Miracle-dz/UniFAR-FTUs huggingface dataset:
Study on the Tabu Search Algorthm for Vehicle Routing Problem

On the basis of describing the vehicle routing problem briefly,this paper presents a new solution indicating method then builds a new tabu search algorithm for the problem and make some experimental computations.The computational results demonstrates that the high quality solutions to the vehicle routing problem can be obtained by using the new tabu search algorithm,and the new algorithm is also efficient and robust.
Moving meshes on general surfaces

Many phenomena in the applied and natural sciences occur on surfaces. To solve accurately the corresponding partial differential equations (PDEs), it is often necessary to adapt the mesh, based upon the geometry of the surface, or based upon the behaviour of the PDE solution. Moving mesh methods are particularly efficient strategies in many situations. PDEs explicitly involving the mesh speed, called moving mesh PDEs (MMPDEs), offer a robust technique to adapt the mesh. In this work, we implement, with the C++ finite-element library deal.II, a mesh adaptation based on the variable diffusion method. We generalize the moving mesh problem to curved surfaces by deriving appropriate mathematical and finite-element formulations. Furthermore, a simple method using surface parameterization is developed and implemented with deal.II. The results, for both fixed and dynamically adapting meshes, demonstrate the effectiveness of our method.
IMPLICIT SOLUTION OF THE NAVIER-STOKES EQUATIONS ON UNSTRUCTURED MESHES

In this paper we describe an algorithm for the solution of the Navier-Stokes equations on unstructured meshes. The algorithm employs a coupled algebraic multigrid (AMG) method to accelerate a two-sweep point implicit Gauss-Siedel relaxation scheme. The equations are preconditioned to permit solution of both compressible and incompressible flows. A cellbased, finite-volume discretization is used in conjunction with flux difference splitting and a linear reconstruction of variables. We present results for flow fields representing a range of Mach numbers and Reynolds numbers. The scheme remains stable for infinite CFL number and exhibits CPU usage that scales linearly with cell count.
Bipartite synchronization of multi‐agent systems under deception attacks via pinning delayed‐impulsive control

This article aims to investigate the bipartite synchronization problem of multi‐agent systems (MASs) under deception attacks. A pinning delayed‐impulsive control scheme is proposed to solve the synchronization of MASs under deception attacks that modify the transmitted data information and destroy the data integrity. The time delay can have a gain effect on the stability of the system by using time delay utilization technology. By selecting the appropriate time delay parameters, the convergence speed of the system is effectively improved. Furthermore, cooperative and competitive interactions between agents are considered. Sufficient conditions of the bipartite mean‐square bounded synchronization of MASs under deception attacks are theoretically obtained. Finally, the designed numerical simulations illustrate the correctness and effectiveness of our results.
FAST TRAINING OF SVDD BY EXTRACTING BOUNDARY TARGETS

Training support vector domain description (SVDD) involves solving a constrained convex quadratic programming, which requires large memory and enormous amounts of training time for large-scale data, an extraction strategy is proposed to extract boundary targets, based on the observation that the description boundary is determined by a small subset of training data called support vectors. Namely, the number of samples that scatter around each training target is calculated and taken as the measure of nearness to boundary targets, according to which the training samples are ranked in ascending order. Those former ranked samples are extracted as the boundary targets and are used for SVDD training. We compare the effectiveness of the proposed SVDD using extraction strategy with SVDD in terms of training accuracy, training scale and training time on artificial and benchmark data. Numerical experiments show that: the training scales and the training times are reduced without any loss of accuracy, which therefore can be used for large- scale data.
Formulation of Business Development Strategies in Gula Aren Temon SMEs in Pacitan District

The development company is one of the important factors underlying the creation of a successful business so that it can maintain its productivity in the long term. Likewise, MSMEs need to design a business strategy to support the progress of their business during increasingly competitive business competition. This study aims to identify business implementation, analyze business potential, and formulate strategic priorities in Gula Aren Temon SMEs. The research method used is a qualitative method with analytical tools in the form of BMC, VRIO, SWOT, and QSPM. The results of the study show that the nine building blocks of the BMC analysis describe the interrelated elements of Gula Aren Temon SME’s business implementation. The VRIO analysis results show that most of the resources owned by the business are at competitive parity. The results of the SWOT Analysis show that there are nine alternative strategies, while the priority chosen strategy to implement is to maximize the quality of service and products to maintain relationships with consumers and other partners. The strategy was chosen based on the highest TAS score, namely 6,280.
An Improved Elliptic Curve Digital Signature Algorithm

An improved elliptic curve digital signature algorithm is proposed in this paper.This algorithm can avoid inversing effectively.Meanwhile the signature algorithm used relatively safe Hash function SHA-256.The SHA-256 has effect on news "m" which waiting for sign and its signature "r". It can ensure the safety of the algorithm further.
Free-surface stretching-sustained intake vortices

An experimental investigation of vortices at deep hydraulic intakes revealed two distinct categories of free surface intake vortex. In case 1 when the strain field was strong enough, without an external, quantifiable source of vorticity, minor asymmetries in the near radial intake flow were concentrated and amplified into a vortex core that was purely stretching sustained (PSS). This vortex did not decay so long as the rate of intake withdrawal did not decrease. In case 2, vortex formation required that there be an ample and regular supply of external vorticity to interact with weaker strain fields. These non-purely stretching-sustained (NPSS) vortices decay with a decrease in externally supplied vorticity while maintaining the rate of intake withdrawal. A parameter, here named the stretching-sustained submergence, was postulated to classify conditions suitable for purely stretching sustained vortex formation.
Bounded real lemma and robust H_∞ control of 2-D Roesser models

The problem for robust H_∞ control of two dimensional Roesser models with time-invariant normbotmded parameter uncertainties was discussed in this paper.The purpose was the design of static state feedback controllers so that the resulting closed- loop system would prescribe H_∞ performance level for all admissible uncertainties.Different versions of bounded real lemma of 2-D Roesser models were established in terms of linear matrix inequalities.Based on this,a sufficient condition for the solvability of the robust H_∞ control problem was given,and a desired state feedback controller can be constructed by solving a set of matrix inequlities.
Combination of multiple classifiers for Chinese recognition

Traditional character recognition systems use a single classifier to determine the true class of a given character. However, by using classifiers of different types simultaneously, classification accuracy could be improved. In this paper, we propose a new approach based on majority vote and statistics to support a combined decision among multiple classifiers. First, we find the strengths and weaknesses of all classifiers through the analysis among test characters, templates and classifiers. Then we devise a combination method that can improve classification performance. Experimental results show the effectiveness of our approach.
Noise reduction using combined trailing edge and leading edge serrations in a tandem airfoil experiment

This work reports the noise reduction obtained using simultaneously serrated trailing edges and serrated leading edges in a tandem airfoil con guration. Two types of serrated trailing edge treatments are applied to the upstream airfoil, i.e., a sawtooth serration and a slitted sawtooth serration. The leading edge serrated airfoil was designed and manufactured by ONERA. Broadband noise reductions of between 5 dB and 8.5 dB are reported. It is shown over most of the frequency range that overall noise radiation is dominated by interaction noise, while trailing edge noise dominates the high frequency power spectrum at low mean ow velocity. Therefore, the overall noise reduction is dominated by the reductions in interaction noise, and particularly by the reduction of the downstream airfoil response R(f) due to leading edge serrations. However, additional reductions of the overall noise radiation of up 3.5 dB are provided by the use of the slitted sawtooth trailing edge rather than the sawtooth trailing edge on the upstream airfoil, at the high ow velocity U0 = 80m=s. This is shown to be most likely due to modi cations of the wake parameters, i.e., notably a faster decay of the turbulence intensity behind the slitted sawtooth trailing edge.
A Model-Based Approach Machine Learning to Scalable Portfolio Selection

This study proposes a scalable asset selection and allocation approach using machine learning that integrates clustering methods into portfolio optimization models. The methodology applies the Uniform Manifold Approximation and Projection method and ensemble clustering techniques to preselect assets from the Ibovespa and S&P 500 indices. The research compares three allocation models and finds that the Hierarchical Risk Parity model outperformed the others, with a Sharpe ratio of 1.11. Despite the pandemic's impact on the portfolios, with drawdowns close to 30%, they recovered in 111 to 149 trading days. The portfolios outperformed the indices in cumulative returns, with similar annual volatilities of 20%. Preprocessing with UMAP allowed for finding clusters with higher discriminatory power, evaluated through internal cluster validation metrics, helping to reduce the problem's size during optimal portfolio allocation. Overall, this study highlights the potential of machine learning in portfolio optimization, providing a useful framework for investment practitioners.
Preparing for a future crisis: using DEA-based performance analysis to assess initial pandemic responses in the Taiwanese hotel industry

PurposeTaiwan’s hotel industry was adversely impacted by the COVID-19 pandemic. This study aims to examine the effect of strategic choices by Taiwanese international tourist hotels before and during the pandemic environments.Design/methodology/approachA data envelopment analysis (DEA)-based Malmquist methodology is used in this study to provide a mechanism to assess Taiwanese hotel strategy performance. Changes in the productivity and performance of Taiwanese international tourist hotels were analyzed in the periods before and during the pandemic to uncover insights useful should a similar crisis occur in the future. Panel data were obtained from the annual report of international tourist hotels published by the Taiwan Tourism Bureau from 2017–2020. Two groups of hotels were analyzed in this study: city hotels and scenic hotels.FindingsThe findings of this study reveal that chain hotels tended to perform better than independent hotels in both city and scenic areas during the global pandemic. Specifically, the crisis caused a substantial decline in productivity and profitability for international tourist hotels in Taipei City during the COVID-19 period. Compared to city hotels, findings also indicate that most international tourist hotels in scenic areas were able to maintain better productivity, including larger-sized scenic hotels.Originality/valueThe DEA-based analysis provides unique and valuable insights for hotel firm leaders on how to better identify and make strategic choices when responding to future crises.
Optimal Guaranteed Service Time and Service Level Decision with Time and Service Level Sensitive Demand

We consider a two-stage supply chain with one supplier and one retailer. The retailer sells a product to customer and the supplier provides a product in a make-to-order mode. In this case, the supplier’s decisions on service time and service level and the retailer’s decision on retail price have effects on customer demand. We develop optimization models to determine the optimal retail price, the optimal guaranteed service time, the optimal service level, and the optimal capacity to maximize the expected profit of the whole supply chain. The results of numerical experiments show that it is more profitable to determine the optimal price, the optimal guaranteed service time, and the optimal service level simultaneously and the proposed model is more profitable in service level sensitive market.
Legal Protection against Patent and Intellectual Property Rights Violations Amidst COVID-19

The concept of legal principles for intellectual property (IP) protection is related to the adequate marketing of drug patents to protect patent rights. The objective of this research is to understand and analyze the factors affecting the market concerning international law, treaties, acts, and declarations, leading to encouraging creativity, production, increased investment, especially amidst the COVID-19 pandemic. The qualitative methodology provided for an in-depth understanding and analysis of primary and secondary research data gathered from key informant interviews and published literature. The collected data were analyzed with Strength, Weakness, Opportunity, and Threats (SWOT), a Delphi panel, and Correct, Adapt, Maintain, and Explore (CAME) analysis. The results found legal problems concerning the lack of rules to protect the rights and freedoms damaged by the monopoly on drug patents, complexities in the process of importing medicinal compounds, and how to access information with limited accessibility during COVID-19. Therefore, it is advisable to amend the law to curtail monopolies and to enact a law that prescribes rules for importing medicinal compounds to produce generic drugs in the country, including identifying the status of the patent holders. The research further paves the way for utilizing micro level research to be conducted in the development of intellectual property rights. Doi: 10.28991/ESJ-2023-SPER-016 Full Text: PDF
Classification of human motions using empirical mode decomposition of human micro-Doppler signatures

The ability to identify human movements can serve as an important tool in many different applications such as surveillance, military combat situations, search and rescue operations and patient monitoring in hospitals. This information can provide soldiers, security personnel and search and rescue workers with critical knowledge that can be used to potentially save lives and/or avoid dangerous situations. Most research involving human activity recognition employs the short-time Fourier transform (STFT) as a method of analysing human micro-Doppler signatures. However, the STFT has time-frequency resolution limitations and Fourier transform-based methods are not well-suited for use with non-stationary and non-linear signals. The authors approach uses the empirical mode decomposition to produce a unique feature vector from the human micro-Doppler signals following which a support vector machine is used to classify human motions. This study presents simulations of simple human motions, which are subsequently validated using experimental data obtained from both an S-band radar and a W-band millimetre wave (mm-wave) radar. Very good classification accuracies are obtained at distances of up to 90 m between the human and the radar.
Observation of second order ion acoustic Peregrine breather in multicomponent plasma with negative ions

The experimental observation of second order ion acoustic Peregrine breathers in multicomponent plasma with negative ions is reported. A long wavelength initial perturbation on a continuous carrier frequency ∼0.5 ωpi (where ωpi is the ion plasma frequency) of finite amplitude is found to undergo self-modulation due to the interplay between nonlinear dispersive effect and group velocity dispersion because of modulational instability. Wave energy focusses to a smaller localized and isolated group of waves within the packet with amplitude amplification up to 5 times of the background carrier wave. The experimental results are compared with second order breather solution of nonlinear Schrodinger equation. The wavelet analysis and fast Fourier transform analysis of the experimental time series data indicate strong nonlinear evolution (wave energy focusing and spectral broadening) conforming to the formation of second order Peregrine solitons.
Discriminating between intentional and unintentional gaze fixation using multimodal-based fuzzy logic algorithm for gaze tracking system with NIR camera sensor

Abstract. Gaze tracking systems are widely used in human–computer interfaces, interfaces for the disabled, game interfaces, and for controlling home appliances. Most studies on gaze detection have focused on enhancing its accuracy, whereas few have considered the discrimination of intentional gaze fixation (looking at a target to activate or select it) from unintentional fixation while using gaze detection systems. Previous research methods based on the use of a keyboard or mouse button, eye blinking, and the dwell time of gaze position have various limitations. Therefore, we propose a method for discriminating between intentional and unintentional gaze fixation using a multimodal fuzzy logic algorithm applied to a gaze tracking system with a near-infrared camera sensor. Experimental results show that the proposed method outperforms the conventional method for determining gaze fixation.
Multi-centre analysis of networks and genes modulated by hypothalamic stimulation in patients with aggressive behaviours

Deep brain stimulation targeting the posterior hypothalamus (pHyp-DBS) is being investigated as a treatment for refractory aggressive behavior, but its mechanisms of action remain elusive. We conducted an integrated imaging analysis of a large multi-centre dataset, incorporating volume of activated tissue modeling, probabilistic mapping, normative connectomics, and atlas-derived transcriptomics. Ninety-one percent of the patients responded positively to treatment, with a more striking improvement recorded in the pediatric population. Probabilistic mapping revealed an optimized surgical target within the posterior-inferior-lateral region of the posterior hypothalamic area. Normative connectomic analyses identified fiber tracts and functionally connected with brain areas associated with sensorimotor function, emotional regulation, and monoamine production. Functional connectivity between the target, periaqueductal gray and key limbic areas – together with patient age – were highly predictive of treatment outcome. Transcriptomic analysis showed that genes involved in mechanisms of aggressive behavior, neuronal communication, plasticity and neuroinflammation might underlie this functional network.
Nearly Complete Genome Sequence of Enterovirus Type A119 from Sewage in France in 2015

A nearly complete genome sequence of enterovirus type A119 was determined from an urban wastewater sample collected during a surveillance campaign in 2015 in Clermont-Ferrand, France. The partial VP1 sequence is a close relative of other partial enterovirus type A119 sequences detected in France and South Africa in the same year. ABSTRACT A nearly complete genome sequence of enterovirus type A119 was determined from an urban wastewater sample collected during a surveillance campaign in 2015 in Clermont-Ferrand, France. The partial VP1 sequence is a close relative of other partial enterovirus type A119 sequences detected in France and South Africa in the same year.
Toxoplasma gondii infection: novel emerging therapeutic targets

ABSTRACT Introduction Toxoplasmosis constitutes a challenge for public health, animal production, and welfare. So far, only a limited panel of drugs has been marketed for clinical applications. In addition to classical screening, the investigation of unique targets of the parasite may lead to the identification of novel drugs. Areas covered Herein, the authors describe the methodology to identify novel drug targets in Toxoplasma gondii and review the literature with a focus on the last two decades. Expert opinion Over the last two decades, the investigation of essential proteins of T. gondii as potential drug targets has fostered the hope of identifying novel compounds for the treatment of toxoplasmosis. Despite good efficacies in vitro, only a few classes of these compounds are effective in suitable rodent models, and none has cleared the hurdle to applications in humans. This shows that target-based drug discovery is in no way better than classical screening approaches. In both cases, off-target effects and adverse side effects in the hosts must be considered. Proteomics-driven analyses of parasite- and host-derived proteins that physically bind drug candidates may constitute a suitable tool to characterize drug targets, irrespectively of the drug discovery methods.
Comparison of three different image enhancement systems for detection of laryngeal lesions

Abstract Objective Image enhancement systems are important diagnostic tools in the detection of laryngeal pathologies. This study aimed to compare three different image enhancement systems: professional image enhancement technology, Image1 S and narrow-band imaging. Method Using the three systems, 100 patients with laryngeal lesions were investigated using a flexible and a 30° rigid endoscope. The lesions were diagnosed by three experts and classified using the Ni classification. The findings were compared. Results Lesions classified as ‘benign’ were histopathologically confirmed in 50 per cent of patients, malignant lesions were confirmed in 41 per cent and recurrent respiratory papillomatosis were confirmed in 9 per cent. There was no significant difference between the experts’ assessments of each image enhancement system. Conclusion The three systems give comparable results in the detection of laryngeal lesions. With two additional systems, more users can perform image-enhanced endoscopy, resulting in a broadly available tool that can help to improve oncological assessment.