Miracle-dz/UniFAR-FTUs huggingface dataset:
Theory and applications of answer set programming

Answer set programming (ASP) is a new form of declarative logic programming. ASP interprets a logic program as a constraint on sets of literals, just as a propositional formula can be viewed as a constraint on assignments of truth values to atoms. The concept of an answer set was originally proposed as a semantics of negation as failure in Prolog. Instead of traditional Prolog systems, ASP uses answer set solvers. The input of Prolog consists of a logic program and a query, and Prolog computes answer substitutions; the input of an answer set solver is a logic program, and the solver computes the program's answer sets. The idea of ASP is to represent a given computational problem as a logic program whose answer sets correspond to solutions, and to use an answer set solver to find an answer set. We have investigated the application of ASP to several combinatorial search problems, including planning, wire routing, and phylogeny reconstruction. Planning is the problem of finding a sequence of actions that leads to a given goal. Wire routing is the problem of determining the physical locations of all wires interconnecting the circuit components on a chip. Phylogeny reconstruction is the problem of constructing and labeling an evolutionary tree for a set of taxa (taxonomic units), which describes the evolution of the taxa in that set from their most recent common ancestor. In our work on phylogeny reconstruction, we have generated several conjectures about the evolutionary history of Indo-European languages. The work on the use of ASP for planning has led us to the investigation of some theoretical questions related to answer sets. One is the problem of equivalent transformations of logic programs: under what conditions can we replace a program by an equivalent program that can be processed by an answer set solver more efficiently? Another problem is related to completion—a process that can translate a logic program into a set of formulas of classical logic. In some cases, the interpretations satisfying the completion of a program are also the answer sets for that program. In such cases, we can use propositional solvers—systems that compute a model of a given set of clauses—to find the program's answer sets. For some problems, propositional solvers are more efficient than answer set solvers. Therefore, we have investigated under what conditions we can use propositional solvers to find the program's answer sets.
Implementation method of Wheatstone bridge for absolute secure communication in physical layer

In this paper, W-SIC, a circuit with self-interference cancellation ability based on Wheatstone bridge is proposed to realize the physical layer absolutely safe communication in wired mode. The communication parties use a coaxial cable to transmit information to each other in different frequency ranges. They send information signals in their respective frequency ranges and release artificial noise signals in each other's frequency range. When the eavesdropper eavesdropping on the coaxial cable, the signals in both frequency ranges have been polluted by artificial noise, the eavesdropper can’t obtain effective signals. However, they can easily eliminate self-interference signals by using W-SIC, receive only the signal sent. Because the two signals are in different frequency ranges, the target information can be easily obtained by filtering, downconversion and other processing by the receiving. Besides W-SIC circuit model, a high-precision sliding rheostat is proposed to more accurately realize Bridge balance inside it, the optimal eavesdropping position of the eavesdropper is analyzed, the channel capacity of legitimate recipients, the formula of self-interference residual factor, the formula of system security capacity and the channel capacity of eavesdroppers are derived. The simulation results show the effectiveness and feasibility of the research method proposed in this paper.
Identification of Several Fractions of Patchouli Alcohol from Patchouli Oil (Pogostemon cablin Benth.) using Combination Method of Infrared Spectroscopy and Principal Component Analysis

Patchouli oil is a type of essential oil that is very popular all over the world because it is rich in benefits and has high economic value as well. The quality of it must be maintained and strictly controlled to prevent counterfeiting. In this study, the patchouli oil sample used was Aceh patchouli oil of the Tapak Tuan variety, Indonesia. The patchouli oil was redistilled and crystallized to obtain 5 varying levels of patchouli alcohol. This study aims to evaluate the application of chemometrics and Fourier transforms infrared (FTIR) spectroscopy as simple, effective and economical methods to assess the quality of patchouli oil and patchouli crystals. FTIR data from the 5 samples were subjected to multivariate analysis consisting of Hierarchical cluster Analysis (HCA), Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). HCA, PCA and LDA managed to distribute the tested samples into informative groups. The results of the HCA analysis showed the pattern of sample classification based on patchouli oil concentration, the results of the PCA analysis showed that the PCA plot scores were quite adequate in distinguishing patchouli oil concentrations. The results of the LDA analysis showed the authenticity of the patchouli oil spectrum based on the results of PCA analysis. From the cross-validation accomplished, this method was successful in predicting accurately the concentration of patchouli oil samples. The results of the analysis concluded that the combination of the FTIR-Chemometric method is a fast, easy, accurate and inexpensive method. In addition, this analysis can be executed without damaging the sample.
Residual learning-based two-stream network for RGB-T object tracking

Abstract. RGB-T object tracking is a branch of visual tracking that has been widely applied to many fields, such as intelligent transportation and urban monitoring. Due to the interference of background clutter and occlusion, the existing trackers still suffer from the problems of unreasonable modal fusion strategy, insufficient feature extraction, and loss of semantic information. To solve these problems, we propose a residual learning-based two stream network for RGB-T object tracking. The overall feature extraction network is composed of three branches, and multi-layer convolutions are utilized to extract the features of visible, thermal, and fused images, respectively. First, aiming at improving the effectiveness of feature extraction, a weight generation module for hierarchical feature weight calculation is designed to guide the direction of feature fusion. Then, the residual block is employed to replace the single-layer convolution in order to increase the depth of the network, by which deeper semantic features are learned and the loss of semantic information is alleviated. Finally, a loss function with a penalty term is developed to adjust our network toward the direction of the best tracking performance of the dual modalities. This overcomes the negative impact of poor mode on model training. Experiments implemented on public RGB-T datasets indicate that our algorithm outperforms the recent state-of-the-art trackers in terms of both precision rate and success rate. Compared with the second-best comparison algorithm, our tracker improves the above two metrics by 0.4% and 1.5%, respectively. Our codes are available at https://github.com/MinjieWan/Residual-learning-based-two-stream-network-for-RGB-T-object-tracking.
Performance of Observation-Based Prediction Algorithms for Very Short-Range, Probabilistic Clear-Sky Condition Forecasting

Abstract Very short-range sky condition forecasts are produced to support a variety of military, civil, and commercial activities. In this investigation, six advanced, observation (obs)-based prediction algorithms were developed and tested that generated probabilistic sky condition forecasts for 1-, 2-, 3-, 4-, and 5-h forecast intervals, for local and regional target types, in six geographic regions within the continental United States. Three of the methods were based on predictive learning algorithms including neural network, random forest, and regression tree. The other three methods were statistical techniques including a k–nearest neighbor algorithm, a classifier based on the Bayes decision rule, and a multialgorithm ensemble. The performances of these six algorithms were compared with forecasts from three benchmark methods: basic persistence, the climatological-expectancy-of-persistence, and satellite cloud climatology. The obs database for each forecast target was composed of a multiyear, half-hour.
Book Review: Econometric Analysis of Count Data. 3d edition, revised and enlarged. By Rainer Winkelmann. Berlin: Spring-Verlag, 2000. 282 pp. $83.00

Abbott, Andrew. 1988. The System of Professions: An Essay on the Division of Expert Labor. Chicago: University of Chicago Press. Bollen, Kenneth A. 1989. Structural Equations With Latent Variables. New York: Wiley. Cochran, William G. 1957. “Analysis of Covariance: Its Nature and Uses. ” Biometrics 13: 261-81. Empson, William. 1947. Seven Types of Ambiguity. 2d ed. revised and reset. London: Chatto and Windus. Jöreskog, K. G. 1973. “A General Method for Estimating as Linear Structural Equation System. ” Pp. 85-112 in Structural Equation Models in the Social Sciences, edited by A. S. Goldberger and O. D. Duncan. New York: Seminar Press. Lindley, D. V. and A. F. M. Smith. 1972. “Bayes Estimates for the Linear Model. ” Journal of the Royal Statistical Society Series B 67:1-19. McCullagh, Peter and J. A. Nelder. 1983. Generalized Linear Models. London: Chapman and Hall. Muthén, B. 1983. “Latent Variable Structural Equation Modeling With Categorical Data. ” Journal of Econometrics 22:48-65. Stolzenberg, Ross M. 1979. “The Measurement and Decomposition of Causal Effects in Nonlinear and Nonadditive Models. ” In Sociological Methodology 1980, edited by Karl Schuessler. San Francisco: Jossey-Bass.
Effective Mixer Design an Important Factor In SSCR Systems for Reduction of NOx from Exhaust of Diesel Engines

In recent decades, the environment has been seriously polluted by the hazardous exhaust components of diesel engines. The international community, which is dedicated to preserving the harmony between nature and humanity, has taken this seriously and imposed strict regulations on Diesel engine manufacturers regarding the quantity of exhaust components from Diesel engines that may apply to the standards of EURO-VI. The SCR technology attempted to reduce the problem somewhat, but the associated problems of solid particle formation on the pipe walls, ammonia slip, and incomplete NOx reduction led to the development of new technology - solid selective catalytic reduction. The use of solid ammonium salt for ammonia generation has shown better results in NOx reduction and reduction of solid particle formation compared to SCR. However, it was not possible to fully resolve the ammonia slip issue. A uniform flow rate of ammonia through the SCR catalyst can reduce NOx efficiently. In this paper, the role of mixer design in achieving a uniform flow rate of ammonia is investigated in detail. The results show that an optimized mixer design leads to efficient reduction of NOx and thus reduces ammonia slip to a great extent. When the mixer is placed near the ammonia injection point, the most homogeneous ammonia distribution is achieved for flow through the SCR catalyst.
Constructing small universal reversible Turing machines

We study the problem of designing small universal reversible Turing machines (URTMs). So far, six kinds of small URTMs that simulate cyclic tag systems have been constructed. In addition, applying general conversion methods to some of these small URTMs, 2-symbol, and 3or 4-state URTMs have been obtained. Here, we give precise descriptions of these URTMs, and show the computer simulation results to see how they work. The description files of the constructed URTMs are also given as attachment files. 2
A Fuel-Efficient BLDC Motor-Driven Light Electric Vehicle With Single-Stage Onboard Charging System

This work proposed a simple, compact, and efficient scheme for an onboard charging configuration for light plug-in electric vehicles (LPEVs) with a dual charging arrangement, i.e., utility grid and solar panel. The system is targeted to develop an LPEV with a low dc bus voltage (< 100 V) and onboard solar support using a brushless dc motor (BLDCM) drive due to its economic controller, compact drive arrangement, and high efficiency. The designed integrated converter works well under all operating modes such as charging, propulsion (PP), and regenerative braking (RB) operations. The developed converter works as a power factor correction (PFC) converter during grid charging as well as performs a maximum power point tracking (MPPT) converter when charging through solar power. A new approach of the controller in regenerative mode exploiting both the drive and proposed integrated control is unique for this system. As a result, the designed system becomes compact and efficient, making it a competitive solution for an onboard charging scheme for LEVs.
Fuzzy Theory in Fog Computing: Review, Taxonomy, and Open Issues

Geographically dispersed Fog Computing architecture ubiquitously connected to a range of heterogeneous nodes at the edge of the network can provide cooperative flexible, and variable computations, communications, and storage services. Several fog computing methods, models, and techniques have been used to solve cloud issues. The fuzzy theory has also been used in many aspects of fog computing. Objectives: This work presents a systematic literature review of the use of fuzzy theory in Fog Computing, highlighting the main practical motivations, classification types in research approaches, fuzzy methods used, popular evaluation tools, open issues, and future trends. Methods: The investigations were systematically performed using fuzzy theory in fog computing, and four databases which are ScienceDirect, Web of Science (WoS), Scopus, and IEEE Xplore Digital Library from 2015 to 2022, were used to analyse their performance evaluation, architecture, and applications. Results: 94 articles were selected based on fuzzy theory in fog computing using different methods, models, and techniques, based on the proposed exclusion and inclusion criteria. The results of the taxonomy were divided into five major classes: task and resource management, intrusion detection systems, trust management, and healthcare services. Discussion: Applications requiring real-time, low latency, and quick responses are well suited for fog computing. These studies show that resource sharing improves the fog computing architecture by delivering reduced latency, distributed processing, improved scalability, better security, fault tolerance, and privacy. Conclusion: The majority of the time, research areas on fuzzy theory in fog computing are crucially significant. We conclude that this review will enhance research capacity, thereby expanding and creating new research domains.
On the mean square formula of the error term in the Dirichlet divisor problem

Abstract Let F(x) be the remainder term in the mean square formula of the error term Δ(t) in the Dirichlet divisor problem. We improve on the upper estimate of F(x) obtained by Preissmann around twenty years ago. The method is robust, which applies to the same problem for the error terms in the circle problem and the mean square formula of the Riemann zeta-function.
Modulational instability of electromagnetic waves in a collisional quantum magnetoplasma

The modulational instability of right-hand circularly polarized electromagnetic electron cyclotron (CPEM-EC) wave in a magnetized quantum plasma is studied taking into account the collisional effects. Employing quantum hydrodynamic and nonlinear Schrodinger equations, the dispersion relation of modulated CPEM-EC wave in a collisional plasma has been derived. It is found that this wave is unstable in such a plasma system and the growth rate of the associated instability depends on various parameters such as electron Fermi temperature, plasma number density, collision frequency, and modulation wavenumber. It is shown that while the increase of collision frequency leads to increase of the growth rate of instability, especially at large wavenumber limit, the increase of plasma number density results in more stable modulated CPEM-EC wave. It is also found that in contrast to collisionless plasma in which modulational instability is restricted to small wavenumbers, in collisional plasma, the interval of instability occurrence can be extended to a large domain.
Compact Waveguide Filters Using Novel Resonant Coupling Structures

In this article, new resonant coupling structures are proposed for designing miniaturized waveguide bandpass filters. The resonant coupling structure is constructed by adding one or two metal blocks in a relatively thick inductive iris, which can be considered as the integration of a resonator, the input, and output coupling structures. Positive or negative coupling can be realized by selecting a different number of metal blocks. The combination of different resonant coupling structures can be used to design higher-order filters with various transverse topologies, realizing different transmission zeros (TZs) distributions. In addition, the weak source–load coupling caused by the resonant coupling structures can introduce an additional TZ, which can further improve the out-of-band suppression level of the filter. For verification, two third-order waveguide bandpass filters based on all resonant coupling structures are designed and simulated to achieve different TZ distributions. Finally, a fifth-order waveguide bandpass filter employing both resonant coupling structures and classical rectangular cavities is designed and fabricated. The measured results verify the proposed method.
Prevalence of irritable bowel syndrome and associated factors in intern doctors at a state university in western black sea, Turkey: A cross-sectional study

Irritable bowel syndrome (IBS) is a functional digestive disorder that affects medical students at a higher rate than the general population. Intern doctors are the most affected class by IBS among medical students. Determining the prevalence of IBS and associated risk factors is essential for taking preventive measures. We aimed to investigate the prevalence and predictors of IBS among intern doctors in this study. This study was conducted using an online survey for intern doctors in the 2021-2022 academic year. Rome IV criteria were utilized for IBS diagnosis. Anxiety and depression scores were calculated according to the Hospital Anxiety and Depression Scale (HADS). The prevalence of IBS in the 249 participants was 31.7% included in this study. 32.1% of all participants had depression, 44.2% had anxiety, 65.1% had sleep disorders, 73.1% had MSE (medical specialty exam) anxiety, and 96% were concerned about carrying out the medical profession after graduating from university. Female gender (p<0.001), using regular medication (p=0.019), not exercising regularly (p=0.002), sleep disturbance (p<0.001), food hypersensitivity (p=0.001), having a history of severe acute gastroenteritis (AGE) before IBS symptoms started (p<0.001), IBS in first-degree relatives (p<0.001), be low income (p<0.001), having a divorced parent (p=0.008), not having siblings (p=0.002), severe emotional stress history in the last six months (p=0.004), having depression (p<0.001), having anxiety (p<0.001), and MSE anxiety (p<0.001) identified as risk factors associated with IBS. The multivariant analysis determined independent risk factors affecting the prevalence of IBS in intern doctors as female gender, depression, anxiety, and severe AGE history before IBS symptoms started. In this study, it was determined that approximately one-third of the interns were diagnosed with IBS. Stress management training can be considered to screen this group for IBS and psychological problems and to help them cope with these stressors.
Transmission Loss-Aware Peer-to-Peer Energy Trading in Networked Microgrids

Networked microgrids (MGs) have a great potential to improve the efficiency, reliability, resilience, security, and sustainability of power supply services. Peer-to-peer (P2P) energy trading built on a smart information system in networked MGs is an emerging economic approach to facilitate energy sharing among networked MGs to achieve mutual cost-effective operation and improve the reliability and stability of energy supply service. Such a distributed and competitive energy trading market urges the need for an efficient energy trading strategy that incentivizes the self-interested MGs with various energy production and consumption profiles to participate in energy trading. In this paper, we propose a distributed real-time P2P energy trading strategy that integrates energy trading into energy management and enables the MGs with renewable energy sources (RESs) and energy storage systems (ESSs) to manage their storage scheduling, energy supply, and energy trading in a dynamic manner, jointly considering the randomness of renewable energy generation and load demand, operational constraints of ESSs and transmission losses associated with energy exchange. The proposed energy control and bidding algorithm allows each MG to dynamically and independently determine its energy control actions and price-quantity bids/offers, while the proposed trading pair matching algorithm matches the MGs on a many-to-many basis with respect to their individual payoffs, which couple price-quantity bids/offers of the MGs with distance-dependent energy transmission losses associated with energy exchange. Numerical simulation results demonstrate that the proposed distributed energy trading system yields significant improvements in terms of energy cost savings and renewable energy utilization efficiency, while reducing energy transmission losses within the system.
The effects of fatigability on shooting skill performance in goalball penalty shots

: This study evaluated the influence of acute fatigue on shooting skill performance in goalball penalty shots. Eleven sub-elite male players were evaluated in an official court using a two-dimensional procedure (60 Hz) to compute the mean radial error, bivariate variable error and accuracy, and three-dimensional kinematics (240 Hz) to measure ball velocity. Rating of perceived exertion, blood lactate concentration and knee extension isometric maximal voluntary contractions were assessed at rest and immediately after the completion of an incremental test specific to goalball. Although significant differences were observed for rating of perceived exertion (pre: M = .36 AU, SD = .67; post: M = 9.27 AU, SD = .79), blood lactate concentration (M = 1.33 mM, SD = .51; M = 9.04 mM, SD = 2.25) and peak force (M = 597.85 N, SD = 132.35; M = 513.37 N, SD =107.00) at the end of the incremental test, no differences were identified in the pre and post moments for mean radial error (M = .10 m, SD = 1.01; M =.37 m, SD = 2.19), bivariate variable error (M = 3.56 m, SD = .70; M = 3.25 m, SD = .70), accuracy (M = 3.68 m, SD = .74; M = 3.78 m, SD = 1.13) and ball velocity (M = 18.53 m . s -1 , SD = 1.53; M =18.46 m . s -1 , SD = 1.65). We concluded that even though significant levels of fatigability can be attributed to the incremental test specific to goalball, shooting skill performance in goalball penalty shots does not appear to be affected. However, further investigations are needed so that goalball coaches can develop strategies that contribute to improve performance in this technical action. Such improvements are decisive for the outcome of a match.