Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of the State, Directions and Problems of the Development of the Catering Market of the Region

The article is devoted to the analysis of the dynamics of indicators of the public catering market of the Belgorod region of Russia. The aim of the article is to analyze the directions and problems of development, identify positive and negative factors, prospects for the development of the regional market of public catering in the region. The analysis method was used to study the dynamics of quantitative indicators of the regional catering market based on static data. The sociological method reveals the factors that affect the regional public catering market. SWOT analysis was used to assess the potential of the region's catering market. The development of the public catering market in the region will allow us to achieve the most complete satisfaction of the needs of the population. To ensure the effective development of the industry's infrastructure, it is necessary to create favorable conditions for the growth of business activity. The main problems of development of public catering on the example of the market of the Belgorod region of Russia are revealed. It is established that the low level of use of modern technologies causes a significant lag in the public catering market from other industries. The directions of development of the regional public catering market are suggested. Keywords—regional catering market, the catering market, catering, the development of catering market, the indicators of the catering market, factors market catering.
Senputing: An Ultra-Low-Power Always-On Vision Perception Chip Featuring the Deep Fusion of Sensing and Computing

Always-on intelligent visual perception applications are widely deployed in edges in the AIoT era. In order to eliminate power costs of data conversion and transmission, this paper proposes Senputing, an ultra-low-power processing-in-sensor chip that completely fuses sensing and computing together for a BNN-based hierarchical processing system. This chip could operate in two modes. In computation mode, photocurrents are directly utilized for computing without being converted into voltages, and the computation results of 1-st BNN layer are directly sent out to subsequent BNN processors for an always-on coarse classification, eliminating conversion power and storage cost of raw images. Once an interested objected is detected, this chip switches to sensor mode and sends raw images to potential full-precision processors or cloud servers for fine-grained recognition or segmentation. A <inline-formula> <tex-math notation="LaTeX">$32\times 32$ </tex-math></inline-formula> prototype is fabricated with 180nm CMOS process. It accomplishes MNIST dataset classification task with the accuracy of 93.76% and the power consumption of 147nW at 156fps, achieving <inline-formula> <tex-math notation="LaTeX">$13.1\times $ </tex-math></inline-formula> energy efficiency compared with state-of-the-art work.
Using author-devised cover letters instead of instructor-devised rubrics to generate useful written peer feedback comments

This study uses both qualitative and quantitative research methods in a mixed-methods approach to investigate whether the principled use of author-devised cover letters (CLs) within doctorate writing groups can result in more useful reviewer feedback comments than would be attained through the use of instructor-devised writing assessment rubrics. In this context, CLs are self-devised written documents that help the reviewers give the author useful and critical written feedback comments. Twenty participants in different discipline-specific writing groups were given explicit instruction about the importance and content of CLs during the peer feedback process. Their perceptions of a useful CL were obtained from post-course questionnaires and analysed qualitatively. In addition, their CLs at various stages of the feedback process were analysed quantitatively for genre, social presence, and evidence of teaching instruction, and compared to the CLs produced by 20 PhD students in similar writing groups who received minimal CL instruction. The study found that author-devised CLs, as opposed to instructor-devised rubrics, can allow the authors the flexibility of providing text-specific background details, requesting reviewer help on specific textual aspects, using social presence to develop a sense of writing community, and provide reflection upon their own writing.
Effects of Different Aboveground Structural Parts of Grass Strips on the Sediment-Trapping Process

Grass strips can decrease erosion, trap sediment in silt-laden water flowing downhill, and control nonpoint source pollution. Determining the effects of different parts of grass strips on silt-laden overland flow will improve our understanding of sediment trapping by grass strips with different structures. Sediment trapping by grass strips was studied using a 5° slope, 30 L min−1 m−1 flow rate, 120 g L−1 sediment concentration, and different aboveground components of grass strips (complete grass, removed green grass, and removed green and withered grass). The whole overland flow process was monitored. Meanwhile, the runoff sediment samples at the outlet were collected and measured. Sediment trapping by aboveground grass parts was quantified at different stages. Of the soil bed surface, green grass, and withered grass, the soil bed surface dominated sediment trapping in the initial stage of the sediment-trapping process, contributing about 90% of total sediment deposition in the first 5 min. As the sediment-trapping process continued, the effect of the soil bed surface weakened, and the green grass played a major role at the later stage of sediment trapping. The ratio of the soil bed surface, green grass, and withered grass contributions to total sediment deposition at the stable stage of the experiments was approximately 3:5:2. The results will help assess the effects of vegetation restoration on sediment transport in entire watersheds.
A More Sustainable Management of Domestic Tourists in Protected Natural Parks: A New Trend in Sport Tourism after the Covid-19 Pandemic?

One of the consequences of the Covid-19 pandemic crisis has been the development of proximity tourism in outdoor spaces being less conducive to the spread of the virus. From a study preceding this pandemic, this article seeks to better understand the experiences lived by domestic tourists from when they visited two typical protected natural parks as specific experiential contexts (extraordinary versus ordinary) providing different experiences. Each experiential context enables the distinction of actual visitors’ experiences inside each park—education, esthetics, entertainment, escapism, physical activity—differentiated, on one hand by the visitors’ participation axis and, on the other hand, by the absorption-immersion axis influencing the visitors’ arousal and memory. A structural equation model tested the data collected (n = 1000) in both experiential contexts and shows their moderator effect. The results underline the link between the experiential context and the actual experiences and highlight the interest of a new global framework including the visitors’ participation and a bodily axis relative to the specific context. This research could help managers of protected natural parks adjust their domestic tourists’ experience offer during pandemic crises by implementing specific sustainable and sanitary strategies.
Estimation of shale volume from well logging data using Artificial Neural Network

The existence of shale has a major effect on reservoir quality because it reduces the rock’s both the porosity and permeability. There are several types of shale, and they can be distributed in the sand in four different ways: laminated, structural, dispersed, or any combination of these. Each of them has various features and physical properties. Therefore, shale volume estimation is one of the most important and challengin tasks to be solved information evaluation. There are many equations proposed to calculate shale volume from Gamma - ray log; however, none of them could be considered the best method that can be applied to all case studies. This study aims to propose a new approach to estimate shale volume from well - logging data. Gamma - ray and other logs were used as input data for an artificial neural network (ANN) to predict the shale volume. We apply this technique to the 1143 data set of the ocean drilling program (ODP) in the East Sea. The authors compared the result to core data and recognized that utilization of several logs and ANN gives a better estimation than conventional methods (more accurate and can reflect the trend of actual shale volume).
A mathematical model for scheduling of transportation, routing, and cross-docking in the reverse logistics network of the green supply chain

Cross-docking refers to the practices of unloading materials from inbound vehicles and then loading them directly into outbound ones. Removing or minimizing warehousing costs, space requirements, as well as inventory utilization, cross-docking simplifies supply chains and makes them deliver goods to markets in a faster and more efficient manner. Accordingly, a mixed-integer linear programming ($MILP $) model is developed in the present study to schedule transportation routing and cross-docking in a reverse logistics network ($RLN$). Furthermore, different traffic modes are also considered to reduce fuel consumption, which reduces emissions and pollution. The proposed model is a multi-product, multi-stage, and non-deterministic polynomial-time that is an NP-hard problem. We use the non-dominated sorting genetic algorithm II ($NSGA-II$) to solve the model. A numerical example has been solved to illustrate the efficiency of the method.
Fuzzy‐based adaptive event‐triggered control for switched stochastic nonlinear systems with state constraints

In this paper, a fuzzy‐based control strategy is devised for switched stochastic nonlinear systems with state constraints. All states are guaranteed to maintain the prescribed regions whereby barrier Lyapunov function. To mitigate the data transmission and save resource, the event‐triggered mechanism (ETM) is exploited to update control signal. To further deal with system uncertainties, the fuzzy logic systems are taken as function approximators. By combining the backstepping technique with event‐triggered mechanism, the adaptive fuzzy control (AFC) strategy is recursively established. In virtue of Lyapunov theory and average dwell time method (ADT), the devised AFC method can guarantee that the entire closed‐loop systems are uniformly bounded and the prescribed state constraints are not violated. Finally, the validity of the developed method is illustrated via a simulation example.
Bright Ring Lattice Generated by Nesting Spiral-Pinhole-Arrays Plate

A nesting spiral-pinhole-arrays plate (NSP) is designed to produce bright ring lattice (BRL) formed by the superposition of two different orbital angular momentum (OAM) modes. The OAM modes of the BRL can be manipulated by the propagation distance and the spiral arrays of the NSP. We have studied the influences of rotating NSP on the BRL, and found that the rotational angle and direction of the BRL are not consistent with the results of the NSP.
Simulation of the Compressive Strength of Cemented Tailing Backfill through the Use of Firefly Algorithm and Random Forest Model

Cemented tailings backfill is widely used in worldwide mining areas, and its development trend is increasing due to the technical and economic benefits. However, there is no reliable and simple machine learning model for the prediction of the compressive strength. In the present study, the research process to use artificial intelligence algorithms to predict the compressive strength of cemented tailing backfill was conducted, overcoming the shortcomings of traditional empirical formulas. Experimental tests to measure the compressive strength of cemented tailing backfill were conducted to construct the dataset for the machine learning. Five input parameters (tailing to cement ratio, percentage of fine tailings, cement type, curing time, and solid to water ratio) were considered for the design of the laboratory tests. The firefly algorithm (FA) was used to tune the random forest (RF) hyperparameters, and it was adopted to combine the RF model to improve the accuracy and efficiency for the prediction of the compressive strength of the cemented tailing backfill. By comparing the predicted and actual results, the reliability and accuracy of the prediction model proposed are confirmed. Tailing to cement ratio and curing time are the two most important parameters to the compressive strength of the cemented tailing backfill.
Method Optimisation in Hydrophilic-Interaction Liquid Chromatography by Design of Experiments Combined with Quantitative Structure–Retention Relationships*

Accurate prediction of the separation conditions for a set of target analytes with no retention data available is fundamental for routine analytical assays but remains a very challenging task. In this paper, a quality by design (QbD) optimisation workflow capable of discovering the optimal chromatographic conditions for separation of new compounds in hydrophilic-interaction liquid chromatography (HILIC) is introduced. This workflow features the application of quantitative structure−retention relationship (QSRR) methodology in conjunction with design of experiments (DoE) principles and was used to carry out a two-level full factorial DoE optimisation for a mixture of pharmaceutical analytes on zwitterionic, amide, amine, and bare silica HILIC stationary phases, with mobile phases containing varying acetonitrile content, mobile phase pH, and salt concentration. A dual-filtering approach that considers both retention time (tR) and structural similarity was used to identify the optimal set of analytes to train the QSRR in order to maximise prediction accuracy. Highly predictive retention models (average R2 of 0.98) were obtained and statistical analysis of the prediction performance of the QSRR models demonstrated their ability to predict the retention times of new compounds based solely on their molecular structures, with root-mean-square errors of prediction in the range 7.6–11.0 %. Further, the obtained retention data for pharmaceutical test compounds were used to compute their separation selectivity, which was used as input into a DoE optimiser in order to select the optimal separation conditions. Experimental separations performed under the chosen optimal working conditions showed good agreement with the theoretical predictions. To the best of our knowledge, this is the first study of a QbD optimisation workflow assisted with dual-filtering-based retention modelling to facilitate the method development process in HILIC.
When Muth’s entrepreneurs meet Schrödinger’s cat

Dans son article-phare de 1961, Muth applique l’hypothese d’anticipations rationnelles a un modele simple de marche concurrentiel d’un bien homogene, produit sous des chocs stochastiques. L’hypothese depasse la simple conception anticipative de l’equilibre marshallien, en ce qu’elle attribue aux entrepreneurs la capacite de former des anticipations de prix sur la base de la theorie. Cette capacite est deja presente chez Cournot [1838], quoique dans un modele sans incertitude fondamentale explicite. L’objectif de cette note est de montrer, dans le cadre du meme modele, que la concurrence oligopolistique ajoute de l’indetermination et donc de l’incertitude strategique au tableau, en affaiblissant ainsi en quelque sorte l’hypothese d’anticipations rationnelles. Avant que l’equilibre ne soit realise, chaque entrepreneur se trouve dans une superposition d’etats colombe et faucon, tout comme le chat de Schrodinger se trouve dans une superposition d’etats mort et vivant.
Lightweight, Ultra‐Wideband, and Polarization‐Insensitive Metamaterial Absorber Using a Multilayer Dielectric Structure for C‐ and X‐Band Applications

The design of a lightweight and ultra‐wideband absorber for C and X bands is still a challenge as the dimension of the absorber is relatively large in such a relatively low‐frequency band. Herein, an ultra‐wideband and lightweight metamaterial absorber (MMA) is presented for C‐ and X‐band applications. The unit cell of the proposed MMA consists of four copper sectors loaded by lumped resistors and a continuous copper ground plane, which is printed on two FR‐4 substrates. Furthermore, an airgap separating the FR‐4 layers is used as the active substrate medium of the MMA to achieve both ultra‐wideband absorption spectra and lightweight design. The MMA is investigated by simulation and measurement, showing that a good agreement is achieved. The result indicates that the absorptivity of the MMA under both transverse electric (TE) and transverse magnetic (TM) polarizations is greater than 90% in a wide range from 3.7 to 13.6 GHz for all polarization angles. Compared to other reported broadband absorbers, the proposed MMA shows an ultra‐wide absorption bandwidth and a lightweight design, which demonstrates a great potential for C‐and X‐band applications.
Arbuscular mycorrhizal fungi as a potential tool for bioremediation of heavy metals in contaminated soil

Human activities have introduced large amounts of heavy metals into natural ecosystems in recent years. As a result, the accumulation of heavy metals and metalloids in plants, animals, and humans, which may have caused some health problems. Chemical and physical methods can remove the heavy metal in contaminated soil, but both are very expensive and ineffective. Arbuscular Mycorrhizal Fungi (AMF) are mutualistic symbionts in most plant roots. Furthermore, AMF are the essential mycorrhizae for phytoremediation, and the extensive hyphal network of them can increase the uptake of micro and macronutrients, water and heavy metals from the soil. However, AMF hyphae colonized in plant roots have an ability for compartmentalizing heavy metals inside plant roots. Furthermore, AMF hyphae are capable of secreting a glycoprotein, named glomalin, which can bind heavy metals and subsequently remove heavy metals absorbed by the plants from contaminated soil. Glomalin can develop the properties and structure of the soil, which helps to enhance soil fertility. This paper presents the role of AMF in the ecosystems and as potential tools for bioremediation of heavy metals in the soil.
Earnings Management. An overview of the relative literature

This article aims to present a critical overview of the traditional studies in earnings management, focusing on the impact on the making decision process. This overview in the literature provides numerous aspects of this topic, in line with the firms’ motivations. Earnings management procedure includes smoothing and opportunistic practices and illustrating accounting rules with a significant effect on accounting information quality. Several researchers have shifted their attention to real activities as a primary method or supplementary to accrual-based methods to obtain a complete view of the earnings management levels. Earnings management is considered an opportunistic instrument, and it can be part of the aggregate long-term business strategy. This review study provides some guidelines, to academics and professionals, in line with the models and the motivations that lead managers to engage in this procedure. This overview creates new research avenues enhancing the existing knowledge. which undoubtedly is a purpose of earnings management activities. The first hypothesis examines the linear relation between earnings management proxies (accrual-based and real earnings) with the firm performance proxy, which is the change of dividends. This stage's results, presenting a significant positive relation with the change of dividends, support that the real earnings management measures provide more information for firm performance trajectories than for unexpected earnings. The second hypothesis is whether the higher levels of earnings management measures are associated with a higher probability of increases in dividends. The logistic regression model uses a dummy variable that controls for firms that present an increase in dividends regressed by earnings management metrics, the unexpected earnings variable. This stage's findings demonstrate a significant positive coefficient of all earnings management measures, which indicates that the firms that intensively engage in earnings management procedures are more likely to increase their dividends. Furthermore, the research states a third hypothesis that examines whether the announcement of dividends change conveys more information to investors than earnings management activities. This regression model's dependent variable is the three-day cumulative stock return around the dividend declaration for the first dividend change of the year. The key independent variables are the change of dividends and earnings management measures, while the set of control variables of the previous stages is also included. The findings are shown to be consistent with the hypothesis and, in particular, indicated a significant positive coefficient of dividend change and an insignificant coefficient of earnings management metrics.
Sub-national government debt sustainability in India: an empirical analysis

ABSTRACT Recognizing the increasing precedence of fiscal shocks leading to a deterioration in states’ debt due to the realization of contingent liabilities, this study assesses the debt sustainability of Indian states by employing both conventional and augmented debts, obtained by incorporating information on states’ guarantees. Results indicate that states’ debt is just sustainable with potential signs of unsustainability. Guarantees given by states, if invoked, could certainly pose a potential risk to debt sustainability for Indian states. The study suggests revisiting and reviewing states’ FRLs with the inclusion of debt as a medium-term anchor, and greater transparency with regard to contingent liabilities.