Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Optimization for Copper Removal Process With Continuous Production Constraints

The copper removal process (CRP) aims to reduce the copper ion concentration in zinc sulphate solution to a specific range by zinc addition. The satisfaction of production constraints and minimization of zinc consumption are vital but difficult to achieve. In this article, the dynamic optimization for CRP is conducted for optimal zinc control trajectory design considering constraints at least cost. First, a dynamic optimization problem with both state and control constraints is constructed for CRP. Then, a constrained dynamic optimization method is proposed, where a wavelet-based control parameterization method and a smooth penalty method are adopted. Specially, a hybrid optimization strategy is proposed to achieve a robust and efficient optimization performance. Numerical experiments are provided to illustrate the effectiveness of the proposed method. Results show that the proposed method can produce not only the optimal control trajectory with a qualified outlet ion concentration but also the less zinc consumption.
A Machine Learning-Enabled Spectrum Sensing Method for OFDM Systems

This paper addresses the spectrum sensing problem in an orthogonal frequency-division multiplexing (OFDM) system based on machine learning. To adapt to signal-to-noise ratio (SNR) variations, we first formulate the sensing problem into a novel SNR-related multi-class classification problem. Then, we train a naive Bayes classifier (NBC), and propose a class-reduction assisted prediction method to reduce spectrum sensing time. We derive the performance bounds by translating the Bayes error rate into spectrum sensing error rate. Compared with the conventional methods, the proposed method is shown by simulation to achieve higher spectrum sensing accuracy, in particular at critical areas of low SNRs. It offers a potential solution to the hidden node problem.
Enabling Reliable, Efficient, and Secure Computing for Energy Harvesting Powered IoT Devices

Energy harvesting is one of the most promising techniques to power devices for future generation IoT. While energy harvesting does not have longevity, safety, and recharging concerns like traditional batteries, its instability brings a new challenge to the embedded systems: the energy harvested from environment is usually weak and intermittent. With traditional CMOS based technology, whenever the power is off, the computation has to start from the very beginning. Compared with existing CMOS based memory devices, emerging non-volatile memory devices such as PCM and STT-RAM, have the benefits of sustaining the data even when there is no power. By checkpointing the processor's volatile state to non-volatile memory, a program can resume its execution immediately after power comes back on again instead of restarting from the very beginning with checkpointing techniques. However, checkpointing is not sufficient for energy harvesting systems. First, the program execution resumed from the last checkpoint might not execute correctly and causes inconsistency problem to the system. This problem is due to the inconsistency between volatile system state and non-volatile system state during checkpointing. Second, the process of checkpointing consumes a considerable amount of energy and time due to the slow and energy-consuming write operation of non-volatile memory. Finally, connecting to the internet poses many security issues to energy harvesting IoT devices. Traditional data encryption methods are both energy and time consuming which do not fit the resource constrained IoT devices. Therefore, a light-weight encryption method is in urgent need for securing IoT devices. Targeting those three challenges, this dissertation proposes three techniques to enable reliable, efficient, and secure computing in energy harvesting IoT devices. First, a consistency-aware checkpointing technique is proposed to avoid inconsistency errors generated from the inconsistency between volatile state and non-volatile state. Second, checkpoint aware hybrid cache architecture is proposed to guarantee reliable checkpointing while maintaining a low checkpointing overhead from cache. Finally, to ensure the security of energy harvesting IoT devices, an energy-efficient in-memory encryption implementation for protecting the IoT device is proposed which can quickly encrypts the data in non-volatile memory and protect the embedded system physical and on-line attacks.
A Comprehensive Overview on PV based Hybrid Energy systems

The need for usage of renewable energy resources is gaining significance due to the depleting fossil fuels and its effect on climate change. Photovoltaic (PV) based power generation is gathering momentum due to the decrease in its cost of components. However solar energy is diurnal and highly uncertain which makes inclusion of storage element indispensable. This will enhance the cost of PV based energy systems. A grid tied PV energy system with proper backup will be a more economical alternative. This paper aims in reviewing the various objectives in designing such a PV based hybrid system to make it convenient and economical. An overview of optimization techniques, energy management methodologies and modeling of PV based hybrid energy systems has been presented. The research challenges in these areas have also been included
Improving ML Applications in Shared Computing Environments

Machine learning (ML) has become a powerful building block for modern services, scientific? endeavors and enterprise processes. The expensive computations required for training ML models often makes it desirable to run them ina distributed manner in shared computing environments (e.g., Amazon EC2, Microsoft Azure, in-house shared clusters). Shared computing environments introduce a number of challenges, including uncorrelated performance jitter, heterogeneous resources, transient resources and limited bandwidth. This dissertation demonstrates that, by structuring software frameworks and work distribution to exploit transient resources and address performance jitter and communication bandwidth limitations, we can improve the e? efficiency of training machine learning models.We support this assertion with three case study systems: FlexRR, Proteus, and PipeDream. FlexRR is a distributed machine learning training system that combines a flexible synchronization model with dynamic peer-to-peer reassignment of work among workers to address stragglers caused by performance jitter. FlexRR observes near ideal run-time, mitigating the adverse effects of stragglers observed in shared computing environments. Proteus is an agile elastic machine learning training system that uses tiers of reliability and intelligent resource management to efficiently utilize transient compute resources.Evaluations on AWS EC2 show that Proteus reduces cost by 85% relative to non-transient pricing, and by 43% relative to previous approaches, while simultaneously reducing runtimes by up to 37%. PipeDream is a distributed trainingsystem for deep neural networks (DNNs) that partitions ranges of DNN layers among machines and aggressively pipelines computation and communication. By reducing the amount of communication, and overlapping communicationand computation, PipeDream provides a 5x or more improvement in \time to accuracy" for training large DNN models.
Reputation Measurement for Online Services Based on Dominance Relationships

Reputation system is an important means to build trust, aid decision making of users, and sustain user loyalty in the context of online services. However, different users inherently have different preferences, and so it is impossible that all users rate services with the same criteria. Thus, aggregating cardinal ratings into reputation will potentially lead to unreliable and misleading result, which makes the impossibility of interpersonal utility comparisons should be considered in reputation systems. In this paper, we propose a novel reputation measurement mechanism that aggregates ordinal user preferences rather than cardinal ratings into reputation. By extending the majority rule naturally, dominance relationship between services is defined based on ordinal preferences. Then, reputation measurement is modelled as a problem to find a ranking that indicates the dominance relationships among services. A directed acyclic graph is constructed based on the dominance relationships of services pairs and then the ranking of services is found from the graph. We prove our method satisfies some basic criteria that a reasonable reputation measurement method should satisfy in the context of the impossibility of interpersonal utility comparisons. We also conduct a comprehensive experimental study and performance analysis to evaluate the effectiveness and efficiency of the proposed method.
Analyzing the Monolithic Integration of a ReRAM-Based Main Memory Into a CPU's Die

Nonvolatile memory, such as resistive RAM (ReRAM), is compatible with standard CMOS logic processes, allowing a sizable main memory system to be integrated into a CPU's die. ReRAM bitcells are fabricated within crosspoint subarrays that leave the bulk of transistors underneath the subarrays vacant. This permits placing the memory system over the CPU, improving area, parallelism, and power. Our work quantifies the impact of integrating ReRAM into a CPU's die. When integrating ReRAM over CPU logic, the best area efficiency occurs when 48% of the die is covered with ReRAM. The CPU's area increases by 18.8%, but we can recoup 35.5% of the die area by utilizing the free transistors underneath the crosspoint subarrays. When integrating ReRAM over CPU cache, up to 85.3% of the cache can be covered with ReRAM. Our work also shows that on-die ReRAM can support very high bandwidth through massively parallel memory access. At 28 nm, 4–16k independent ReRAM banks could be integrated onto the CPU die, providing 512–1024–GB/s peak bandwidth. At more advanced technology nodes, 5–10 TB/s may be possible.
Multi-View Saliency Guided Deep Neural Network for 3-D Object Retrieval and Classification

In this paper, we propose the multi-view saliency guided deep neural network (MVSG-DNN) for 3D object retrieval and classification. This method mainly consists of three key modules. First, the module of model projection rendering is employed to capture the multiple views of one 3D object. Second, the module of visual context learning applies the basic Convolutional Neural Networks for visual feature extraction of individual views and then employs the saliency LSTM to adaptively select the representative views based on multi-view context. Finally, with these information, the module of multi-view representation learning can generate the compile 3D object descriptors with the designed classification LSTM for 3D object retrieval and classification. The proposed MVSG-DNN has two main contributions: 1) It can jointly realize the selection of representative views and the similarity measure by fully exploiting multi-view context; 2) It can discover the discriminative structure of multi-view sequence without constraints of specific camera settings. Consequently, it can support flexible 3D object retrieval and classification for real applications by avoiding the required camera settings. Extensive comparison experiments on ModelNet10, ModelNet40, and ShapeNetCore55 demonstrate the superiority of MVSG-DNN against the state-of-art methods.
Deep Cognitive Perspective: Resource Allocation for NOMA-Based Heterogeneous IoT With Imperfect SIC

The Internet of Things (IoT) has attracted significant attentions in the fifth generation mobile networks and the smart cities. However, considering the large numbers of connectivity demands, it is vital to improve the spectrum efficiency (SE) of the IoT with an affordable power consumption. To improve the SE, the nonorthogonal multiple access (NOMA) technology is newly proposed through accommodating multiple users in the same spectrums. As a result, in this paper, an energy efficient resource allocation (RA) problem is introduced for the NOMA-based heterogeneous IoT. At first, we assume the successive interference cancelation (SIC) is imperfect for practical implementations. Then, based on the analyzing method for cognitive radio networks, we present a stepwise RA scheme for the mobile users and the IoT users with the mutual interference management. Third, we propose a deep recurrent neural network-based algorithm to solve the problem optimally and rapidly. Moreover, a priorities and rate demands-based user scheduling method is supplemented, to coordinate the access of the heterogeneous users with the limited radio resource. At last, the simulation results verify that the deep learning-based scheme is able to provide optimal RA results for the NOMA heterogeneous IoT with fast convergence and low computational complexity. Compared with the conventional orthogonal frequency division multiple access system, the NOMA system with imperfect SIC yields better performance on the SE and the scale of connectivity, at the cost of high power consumption and low energy efficiency.
Concept of Reconfigurability in Interoperation Manufacturing Buffers for Smart Factory

Abstract In this article, it is described how the reconfigurable inter-operational buffers system built on the Digital Twin platform. Interoperating production buffers are now widely used in production. Their effect on the production system can be seen in decreasing downtime. From a cost-based point of view, the interoperating production buffers may generate a gain from the reduction in the volume of work-in-process, with which we increase production performance. This ratio depends on the average number of products that the buffers contain. The average number of pieces in the buffer is limited by the capacity of the buffer. The impact of turbulence in production is seen precisely on the average content of inter-operational production buffers. If we want to maintain work-in-process on optimal values, it is necessary to calculate and maintain the optimal capacity of each interoperating production buffer on the line. In the context of Smart Factory, it is currently possible that the current capacity of the interoperating production buffers is maintained according to the current state of production. In the subject system, real production facilities communicate with each other through the IoT as autonomous agents, which are decided on the basis of a formula to calculate the optimal capacity of the buffers, the prediction of faults and negotiation, thus actively maintaining the optimal capacity of intermediate operating production buffers for Smart Factory support.
Rice Husk Biochars Modified with Magnetized Iron Oxides and Nano Zero Valent Iron for Decolorization of Dyeing Wastewater

This study investigated if biochar, a low-cost carbon-rich material, can be modified with reactive materials for decolorization of dyeing wastewater. Two types of rice husk biochars were produced by using different processes of gasification and pyrolysis in limited air condition. The biochars were first magnetized and then modified with nano-scale zero-valent iron (nZVI) to achieve the final products of magnetic-nZVI biochars. Batch experiments were conducted to investigate the efficiency of the modified biochars for reducing color of the reactive dyes yellow (RY145), red (RR195), and blue (RB19) from dyeing solutions. Results showed that color removal efficiency of the modified biochars was significantly enhanced, achieving the values of 100% for RY145 and RR195 and ≥65% for RB19, while the effectiveness of the original biochar was significantly lower. In addition, with increasing dose of the modified biochars, the color removal efficiency increased accordingly. In contrast, when the dose of nZVI was increased beyond a certain value then its color removal efficiency decreased accordingly. It is reported that the magnetic-nZVI rice husk biochars effectively removed the reactive dyes. The impregnation of nZVI particles on the biochar surface spatially separates the nZVI particles, prevents its aggregation and therefore enhances the decolorization efficiency.
Laparoscopic Surgery of the Adrenal Gland

Surgical robots have begun to appear on the market in the last few years and have started to populate the operating rooms in large medical centers. These systems have already established their ability to augment a surgeon’s dexterity in minimally invasive procedures and have the potential to maximize patient outcome on a larger basis even though, for the moment, their cost is prohibitively high for widespread application. As surgeons become increasingly aware of the clinical benefits of these systems and costs are driven down by technological advance and availability, we foresee that robots will become standard operating room tools. Initial applications of a handful of robots have already demonstrated their surgical potential. As technology evolves, robots could not only improve performance in minimally invasive procedures, but also enhance other existing procedures or even enable entirely new kinds of surgeries.
Differences in metal content in liver of Heteromyids from deposits with and without previous mining operations

Some rodents of the family Heteromyidae can survive without drinking water, as they obtain it from food. All these species have in common that they eat seeds and fruits. The content of trace metals in food varies depending on the local geology and anthropogenic activities. Baja California Sur has mineral deposits that have been exploited; thus, the metals released may be incorporated into seeds and fruits that are consumed by Heteromyidae. Therefore, metal content in the liver is expected to reflect the presence of mining in soil where these rodents thrive. Individuals from different species of Heteromyidae were collected at sites with mineral deposits; these were divided into two groups: rodents captured in sites with a history of mining operations (Santa Rosalia, San Juan de la Costa, and El Triunfo), and rodents captured in areas with no mining activities (El Vizcaino, Punta Abreojos, Magdalena Island, and Santiago). The liver of these rodents was excised and its manganese, copper, cadmium, lead, zinc, nickel, and iron contents were determined using atomic absorption spectrophotometry. Manganese was the element that showed the highest significant differences between species, followed by zinc and copper. Iron and cadmium showed the lowest differences. Nickel and lead showed no differences. Chaetodipus arenarius showed significant differences between sites in copper and lead content; C. spinatus did not show significant differences for manganese, copper, nickel, and iron, but it did for cadmium and lead content. Differential accumulation of metals occurs across species. These results indicate that the same species could be used for comparative purposes in pollution monitoring. The comparison of different heteromid species, despite their sharing similar feeding habits and belonging to the same family, can lead to misinterpretation due to the variability of the results. This is probably due to the requirements and tolerances regarding essential elements, such as manganese and zinc, to or tolerance to non-essential elements such as cadmium and lead, which may vary across species.
Detecting At-Risk Students With Early Interventions Using Machine Learning Techniques

Massive Open Online Courses (MOOCs) have shown rapid development in recent years, allowing learners to access high-quality digital material. Because of facilitated learning and the flexibility of the teaching environment, the number of participants is rapidly growing. However, extensive research reports that the high attrition rate and low completion rate are major concerns. In this paper, the early identification of students who are at risk of withdrew and failure is provided. Therefore, two models are constructed namely at-risk student model and learning achievement model. The models have the potential to detect the students who are in danger of failing and withdrawal at the early stage of the online course. The result reveals that all classifiers gain good accuracy across both models, the highest performance yield by GBM with the value of 0.894, 0.952 for first, second model respectively, while RF yield the value of 0.866, in at-risk student framework achieved the lowest accuracy. The proposed frameworks can be used to assist instructors in delivering intensive intervention support to at-risk students.
Stochastic Differential Equations With Markovian Switching

This textbook provides the first systematic presentation of the theory of stochastic differential equations with Markovian switching. It presents the basic principles at an introductory level but emphasizes current advanced level research trends. The material takes into account all the features of Ito equations, Markovian switching, interval systems and time-lag. The theory developed is applicable in different and complicated situations in many branches of science and industry.
Administrative reforms and global managerialism: a critical analysis of three Brazilian state reforms

Administrative reforms implemented in the last two decades in Brazil have been influenced by the New Public Management (NPM) literature. This manuscript compares and critically analyzes the administrative, social, and political implications of reforms in the executive branch of three Brazilian states. Based on the concept of global managerialism, we consider the impact of NPM-based reforms and the corresponding dissemination of managerialism within governments and public organizations. We conclude by highlighting the threat of global managerialism to democratic institutions in an era of globalization. Copyright © 2010 ASAC. Published by John Wiley & Sons, Ltd. Les reformes administratives mises en œuvre au cours des deux dernieres decennies au Bresil ont ete influencees par les publications sur la nouvelle gestion publique (NGP). Cet article compare et analyse de maniere critique les implications administratives, sociales et politiques de ces reformes sur le pouvoir executif de trois Etats de la federation du Bresil. Sous l'angle du concept de gestionariat mondial, l'etude examine l'impact des reformes basees sur la NGP et la dissemination du gestionariat qui en decoule, entre les gouvernements et les organismes publics. La conclusion met en exergue la menace du gestionariat mondial sur les institutions democratiques a l'ere de la mondialisation. Copyright © 2010 ASAC. Published by John Wiley & Sons, Ltd.
Traffic Density Analysis based on Image Segmentation with Adaptive Threshold

Traffic congestion has become an important problem in recent years. The main reason is the increase in the population in big cities and respective increase in number of vehicles. Traffic jams not only affect the human routine lives but also lead to a rise in the cost of transportation. So, an automatic traffic control system is required to manage the traffic congestion problem. The traffic density analysis will support the traffic management problems such as intelligent traffic signal control, traffic planning, etc. This paper has proposed a traffic density analysis method based on image segmentation with adaptive threshold. The system was designed and evaluated with the traffic images taken in Ho Chi Minh City, Viet Nam. The proposed method provides a accuracy analysis rate higher than 97% and a verification error lower than 3%.
Light Scattering by Cholesteric Spherulites

Abstract In this work, we analyze the scattering of light by the so-called spherulites or skyrmions in cholesteric liquid crystals. These are quasi-planar localized excitations of the director configuration. We compute the cross section of the polarization conversion for polarized incident light in the Born approximation, considering the anisotropic optical properties of the liquid crystal and the shape of the spherulite. We compare the results obtained by the analytical and the numerical skyrmion solutions found for several values of the model parameters. Finally, possible practical employments and theoretical extensions in the study of the phenomenon are foreseen.