Miracle-dz/UniFAR-FTUs huggingface dataset:
Artificial Intelligence Control Logic in Next-Generation Programmable Networks

The new generation of programmable networks allow mechanisms to be deployed for the efficient control of dynamic bandwidth allocation and ensure Quality of Service (QoS) in terms of Key Performance Indicators (KPIs) for delay or loss sensitive Internet of Things (IoT) services. To achieve flexible, dynamic and automated network resource management in Software-Defined Networking (SDN), Artificial Intelligence (AI) algorithms can provide an effective solution. In the paper, we propose the solution for network resources allocation, where the AI algorithm is responsible for controlling intent-based routing in SDN. The paper focuses on the problem of optimal switching of intents between two designated paths using the Deep-Q-Learning approach based on an artificial neural network. The proposed algorithm is the main novelty of this paper. The Developed Networked Application Emulation System (NAPES) allows the AI solution to be tested with different patterns to evaluate the performance of the proposed solution. The AI algorithm was trained to maximize the total throughput in the network and effective network utilization. The results presented confirm the validity of applied AI approach to the problem of improving network performance in next-generation networks and the usefulness of the NAPES traffic generator for efficient economical and technical deployment in IoT networking systems evaluation.
Improving Website Usability by Mining Trending and Popular Web Resources Based on User Access Patterns from Weblogs

Websites are now mode of education, business and entertainment for almost every entity on Internet. To retain interests of users in the website, it is necessary to provide advance usability features. For example, in online shopping, if information about frequently visited brands of a specific item is provided to the users, then it will help users to identify the purchasing trends of that item, and help them to buy a product. Also, it will help companies to identify popularity of products. But, as interests of users frequently changes with time, this paper proposes a web usage mining technique that retrieves popular and trending visited web resources of website on daily basis, to improve site usability. Experimental result shows trending and popular web resources for different access patterns of users.
Methodisches Vorgehen bei experimentellen Untersuchungen zur Oberflächenfiltration mit flexiblen Filtermedien an einer genormten Labor‐Filterprüfapparatur

Die Partikelabscheidung aus Gasen mit Oberflachenfiltern ist sowohl bei der Emissionsminderung als auch bei der Wertstoffgewinnung ein bewahrtes Standardverfahren. Experimentelle Untersuchungen zur Oberflachenfiltration werden haufig mit Testfiltern, die an Filteranlagen im Labormasstab eingebaut sind, durchgefuhrt. Am Beispiel einer bekannten genormten Labor-Filterprufapparatur werden Ansatzpunkte zum methodischen Vorgehen bei experimentellen Untersuchungen zur Oberflachenfiltration mit flexiblen Filtermedien beschrieben und diskutiert, die einen schlussigen quantitativen Vergleich der experimentellen Ergebnisse ermoglichen bzw. erleichtern sollen. Surface filters are used widely because of their excellent collection performance and their adaptability to the most diverse conditions. Furthermore, significant advances have been made in the development of new filter media. Experimental investigations on surface filtration are often performed on laboratory filter test rigs using test filters. This paper describes and discusses starting points for methodical approaches to experimental investigations on surface filtration using flexible filter media on an established standard laboratory filter test rig intending to make a quantitative, conclusive comparison of experimental data possible or easier.
Toward Reference Models for

Requirements traceability is intended to ensure continued alignment between stakeholder requirements and various outputs of the system development process. To be useful, traces must be organized according to some modeling framework. Indeed, several such frameworks have been proposed, mostly based on theoretical considerations or analysis of other literature. This paper, in contrast, follows an empirical approach. Focus groups and interviews conducted in 26 major software development organizations demonstrate a wide range of traceability practices with distinct low-end and high-end users of traceability. From these observations, reference models comprising the most important kinds of traceability links for various development tasks have been synthesized. The resulting models have been validated in case studies and are incorporated in a number of traceability tools. A detailed case study on the use of the models is presented. Four kinds of traceability link types are identified and critical issues that must be resolved for implementing each type and potential solutions are discussed. Implications for the design of next-generation traceability methods and tools are discussed and illustrated.
Error Analysis of Harmonic Measurement Device and Solution

Aiming at the shortage of lowprecision harmonic wave's analysis in China, and the inadaptability of error transmitting, we analyze the error sources and come up with an approach, Direct Data frequency Synthesizer (DDS), to replace the traditional analog Phase Locked Loop (PLL) to track the frequency of measured signal. Non-integer-period Discrete Fourier Transform (DFT) compensation algorithm is used for processing data. Accordingly a high-precision harmonic standard device is designed in this paper. The final test shows that this solution can efficiently improve the precision.
Quantification of Knowledge Exchange Within Classrooms: An AI-based Approach

Knowledge management improves efficiency and productivity of a company. A typical knowledge transfer pipeline, an enabler of knowledge management, consists of academics, higher education institutions, research funding bodies and companies. While the knowledge exchange mainstream sheds light on research collaborations, the evaluation of in-classroom knowledge exchange is often omitted, underestimating the impact this would have on the student employability. Current work on knowledge exchange at higher education institutions primarily focuses on: (i) collaborations with external parties, and (ii) identifying factors that affect knowledge sharing behaviours. This paper extends knowledge exchange to classroom teaching through: (i) formulating a framework among undergraduate Engineering students, and (ii) proposing an Artificial Intelligence based approach for evaluating the knowledge exchange process. The framework comprises of two group coursework with an intermediate handover event emulating an industrial workplace scenario in which knowledge exchange plays a key role. Then, an artificial intelligence-based visualisation technique processes data from two coursework-based surveys, completed before and after the abrupt handover event, to assess the change in the student intellectual backgrounds using two-dimensional maps embedding students as datapoints. The results interestingly reveal correlations between standard student evaluation metrics (for example grades, peer review and survey scores) and the formation of datapoint clusters. It is argued in the paper that the proposed artificial intelligence tool lends educators with tools to better understand the individual student performance in ways that are not captured by conventional academic assessments.
Optimum Design and Management of Pressurized Branched Irrigation Networks

The scarcity of water resources is the driving force behind modernizing irrigation systems in order to guarantee equal rights to all beneficiaries and to save water. Traditional distribution systems have the common shortcoming that water must be distributed through some rotational criteria. This type of distribution is necessary to spread the benefits of scarce resources. Irrigation systems based on on-demand delivery scheduling offer flexibility to farmers and greater potential profit than other types of irrigation schedules. However, in this type of irrigation system, the network design has to be adequate for delivering the demand during the peak period whilst satisfying minimum pressure constraints along with minimum and maximum velocity constraints at the farm delivery points (hydrants) and in the pipes, respectively. In this paper, optimum design and management of pressurized irrigation systems are considered to be based on rotation and on-demand delivery scheduling using a genetic algorithm. Comparison is made between the two scheduling techniques by application to two real irrigation systems. Performance criteria are formulated for the optimum design of a new irrigation system and better management of an existing irrigation system. The design and management problems are highly constrained optimization problems. Special operators are developed for handling the large number of constraints in the representation and fitness evaluation stages of the genetic algorithm. The performance of the developed genetic algorithm is assessed in comparison to traditional optimization techniques. It is shown that the methodology developed performs better than the linear programming method and that solutions generated by the modified genetic algorithm show an improvement in capital cost. The method is also shown to perform better in satisfying the constraints. Comparison between on-demand and rotation delivery scheduling shows that a greater than 50% saving can be achieved in total cost at the cost of reducing flexibility in the irrigation time. Finally, it is shown that minimizing standard deviation of flow in pipes does not result in the best distribution, and therefore minimum cost, neither for systems with uniform flows or those with large variations in discharge at hydrants.
Steady state reference current determination technique for brushless DC motor drive system

The permanent magnet (PM) brushless DC motor (BDCM) has been widely used in industrial applications because of its low inertia, fast response, high power density, high reliability and no maintenance requirements. It exhibits the operating characteristics of a conventional commutated permanent magnet DC motor but eliminates the mechanical commutators and brushes. Consequently, many problems associated with brushes and commutators are eliminated. A steady state reference current (SSRC) determination technique based on a neural network is proposed for the PM BDCM drive system. A conventional controller combined with this technique can improve system response speed, and reduce overshoot and oscillation. The design of the conventional controller is introduced. The SSRC determination technique is described in detail. Hardware implementation and experimental results are proposed. The drive system is tested under PI and fuzzy control.
Above-threshold parameter extraction and modeling for amorphous silicon thin-film transistors

This paper presents modeling and parameter extraction of the above-threshold characteristics of hydrogenated amorphous silicon (a-Si:H) thin-film transistors (TFTs) in both linear and saturation regions of operation. A bias- and geometry-independent definition for field effect mobility considering the ratio of free-to-trapped carriers is introduced, which conveys the properties of the active semiconducting layer. A method for extraction of model parameters such as threshold voltage, effective mobility, band-tail slope, and contact resistance from the measurement results is presented. This not only provides insight to the device properties, which are highly fabrication-dependent, but also enables accurate and reliable TFT circuit simulation. The techniques presented here form the basis for extraction of physical parameters for other TFTs with similar gap properties, such as organic and polymer TFTs.
Dynamic modeling of refrigerated transport systems with cooling-mode/heating-mode switch operations

This article presents dynamic modeling approaches to predict system performance characteristics of cooling-/heating-mode switch cycling operation, a commonly used temperature regulation approach in refrigerated transport systems. A dynamic model of a commercially available transport refrigeration system is presented, which describes the system dynamics during the mode switch transients. The development of the heat exchanger and accumulator models is highlighted using the switched modeling framework. Model validation against experimental data demonstrates the capabilities of the modeling approach in representing the transient behavior of the mode switch process. Simulation case studies to predict refrigerant mass distribution during transients and system performance with the influence of door-opening events are also provided to demonstrate modeling capabilities. The presented dynamic modeling framework can serve as a valuable tool to evaluate performance with different system configurations and operating strategies in transport refrigeration applications.
Predicting the current and future distributions of major food crop designated geographical indications (GIs) in China under climate change

Abstract We first predicted the potential climate suitability of food crops with geographical indications (GIs) using the maximum entropy (MaxEnt) model and eight bioclimatic predicators under three shared socioeconomic pathway (SSP) scenarios during 2021–2040. The results showed that highly suitable rice, wheat, and corn areas increased in the three scenarios. Specifically, the growth rate under the SSP2–4.5 scenario was higher than that under the other scenarios. The potentially suitable area of wheat GIs was the largest with the most dramatic change. In contrast, the highly suitable area of millet and soybean GIs decreased under the SSP2–4.5 scenario but increased under the SSP1–2.6 and SSP3–7.0 scenarios. The increased greenhouse gas concentrations and radiative forcing under various scenarios had different effects on the dominant factors, changing the highly suitable areas of various food crop GIs. This study provides scientific evidence for the future cultivation of food crop GIs.
Relationship between TQM and TPM implementation factors and business performance of manufacturing industry in Indian context

Purpose – To study the strategic implications of TQM and TPM in an Indian manufacturing set‐up and to detail literature reviews to highlight gap areas. To examine the relationship between factors influencing the implementation of TQM and TPM and business performance for the following three approaches in an Indian context: TQM alone; TPM alone; both TQM and TPM together. This is done to extract significant factors for the above three approaches.Design/methodology/approach – Empirical survey‐based research on a sample size of 108 manufacturing companies. Uses bivariate correlation and multiple regression analysis techniques to extract significant factors using SPSS.Findings – The research identifies two sets of factors which are critical for the effectiveness of TQM and TPM: universally significant factors for all the three approaches like leadership, process management and strategic planning; and approach‐specific factors like equipment management and focus on customer satisfaction. The study also highligh.
A new parametric differential operator generalized a class of d'Alembert's equations

The studies in operator theory are attracting many researchers. The central aim of this investigation is to formulate a special parametric differential operator (PDO) based on the error function in the open unit disk. The suggested operator is related to the well-known Carlson–Shaffer operator and the differential operator due to Salagean. We express a class of analytic functions to study its performance in view of the geometric function theory. As application, we employ PDO to present the conformable d'Alembert's equation. We discuss the univalent solution of the suggested equation and study some of its geometric behaviours.
Convergence Failure and Stall Hysteresis in Actuator-Disk Momentum Models Applied to Vertical Axis Wind Turbines

The application of actuator-disk momentum models to highly loaded vertical axis rotors (large tip speed ratios/solidities) frequently results in a convergence failure of the iterative method employed and the production of nonphysical erratic solutions. It is suggested that this convergence failure is due to an inflection in Glauert's empirical fit for streamwise momentum loss versus disk blockage. The erratic solutions are found to be due to the presence of multiple roots occurring near stall between the blade-element and the momentum models. A new method is proposed that employs a graphical root finding scheme coupled with knowledge of a blade's flow history to correctly identify the turbine's % operating point, regardless of disk loading.
The Effect of Knowledge Sharing and Innovativeness on Organizational Performance: An Empirical Study in Vietnam

The emerging competitive environment in today’s global marketplace is one where businesses no longer compete with each other as autonomous, individual firms. Global, economic, and technological development pressure forces organizations to continually enhance their performance through knowledge sharing and innovativeness. The purpose of this paper is to explore the moderating role of knowledge sharing and the mediating role of innovativeness in the relationship between transformational leadership and organizational performance. The data was collected from 389 employees in Vietnamese industrial enterprises through a questionnaire survey. The information was then analyzed by explanatory factor analysis (EFA) confirmatory factor analysis (CFA) as well as structural equation modeling (SEM). The results show that the mediating role of innovativeness and the moderating role of knowledge sharing in the relationship between transformational leadership and performance, are supported. Organizations may reap the benefits of an innovative workforce by selecting, nurturing transformational leaders. This study contributes to the field of human resources management, particularly leadership, by exploring the role of transformational leadership. Moreover, this is the first study to test the moderating role of knowledge sharing and the mediating role of innovativeness in the relationship between transformational leadership and the organizational performance.
Automatic Blob Detection for Dental Caries

Dental Caries are one of the most prevalent chronic diseases around the globe. Detecting carious lesions is a challenging task. Conventional computer aided diagnosis and detection methods in the past have heavily relied on the visual inspection of teeth. These methods are only effective on large and clearly visible caries on affected teeth. Conventional methods have been limited in performance due to the complex visual characteristics of dental caries images, which consist of hidden or inaccessible lesions. The early detection of dental caries is an important determinant for treatment and benefits much from the introduction of new tools, such as dental radiography. In this paper, we propose a deep learning-based technique for dental caries detection namely: blob detection. The proposed technique automatically detects hidden and inaccessible dental caries lesions in bitewing radio-graphs. The approach employs data augmentation to increase the number of images in the data set to have a total of 11,114 dental images. Image pre-processing on the data set was through the use of Gaussian blur filters. Image segmentation was handled through thresholding, erosion and dilation morphology, while image boundary detection was achieved through active contours method. Furthermore, the deep learning based network through the sequential model in Keras extracts features from the images through blob detection. Finally, a convexity threshold value of 0.9 is introduced to aid in the classification of caries as either present or not present. The process of detection and classifying dental caries achieved the results of 97% and 96% for the precision and recall values, respectively.
The development of an analytical method for constructing noise contours

Noise is one of the most pervasive occupational health problems. Noise contours provide useful information by clearly identifying areas where noise hazards exist. This study finds an efficient algorithm for computing sound level in industrial buildings that accounts for reverberation effects. This study proposes an analytical image method to calculate workplace noise distribution by using machinery noise labeling. Contours are then obtained by joining locations with equal noise levels using commercially available software. To demonstrate the application of the proposed method, a manufacturing plant building is considered. The computation algorithm accounts for distance and sound reflection to construct noise contours. The proposed approach could be used in industry for noise management when designing new plant buildings and installing machines. Practical applications: Machine noise emission information will be widely used in practice to evaluate the potential and performance of noise levels in industrial buildings. Plant buildings should be designed to prevent or minimize any risks to health or safety resulting from the emission of noise. The construction of noise contours will have direct implications for the hearing conservation program.
Beitrag zur Prozessstandardisierung in der Intralogistik

To sustain performance and competitiveness of enterprises in a dynamic environment a permanent adaptation of (intra-)logistic processes was required. Standardization of processes and corporate overlapping process models were considered as key factors for an efficient business process management, particularly in networks. In Practice there were no scientific established and detailed reference process models for the intra logistic sector. With the research and development of a reference process model and the prototypic implementation of a process workbench for the generic creation of changeable process chains a contribution to process standardization in logistics was made. Below the chosen solution process is described, which consisted first in the development of a metamodel for the reference modelling, second in the empiric confirmation of the generation of a reference process model from “atomic” elements and third in the model evaluation.