively represent the local information of data, we introduce multiple convolution layers in QTNN to extract the local features. We also develop a high-order back-propagation algorithm for training the parameters of QTNN. We conducted classification experiments on multiple representative datasets to further evaluate the performance of proposed models, and the experimental results show that QTNN is simpler and more efficient while compared to the classic deep learning models.
Design and fabrication of micro rotational generators

In order to provide continuous electric powers to micro systems by using micro rotational generators as power sources,the design approach,fabrication technique and test method of a micro rotational generator were researched.Firstly,a model of three-phase and permanent magnet synchronous generator with a rotor and a stator was designed.The formulas for air density and induced Electromotive Force(EMF) were given by a magnetic circuit analysis and main design parameters of the micro generator were determined.Then,Finite Element Analysis(FEA) was used to analyze the model to obtain the waveform of induced EMF.Furthermore,the precise machining technique was used to fabricate the components of the rotor and stator and to assemble to a micro generator with a size of(Φ7×2) mm3.Finally,micro generator was tested in variations of air-gap thickness and rotation speed.Experimental results indicate that with a air-gap thickness of 300 μm and a rotation speed of 10 kr/min,the induced EMF in one phase is 26.2 mVp-p;when symmetrical loads of 0. 23Ω are connected to the generator,the sum of output power is 357.3 μW. Compared to the FEA results,the micro generator shows an ideal output,which means that the generator may be used in micro systems as the power sources.
An Animation Character Robot That Increases Sales

Performing the role of a shopping assistant is one promising application for social robots. Robot clerks can provide a richer experience for customers and increase sales; however, the scant opportunities for interaction with customers in real shopping environments is a typical drawback. We solve this problem by developing a unique networked salesclerk system that consists of a virtual agent that acts through the customer’s smartphone and a physical agent that performs as a robot salesclerk in an actual store environment. Toward this capability, in cooperation with Production I.G. Inc., an animation production company, we adopted a character named Tachikoma from “Ghost in the Shell: Stand Alone Complex” (commonly known as the S.A.C. series) when designing the appearance and features of both agents. We conducted a field test to investigate how our system contributed to the sales of Ghost in the Shell anime-themed products, and the results showed the advantages of our system for increasing sales.
A capacitor-free high PSR CMOS low dropout voltage regulator

This paper presents a capacitor-free CMOS low dropout voltage regulator which has high PSR performance and low chip area. Pole splitting and gm boosting techniques are employed to achieve good stability. The capacitor-free chip LDO was fabricated in commercial 0.18 μm CMOS technology provided by GSMC (Shanghai, China). Measured results show that the capacitor-free LDO has a stable output voltage 1.79 V, when supply voltage changes from 2.5 to 5 V, and the LDO is capable of driving maximum 100 mA load current. The LDO has high power supply rejection about −79 dB at low frequency and −40 dB at 1 MHz frequency, while sacrifice of the LDO's active chip-area is only smaller than 0.02 mm2.
DETERMINATION OF STABILITY RAY OF A DECISION MAKING UNIT IN DEFINITION OF THE RIGHT AND LEFT RETURNS TO SCALE

Data envelopment analysis (DEA) is a non-parametric technique for evaluating decision making units (DMUs) with common input and output terms that its first introduction by Charnes, Cooper and Rhodes (1978) provided, as the CCR model. One of great importance provided investigations in DEA, is the concept of returns to scale. some methods are obtained for determination this concept in DEA (Banker et al., 2004; Zarepisheh et al., 2006). With determination the right and left RTS, behavior of a o DMU , in direction of increasing and decreasing of inputs, is determined. Hadjicostas and Soteriou (2006), proposed a method for determination of these two notions in RTS, that needs solving two linear programming models. Zarepisheh and Soleimani-damaneh (2009), a new method based on the dual-simplex algorithm provided to do this. In this paper we provide models for determination alteration level of inputs and outputs of a o DMU , that the right and left its RTS is given, such that its RTS characteristic, in the RTS classification remains.
Sweat‐Permeable, Biodegradable, Transparent and Self‐powered Chitosan‐Based Electronic Skin with Ultrathin Elastic Gold Nanofibers

The simultaneous achievement of multiple functional attributes, such as flexibility, stretchability, transparency, comfortability, biodegradability, and self‐powered ability into electronic skins (e‐skins) is vital to their long‐term practical applications. Due to the internal contradiction between functional material combination and simple structural design, this kind of multifunctional e‐skin has rarely been fabricated or even reported. To this end, chitosan (CS), a natural material with remarkable biocompatibility, biodegradability, and electron‐donating ability, is integrated with a single‐mode triboelectric nanogenerator (TENG) to develop a multifunctional e‐skin, which includes sweat permeability, controllable biodegradability, high transparency, and self‐powered sensing ability. In addition, a facile, efficient, and large‐scale fabrication strategy is proposed to construct stretchable, ultrathin, transparent, and shape‐adaptable gold nanofibers (Au NFs) electrodes. Furthermore, the e‐skin can achieve a voltage response pressure sensitivity of 0.012 kPa−1 in the pressure range of 0–70 kPa and a fast response time of 70 ms. Finally, it shows controllable and excellent degradability in various solutions. It is believed that the proposed e‐skin based on the design and integration of CS and Au NFs will provide a paradigm shift for the next‐generation self‐powered transient electronics.
The Impact of Organizational Culture and Work Motivation on Employee Performance through Employee Satisfaction of Air Mas Perkasa

The goal of this study is to look at the direct and indirect effects of organizational culture and job motivation on employee performance. A total of 120 employees were sampled. The company culture has an impact on employee performance, as according data analysis approaches employing SEM. Employee motivation has a positive impact on their performance. Employee job satisfaction is positively influenced by organizational culture. Employee work satisfaction has a favorable correlation with work motivation. Job satisfaction has a favorable impact on Air Mas Perkasa's staff performance. The corporation should always maintain and harmonize the function of organizational culture in the life of corporate activities, focusing on the company's interests and objectives in order to increase employee performance and be sensitive to factors that can improve work motivation.
Parametric HBIM Procedure for the Structural Evaluation of Heritage Masonry Buildings

In the architecture, engineering and construction sectors, Building Information Modeling (BIM)-based procedures have become adopted more and more. In fact, the development of suitable BIM models facilitates the management of the design and construction phases and improves the efficiency of the maintenance policies during the life cycle of the building. Although the BIM method is mostly implemented in the building industry for new constructions, in recent years, the deployment of this technology has also attracted increasing attention for existing structures to rebuild their geometry and gather relevant data, especially for historical buildings; in this case, we refer to Heritage BIM (HBIM). A HBIM procedure requires a multidisciplinary approach involving not only historical, conservation, and restoration considerations but also suitable maintenance and repair plans, duly balancing the structural needs with the preservation of the historical value of the building and its content. Although the integration of the structural assessment in the HBIM process would be highly beneficial, its practical implementation is often in the early stages. In the paper, an original parametric procedure for the assessment of existing masonry buildings is proposed in the BIM environment. The procedure combines E-PUSH, a software program for the structural analysis of masonry structures under seismic and non-seismic actions with an appropriate BIM approach, so improving the management of gathered data through cognitive phases. The assessment process is, thus, simplified since data required for the structural analysis are directly retrieved from the BIM model, and the structural analyses and verifications are performed without using external programs. The proposed BIM workflow is finally illustrated and discussed referring to a relevant case study, the seismic vulnerability assessment of the “Bernardo Rucellai” school in Florence (Italy).
In-and-Out: a data augmentation technique for computer vision tasks

Abstract. This study focuses on the over-fitting problem in the training process of the deep convolutional neural network model and the problem of poor robustness when the model is applied in an occlusion environment. We propose a unique data augmentation method, In-and-Out. First, the information variance is enhanced through dynamic local operation while maintaining the overall geometric structure of the training image; compared with the global data augmentation method, our method effectively alleviates the overfitting problem of model training and significantly improves the generalization ability of the model. Then through the dynamic information removal operation, the image is hidden according to the dynamic patch generated by multiple parameters. Compared with other information removal methods, our method can better simulate the real-world occlusion environment, thus improving the robustness of the model in various occlusion scenes. This method is simple and easy to implement and can be integrated with most CNN-based computer vision tasks. Our extensive experiments show that our method surpasses previous methods on the Canadian Institute for Advanced Research dataset for image classification, the PASCAL Visual Object Classes dataset for object detection, and the Cityscapes dataset for semantic segmentation. In addition, our robustness experiments show that our method has good robustness to occlusion in various scenes.
Quantifying the Thermomechanical Response of Bitumen from Microphase Properties

The macroscopic mechanical response properties of bituminous materials originate from the mechanical properties at the microstructural level. From atomic force microscopy (AFM) investigations, it is evident that mainly two material phases are present in bitumen; these phases can be loosely associated with bitumen’s chemical composition (i.e., crude oil origin). However, little is known about the mechanical properties of the constituent phases of bitumen. In this research, an AFM technique was used to obtain mechanical property maps of two bitumens. This technique can distinguish between phases and provide quantitative results. The mechanical properties at the nano- to micrometer-length scale govern the overall properties of bitumen when considered as a microscale composite material. A mechanics approach is followed to derive the composite modulus from the individual phase properties. Furthermore, the temperature dependence of mechanical properties is determined on heating the bitumens from ambient conditions. With an increase in temperature, the moduli of both phases decrease, whereas the phases become more adhesive. The results demonstrate a successful quantitative characterization of the mechanical properties of bitumen microphases and the subsequent coarse graining of these properties into composite mechanical response properties. These mechanical properties (i.e., stiffness and adhesion potential) are important input parameters for material design and modeling and will allow one to predict the macroscopic behavior of asphalt concrete according to fundamental quantities. Finally, a better understanding of the temperature dependence of microstructural mechanical properties can contribute to the understanding of the thermorheological properties of bitumen for optimal processing conditions and best performance.
A Comprehensive Investigation of the Structural, Thermal, and Biological Properties of Fully Randomized Biomedical Polyesters Synthesized with a Nontoxic Bismuth(III) Catalyst

Aliphatic polyesters are the most common type of biodegradable synthetic polymer used in many pharmaceutical applications nowadays. This report describes the ring-opening polymerization (ROP) of l-lactide (L-LA), ε-caprolactone (CL) and glycolide (Gly) in the presence of a simple, inexpensive and convenient PEG200-BiOct3 catalytic system. The chemical structures of the obtained copolymers were characterized by 1H- or 13C-NMR. GPC was used to estimate the average molecular weight of the resulting polyesters, whereas TGA and DSC were employed to determine the thermal properties of polymeric products. The effects of temperature, reaction time, and catalyst content on the polymerization process were investigated. Importantly, the obtained polyesters were not cyto- or genotoxic, which is significant in terms of the potential for medical applications (e.g., for drug delivery systems). As a result of transesterification, the copolymers obtained had a random distribution of comonomer units along the polymer chain. The thermal analysis indicated an amorphous nature of poly(l-lactide-co-ε-caprolactone) (PLACL) and a low degree of crystallinity of poly(ε-caprolactone-co-glycolide) (PCLGA, Xc = 15.1%), in accordance with the microstructures with random distributions and short sequences of comonomer units (l = 1.02–2.82). Significant differences in reactivity were observed among comonomers, confirming preferential ring opening of L-LA during the copolymerization process.
Nowcasting tracks of severe convective storms in West Africa from observations of land surface state

In tropical convective climates, where numerical weather prediction of rainfall has high uncertainty, nowcasting provides essential alerts of extreme events several hours ahead. In principle, short-term prediction of intense convective storms could benefit from knowledge of the slowly evolving land surface state in regions where soil moisture controls surface fluxes. Here we explore how near-real time (NRT) satellite observations of the land surface and convective clouds can be combined to aid early warning of severe weather in the Sahel on time scales of up to 12 h. Using land surface temperature (LST) as a proxy for soil moisture deficit, we characterise the state of the surface energy balance in NRT. We identify the most convectively active parts of mesoscale convective systems (MCSs) from spatial filtering of cloud-top temperature imagery. We find that predictive skill provided by LST data is maximised early in the rainy season, when soils are drier and vegetation less developed. Land-based skill in predicting intense convection extends well beyond the afternoon, with strong positive correlations between daytime LST and MCS activity persisting as far as the following morning in more arid conditions. For a Forecasting Testbed event during September 2021, we developed a simple technique to translate LST data into NRT maps quantifying the likelihood of convection based solely on land state. We used these maps in combination with convective features to nowcast the tracks of existing MCSs, and predict likely new initiation locations. This is the first time to our knowledge that nowcasting tools based principally on land observations have been developed. The strong sensitivity of Sahelian MCSs to soil moisture, in combination with MCS life times of typically 6–18 h, opens up the opportunity for nowcasting of hazardous weather well beyond what is possible from atmospheric observations alone, and could be applied elsewhere in the semi-arid tropics.
Voice-Enabling the Data Network: H.323, MGCP, SIP, Q.S, SLAs, and Security

From the Publisher: Assist Telco and ISP engineers and technicians in their transition to IP telephony Assists traditional telco engineers and technicians in designing, implementing, and supporting VoIP networks Provides a step-by-step approach to designing a VoIP network Outlines each VoIP technology components with detailed descriptions of possible applications Provides a thorough description of Voice over IP for those new to the technology Voice over IP (VoIP) is a fast-growing technology within telcos and ISPs. VoIP is significantly reducing the cost of transmitting telephone calls and enabling small companies to enter the +$50 billion marketplace. Many telcos and ISPs are entering this market while VoIP is still in its infancy. As a result, there is an urgent need for telco and ISP engineers to learn this new technology. This book will assist telco engineers to transition to IP networking with a focus on VoIP networks. This book introduces the fundamentals of IP networking as it applies to transmitting voice calls using IP packets. Key telco-based technologies such as SS7 are reviewed. A unique approach is that the book outlines each component of VoIP technology while describing the application of the technology. This is accomplished by describing key steps in designing an operational VoIP network, thereby enabling the reader to understand how this technology is applied.
Security Assessment for the Islanding Transition of Microgrids

Grid-connected microgrids pose a risk of unsuccessful transition to islanded operation due to unintentional islanding. The success of this transition is influenced by different factors, such as the operating point, the controls, and the islanding detection scheme. This work proposes a methodology to access the probability of a successful transition of a microgrid from grid-connected to islanded mode. The methodology is based on Monte Carlo simulations using probabilistic curves for representing the operating point of the distributed energy resources, loads, and storage devices. The method is validated by performing an analysis of the islanding transition of a hybrid RE-storage-diesel microgrid, either employing a Battery Energy Storage System (BESS) or Flywheel Energy Storage (FES). The use of the proposed method allowed an assessment of the influence of the capacity of the storage units on the success of the islanding transition.