Miracle-dz/UniFAR-FTUs huggingface dataset:
Three lines model paradigm shift: a blockchain-based control framework

PurposeWhile the three lines model (TLM) provides an organizational structure to execute risk and control duties, research and practice show limitations in the model's implementation. These limitations result in governance issues. Such issues, together with control weaknesses, could be addressed by leveraging properties of distribution, transparency, and immutability of blockchain technology. To this end, in this paper the authors propose a conceptual control framework based on blockchain technology to augment control practice.Design/methodology/approachThe design of the resulting blockchain-based control framework (BBCF) and its prototype, based on the design science research methodology (DSRM), is presented and discussed in terms of the potential impact in the context of the identified problems within the TLM.FindingsOne potential outcome of BBCF could be to redefine the scope and boundaries of some of the activities in audit and control practices from a more static to a more dynamic and prospective role. In a larger context of improving governance practices, the BBCF could set the path for a more inclusive and participatory interaction between the different governance actors of an organization.Research limitations/implicationsHowever, this assumes that blockchain is more widely adopted despite its complexity and rigidity.Practical implicationsBBCF covering both a conceptual model design and a reference implementation provides an innovation in audit and control. BBCF could include all relevant stakeholders who have an interest in corporate governance and control activities, including the regulators.Originality/valueThe contribution intends to serve both as a starting point for discussing the evolution of audit and control practice based on blockchain technology, as well as an initial actionable prototype for experimentation and further development.
Obstructed group nearest neighbor queries in spatial databases

A group nearest neighbor (GNN) query is an important class of information and enquiry services. Researchers have focused on developing efficient algorithms to evaluate variant of GNN queries in recent years. In this thesis, we introduce obstructed group nearest neighbor (OGNN) queries that enable a group of pedestrians located at different places to meet at a data point such as a restaurant that minimizes their aggregate travel distance in presence of obstacles such as buildings and lakes. We propose the first comprehensive approach to process an OGNN query. The obstructed distance between two point locations is defined as the length of the shortest path that connects them without crossing any obstacles. The aggregate obstructed distance is measured in terms of the total or the maximum travel distance of the group members in an obstructed space. We develop two techniques, Single Point Aggregate Obstructed Distance (SPAOD) computation and Multi Point Aggregate Obstructed Distance (MPAOD), for computing aggregate obstructed distance between a data point and a set of query points, which is an essential part in evaluating GNNs in an obstructed space. SPAOD computation does not retrieve an obstacle multiple times but may retrieve additional obstacles, which are not required for computing the aggregate obstructed distance. On the other hand, MPAOD may retrieve the same obstacle multiple times but does not retrieve any obstacle, which is not required for computing the aggregate obstructed distance. We propose two methods: Group Based Query Method(GBQM) and Centroid Based Query Method (CBQM) to evaluate OGNN queries. The key idea of GBQM and CBQM is to incrementally retrieve data points until the actual obstructed GNN has been found. GBQM retrieves the Euclidean group nearest neighbors with respect to the locations of group members, whereas CBQM retrieves the Euclidean nearest neighbors with respect to the centroid of the locations of group members. We validate the efficacy and efficiency of our solutions with an extensive experiments using both real and synthetic datasets and present a comparative analysis among our proposed algorithms in terms of query processing overhead.
An efficient algorithm for non-convex sparse optimization

It is a popular research topic in computer vision community to find a solution for the zero norm minimization problem via solving its non-convex relaxation problem. In fact, there are already many existing algorithms to solve the non-convex relaxation problem. However, most of them are computationally expensive due to the non-Lipschitz property of this problem and thus these existing algorithms are not suitable for many engineering problems with large dimensions. In this paper, we first develop an efficient algorithm to solve the non-convex relaxation problem via solving a sequence of non-convex sub-problems based on our recent work. To this end, we reformulate the minimization problem into another non-convex one but with non-negative constraint. Then we can transform the non-Lipschitz continuous non-convex problem with the non-negative constraint into a Lipschitz continuous problem, which allows us to use some efficient existing algorithms for its solution. Based on the proposed algorithm, an important relation between the solutions of relaxation problem and the original zero norm minimization problem is established from a different point of view. The results in this paper reveal two important issues: ⅰ) The solution of non-convex relaxation minimization problem converges to the solution of the original problem; ⅱ) The general non-convex relaxation problem can be solved efficiently with another reformulated high dimension problem with nonnegative constraint. Finally, some numerical results are used to demonstrate effectiveness of the proposed algorithm.
Novel doping alternatives for transition metal dichalcogenides from high-throughput DFT calculations

Successful doping of single layer transition metal dichalcogenides (TMDs) remains a formidable barrier to their incorporation into a range of technologies. We use density functional theory within the generalized gradient approximation to assess the possibility of substitutional doping the metal and chalcogen sites in molybdenum and tungsten disulfide as well as diselenide against a large fraction of the periodic table. An automated analysis of the energetics, atomic and electronic structure of thousands of calculations results in insightful trends across the periodic table and points out promising dopants to be pursued experimentally. The automated analysis of the electronic structure is able to capture and graphically represent subtleties in the electronic structure of doped TMDs including the presence of gap states and the results are in good agreement with the limited experimental data available. Beyond previously studied cases, our predictions suggest promising candidates for p-type doping and reveal interesting physics behind the doping of the metal site. Doping with early transition metals (TMs) leads to tensile strain and a significant reduction in the bandgap. The bandgap increases and strain is reduced as the d-states are filled into the mid TMs; these trends reverse are we move into the late TMs. Thus, strain and bandgaps are dominated by the non-monotonous variation in atomic radius of the series. Additionally, the Fermi energy increases monotonously as the d-shell is filled from the early to mid TMs and we observe few to no gap states indicating the possibility of both n- (early TMs) and p- (mid TMs) type doping.
NESSY: NLTE spectral synthesis code for solar and stellar atmospheres

Context. Physics-based models of solar and stellar magnetically-driven variability are based on the calculation of synthetic spectra for various surface magnetic features as well as quiet regions, which are a function of their position on the solar or stellar disc. Such calculations are performed with radiative transfer codes tailored for modeling broad spectral intervals. Aims. We aim to present the NLTE Spectral SYnthesis code (NESSY), which can be used for modeling of the entire (UV-visible-IR and radio) spectra of solar and stellar magnetic features and quiet regions. Methods. NESSY is a further development of the COde for Solar Irradiance (COSI), in which we have implemented an accelerated Λ-iteration (ALI) scheme for co-moving frame (CMF) line radiation transfer based on a new estimate of the local approximate Λ-operator. Results. We show that the new version of the code performs substantially faster than the previous one and yields a reliable calculation of the entire solar spectrum. This calculation is in a good agreement with the available observations.
Music Audio Sentiment Classification Based on Improvied Vision Transformer

: Common neural network models have problems of low accuracy and low efficiency in music sentiment classification tasks. In order to further excavate sentiment information contained in the audio spectrum and increase the accuracy of music sentiment classification, an improved Vision Transformer model is proposed. Since the public data set does not meet the requirements of the task of music sentiment classification, this paper makes a four-category music sentiment data set. After the audio is preprocessed, the processed audio features are trained by Vision Transformer. Modify the input of Vision Transformer to fit the structure of Vision Transformer. Position parameters of Vision Transformer model can better preserve the connection between audio features. Encoder structure can also fully learn local features and global features. Due to the long training time of this model, softpool pooling layer is introduced into the model, which can better retain the emotional features, speed up the calculation of the model, but also retain the model accuracy. Experimental results show that the classification accuracy of Vision Transformer model reaches 86.5%, which has better classification effect compared with neural networks such as ResNet. Meanwhile, the improved Vision Transformer reduces training time by 10.4% and accuracy by only 0.3%. On the public data set gtzan, the accuracy of this model reaches 90.7%.
Comparison for Level Control of a Coupled-Tank using PI, PI-plus-Feedforward, and IMC Controllers

This paper investigates the performance of various control schemes for level control of a coupled tank process. The nonlinear dynamic model of the system was derived using the analytical and empirical approaches. To investigate the performance of the controllers, proportional plus integral control, proportional plus integral plus feedforward control and internal model control (IMC) have been proposed. The PI gains were determined using pole placement, Ciancone correlation, and Cohen-Coon tuning techniques. Time response specification and mean absolute error (MAE) are used to assess the level control performance of the designed controllers. Comparative MATLAB simulation assessments have shown that IMC with the least MAE value and fastest settling time has the best tracking performance as compared to other controllers.
Validation of a three‐dimensional constitutive model for nonlinear site response and soil‐structure interaction analyses using centrifuge test data

The capability of a bounding surface plasticity model with a vanishing elastic region to capture the multiaxial dynamic hysteretic responses of soil deposits under broadband (eg, earthquake) excitations is explored by using data from centrifuge tests. The said model was proposed by Borja and Amies in 1994 (J. Geotech. Eng., 120, 6, 1051‐1070), which is theoretically capable of representing nonlinear soil behavior in a multiaxial setting. This is an important capability that is required for exploring and quantifying site topography, soil stratigraphy, and kinematic effects in ground motion and soil‐structure interaction analyses. Results obtained herein indicate that the model can accurately predict key response data recorded during centrifuge tests on embedded specimens—including soil pressures and bending strains for structural walls, structures' racking displacements, and surface settlements—under both low‐ and high‐amplitude seismic input motions, which was achieved after performing only a basic material parameter calibration procedure. Comparisons are also made with results obtained using equivalent linear models and a well‐known pressure‐dependent multisurface plasticity model, which suggested that the present model is generally more accurate. The numerical convergence behavior of the model in nonlinear equilibrium iterations is also explored for a variety of numerical implementation and model parameter options. To facilitate broader use by researchers and practicing engineers alike, the model is implemented as a “user material” in ABAQUS Standard for implicit time stepping.
Measuring service quality perceptions of hotel management institutes on hotel recruiters’ hiring intentions: effect of sacrifice, satisfaction and service value

ABSTRACT This study examined how service quality by institutes of hospitality management (IHM) in India affected hotel recruiters’ behavioral intentions of returning to campus for sustained recruitment activities. It further examined the underlying mechanisms of service value perceived by hotel recruiters, affected by the sacrifice made by hotel recruiters, as well as the satisfaction of hotel recruiters with IHM service quality and their behavioral intentions. A sample of 132 hotel recruiters from four international and domestic hotel organizations in India participated in a survey. Outcomes of structural equation modeling revealed that service quality, service value, and satisfaction were all predictors of behavioral intentions of the hotel recruiters. Sacrifice of hotel recruiters positively influenced their service value; service value perceived by hotel recruiters and their satisfaction were positively impacted by service quality; satisfaction and service value were the significant underlying mechanisms between service quality and behavioral intentions; and the relationship between service value and satisfaction was insignificant. These findings emphasize that IHMs improve performance by developing effective administrative policies and procedures that enhance the hiring intentions of hotel recruiters and in turn can elevate the reputation of the institutions with closely aligned academia-industry collaborations to achieve the sustainable development goals related to quality education.
Authentic Movement: The embodied experience of the text

A few years ago I was watching a live theatre performance with a friend. He is, I think, an average theatre-goer: intelligent, educated, enjoys theatre, but with no professional interest or expertise. He was not impressed. He commented, however, that he found one actor exceptional. “When he was on stage, everything changed, even the other actors. ” I questioned him about what it was about this actor that made a difference, and he finally said: “He was so in his body. I could see everything he said. ” I asked him to repeat his comment. No, it was not a slip of the tongue or a sudden onset of synaesthesia. Hardly – he meant exactly what he said.
Characteristics of effective models for classroom demonstrations

ABSTRACT Use of peer models during classroom demonstrations can be an effective teaching practice to support student learning and self-efficacy. According to social cognitive theory, modeling is a form of vicarious experience: those experiences through which students are able to learn and acquire knowledge through observation. As a critical source of self-efficacy, vicarious experiences involving peer models are likely to support students’ efficacy. Although qualities of effective peer models have been identified, there has been less translational work that discusses the tensions between best practice and the practical considerations by educators in identifying, selecting, and using peer models for classroom demonstrations. The aim of this paper is to review the qualities of peer models research and discuss how effective peer models can be implemented in diverse classrooms. Here we pay particular attention to the perceived similarity of ability and identity between the model and student observers. This paper also outlines gaps in the research and identifies pathways for future research.
An air-to-air unmanned aerial vehicle interceptor using machine-learning methods for detection and tracking of a target drone

The proliferation of small or micro unmanned aerial vehicles (UAV) gives rise to a potential threat for both public and military security. The small footprint and unpredictable dynamics of drones make detection and tracking difficult. Traditional methods of defence and protection may be ineffective against this new danger. This paper presents the work on developing DroneSwatter, a counter unmanned aerial system developed to track, follow, and take down a drone threat (Target Drone) using an agile, low-cost drone interceptor (Hunter Drone). The DroneSwatter project aims to apply machine learning techniques for counter-drone scenarios. Detection tasks are performed using deep learning detection algorithms. Simulation is used to build a tracking control model via proportional-derivative (PD) and machine learning algorithms. Optical pursuit based on images collected from the onboard camera of a Hunter Drone is implemented to track a Target Drone. Field experiments were conducted to test the feasibility and functionality of the current software and hardware methods for the DroneSwatter system. A benchmark was established by flying a target drone in designed patterns and the performance of the DroneSwatter tracking system was evaluated based on what speeds the Hunter Drone could follow the Target Drone in the field testing.
Distribution of the zeros of canonical products and weighted condensation index

Canonical products with symmetrically positioned real zeros are considered. The question of the measurability of the sequence of zeros in terms of the weighted condensation index is treated. A natural class of weight functions, for which a finite condensation index ensures that the sequence of zeros is measurable, is distinguished. The main condition characterizing this class is shown to be sharp. Bibliography: 31 titles.
Digital Twin Concept, Method and Technical Framework for Smart Meters

Smart meters connect smart grid electricity suppliers and users. Smart meters have become a research hotspot as smart grid applications like demand response, power theft prevention, power quality monitoring, peak valley time of use prices, and peer-to-peer (P2P) energy trading have grown. But, as the carriers of these functions, smart meters have technical problems such as limited computing resources, difficulty in upgrading, and high costs, which to some extent restrict the further development of smart grid applications. To address these issues, this study offers a container-based digital twin (CDT) approach for smart meters, which not only increases the user-facing computing resources of smart meters but also simplifies and lowers the overall cost and technical complexity of meter changes. In order to further validate the effectiveness of this method in real-time applications on the smart grid user side, this article tested and analyzed the communication performance of the digital twin system in three areas: remote application services, peer-to-peer transactions, and real-time user request services. The experimental results show that the CDT method proposed in this paper meets the basic requirements of smart grid user-side applications for real-time communication. The container is deployed in the cloud, and the average time required to complete 100 P2P communications using our smart meter structure is less than 2.4 seconds, while the average time required for existing smart meter structures to complete the same number of P2P communications is 208 seconds. Finally, applications, the future development direction of the digital twin method, and technology architecture are projected.
Pharmacological Properties of Genista sagittalis L. (Fabaceae) Grown in Turkey

The genus Genista L. (Family: Fabaceae) is a plant having several traditional uses for treating common ailments such as diabetes, ulcer, and respiratory diseases. In this current study, the composition of essential oil and the biological activities of Genista sagittalis L. (Fabaceae) from Kocaeli: Yuvacık Dam Basin have been studied. A total of fourteen components were identified in the essential oil. The identified compounds belonged to straight-chain alkane, aromatic ether, and terpenoid derivatives. The antibacterial activity analyses demonstrated that G. sagittalis flower extract only had low activity against P. mirabilis and P. aeruginosa with MICs 1 to 2 mg/mL, as the peduncle extract showed strong anti-QS activity at 1.3 mg/mL. To the best of our knowledge, the current work is the first to report the antimicrobial and anti-quorum sensing activity of G. sagittalis growing in Turkey. Double-stranded DNA binding affinity investigations of the flower and peduncle ethanol extracts indicate that there are interactions with double-stranded DNA and related binding constants (Kb) were found as 1.97×103±0.37 and 3.68×102±0.44 for the flower and peduncle extract, respectively.
Anaplastic meningioma in an adolescent: Case report and brief literature review

Anaplastic meningioma is an uncommon neoplasm in childhood and adolescence. In contrast to the case in adults, pediatrics have a predominance of males over females. We report a case of a 17 years old with progressive scalp swelling on the right side. Patient underwent subtotal tumor resection at previous hospital three months ago. The operation was stopped due to extensive bleeding. Radiological investigations revealed a dura-based tumor with a cystic component. Following preoperative embolization, a craniotomy was done, and the tumor was almost completely removed. The tumor's histopathological analysis revealed characteristics of an anaplastic meningioma. The aggressive behavior of this tumor necessitates meticulous monitoring of therapeutic measures in such patients.