Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation Method of Logistics Optimization Scheme based on Multi Preference Group Decision Making

To overcome the limitation of standard PSO algorithm in solving process of site selection of logistics distribution center, catfish effect in nature is introduced and a site selection scheme of the logistics distribution center based on CFPSO is proposed, besides, simulation experiments are carried out through actual problem of site selection of logistics distribution center and compared with standard PSO algorithm and genetic algorithm, which is adopted to verify effectiveness of CFPSO on solution to site selection of logistics distribution center. Simulation results indicate that CPSO algorithm preferably overcomes differences compared with other algorithms and obtains global optimum scheme of site selection of logistics distribution center with incomparable advantages that other comparative algorithms fail to catch up with.
Providing energy services to prosumer communities

Recent years have shown the emergence of numerous local energy initiatives (prosumer communities) in the Netherlands. Many of them have set the goal to establish a local and sustainable energy provision on a not-for-profit basis. In this study we carried out exploratory case studies on a number of Dutch prosumer communities. The objective is to analyse their development process, to examine the barriers they encounter while organising their initiative, and to find how ICT could be applied to counteract these barriers and support communities in reaching their goals. The study shows that prosumer communities develop along a stepwise, evolutionary growth path, while they are struggling with organising their initiative, because the right expertise is lacking on various issues (such as energy technology, finance and legislation). Participants stated that, depending on the development phase of their initiative, there is a strong need for information and specific expertise. With a foreseeable growing technical complexity they indicated that they wanted to be relieved with the right tools and services at the right moment. Based on these findings we developed a generic solution through the concept of a prosumer community shopping mall. The concept provides an integrated and scalable ICT environment, offering a wide spectrum of energy services that supports prosumer communities in every phase of their evolutionary growth path. As such the mall operates as a broker and clearing house between 2 prosumer communities and service providers, where the service offerings grow and fit with the needs and demands of the communities along their growth path. The shopping mall operates for many prosumer communities, thus providing economies of scale. Each prosumer community is presented its own virtual mall, with specific content and a personalised look-and-feel.
ROLLING HORIZON PROCEDURES FOR THE SOLUTION OF AN OPTIMAL REPLACEMENT PROBLEM OF n-MACHINES WITH RANDOM HORIZON

In this paper a system consisting of independently operating n-machines is considered, where each machine follows a stochastic process of deterioration, which is associated with a cost function. It is assumed that the system is observed at discrete time and the objective function is the total expected cost. Also, it is considered that the horizon of the problem is random with an infinite support. In this case, the optimal replacement problem with a random horizon is modelled as a non-homogeneous optimal control problem with an infinite horizon and solved by means of the rolling horizon procedure. Then, a replacement policy is provided, which approximate the optimal solution. Finally, a numerical example through a program in Maple is presented.
Coupling and Interaction between Regional Science and Technology Finance and High-quality Economic Development based on the Empirical Data of Anhui Province

This paper selects the panel data of 16 prefecture level cities in Anhui Province from 2010 to 2019, uses the entropy method to calculate science and technology finance and high-quality economic development, and uses the coupling coordination model and PVAR model to measure the coupling and interaction relationship between them. The research shows that science and technology finance and high-quality economic development in Anhui Province is developing steadily and well. The coupling and coordination degree of local level cities is developing to a higher level, but more than half of prefecture level cities are still in the stage of low coupling and coordination. There is no significant promotion between science and technology finance and high-quality economic development. In the long run, high-quality economic development can promote science and technology finance, which has positive feedback on high-quality economic development.
NEURAL NETWORKS USAGE AT CRYSTALLIZERS DIAGNOSTICS

This paper deal with crystallizers diagnostics, which is part of continuous casting devices. The diagnostics is made by software analysis of vibration spectrums respectively of acoustics emission. These emissions can cause when unexpected situation occur. Principle of this method is analysis of spectrum of gained signal, its processing in Matlab environment and subsequent verification in Statistica software. Last step is most important, because thanks to it we are able to tell, with certain confidence, in which state the examined object occur. If the initial neural network has good starting data, it is able to analyze other samples and with successfulness over 70% tell, in which state the current objet remain, eventually if occur a critical (limiting) state (untighten screws in critical spots and so on). Data from accelerometer (microphone) are evaluated in Matlab environment and special filter is applied on them. Thos filter will highlight relevant data. Analog signal is digitized with special I/O module from National Instrument company. Type prefix is NI PCI 6221. From FFT result is obvious correlation of power spectrum and the inner state of examined object. Far in this paper will be described process of solving this problematic and current result. To achieve sufficient results, we are using multilayer neural networks with help of Statistica program. Resulting diagnostics system, which should be the final solution, should dramatically innovate and rationalize optimization problems of preventive maintenance of crystallizers desks, where principle of control on maximum relevant data basis should take apart. Relevant data are obtained from maintenance department.
Creating Believable, EmergentBehaviour in Virtual Agents, Using a‘Synthetic Psychology’ Approach

Drawing on the fields of robotics, computer games and usability testing, this thesis contextualises the work of cyberneticist Valentino Braitenberg within the current field of game AI and demonstrates how there remain aspects of his propositions that cannot yet be adequately captured theoretically, or tested empirically or conclusively, in simulations of complex biologically inspired behaviours. Justification for this stems from the view that believable, simulated characters are increasingly prevalent and exposed to mass-market scrutiny in visual entertainment media, such as visual effects in films and in particular the growing video games business, but will also have a growing, significant role in serious games and educational applications. This thesis combines analytical, synthetic and empirical methods and discusses the contributions and limitations of each to the evaluation of believability in the context of human computer interaction scenarios. It first analyses Braitenberg’s more intuitive architectures and demonstrates how these can be adapted, using a synthetic approach, to function as controllers for believable virtual agents, in a series of typical gaming scenarios. It then presents a set of empirical studies that were performed to develop a method for evaluating agents for their ability to elicit suspension of disbelief in the user. The resulting method, which is user centred and combines a qualitative content analysis approach with believability metrics for virtual agents sourced from the literature, was used to evaluate a series of increasingly complex agent models in a simple game scenario. The data was then compared to the results of the analysis of the underlying agent architecture to determine the correlation between AI design intent and the resulting user reactions and observations gathered from the empirical study.
PERBANDINGAN ALGORITMA PERLUASAN JARINGAN TELEKOMUNIKASI DENGAN MOBILE ROBOT DI DAERAH BENCANA MENGGUNAKAN OPEN DYNAMIC ENGINE

Putusnya jaringan komunikasi sebagai dampak suatu bencana merupakan permasalahan yang perlu cepat diatasi agar tidak menimbulkan kerugian yang lebih besar. Komunikasi antara lokasi bencana dengan dunia luar menjadi sangat penting dalam menentukan tindakan yang tepat untuk meminimalisasi kerugian akibat bencana. Kajian penggunaan mobile robot otonom untuk menghubungkan menara-menara komunikasi yang terputus dapat menjadi solusi alternatif untuk mengatasi masalah ini. Dalam penelitian ini, penulis mengkaji beberapa algoritma perluasan jaringan dalam bentuk simulasi menggunakan Library Open Dynamic Engine. Library Open Dynamic Engine merupakan library yang telah menggunakan perhitungan fisika secara efisien dan teruji sehingga dapat mendekati keadaan dunia sebenarnya. Simulasi ini bertujuan untuk mendemontrasikan serta membandingkan bahwa algoritma ini dapat dikembangkan untuk menghubungkan menara komunikasi. The disconnection in communication networks as the impact of a disaster is a problem that needs to be addressed quickly in order to avoid greater losses. Communication between the disaster site with the outside world becomes very important in determining the appropriate action to minimize losses due to disasters. Study the use of autonomous mobile robots for connecting communication towers that was disconnected can be alternative solutions to address these issues. In this study, the authors examine some of the network expansion algorithm in the form of a simulation using Open Dynamics Engine library. Library of Open Dynamic Engine is a library that has used physics calculations in an efficient and tested so can approach the state of the real world. This simulation aims to demonstrate and compare that this algorithm can be developed to connect the communication towers.
Software Defined Millimeter Wave 5th Generation Communications System

A row column Diagonal (RCD) based data security method has been disused here for software defined millimeter wave (SDMW) based 5th generation (5G) millimeter wave (MW) communication system. The RCD based security method in this research paper discusses encryption and decryption technique for 5G SDMW based cloud computing environment (CCE) and fog computing environment (FCE). The Fog computing is an emerging technology trend and complementing along with Cloud computing. Fog Computing along with Cloud computing considers various emerging applications of ubiquitous computing, Internet of Flying-Things (IoT) and 5G millimeter wave (MW) communication system. FCE also provides data storage for user and groups at personal and professional level along with the application services. In this paper we are discussing, data security need and demand for Fog computing and Cloud computing. RCD based encryption and decryption module is very secure and useful. This proposed security method is easy to develop and integrate in SDMW based 5G millimeter wave (MWe) communications system. This research is promising high quality security for the 5G MW communications system to provide efficient and optimum security.
Seed vigor of maize (Zea mays) cultivars affected by position on ear and water stress

Accepted: 10 Apr. 2015 A field experiment was conducted in 2014 to investigate the effects of different irrigation treatments (I1, I2, I3 and I4: irrigation after 60, 80, 100 and 120 mm evaporation from class A pan, respectively), cultivars (SC704, NS640 and DC303: late, mid and early maturing, respectively) and positions on ear (upper, lower and middle) on seed vigor of maize. The experiment was arranged as split-split plot based on RCB design with three replications. Irrigation treatments, cultivars and seed positions were considered as main, sub and sub-sub plots, respectively. Although, water limitation had no significant effect on seed weight, seeds produced under limited irrigation conditions showed low vigor as measured by electrical conductivity of seed leachates, germination rate and seedling dry weight. Seeds of SC704 germinated earlier and consequently produced larger seedlings in comparison with other cultivars. The higher seedling dry weight of the lower position seeds of ear was mainly the result of larger reserve accumulation and rapid germination. The advantage of the lower position seeds was more evident under severe water deficit. Thus, selection of the large and uniform seeds could be a simple way for improving seed and seedling vigor in maize cultivars.
Improving Pulse Shape Discrimination in Organic Scintillation Detectors by Understanding Underlying Data Structure

Abstract Various machine learning techniques have been implemented to assist in neutron-gamma discrimination with great success compared to traditional methods. Despite this, the fundamental structure of a pulse shape as it relates to machine learning has not yet been explored in detail, and the optimal number of pulse vector features needed for training is still unknown. In this study, support vector machines (SVMs) using linear, radial basis, and exponential kernel functions are fitted on data of two different forms: waveforms that partially cover the original pulses and principal components extracted from those pulses. The described methods correctly classified 98.02% for neutrons and 97.84% for gamma rays. The efficiency of the SVM was improved by extracting principal components from the waveforms. That is, fewer features were needed to discriminate between neutrons and gamma rays without negatively impacting the classification accuracy. This study also shows that utilizing a nonlinear kernel significantly reduces the number of features required to reach high classification accuracy. SVMs that did this could make accurate classifications 97% of the time with data that had fewer than 50 features.
A deep learning‐based approach for diagnosing COVID‐19 on chest x‐ray images, and a test study with clinical experts

Pneumonia is among the common symptoms of the virus that causes COVID‐19, which has turned into a worldwide pandemic. It is possible to diagnose pneumonia by examining chest radiographs. Chest x‐ray (CXR) is a fast, low‐cost, and practical method widely used in this field. The fact that different pathogens other than COVID‐19 also cause pneumonia and the radiographic images of all are similar make it difficult to detect the source of the disease. In this study, automatic detection of COVID‐19 cases over CXR images was tried to be performed using convolutional neural network (CNN), a deep learning technique. Classifications were carried out using six different architectures on the dataset consisting of 15,153 images of three different types: healthy, COVID‐19, and other viral‐induced pneumonia. In the classifications performed with five different state‐of‐art models, ResNet18, GoogLeNet, AlexNet, VGG16, and DenseNet161, and a minimal CNN architecture specific to this study, the most successful result was obtained with the ResNet18 architecture as 99.25% accuracy. Although the minimal CNN model developed for this study has a simpler structure, it was observed that it has a success to compete with more complex models. The performances of the models used in this study were compared with similar studies in the literature and it was revealed that they generally achieved higher success. The model with the highest success was transformed into a test application, tested by 10 volunteer clinicians, and it was concluded that it provides 99.06% accuracy in practical use. This result reveals that the conducted study can play the role of a successful decision support system for experts.
Heterogenous Graph Mining for Measuring the Impact of Research Institutions

Mining influential nodes in a social network for identifying patterns or maximizing information diffusion has been an active research area with many practical applications. In the research community, influential institutions usually attract denser attention than others. Based on the prediction on how many papers will be accepted by some top conferences held in 2016, the KDD Cup 2016 hosts an international competition for evaluating the importance of academic institutions. This paper describes our solution to the competition. Specifically, the proposed scheme involved in the competition mainly comprises of feature engineering and application of decision tree models. Finally, as claimed by the competition organizer, our approach scored 0.6599, 0.8169, 0.7213 with NDCG@20 in phases 1-3, and resulted in 0.7472 in overall score. With the above scores, our team ranked the first place in phase 2 and fourth place in overall rank.
Speed of light-induced stomatal movement is not correlated to initial or final stomatal conductance in rice

Abbreviations : C i – intercellular CO 2 concentration; C if – final intercellular CO 2 concentration; g s – stomatal conductance; g s,300 – stomatal conductance at 300 s of induction; g sf – final stomatal conductance; g si – initial stomatal conductance; LB – transient biochemical limitation; LS – transient stomatal limitation; P f – final photosynthetic rate; P i – initial photosynthetic rate; P N – photosynthetic rate; P 50 of g s – the time taken for g s to increase 50% of the difference between the first and final values; P 90 of g s – the time taken for g s to increase 90% of the difference between the first and final values; P 50 of P N – the time taken for P N to increase 50% of the difference between the first and final values; P 90 of P N – the time taken for P N to increase 90% of the difference between the first and final values; P 300 – photosynthetic rate at 300 s of induction; R d – dark respiration rate; W f – final intrinsic water-use efficiency. W i – initial intrinsic water-use efficiency; WUE i – intrinsic water-use efficiency; Γ * – CO 2 -compensation point in the absence of photorespiration. Acknowledgments : This work was supported by the National Natural Science Foundation of China (31671620). Conflict of interest : The authors declare that they have no conflict of interest. In nature, plants are often confronted with wide variations in light intensity, which may cause a massive carbon loss and water waste. Here, we investigated the response of photosynthetic rate and stomatal conductance to fluctuating light among ten rice genotypes and their influence on plant acclimation and intrinsic water-use efficiency (WUE i ). Significant differences were observed in photosynthetic induction and stomatal kinetics across rice genotypes. However, no significant correlation was observed between steady-state and non-steady-state gas exchange. Genotypes with a greater range of steady-state and faster response rate of the gas exchange showed stronger adaptability to fluctuating light. Higher stomatal conductance during the initial phase of induction had little effect on the photosynthetic rate but markedly decreased the plant WUE i . Clarification of the mechanism influencing the dynamic gas exchange and synchronization between photosynthesis and stomatal conductance under fluctuating light may contribute to the improvement of photosynthesis and water-use efficiency in the future.
Fuel Poverty and Health: A Shared Agenda for Policy

of health as we regard health status to be closely related to previous health trajectories. Considering that unobserved heterogeneity might influence health status and fuel poverty simultaneously, we have corrected for the endogeneity bias that could affect our results. We conclude that being fuel-poor increases the risk of bad health by slightly more than a factor of 7 for those whose health is already poor and by 1.82 for those in good health. For policy makers, combatting fuel poverty reduces sources of discomfort which might also severely affect the health of a dwelling’s inhabitants, and reduce health spendings.
A mathematical model for balancing safety and economy of UAVs in forest firefighting

Forest fires have a tremendous negative impact on the global ecology and the national economy. Advances in information machine technology have paved the way for the use of Unmanned Aerial Vehicles (UAVs) to fight forest fires, including monitoring fires and expanding signal propagation. However, in practical applications, we must consider the characteristics of UAVs such as monitoring range and the difficulties in balancing safety and economics. We take the 2019-2020 bushfires in Victoria and New South Wales as a case study and explore how best to combine two types of UAVs, SSA drones and radio repeater drones, to balance safety and cost-effectiveness. In this paper, we first employ the particle swarm algorithm to reasonably schedule the minimum number of SSA drones patrolling the sky while maximizing their surveillance area for the fire area. Then, we propose a Topology Spanning Tree (TST) algorithm to determine the locations of Emergency Operations Centers (EOCs) and Radio Repeater drones and the minimum number required. Then, we use the Ant Colony Algorithm to find the shortest path of the drones from the EOCs to the designated patrol location to reduce the loss of their flight power. Finally, we set up a multiple regression equation to find the minimum cost using the size and frequency of fire events as parameters.