Miracle-dz/UniFAR-FTUs huggingface dataset:
Uncertainty propagation of storage reliability evaluation using two-level MCS/NIPC

In order to solve the problem of limited sample size and life data in the storage reliability evaluation of high-value weapons, the storage reliability evaluation method of electronic product based on storage performance modeling and simulation was studied. The problem of epistemic-aleatory mixed uncertainty propagation in product storage performance model with time degradation factor was discussed emphatically. The calculation method of two-level Monte Carlo simulation and non-intrusive polynomial chaos is proposed, which solved the problem that the computational capacity of storage performance simulation would increase explosivelly when the number of uncertainty parameters is large. Finally, the effectiveness of the method is verified by an example of circuit storage reliability evaluation, and the calculation efficiency is about 100 times higher than that of the two-lever MCS method.
Selection of optimal electric vehicle charging station location using AHP-fuzzy TOPSIS approach

Electric vehicles have developed into one of the most desirable transportation alternatives in recent times. India and many countries such as the USA and the UK are working on various projects and policies to increase the amount of electrically powered vehicles onto their roads soon to deal with continuously rising fuel prices and CO2 emissions. For effective implementation of such plans, suitable charging infrastructure requires supplying the electrical fuel to these vehicles. In this paper, we studied and different types of factors, such as traffic density, weather, parking space, solar power capacity, and many other factors, to identify the optimum location for public charging stations within a specified area with the help of AHP-fuzzy TOPSIS approach.
Co-Maintained Database Based on Blockchain for IDSs: A Lifetime Learning Framework

Intrusion Detection System (IDS) is one of the most important approaches in cyber security to protect networks against both inner and outer threats. Apart from traditional networks, IDSs have been implemented in various emerging networks, such as mobile networks and Vehicle Ad hoc Networks (VANETs). However, a critical problem in IDSs is that the detection capacity is gradually decaying with the emergence of unknown attacks. It is necessary to constantly retrain IDSs with a more extensive database, but the security institutes usually lack the motivation to persistently update and maintain the database for public. Thus, in this paper, a lifetime learning framework is proposed for IDSs with a blockchain-based database (bc-DB). In the proposed framework, the blockchain-based database is multilaterally maintained by the security institutes and universities using Data Coins (DCoins) as the incentives. In addition, a Lifetime Learning IDS (LL-IDS) is further designed as the supplement of the bc-DB for common IDS users. For the LL-IDS, the Growing Hierarchical Self-Organizing Map with probabilistic relabeling (GHSOM-pr) having flexible and hierarchical architecture is employed as the classifier, which grows to make itself perfectly fit the changeable bc-DB. Security analysis and simulation experiments show that the proposed lifetime learning framework are both secure and effective in attacks detection.
Resistance-Emulating Control Strategy for Three-Phase Voltage Source Rectifiers Under Unbalanced Grids

In this article, a resistance-emulating control strategy without grid voltage sensors is proposed for three-phase voltage source rectifiers under unbalanced grid conditions. It consists of angle estimator, coordinate correction, resistance-emulating, common-mode resistance (CMR) and the differential-mode resistance (DMR) regulation, extended state observer, and other critical steps. In this scheme, the CMR regulates the average power, and the DMR controls the ripple power caused by the unbalanced supplies. Three different control objectives including constant active power, symmetrical input currents, and constant reactive power are achieved by regulating the DMR. Since the grid voltage sensors and the information of the grid-side circuit parameters are not required, the cost is reduced and the robustness and reliability of the scheme is improved. Experiments verify the effectiveness of the proposed method.
Vehicle detection based on point cloud intensity and distance clustering

In the intelligent transportation system, vehicle detection is one of the essential technologies in obstacle avoidance and navigation, however the existing vehicle detection methods cannot meet the actual needs. This paper presents a vehicle detection method combines the intensity and distance information of point cloud, which improves the segmentation performance of nearby objects. Specifically, the data of point cloud collected by lidar is preprocessed first. Then the processed point cloud is clustered by combining its coordinate and intensity information. Finally, the clustered suspected targets are fed to the random forest classifier. Our method can efficiently detect and classify targets in large-scale disordered 3D point cloud with high accuracy. In the real-scanned Livox Mid-40 Lidar dataset, our proposed method improves the detection accuracy by 31% compared with the traditional Euclidean clustering.
PARTIÇÃO DE PROTEÍNAS DE SORO DE LEITE EM SISTEMAS AQUOSOS BIFÁSICOS BASEADOS EM LÍQUIDOS IÔNICOS

In this work, we used an aqueous two-phase system (ATPS) consisting of the ionic liquid [C4mim][Cl] and the salt K2HPO4 to partition α-lactalbumin, β-lactoglobulin, and lactoferrin whey proteins. Extraction efficiency values above 87% indicate that the proteins primarily migrated to the ionic liquid-rich phase (top phase). Partition coefficient values ranged from 6.17 to 8.93 for a-lactalbumin, from 22.80 to 34.55 for β-lactoglobulin, and from 26.46 to 40.06 for lactoferrin. Therefore, the saline ATPS with the ionic liquid examined in this study can be considered to be a promising alternative for extracting whey proteins.
An Robot Vision Grasping Network Based on Inception-Lite

Using computer vision algorithms to find the grasping position of unknown objects is a key issue in the development of intelligent robot technology. In order to address the problem of robot grasping, firstly, based on the analysis of Inception-v1, a novel Inception-Lite convolution module is proposed, and then a grasping network GRASPNET-CLF is designed based on the convolution module. Finally, the grasping network GRASPNET-CLF is trained and verified by GPU on Cornell grasping dataset. The experimental results show that the GRASPNET-CLF has better performance in the same kind of network, and meets the real-time requirements.
Suitability of VVC and HEVC for Video Telehealth Systems

: Video compression in medical video streaming is one of the key technologies associated with mobile healthcare. Seamless delivery of medical video streams over a resource constrained network emphasizes the need of a video codec that requires minimum bitrates and maintains high perceptual quality. This paper presents a comparative study between High Efficiency Video Coding (HEVC) and its potential successor Versatile Video Coding (VVC) in the context of healthcare. A large-scale subjective experiment comprising of twenty-four non-expert participants is presented for eight different test conditions in Full High Definition (FHD) videos. The presented analysis highlights the impact of compression artefacts on the perceptual quality of HEVC and VVC processed videos. Our results and findings show that VVC clearly outperforms HEVC in terms of achieving higher compression, while maintaining high quality in FHD videos. VVC requires upto 40% less bitrate for encoding an FHD video at excellent perceptual quality. We have provided rate-quality curves for both encoders and a degree of overlap across both codecs in terms of perceptual quality. Overall, there is a 71% degree of overlap in terms of quality between VVC and HEVC compressed videos for eight different test conditions.
Power Enhancement under Partial Shading Condition Using a Two-Step Optimal PV Array Reconfiguration

Under partial shading conditions, photovoltaic (PV) arrays are subjected to different irradiance levels caused by nonuniform shading. As a result, a mismatch between the modules, a reduction in the power generated, and the hotspot phenomenon will be observed. One method to reduce mismatch losses is to reconfigure the total-cross-tied (TCT) array in dynamic and static forms, where improved performance can be achieved through more efficient shading distribution thanks to increased dimensions. However, the increase in dimensions leads to the complexity of wiring and installation in static reconfiguration and the large number of switches and sensors required in dynamic reconfiguration. To rectify these problems, a two-step method is proposed in this paper. In the first step, the modules inside the PV array are divided into subarrays with wiring in static reconfiguration, rather than being wired as large-scale PV arrays. In the second step, an algorithm is developed for dynamic reconfiguration. The introduced algorithm searches for all possible connections and finally identifies the most optimal solution. As an advantage, this algorithm employs only the short-circuit current values of the subarray rows, which reduces the number of switches and sensors required in comparison to dynamic reconfiguration. Under 8 different partial shading patterns, simulations are conducted and results confirm that the proposed method outperforms the TCT array and statically modified TCT array in terms of power and mismatch losses. Among these, the highest power improvement is obtained with regard to the TCT array and statically modified TCT array under the fourth and eighth shading patterns, respectively.
Evaluation of Mental Workload Associated With Time Pressure in Rapid Serial Visual Presentation Tasks

Existing studies related to brain–computer interface (BCI) based on rapid serial visual presentation (RSVP) mainly focused on performance improvement or paradigm expanding. However, less work was reported about evaluating mental workload in RSVP tasks applied in the surveillance field. This article aimed to validate the separability of multilevels of the mental workload associated with time pressure in RSVP tasks, and additionally to explore the influence of mental workload on target detection performance and neural patterns. Time pressure was simulated by setting three levels of presentation rate. Both behavioral and single-trial target detection performance presented a descending trend as an increase of presentation rate. The changes of mental workload mainly affected occipital N2 and P3 components, whose amplitudes presented a significantly negative correlation with $z$ -scored rating scale mental effort (RSME) value and significantly positive correlation with $z$ -scored single-trial hit rate. The average classification accuracy on three levels of mental workload reached to 71.0%. This work implies that the parameter associated with time pressure could affect the mental workload in RSVP tasks, and further influence the neurophysiological system and task performance. Mental workload induced by different presentation rates could be effectively estimated, which provides a potential approach to monitor mental workload in RSVP-based BCI.
Dynamics of Strong Coupling between J‐Aggregates and Surface Plasmon Polaritons in Subwavelength Hole Arrays

A prototypical hybrid system formed by strong coupled gold hole arrays and J‐aggregate molecules is investigated by using both steady‐state spectroscopic method and ultrafast pump‐probe approach. In particular, the plasmonic response of the device has been tuned by modifying its periodicity thus to achieve the strongest possible coupling regime. It is found that in the transient absorption spectra, under upper band excitation, the bleaching signal from uncoupled J‐aggregate molecules completely disappears. Instead, two distinctive period dependent bleaching bands are formed, clearly fingerprint of the hybrid exciton‐plasmon state. The dynamics of these bands is also directly analyzed. A remarkable long lifetime is found especially for the upper band, corresponding to the presence of a trap state in its transient absorption spectra under resonance excitation. Such unique feature should provide a new approach to control quantum‐mechanical states under coherent coupling.
Performance and Volatility Modelling for Shariah Compliant Stocks in Malaysia Using Exponentially Weighted Moving Average

The diversity of investment in Malaysia provides an excellent platform to gauge volatility. Malaysia as an emerging market with a rich Islamic culture serves as an inspiration to randomly model a portfolio of 50 Shariah compliant stock returns from 2015 to 2020. The systematic risk of a company’s stock returns is measured by computing the volatility and downside volatility for the said period. The Exponentially Weighted Moving Average (EWMA) method is used to outline the risk levels of Shariah compliant stocks for the recent stipulated period. The results indicate a statistical difference between beta and downside beta for Shariah compliant portfolio. This signals investors to be cognisant of the semi-variant characteristics of returns in estimating volatility. Meanwhile, there is no significant difference in performance using the Sharpe and Sortino ratio on the beta and downside beta scores respectively. Consequently, this suggests that investors can always measure performance to a sufficient degree of accuracy regardless of their volatility choice.
Exploring fluid mechanics in energy processes: Viscous flows, interfacial instabilities & elastohydrodynamics

The displacement of fluids in porous media and fluid-structure interactions are major elements in upstream oil and gas processes. For instance, water flooding, which is an enhanced oil recovery process, consists of injecting water into petroleum reservoirs to displace and, hence, recover residual resources. This process is limited by flow instabilities since water cannot sweep the viscous oil efficiently. Moreover, hydraulic fracturing uses pressurized water to crack the tough, yet compliant, shale rock and release the trapped natural gas. In this thesis, we provide new insights on controlling interfacial instabilities that occur in two-phase fluid displacements by leveraging flow geometry. When a flow passage is nonuniform, surface tension forces can either suppress or trigger interfacial instabilities in two-phase fluid displacements. We demonstrate this phenomenon experimentally and theoretically in a variety of geometrically varying configurations. In particular, we study two-phase flows confined to elastic boundaries. In this case, the flow geometry is provided by the subtle coupling between fluid flow and the compliant structure. Also in the spirit of fluid-structure interactions, we investigate boundary-layer flows, which are coupled to elastic and soft surfaces. Beyond conventional energy sources, we shed light on the effect of fluid flow on the performance of microfluidic microbial fuel cells. These devices, which rely on bacteria to consume nutrients and generate electricity in return, exhibit a surprising dependence on fluid flow. We report the existence of an optimal flow rate range in which microfluidic microbial fuel cells achieve a maximum current production.
Development Strategy of Sago Local Food Agroindustry Using Analytical Hierarchy Process Method

This study aims to formulate the development strategy for sago local food agroindustry in Maluku Province. The sample was taken deliberately (purposive sampling) because respondents realize the sago development in the province. The respondents, totaling 15 people, consisted of farmers, traders, and experts from several agencies, namely the Food Security Agency, the Department of Agriculture, the Department of Industry and Commerce, the Department of Cooperatives and Small and Medium Micro Enterprises, Universities, and Non-Governmental Organizations. Data analysis utilized the Analytic Hierarchy Process method. The priority analysis results of sago agroindustry development strategy revealed technology as a top priority in the agroindustry development for being associated with very low and limited assistance and access to processing technology. The sago local food agroindustry development focuses on linkages between factors, sub-factors, actors, objectives, and policy scenarios. The recommended policy scenarios are preserving local food, improving technology, arranging marketing strategies, improving production facilities and infrastructure, and improving institutional systems.
Overview of Pest Status, Potential Risk, and Management Considerations of Helicoverpa armigera (Lepidoptera: Noctuidae) for U.S. Soybean Production

Since its introduction and establishment in Brazil in 2013 and subsequent spread through most of Latin America, the Old World bollworm, Helicoverpa armigera (Hübner), has become an economically important pest of soybean, cotton, and other crops. Because this pest can migrate long distances aided by wind currents, it is expected to arrive and establish in the United States. There were reports of H. armigera moth incursions collected in pheromone traps from Puerto Rico and Florida, but no reports yet of field establishment in the United States. Although H. armigera is a polyphagous pest and poses threats to multiple crops, it has shown preference for and is considered a key pest of soybean and cotton in Brazil. Soybean and cotton in Mid-Southern states will be at greater risk for economic loss when this pest establishes in the United States due to similarities of H. armigera with the native sibling species, Helicoverpa zea (Lepidoptera: Noctuidae) (Boddie). Both are polyphagous, could damage the same crops, and have documented cases of resistance. Establishment of H. armigera in the United States presents uncertainty about how the two species may interact in U.S. agroecosystems. Management strategies for H. armigera consist of correct species identification, monitoring and early detection, and determining the susceptibility of populations to multiple control tactics to deploy effective management tools. Although it is expected to be a key pest of soybean and cotton, in this article, we discuss the overview of the potential threats which H. armigera poses to soybean production in Mid-Southern states when it establishes in the United States.
The Relevance of Professional Use of Smart-Complexes in the Training Process of Future Labor and Technology Teachers

Transformational changes both in the world on the whole and in the education system in particular are constanly creating new challenges for the professional qualities of teachers. There is a profound discrepancy between the needs of the modern student and the abilities to meet these needs on the part of the teacher. There are a number of reasons for this, namely, the difference between generations, teachers’ low motivation for self-development, and low digital literacy. It is very important to take into account all of these issues while training future teachers. We have experimentally determined the relevance of using SMART complexes in the process of professional training of bachelors studying in the program "Secondary education: labor training and technology", Additionally, we have proved the need for active implementation of SMART complexes into educational and professional activities of teachers.
Preferential solvation of nifedipine in some aqueous co-solvent mixtures

ABSTRACT Preferential solvation parameters of nifedipine (NIF) in ethanol (EtOH) + water and propylene glycol (PG) + water mixtures were obtained from their thermodynamic properties in solution using the inverse Kirkwood–Buff integrals. Preferential solvation parameter (δx1,3) by both co-solvents is negative in the water-rich mixtures but positive in almost all the other compositions at 293.2, 303.2 and 313.2 K. Nevertheless, in EtOH-rich mixtures the values of δx1,3 are also negative. It can be assumed that in water-rich mixtures the hydrophobic hydration around the non-polar groups of NIF plays a relevant role in the solvation. The higher drug solvation by co-solvent in mixtures of similar solvent proportions and in co-solvent-rich mixtures could be due mainly to polarity effects. Moreover, in these mixtures the drug could be acting as a Lewis acid with the co-solvents molecules. Finally, in EtOH-rich mixtures the drug could be acting as a Lewis base with water molecules.
The Implementation of Integrative and Complementary Health Practices in the State of Rio Grande do Sul, Brazil

— This study aims to describe the implementation of the Integrative and Complementary Health Practices (PICS) in the state of Rio Grande do Sul, located in the extreme south of Brazil. It uses the norms related to the National Policy of Integrative and Complementary Health Practices (PNPIC) and the State Policy of Integrative and Complementary Practices of Rio Grande do Sul (PEPIC / RS) in addition to other government documents and published papers reports on policies for describe this process that started in 2006 with the publication of the PNPIC. The main results include the standardization of twenty-nine PICS within the scope of Unified Health System (SUS) and the preparation of a manual for their implementation, in addition to the preparation of nine Technical Notes in Rio Grande do Sul as a way to encourage municipal managers to bid the PICS. There
Accuracy of Isoperibolic Calorimetry Used in a Cold Fusion Control Experiment

This control or "blank" experiment proves that anomalous excess power is measurable to within ±0.1 mW using this electrochemical calorimetry. The precision and accuracy is better than 99.99%. The most accurate results were obtained using backward integration of the experimental data sets. The rate of enthalpy production due to the reduction of electrogenerated oxygen (recombination) was measurable at ± 0.1 mW in these experiments where the cell input power was generally about 800 to 1000 mW. The logical conclusion from this control study is that excess enthalpy measurements in cold fusion experiments using this calorimetric system cannot be scientifically dismissed simply as calorimetric errors.
A Zero-Velocity Detection Algorithm Robust to Various Gait Types for Pedestrian Inertial Navigation

In this article, we develop a pedestrian inertial navigation (PIN) that uses an inertial measurement unit (IMU) mounted on a shoe to locate a pedestrian in an infrastructure-free environment. In the PIN, stand-alone navigation is possible by calculating the navigation solution using the inertial navigation system (INS) mechanism. For accurate navigation, however, it is necessary to compensate for accumulated errors over time. In a foot-mounted PIN, the error can be compensated through zero-velocity (ZV) correction method by detecting the moment the foot touches the ground. One important point here is to detect ZVs accurately. In most of the literature related to this, ZV detection techniques for normal gait have been proposed. In this article, we first propose a powerful ZV detection algorithm for various gait types. This algorithm consists of a new buffering method of the sensor output power, an exact ZV detection method through glitch removal, and a reinforcement method for ZV detection based on auxiliary step detection. Second, we design a PIN/ZV integration filter, and describe the real-time operation plan of all these functions. The performance of the PIN using the proposed algorithm is verified through experiments.