Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonlinear Model Predictive Control using Derivative-Free Optimization

Today it is common to solve the Non-Linear Programming (NLP) problem arising in NMPC by the use of gradient-based optimization. However, these techniques may not be suited if the prediction model, and thus the optimization problem, is not differentiable. Such cases can arise when the prediction model contains logic operators, lookup-tables or a variable-step ODE-solver is used to simulate the prediction model. Although the model may be made differentiable by alterations or using another ODE-solver, this can compromise the accuracy of the prediction, and thus the performance of the NMPC can suffer. Therefore it is desirable to investigate techniques that can solve a NLP without requiring the gradient of the objective function or its constraints. Derivative-Free Optimization(DFO) has been subject to substantial research, as it is common for gradient information to be unavailable in optimization. This class of algorithms have been frequently applied to simulation-based optimization, as well as to some extent to NMPC. However, for the latter the studies have mainly been limited to smaller and simpler systems. This thesis will investigate some theoretical fundamentals of DFO, and present some common practical algorithms. Then a case-study is performed which develops a NMPC for a particular industrial crude-oil separator. This controller is simulated using the presented algorithms, as well as a gradient-based SQP. The experience gained is then used to modify an existing algorithm to improve real-time performance. The findings of the thesis are that DFO is significantly more robust against the numerical issues which the model of the case-study inhabits compared to the SQP tested. Further, one of the DFO-algorithms is comparable with the particular SQP, both with respect to computational consumption and accuracy of the solutions. Another algorithm is also very robust against numerical issues. Distribution of the computational load over several processing cores can improve real-time performance significantly, as well as minimizing the computational consumption by reducing the number of predictions required at each time step. This makes DFO a promising alternative to gradient-based optimization in NMPC.
CLOUD MANUFACTURING: IDENTIFICATIONS AND PRIORITIZATION OF OPPORTUNITIES USING AHP

The origin of a cost-efficient, service-oriented, customer-centric, manufacturing system called cloud manufacturing has evolved due to advancements in cyber systems and the availability of internet facilities worldwide. However, there is a significant number of opportunities before the adoption of cloud manufacturing. Through literature survey, expert opinions from academicians and industrialists, various opportunities, namely, pay-as-use, scalability, cost efficiency, flexibility, autonomy, low-risk backup and recovery, low startup cost and location independence associated with the espousal of cloud manufacturing are identified. Further, the Analytic Hierarchy Process (AHP) model is applied to find the weights and prioritize these opportunities, thereby finding the significant key opportunities. Moreover, the consistency ratio is calculated for the accuracy and consistency of the results. As the obtained value of consistency ratio is less than . 1, it shows that the result obtained is consistent and accurate. The managerial implication of these outcomes is that the results would indirectly help entrepreneurs in the adoption of cloud manufacturing.
Statistical Analysis of Non-Lattice Data

In rather formal terms, the situation with which this paper is concerned may be described as follows. We are given a fixed system of n sites, labelled by the first n positive integers, and an associated vector x of observations, Xi, . , Xn, which, in turn, is presumed to be a realization of a vector X of (dependent) random variables, Xi, . , X. In practice, the sites may represent points or regions in space and the random variables may be either continuous or discrete. The main statistical objectives are the following: firstly, to provide a means of using the available concomitant information, particularly the configuration of the sites, to attach a plausible probability distribution to the random vector X; secondly, to estimate any unknown parameters in the distribution from the realization x; thirdly, where possible, to quantify the extent of disagreement between hypothesis and observation.
MERS-CoV in Dromedary Camels: Sequence-Based Comparison of Antigenicity and Pathogenicity of Structural and Non-Structural Proteins

The goal of this researcher was to map the camel MERS-CoV structural and non-structural proteins and track changes in antigenicity and pathogenicity from 2013 to 2018. This is critical for developing effective diagnostic and control methods. Therefore, the sequences of MERS-CoV structural proteins such as Spike (S), Membrane (M), Nucleocapsid (N) and Envelope (E) and nonstructural proteins, comprising polyprotein-ab (polyab) and Open reading frame-3 (ORF3) were retrieved. The amino acid sequences of each of these proteins were analysed to estimate their antigenic and pathogenic properties from 2013 to 2018. The antigenicity profiles showed variations in antigenicity minimum, maximum, range, and average from 2013 to 2018. MERS-CoV proteins’ maximum antigenicity score was declining in the examined time frame. In 2013, the maximum score was 0.86, and by 2018, it had dropped to 0.6. This resulted in a greater range of antigenicity with the onset of MERS-CoV in 2013, with a range of 0.56, which eventually decreased to 0.22 in 2018. The net effect on the mean value of antigenicity score revealed that MERS-CoV antigenicity decreased gradually. Between 2013 and 2018, the mean antigenicity score of structural and nonstructural proteins decreased. Non-structural proteins, on the other hand, had the greatest alterations, with a mean value of 0.52 in 2013 and 0.42 in 2018, representing a loss of 19.2 per cent of their antigenicity score. In contrast, the structural protein showed a mean value of 0.55 in 2013 and 0.53 in 2018, i.e. lost only 3.6% of its antigenicity score. Given that ORF3 was nonantigenic, therefore, all changes in antigenicity changes were attributed to polyab protein. Pathogenicity score has decreased from 0.81 in 2013 to 0.78 in 2018. Further studies are required to map these changes on the virus-host cell interaction level.
Performance promotion of broiler chickens: the role of the food supplements

In order to increase egg and chicken meat production, poultry growth is critical. The purpose of this study is to see how well broiler chickens perform when provided different additives in their meals (trace minerals and vitamins). Nutraceuticals, in addition to providing nutritional requirements for birds, offer advantageous pharmacological effects, such as assisting in the establishment of normal state of physiological health, preventing various diseases, and thus improving production performance. The vitamins, polypeptides and amino acids, minerals, enzymes, and other nutraceuticals are examples. Vitamins are dietary substances that are required for vital cell functions such as cell development, growth, and metabolism process. Mineral nutrition is critical in broiler chicken production, with an emphasis on manganese (Mn), zinc (Zn), selenium (Se) and copper (Cu). Due to the availability of organic trace elements and today's fast-growing birds, broiler chickens' Mn, Zn, Se, and Cu requirements must be revised.
Reinforcement Learning-Based Dynamic Anti-Jamming Power Control in UAV Networks: An Effective Jamming Signal Strength Based Approach

Unmanned aerial vehicle (UAV) assisted air-to-ground (A2G) communication is vulnerable to malicious jamming due to the broadcast nature of wireless communications. In this letter, an anti-jamming power control framework with an unknown jamming model and unknown transmission power is proposed. In particular, the probability density function (PDF) of the effective jamming signal strength (EJSS) is first estimated via kernel density estimation (KDE). Then, utilizing the EJSS, a deep deterministic policy gradient (DDPG) based framework is proposed to acquire the power control strategy in real time. Moreover, a trajectory design scheme based on K-means++ is proposed to track the location of users. The simulation results show that the proposed framework yields an improved sum rate and energy efficiency over the reference schemes.
Neuromuscular Fatigue Responses of Endurance- and Strength-Trained Athletes during Incremental Cycling Exercise

This study explored the development of neuromuscular fatigue responses during progressive cycling exercise. The sample comprised 32 participants aged 22.0 ± 0.54 years who were assigned into three groups: endurance-trained group (END, triathletes, n = 10), strength-trained group (STR, bodybuilders, n = 10) and control group (CG, recreationally active students, n = 12). The incremental cycling exercise was performed using a progressive protocol starting with a 3 min resting measurement and then 50 W workload with subsequent constant increments of 50 W every 3 min until 200 W. Surface electromyography (SEMG) of rectus femoris muscles was recorded during the final 30 s of each of the four workloads. During the final 15 s of each workload, participants rated their overall perception of effort using the 20-point rating of the perceived exertion (RPE) scale. Post hoc Tukey’s HSD testing showed significant differences between the END and STR groups in median frequency and mean power frequency across all workloads (p < 0.001 and p < 0.01, respectively). Athletes from the END group had significantly lower electromyogram amplitude responses than those from the STR (p = 0.0093) and CG groups (p = 0.0006). Increasing RPE points from 50 to 200 W were significantly higher in the STR than in the END group (p < 0.001). In conclusion, there is a significant variation in the neuromuscular fatigue profiles between athletes with different training backgrounds when a cycling exercise is applied. The approximately linear trends of the SEMG and RPE values of both groups of athletes with increasing workload support the increased skeletal muscle recruitment with perceived exertion or fatiguing effect.
Improvement in calculation of some surface integrals: application to junction characterization in cavity filter design

A rigorous method which makes it possible to reduce a surface integral to a contour integral is developed. The application of this formulation to the study of a circular-to-rectangular waveguide junction reduces the time of computation of the scattering parameters by more than 50%. The method is used to calculate the electromagnetic coupling of circular cavities by a rectangular iris. This approach, together with an optimization procedure, has allowed the direct design of a filter without any further mechanical adjustments. The results obtained are in good agreement with experimental data. <<ETX>>
A sulfur-containing coordination polymer: remarkable heavy metal removal capacities and broad-spectrum antibacterial activities

Coordination polymers (CPs) represent a potentially highly valuable class of materials for next-generation applications in heavy metal removal and as antibacterial agents. Ligands bearing sulfur donors are ideal candidates for the preparation of CPs for these applications since sulfur atom has lone pairs of electrons which could be donated to some metal ions, thereby binding the metals selectively; and it is also effective in disrupting bacterial functioning. Herein, we report, the synthesis of the crystal (1crys ) and the powder (1powd ) forms of the Zn(II)-based CP [Zn(TDPA)2(TMPy)2] n (1) (where TDPA = 3,3-thiodipropionic acid and TMPy = 4,4-trimethylenedipyridine) . Structural elucidation of the CPs – 1crys and 1powd were undertaken by means of FTIR, TGA and PXRD. The 1powd was obtained in much higher yields than the 1crys , and heavy metal removal behavior and antibacterial activity of the former were extensively investigated. Excellent removal capacities of 667 mg/g for Ag(I) and for Pb(II) by 1powd in single batch studies were observed. Additionally, 1powd inhibited the growth of six common bacterial strains and showed minimum inhibitory concentrations comparable to reported antibacterial agents. The efficiency of 1powd for these applications is linked to the sulfur-containing ligand used in the construction of the CP and its polymeric structure. Overall, the ease of preparation, yield, outstanding heavy metal removal capacities and potency in inhibiting bacterial growth offers 1powd as a highly adaptable and multifunctional material. GRAPHICAL ABSTRACT
«Lublin Triangle»: Prospects for the Formation of a Transport and Logistics Cluster (Aspects of Modern Marketing Management)

The generalizing analysis of modern pan-European disintegration tendencies, especially against the background of forecasted such new tendencies, necessitates the search for other determinants of reliable international cooperation and the formation of stable international organizations. The new Lublin Triangle platform, consisting of Poland, Lithuania and Ukraine, which was founded in 2020, could be one of the few international organizations. The greatest economic effect and a number of concomitant institutional advantages of cooperation between these countries can give in the transport and logistics sphere with the formation of the transport and logistics cluster "Lublin Triangle". Taking into account a number of institutional and technical-functional factors, Latvia and Estonia can also be full participants in the proposed cluster; in this case, the proposed cluster may be called, for example, the Baltic-Eastern European Transport and Logistics Cluster. The categorical definition of the definition of "transport and logistics cluster" is substantiated. It is a set of spatially defined entities of all types of transport, infrastructure of their modes of transport and engineering industries, as well as related enterprises, institutions and governing bodies, which operates on the basis of a network of hubs. The innovative nature of the proposal is that in contrast to the existing approaches in domestic and foreign science, according to which transport and logistics clusters are considered only within certain regions of a country, in this case: the understanding of the cluster, provisions and procedures of the Trans-European Transport Network (TEN-T) policy on the basis of modern marketing management; the cluster itself is offered in the form of appropriate transport and logistics hubs. The article, in addition to identifying the definition of "transport and logistics cluster" highlights the parameters of the proposed cluster with the disclosure of their content; cluster functions are classified; the structure of the network of geographical location of hubs is given.
A High-Resolution Capacitance-to-Digital Converter based on Iterative Discharging

This work focuses on the improvement of a capacitance-to-digital converter (CDC) based on iterative discharging. Time-domain interpolation is applied on the CDC. With such technology, a high-resolution CDC can be achieved while its fully digital feature can be kept. The proposed CDC achieves a resolution of 2.8fF and a FoM of 1.32pJ/step at 5.5pF capacitance. A MEMS capacitive pressure sensor has been bonded to the chip and the output with varying capacitance has been checked.
An Automatic Prostate Surgical Region Reconstruction Method Based on Multilevel Learning

Benign prostatic hyperplasia (BPH) is one of the main diseases affecting the health of middle-aged and elderly men. The accurate measurement and reconstruction of the surgical region (SR) from medical images is a vital and challenging step before the surgery. In this article, an automatic prostate SR reconstruction method based on multilevel learning is proposed. This method divides the reconstruction problem into two levels: prostate segmentation learning and reconstruction parameter learning. It can not only segment the prostate accurately, but also fuse various surgical constraints flexibly and doctors’ clinical experience. Compared with traditional methods, the proposed method has better reconstruction accuracy and flexibility. The proposed method was comprehensively validated on multiple datasets. It achieved better accuracy and robustness than current baselines on the magnetic resonance (MR) images of 20 patients from public and clinical datasets. Moreover, the clinical patients’ postoperative MR images were collected, and a preoperative–postoperative comparative study was carried out, which further proved the effectiveness of this method from a clinical perspective. Furthermore, this method has the potential to promote the development of BPH robotic surgery navigation and autonomy as well as improve the safety and efficiency of BPH surgery.
A method for coupling analysis of association modules in product family design

ABSTRACT For the coupling between modules in product family design, an approach to analysing coupled design for product family based on coupling incidence path and correlation impact degree is presented. The coupling of three cases is discussed, i.e. the coupling between platform modules, between platform module and customisation module, as well as between customisation modules. Then the interface among modules can be identified, and instigating components are considered as controllable factors and experimental design techniques are utilised to analyse their influence on the design objectives. According to analysis on coupling incidence path and correlation impact degree, the implementation sequence of modules is identified and corresponding decoupling strategies are proposed, if any, that may weaken the coupling of product family design. Finally, an example on the trolley design of gantry crane is provided to illustrate the proposed method for coupling analysis and decoupling of association modules in product family design.
Assessment of on-line burnup monitoring of pebble bed reactor fuel using passive gamma-ray spectrometry

An investigation was performed to assess the feasibility of passive gamma-ray spectrometry assay as an approach for on-line burnup determination for the Modular Pebble Bed Reactor (MPBR). In addition to its inherently safe design, a unique feature of this reactor is its multi-pass fuel cycle in which graphite fuel pebbles are randomly loaded and continuously cycled through the core until they reach their prescribed End-of-Life burnup limit (/spl sim/80,000 MWD/MTU). Therefore, unlike the situation with conventional light water reactors, depending solely on computational methods to perform in-core fuel management will be highly inaccurate. As a result, an on-line measurement approach becomes the only accurate method to assess whether a particular pebble has reached its End-of-Life burnup limit. The results of this investigation indicate that the fission products Cs-137 and Eu-154 provide accurate, and power-history-independent signatures that can be directly correlated with burnup. Furthermore, depending on the fuel manufacturing process, artificially introduced dopants (e.g., Co) can provide gamma-ray lines that are usable for burnup monitoring. In fact, it was found that the relative activity of Co-60 to Cs-134 could form an accurate burnup indicator that is independent of power history. In this case, the use of a relative indicator has several advantages, among which is the elimination of the need for absolute knowledge of the detector full energy peak efficiency curve, and the establishment of a system quality assurance figure of merit based on the peak area ratio of the Co lines.
Arabic Language Modeling Based on Supervised Machine Learning

Misinformation and misleading actions have appeared as soon as COVID-19 vaccinations campaigns were launched, no matter what the country’s alphabetization level or growing index is. In such a situation, supervised machine learning techniques for classification appears as a suitable solution to model the value & veracity of data, especially in the Arabic language as a language used by millions of people around the world. To achieve this task, we had to collect data manually from SM platforms such as Facebook, Twitter and Arabic news websites. This paper aims to classify Arabic language news into fake news and real news, by creating a Machine Learning (ML) model that will detect Arabic fake news (DAFN) about COVID-19 vaccination. To achieve our goal, we will use Natural Language Processing (NLP) techniques, which is especially challenging since NLP libraries support for Arabic is not common. We will use NLTK package of python to preprocess the data, and then we will use a ML model for the classification.
Cellular Automata in Modeling and Predicting Urban Densification: Revisiting the Literature since 1971

The creation of an accurate simulation of future urban growth is considered to be one of the most important challenges of the last five decades that involves spatial modeling within a GIS environment. Even though built-up densification processes, or transitions from low to high density, are critical for policymakers concerned with limiting sprawl, the literature on models for urban study reveals that most of them focus solely on the expansion process. Although the majority of these models have similar goals, they differ in terms of implementation and theoretical assumptions. Cellular automata (CA) models have been proven to be successful at simulating urban growth dynamics and projecting future scenarios at multiple scales. This paper aims to revisit urban CA models to determine the various approaches for a realistic simulation and prediction of urban densification. The general characteristics of CA models are described with respect to analysis of various driving factors that influence urban scenarios. This paper also critically analyzes various hybrid models based on CA such as the Markov chain, artificial neural network (ANN), and logistic regression (LR). Limitation and uncertainties of CA models, namely, neighborhood cell size, may be minimized when integrated with empirical and statistical models. The result of this review suggests that it is useful to use CA models with multinomial logistic regression (MLR) in order to analyze and model the effects of various driving factors related to urban densification. Realistic simulations can be achieved when multidensity class labels are integrated in the modeling process.
An Integrated Traffic and Vehicle Co-Simulation Testing Framework for Connected and Autonomous Vehicles

Simulation testing is critical for the development and optimization of connected and autonomous vehicle (CAV) driving systems. Existing simulation tools for autonomous driving testing generally focus on single-vehicle-based perception, decision, and control. Nevertheless, cooperative maneuvering among CAV and human-driven vehicles is of utmost importance in many high-value scenarios, e.g., intersections, platooning, and bottlenecks. To this end, this article aims to establish a comprehensive and realistic co-simulation framework that combines both vehicle and traffic simulation. For the sake of presentation and without loss of generality, CARLA is employed for vehicle simulation as it features high-fidelity vehicle dynamics models, while Simulation of Urban Mobility is employed for traffic simulation as it provides advanced traffic models. Further, vehicle trajectory extraction technology is applied to extract vehicle trajectories from videos and use them as an input of the co-simulation framework. Moreover, three different scenarios comprising the presence of obstacles on the highway, congested city intersections, and complete CAV testing are described to verify the rationality of the framework. The real data-driven, full-chain co-simulation testing method proposed in this article can provide a realistic virtual environment for testing decision- and motion-planning level vehicular functions of CAVs.