Miracle-dz/UniFAR-FTUs huggingface dataset:
PRICE-DRIVEN CHARGING OF PLUG-IN ELECTRIC VEHICLES IN THE SMART GRID

As the number of Plug-in Electric Vehicles (PEVs) increases, it is essential to control their charging schedules and spread the PEV load over time to reduce the energy generation and distribution costs due to this additional demand. Furthermore, due to the limited power capacity of the transmission feeders and the sensitivity of the mid-way distribution transformers to excessive load, it is crucial to control the amount of power through each specific feeder in the distribution network to avoid system overloads that may lead to breakdowns. In this thesis we develop, analyze and evaluate price-driven charging algorithms for PEVs in a smart grid environment. The algorithms we propose minimize the cost incurred on the power distribution system (or the supply cost of the electric utility or aggregator) due to the PEV load, and at the same time prevents overloading of the transmission feeders. We first develop two convex optimization algorithms for PEV charging that minimize the aggregator’s convex cost function subject to transmission feeder overload constraints. The two algorithms are amenable to decentralized implementation, in which the PEVs react to the load signals on their supply paths and the distribution grid on the whole (by adjusting their charging schedules). We next analyze the equilibrium properties of a natural price-driven charging control game in the distribution grid, between the utility (that sets the timedependent energy usage price) and selfish PEVs (that choose their own charging schedules to minimize individual cost). We demonstrate, through analysis and simulations, that individual best-response strategies converge to socially optimal charging profiles (also equilibrium solutions) under fairly weak assumptions about the (asynchronous) charging profile update processes. We also discuss how the framework can be extended to consider the topology of the distribution tree and associated transmission feeder capacity constraints. We then consider the day-ahead price-setting question from the perspective of the utility (or aggregator) that is interested in minimizing the average energy supply costs given the uncertainty in the charging preferences of the PEV owners.
Study on formation mechanism of edge defects of high-volume fraction SiCp/Al composites by longitudinal-torsional ultrasonic vibration-assisted milling

SiCp/Al composites are a kind of particle-reinforced composite material, which has been widely used in various fields due to its excellent performance. However, during the machining, the damage and failure of the SiC particles, the aluminum matrix, and the interface phase will cause many surface and edge defects. These defects will seriously affect the application of SiCp/Al composites. In this paper, a finite element model of randomly distributed multi-cell SiCp/Al composites with longitudinal-torsional ultrasonic vibration-assisted milling was established to analyze the formation mechanism of edge defects of the material. The simulation results showed that the main reason for the formation of edge defects was that the interface, SiC particles, and the Al matrix will produce cracks during machining, and these cracks will propagate and cause particle breakage, matrix tearing, and edge gaps. According to different machined parameters, the ultrasonic vibration-assisted milling experiment was carried out. The test results showed that the actual processed workpiece does have defects such as edge gaps. The depth of cut and the feed per tooth had a serious influence on the edge defect value, while the cutting speed had a small effect. Moreover, under the condition of applying appropriate ultrasonic amplitude, the phenomenon of edge defects and the surface quality were significantly improved. Therefore, the application of ultrasonic vibration-assisted milling can improve the surface and edge quality of SiCp/Al composites.
Based on Scene Classification Method of Wind-PV Output Coupling Mechanism

Load tracking and output forecast error are less researched in wind and photovoltaic (PV) systems synthetic of output coupling power in present time. This paper using the technology of data mining of according to the wind and PV output characteristics with different scenario data partitioning method to the Wind and PV resource rich region of the actual wind power and PV output data divides the scene under the typical scenario data of division of Wind and PV complementary output coupling characteristics are analyzed and studied the mechanism of synthesis of output tracking system load characteristics and improve the output prediction error problem. Studies have shown that Wind and PV complementary synthetic efforts to a certain extent, reduced the coupling characteristics of output volatility, the system load tracking degree reached 14.1%.
Coefficient of Drag and Trajectory Simulation of 130 mm Supersonic Artillery Shell with Recovery Plug or Fuze

In the present study, the drag variation and trajectory elements estimation of a supersonic projectile having two different nose shapes are made numerically. The study aims at finding the coefficient of drag and shock wave pattern for 130 mm artillery shell fitted with recovery plug or with fuze, when travelling at zero angle of attack in a supersonic flow of air. The coefficient of drag (CD) obtained from the simulation is used as an input parameter for estimation of trajectory elements. The numerical results, i.e., the coefficient of drag at different Mach numbers and trajectory elements are validated with the data recorded by tracking radar from an experimental firing. Based on numerical results and data recorded in experimental firing, the coefficient of drag in the case of the shell with recovery plug is 2.7 times more than for the shell with fuze. The shock wave in the case of the shell with recovery plug is detached bow shock wave, whereas in the case of a shell with fuze, the shock is attached. The results indicate that the coefficient of drag increases with detached shock wave and an increase in the radius of the shell nose. Good agreements were observed between numerical results and experimental observations. Defence Science Journal, Vol. 64, No. 6, November 2014, pp.502-508, DOI:http://dx.doi. org/10.14429/dsj.64. 8110
FULLY AUTOMATIC AND OPERATOR-LESS ANOMALY DETECTING GROUND SUPPORT SYSTEM FOR MARS PROBE "NOZOMI"

This paper describes a fully automatic and operatorless anomaly detecting ground support system named ISACS-DOC for Japan’s first Mars probe “NOZOMI”. This system is the second application of expert technique to daily operation of scientific spacecraft at the Institute of Space and Astronautical Science (ISAS) in Japan. Many improvements are achieved based on the experiences of the first expert application to monitoring and diagnosis system for geomagnetic observation satellite “GEOTAIL”. ISACS-DOC is now used to keep the safer operation of “NOZOMI” as well as the first system for “GEOTAIL” at ISAS’ Sagamihara Spacecraft Operation Center (SSOC). ISACS-DOC has caught many abnormalities including serious problems which actually happened to “NOZOMI” and warned to “NOZOMI” operators about them immediately at SSOC. ISACS-DOC for “NOZOMI” will be continuously used until the end of the “NOZOMI” mission to keep the operation safe under strict limitation of the operation budget at ISAS. In this paper, the process of ISACS-DOC development, the improvement points that were performed, and some actual examples of abnormalities that were detected by ISACS-DOC will be reported as well as the outline of ISACS-DOC itself.
Effective Inhibition of Invasive Pulmonary Aspergillosis by Silver Nanoparticles Biosynthesized with Artemisia sieberi Leaf Extract

Aspergillus fumigatus is one of the most common fungal pathogens that can cause a diversity of diseases ranging from invasive pulmonary aspergillosis (IPA) and aspergilloma to allergic syndromes. In this study, we investigated the antifungal effect of silver nanoparticles biosynthesized with Artemisia sieberi leaf extract (AS-AgNPs) against A. fumigatus in vitro and in vivo. The biosynthesized AS-AgNPs were characterized by imaging (transmission electron microscopy (TEM)), UV−VIS spectroscopy, X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR). The microdilution method showed the antifungal activity of AS-AgNPs against A. fumigatus, with an MIC of 128 µg/mL. AS-AgNPs significantly inhibited the growth of hyphae in all directions, as imaged by SEM. Additionally, TEM on biofilm revealed invaginations of the cell membrane, a change in the vacuolar system, and the presence of multilamellar bodies within vacuoles. Interestingly, AS-AgNPs displayed low cytotoxicity on the A549 human lung cell line in vitro. Treatment of an invasive pulmonary aspergillosis (IPA) mouse model with AS-AgNPs demonstrated the potency of AS-AgNPs to significantly reduce lung tissue damage and to suppress the elevated levels of pro-inflammatory cytokines, tumor necrosis factor-alpha (TNF-α), interleukin-1 (IL-1), and interleukin-17 (IL-17). The therapeutic potential of AS-AgNPs was found to be due to their direct action to suppress the fungal burden and gliotoxin production in the lungs. In addition, AS-AgNPs reduced the oxidative stress in the lungs by increasing the enzymatic activities of catalase (CAT) and superoxide dismutase (SOD). Thus, our data indicate the biosynthesized AS-AgNPs as a novel antifungal alternative treatment against aspergillosis.
Correlation of maximum temperature increase and peak SAR in the human head due to handset antennas

This paper attempts to correlate the maximum temperature increase in the head and brain with the peak specific absorption rate (SAR) value due to handset antennas. The rationale for this study is that physiological effects and damage to humans through electromagnetic-wave exposure are induced by temperature increases, while the safety standards are regulated in terms of the local peak SAR. For investigating these correlations thoroughly, the total of 660 situations is considered. The numerical results are analyzed on the basis of statistics. We find that the maximum temperature increases in the head and brain can be estimated in terms of peak SARs averaged over 1 and 10 g of tissue in these regions. These correlations are less affected by the positions, polarizations, and frequencies of a dipole antenna. Also, they are reasonably valid for different antennas and head models. Further, we discuss possible maximum temperature increases in the head and brain for the SAR values prescribed in the safety standards. They are found to be 0.31/spl deg/C and 0.13/spl deg/C for the Federal Communications Commission Standard (1.6 W/kg for 1 g of tissue), while 0.60/spl deg/C and 0.25/spl deg/C for the International Commission on Non-Ionizing Radiation Protection Standard (2.0 W/kg for 10 g of tissue).
On the Use of Deep Neural Networks to Improve Flights Estimated Time of Arrival Predictions

Predictable operations are the basis of efficient air traffic management. In this context, accurately estimating the arrival time to the destination airport is fundamental to make tactical decisions about an optimal schedule of landing and take-off operations. In this paper, we evaluate different deep learning models based on LSTM architectures for predicting estimated time of arrival of commercial flights, mainly using surveillance data from OpenSky Network. We observed that the number of previous states of the flight used to make the prediction have great influence on the accuracy of the estimation, independently of the architecture. The best model, with an input sequence length of 50, has reported a MAE of 3.33 min and a RMSE of 5.42 min on the test set, with MAE values of 5.67 and 2.13 min 90 and 15 min before the end of the flight, respectively.
RAFT Emulsion Polymerization of Styrene Using a Poly((N,N-dimethyl acrylamide)-co-(N-isopropyl acrylamide)) mCTA: Synthesis and Thermosensitivity

Thermoresponsive poly((N,N-dimethyl acrylamide)-co-(N-isopropyl acrylamide)) (P(DMA-co-NIPAM)) copolymers were synthesized via reversible addition−fragmentation chain transfer (RAFT) polymerization. The monomer reactivity ratios were determined by the Kelen–Tüdős method to be rNIPAM = 0.83 and rDMA = 1.10. The thermoresponsive properties of these copo-lymers with varying molecular weights were characterized by visual turbidimetry and dynamic light scattering (DLS). The copolymers showed a lower critical solution temperature (LCST) in water with a dependence on the molar fraction of DMA in the copolymer. Chaotropic and kosmotropic salt anions of the Hofmeister series, known to affect the LCST of thermoresponsive polymers, were used as additives in the aqueous copolymer solutions and their influence on the LCST was demonstrated. Further on, in order to investigate the thermoresponsive behavior of P(DMA-co-NIPAM) in a confined state, P(DMA-co-NIPAM)-b-PS diblock copolymers were prepared via polymerization induced self-assembly (PISA) through surfactant-free RAFT mediated emulsion polymerization of styrene using P(DMA-co-NIPAM) as the macromolecular chain transfer agent (mCTA) of the polymerization. As confirmed by cryogenic transmission electron microscopy (cryoTEM), this approach yielded stabilized spherical micelles in aqueous dispersions where the PS block formed the hydrophobic core and the P(DMA-co-NIPAM) block formed the hydrophilic corona of the spherical micelle. The temperature-dependent behavior of the LCST-type diblock copolymers was further studied by examining the collapse of the P(DMA-co-NIPAM) minor block of the P(DMA-co-NIPAM)-b-PS diblock copolymers as a function of temperature in aqueous solution. The nanospheres were found to be thermosensitive by changing their hydrodynamic radii almost linearly as a function of temperature between 25 °C and 45 °C. The addition of kosmotropic salt anions, as a potentially useful tuning feature of micellar assemblies, was found to increase the hydrodynamic radius of the micelles and resulted in a faster collapse of the micelle corona upon heating.
Improving Customer Satisfaction of Smartcard System: Integration of Human Factors and Quality Function Deployment

Octopus card is an electronic travel ticket with built-in microchip jointly issued by five major public transportation companies in Hong Kong. It can be used for riding on six kinds of transportation vehicles, viz. Mass Transit Railway (MTR), Kowloon Canton Railway (KCR) East Rail, KCR Light Rail, Kowloon Motor Bus (KMB), Citybus, and Hong Kong & Yaumati Ferry (HYF). The system has been launched in Hong Kong since September 1997. At the launching phase of this system, some operational problems like wrong fare deduction, card damage, inaccurate display of remaining balance on buses with the use of Octopus card were observed and brought up in the public. This study aimed at finding out the ways of satisfying the customer needs with the use of the system with consideration of changing technology development. The work in this study included identification of the quality dimensions of using Octopus card, conducting questionnaires survey on the users, applying usability testing to evaluate the overall performance of Octopus card and employing Quality Function Deployment to translate the customer voices into technical specifications. With the results of this study, strategies for improving the performance of Octopus card were finally recommended.
Encouraging Resource-Conscious Behavior Through Product Design: The Principle of Discretization

Lead-user methods were applied to develop product design principles that encourage resource-conscious behavior in individuals. Old Order Mennonites (OOMs) were chosen as lead users because of their low-resource consumption lifestyles. Ethnographic analysis revealed that discretizing resource consumption facilitates and encourages conservation behaviors in OOMs. An experimental study demonstrated the effectiveness of discretization in reducing water consumption by non-OOMs. We then generated concepts for products that applied discretization and tested them with users. Concepts were revised and a prototype for saving water was created. [DOI: 10.1115/1.4024225]
Optimized Design and Testing of a Prototype Military Bridge System for Rapid In-Theater Construction

Abstract : A prototype military bridge system using in-theater concrete with a deployable, folding truss support, and stay-in-place-form system was designed and tested. The bridge's primary advantage is a reduction in deployment requirements because of the use of in-theater materials. Numerical optimization was used in the design to reduce the deployable component weights. The results showed a potential 50% weight savings compared to the US Army Rapidly Emplaced Bridge. An experimental program investigated the critical deck component capacity, and physical and statistical analysis confirmed the deck has a military load class 70 axle load capacity.
AN OVERVIEW ON IVIVC AND DISSOLUTION METHODOLOGY

The concept and application of the in vitro-in vivo correlation (IVIVC) for pharmaceutical dosage forms have been a main focus of attention of pharmaceutical industry, academia, and regulatory sectors. Development and optimization of formulation is an integral part of manufacturing and marketing of any therapeutic agent which is indeed a time consuming and costly process An IVIVC should be evaluated to demonstrate that predictability of in vivo performance of a drug product from its in vitro dissolution characteristics is maintained over a range of in vitro dissolution release rates and manufacturing changes. Certainly, implementation of these requirements not only halts the marketing of the new formulation but also increases the cost of the optimization processes. It would be, desirable, therefore, to develop in vitro tests that reflect bioavailability data. The FDA Guidance on IVIVC provides general methods is considered as an important complimentary tool for use in in vitro dissolution techniques, allowing more information to be collected about drug behaviour in various GI conditions & in which all drug of high dosage can be easily administered. The ADS method is sensitive, effective & reliable in predicting in vivo performance of dosage forms. The number of studies reported in the area of establishing IVIVCs for non-oral dosage forms are very scarce and further research is necessary in the development of more meaningful dissolution and permeation methods. Pharmaceutical Sciences
Reliable Fault Diagnosis of Bearings Using an Optimized Stacked Variational Denoising Auto-Encoder

Variational auto-encoders (VAE) have recently been successfully applied in the intelligent fault diagnosis of rolling bearings due to its self-learning ability and robustness. However, the hyper-parameters of VAEs depend, to a significant extent, on artificial settings, which is regarded as a common and key problem in existing deep learning models. Additionally, its anti-noise capability may face a decline when VAE is used to analyze bearing vibration data under loud environmental noise. Therefore, in order to improve the anti-noise performance of the VAE model and adaptively select its parameters, this paper proposes an optimized stacked variational denoising autoencoder (OSVDAE) for the reliable fault diagnosis of bearings. Within the proposed method, a robust network, named variational denoising auto-encoder (VDAE), is, first, designed by integrating VAE and a denoising auto-encoder (DAE). Subsequently, a stacked variational denoising auto-encoder (SVDAE) architecture is constructed to extract the robust and discriminative latent fault features via stacking VDAE networks layer on layer, wherein the important parameters of the SVDAE model are automatically determined by employing a novel meta-heuristic intelligent optimizer known as the seagull optimization algorithm (SOA). Finally, the extracted latent features are imported into a softmax classifier to obtain the results of fault recognition in rolling bearings. Experiments are conducted to validate the effectiveness of the proposed method. The results of analysis indicate that the proposed method not only can achieve a high identification accuracy for different bearing health conditions, but also outperforms some representative deep learning methods.
COVID-19 post-implications for sustainable banking sector performance: evidence from emerging Asian economies

Abstract The novel COVID-19 virus, and the pandemic that has followed its spread, has had a massive impact on the banking industry on a global level. Therefore, considering this context, this study examines the effect of the COVID-19 pandemic on the banking sector’s performance in the Asian emerging economics, over a time span ranging from 2016Q1–2021Q2. For this purpose, this study makes use of the FE, RE, 2SLS, and PPML regression models. The empirical findings show that the GDP and banking size are positively related to bank performance in the pre-COVID era, and the COVID pandemics era thus far in the Asian emerging economies. In addition to this, the findings also reveal that the impact of GDP and bank size on banking performance is lower in the pandemic period, as compared to the pre-COVID period. Furthermore, the results show that the COVID pandemic has significantly affected the banking sector performance in the Asian emerging economies. Findings have also suggested that the central bank should shift their focus on the banking industry in the Asian emerging economies. Various other policy implications have also been suggested under the present study.
Uncooled IR focal plane arrays

Uncooled IR focal plane arrays employ thermal detection mechanisms including the resistive bolometric, pyroelectric, and thermoelectric effects. Readout electronics are incorporated at each pixel. Two thermal isolation structures are possible: hybrid and monolithic. The ultimate limit to the performance is set by temperature fluctuation noise, which depends upon the structure. The state-of-the-art of the approaches employing the three mechanisms is reviewed.
Nonlinear Associations between Medical Expenditure, Perceived Medical Attitude, and Sociodemographics, and Older Adults’ Self-Rated Health in China: Applying the Extreme Gradient Boosting Model

Background: although China’s total health expenditure has been dramatically increased so that the country can cope with its aging population, inequalities among individuals in terms of their medical expenditures (relative to their income level) have exacerbated health problems among older adults. This study aims to examine the nonlinear associations between each of medical expenditure, perceived medical attitude, and sociodemographics, and older adults’ self-rated health (SRH); it does so by using data from the 2018 China Family Panel Studies survey. Method: we used the extreme gradient boosting model to explore the nonlinear association between various factors and older adults’ SRH outcomes. We then conducted partial dependence plots to examine the threshold effects of each factor on older adults’ SRH. Results: older adults’ medical expenditure exceeded their overall income. Body mass index (BMI) and personal health expenditure play an essential role in predicting older adults’ SRH outcomes. We found older adult age, physical exercise status, and residential location to be robust predictors of SRH outcomes in older adults. Partial dependence plots of the results visualized the nonlinear association between variables and the threshold effects of factors on older adults’ SRH outcomes. Conclusions: findings from this study underscore the importance of medical expenditure, perceived medical attitudes, and BMI as important predictors of health benefits in older adults. The potential threshold effects of medical expenditure on older adults’ SRH outcomes provide a better understanding of the formation of appropriate medical policy interventions by balancing the government and personal medical expenditure to promote health benefits among older adults.