Miracle-dz/UniFAR-FTUs huggingface dataset:
A high capacitive-coupling ratio (HiCR) cell for 3 V-only 64 Mbit and future flash memories

A novel contactless cell with high capacitive-coupling ratio (HiCR) of 0.8, which is programmed and erased by Fowler-Nordheim tunneling, has been developed for 3 V-only 64 Mbit and future flash memories. A 1.50 /spl mu/m/sup 2/ cell area is obtained by using a 0.4 /spl mu/m technology. The HiCR cell structure is realized by 1) self-aligned definition of small tunneling regions underneath the floating-gate side wall and 2) advanced rapid thermal process for 7.5-nm thick tunnel oxynitride. The internal voltages used for program and erase are +8 V and +12 V, respectively. The total process-step numbers can be reduced to 85% compared to reported memory cells so far. <<ETX>>
Harnessing mode-selective nonlinear optics for on-chip multi-channel all-optical signal processing

All-optical signal processing based on nonlinear optical effects allows for the realization of important functions in telecommunications including wavelength conversion, optical multiplexing/demultiplexing, Fourier transformation, and regeneration, amongst others, on ultrafast time scales to support high data rate transmission. In integrated photonic subsystems, the majority of all-optical signal processing systems demonstrated to date typically process only a single channel at a time or perform a single processing function, which imposes a serious limitation on the functionality of integrated solutions. Here, we demonstrate how nonlinear optical effects can be harnessed in a mode-selective manner to perform simultaneous multi-channel (two) and multi-functional optical signal processing (i.e., regenerative wavelength conversion) in an integrated silicon photonic device. This approach, which can be scaled to a higher number of channels, opens up a new degree of freedom for performing a broad range of multi.
Data Aggregation in Wireless Sensor Networks. (Agrégation de données dans les réseaux de capteurs sans fil)

Wireless Sensor Networks (WSNs) have been regarded as an emerging and promis- ing field in both academia and industry. Currently, such networks are deployed due to their unique properties, such as self-organization and ease of deployment. How- ever, there are still some technical challenges needed to be addressed, such as energy and network capacity constraints. Data aggregation, as a fundamental solution, pro- cesses information at sensor level as a useful digest, and only transmits the digest to the sink. The energy and capacity consumptions are reduced due to less data packets transmission. As a key category of data aggregation, aggregation function, solving how to aggregate information at sensor level, is investigated in this thesis. We make four main contributions: firstly, we propose two new networking-oriented metrics to evaluate the performance of aggregation function: aggregation ratio and packet size coefficient. Aggregation ratio is used to measure the energy saving by data aggregation, and packet size coefficient allows to evaluate the network capac- ity change due to data aggregation. Using these metrics, we confirm that data ag- gregation saves energy and capacity whatever the routing or MAC protocol is used. Secondly, to reduce the impact of sensitive raw data, we propose a data-independent aggregation method which benefits from similar data evolution and achieves better re- covered fidelity. Thirdly, a property-independent aggregation function is proposed to adapt the dynamic data variations. Comparing to other functions, our proposal can fit the latest raw data better and achieve real adaptability without assumption about the application and the network topology. Finally, considering a given application, a tar- get accuracy, we classify the forecasting aggregation functions by their performances. The networking-oriented metrics are used to measure the function performance, and a Markov Decision Process is used to compute them. Dataset characterization and classification framework are also presented to guide researcher and engineer to select an appropriate functions under specific requirements.
MODELLING AND S IMULATION OF S OLAR PHOTOVOLTAIC SYSTEM AND I NTERFACING WITH C LOSED LOOP BOOST CONVERTER AND NEUTRAL POINT CLAMPED MULTILEVEL

In the power generation sector, Natural Resources like Solar, Wind, Tidal, Geothermal, Hydro etc have always played a very important role. Out of these solar PV (photo voltaic) is most popular due to its significant advantages. Controlling the output of solar PV system and harmonics at the load end are key aspects. The main theme of this paper is to control output of solar PV system with the help of PI controller and reduction of harmonics at the load end by using Neutral Point Clamped Multilevel Inverter. In this paper, commercial solar arrays of 1.2 kW along with close loop boost converter have been interfaced with neutral point clamped multilevel inverter. In this paper mathematical model of PV system have been presented and the characteristic of pv cell have been verified experimentally with the help of solar simulator under varying climate and load condition. Also, design and simulation of a Boost converter which works in continuous conduction mode (CCM) using Solar PV array voltage as input has been done. PV model has been interfaced with Multilevel inverter (MLI) and the results for three level, five level, seven level, nine level and eleven level are presented. Models have been developed for different level of inverter in MATLAB to achieve this purpose .
Parallel strings of IGBTs in short circuit transients: analysis of the parameter influence and experimental behavior

In this paper the behavior analysis of parallel connection of IGBTs under short circuit conditions is presented. The issues of hard switching fault (HSF) and fault under load (FUL) short circuit types are faced by taking into account for the influence of the layout and gate driving parameters. The role of the temperature has been considered too in order to investigate how this quantity affects the IGBTs short circuit phenomenon. An analytical description of the FUL transient is introduced to put in correlation the current and voltage peaks, which are suffered by the IGBT, to the circuit and device parameters. Indeed, the current peak imbalance appearing in a FUL condition is depending on the power layout, on the gate driving conditions and spread on device parameters.
Uncovering Cross-Context Inconsistent Access Control Enforcement in Android

—Due to the complexity resulted from the huge code base and the multi-context nature of Android, inconsistent access control enforcement exists in Android, which can be exploited by malware to bypass the access control and perform unauthorized security-sensitive operations. Unfortunately, existing studies only focus on the inconsistent access control enforcement in the Java context of Android. In this paper, we conduct the ﬁrst systematic investigation on the inconsistent access control enforcement across the Java context and native context of Android. In particular, to automatically discover cross-context inconsistencies, we design and implement IAceFinder , a new tool that extracts and contrasts the access control enforced in the Java context and native context of Android. Applying IAceFinder to 14 open-source Android ROM s, we ﬁnd that it can effectively uncover their cross-context inconsistent access control enforcement. Speciﬁcally, IAceFinder discovers 23 inconsistencies that can be abused by attackers to compromise the device and violate user privacy.
Studies of light perception in marine diatoms and discovery of a novel blue light cryptochrome photoreceptor

The research project has focused on the structural and functional characterization of a gene denoted PtCPF1 (Cryptochrome/Photolyase Family 1) that encodes a putative photoreceptor in the diatom Phaeodactylum tricomutum. Cryptochromes (cry) are blue light receptors that share sequence similarity with photolyases, flavoproteins that catalyze the repair of UV light-damaged DNA. In order to characterize the diatom PtCPF1 gene, expression at both transcriptional and translational levels have been performed in time course experiments designed to study circadian rhythmicity and acute light induction responses. From this analysis PtCPF1 was shown to be strongly induced under blue light and to be expressed diurnally. In order to understand better the function of the gene product, the protein has been expressed and purified in E. coli. Spectral and biochemical analyses of the purified protein have shown that PtCPF1 is a blue-light-absorbing protein with DNA repair activity. On the other hand, localization studies in diatom cells have evidenced the constitutive nuclear localization of the protein. Interestingly, comparative analysis of the diatom PtCPF1 protein has revealed it to be more similar to the animal cryptochromes than to plant counterparts. Since animal crys act as components of the circadian clock controlling daily physiological and behavioural rhythms and as photoreceptors that mediate entrainment of the circadian clock to light, it was important to elucidate the function of the PtCPF1 protein both in a heterologous system and in an in vivo system. Remarkably, transcription assays developed in mammalian cells have evidenced a repressor activity of the PtCPF1 protein within the clock machinery, mimicking the function of animal crys. Furthermore, gene expression studies of transgenic diatom lines overexpressing PtCPF1 have indicated that the protein acts as a blue light photoreceptor because it can modulate several blue light-dependent responses. Therefore, this research project has identified a novel protein that displays both blue light photoreceptor activity as well as DNA repair activity. This protein could, in fact, be considered the missing link in the evolutionary history of the Cryptochrome/Photolyase family.
The Sociodemographic Digital Divide in Mobile Health App Use Among Clients at Outpatient Departments in Inner Mongolia, China: Cross-sectional Survey Study

Background Mobile health (mHealth) apps have become part of the infrastructure for access to health care in hospitals, especially during the COVID-19 pandemic. However, little is known about the effects of sociodemographic characteristics on the digital divide regarding the use of hospital-based mHealth apps and their benefits to patients and caregivers. Objective The aim of this study was to document the cascade of potential influences from digital access to digital use and then to mHealth use, as well as the potential influence of sociodemographic variables on elements of the cascade. Methods A cross-sectional survey was conducted from January to February 2021 among adult clients at outpatient departments in 12 tertiary hospitals of Inner Mongolia, China. Structural equation modeling was conducted after the construct comprising digital access, digital use, and mHealth use was validated. Results Of 2115 participants, the β coefficients (95% CI) of potential influence of digital access on digital use, and potential influence of digital use on mHealth use, were 0.28 (95% CI 0.22-0.34) and 0.51 (95% CI 0.38-0.64), respectively. Older adults were disadvantaged with regard to mHealth access and use (β=–0.38 and β=–0.41), as were less educated subgroups (β=–0.24 and β=–0.27), and these two factors had nonsignificant direct effects on mHealth use. Conclusions To overcome the mHealth use divide, it is important to improve digital access and digital use among older adults and less educated groups.
The Potential Increase of Household Consumption Through Online Shopping as East Java Economic Growth Acceleration Effort in The Covid-19 Pandemic: Optimization of Social Demographic and Spatial Factors

The economic shock as a result of the Covid-19 pandemic resulted in an economic contraction, including East Java. The shift in consumer behavior in utilizing e-commerce to adapt to pandemic constraints is a good catalyst for stimulating household final consumption as the largest shareholder in the East Java economy. This study tries to identify socio-demo graphic and spatial factors that can be optimized for the acceleration of East Java’s eco nomic growth during the disruption from the demand side. The results of the identification of individual behavior in online shopping as well as district/city aggregates indicate several socio-demographic and spatial factors that can be optimized. Mapping districts/cities based on the dimensions of online shopping activities and ICT infrastructure conditions can be a more specific alternative solution to further optimize the potential for increasing household consumption as an effort to accelerate economic growth during the Covid-19 pandemic appropriate with the characteristics of each region.
Benefits, Security and Issues in Software Defined Networking (SDN)

Applications used now a day are bandwidth hungry like Online Shopping, IPTV, E-Commerce and many other which require more and more bandwidth as well as continuous bandwidth. The Software Defined Networking (SDN) decouples control and forward plane allowing the flexibility to program network control plane and empowers distinctive approaches to network security than those existing in present IP system. In SDN, the centralized controllers keep an eye on the changing scenarios of the network. Because of controller view of the network, SDN can facilitate and enhance the network related security. SDN architecture is directly programmable and opens standard-based but SDN itself have numerous issues like performance vs. flexibility, scalability, security and interoperability. This paper discusses security issues regarding logically centralized controller, OpenFlow constraints and absence of middle-boxes in SDN.
Awareness Regarding the Dietary Practices Among Osteoarthritis Patients

Osteoarthritis is a significant outset of pain, incapacity, and financial cost around the world[[i]]. By 2050 people aging above 60 years will represent over 20% of the total population[[ii]]. In Pakistan 28.1% urban population and 25.07% rural population has been affected by knee osteoarthritis[[iii]]. Elderly people are more affected as compared to younger population. Obesity and overweight are major risk factors of osteoarthritis[[iv]]. Other contributing factors are smoking, gender, sarcopenia, regular stairs climbing and vitamin D deficiency[[v]]. Nowadays different studies are conducted to check the relation between life style and dietary factors with the occurrence of osteoarthritis[[vi]]. Losina E et al., in 2013 concluded that patients above 50 years of age have more incidence rate of osteoarthritis about 57.16%[[vii]]. Statistical analysis of this study showed that 61% patients with osteoarthritis were overweight or obese. Out of these 35% were overweight and 26%were obese. Pereira D et al., in 2017 concludedin their study 68.3% patients were overweight and obese. Total 43.3% individuals were overweight with BMI less than 30 mg/m2 and individuals with BMI greater than 30 were 24.9% [[viii]]. Socioeconomical status was considered as a major factor when knowledge, attitude and practices were being discussed, in a study 45% osteoarthritis patients were from lower class and lower middle class and 40% patients were from middle class. Out of these, 75% patients were from urban areas while 25% osteoarthritis patients came from rural areas. The association of socioeconomic status with respect to knowledge, attitude and practices was also shown by Cleveland RJ and colleagues. It concluded that the socioeconomical status played a role in increasing pain caused by osteoarthritis in lower class, were more prone towards developing osteoarthritis as compared to upper class, as they were performing more heavy domestic work as compared to upper class [[ix]]. Perruccio AV et al., in 2016 investigated that knowledge played a major role, there was a significant association between lower education and increased pain. A person with knowledge, avoid excessive work out which is directly linked with pain intensity.Lower level of awareness and education about how to cope up with disease symptoms also played an important role in osteoarthritis[[x]]. Jones AC et al., in 2017 concluded that carbonated drinks played a major role in reducingbone mineral density (BMD) because of phosphoric acid present in it which leech out calcium from bones making them fragile and porous[[xi]]. Based on results it was concluded that lack of knowledge and awareness about diet, disease, life style modifications, obesity, false, low income, gender, age, poor socioeconomic status and attitude and beliefs proved to be directly related with osteoarthritis. Majority of patients had inappropriate knowledge about disease which aggravate the symptoms of the disease. It was found that majority of patients follow inappropriate attitude and practices towards disease
Evaluation of Performance of Different Japanese Quail (Coturnix Coturnix Japonica) Varieties

The study was conducted to assess three varieties (CARI, PES and WB) of Japanese quails (Coturnix Coturnix Japonica) during the growing and laying periods. In this trail, 360 quails consisting of three varieties each 120 quails were distributed in three treatments with six replicates each consist of 20 chicks. The diets were prepared iso-nitrogenous and iso-caloric. The performance and egg production parameters were studied from 0 to 22 weeks of age. There were no significant differences (P> 0.05) found on body weight gain, feed consumption and feed conversion ratio among varieties from 0 to 6 weeks. The age at first egg was found to be early in the CARI variety 43days. The peak egg production level (79.83±0.64%) during fourth laying cycle in the PES at the age of 18–22 weeks followed by CARI (71.62±0.62%) and WB (49.28±0.43%). The results of the present study indicated that, the three varieties performance and production were significantly there was no difference.
Natural Bioactive Compounds Targeting Histone Deacetylases in Human Cancers: Recent Updates

Cancer is a complex pathology that causes a large number of deaths worldwide. Several risk factors are involved in tumor transformation, including epigenetic factors. These factors are a set of changes that do not affect the DNA sequence, while modifying the gene’s expression. Histone modification is an essential mark in maintaining cellular memory and, therefore, loss of this mark can lead to tumor transformation. As these epigenetic changes are reversible, the use of molecules that can restore the functions of the enzymes responsible for the changes is therapeutically necessary. Natural molecules, mainly those isolated from medicinal plants, have demonstrated significant inhibitory properties against enzymes related to histone modifications, particularly histone deacetylases (HDACs). Flavonoids, terpenoids, phenolic acids, and alkaloids exert significant inhibitory effects against HDAC and exhibit promising epi-drug properties. This suggests that epi-drugs against HDAC could prevent and treat various human cancers. Accordingly, the present study aimed to evaluate the pharmacodynamic action of different natural compounds extracted from medicinal plants against the enzymatic activity of HDAC.
Significance and Confidence in Evaluation

Significance is becoming a matter of considerable concern for Machine Learning, but remains of little concern in other areas of Artificial Intelligence, particularly where Machine Learning paradigms are not rigorously applied. The Machine Learning paradigm of independent Validation and Evaluation, or more complex paradigms such as Cross-Validation or Bootstrapping, allows a quantifiable measure of confidence in the results of an evaluation. However, over the last decade there has been increasing concern about the biases embodied in traditional evaluation methods, as well as questions about how to deal with prevalence and bias. This paper briefly introduces unbiased alternatives to Recall, Precision and Accuracy and shows how they can be used to directly estimate familiar estimates of significance and confidence. In this paper we will develop significance and confidence estimates theoretically, as well as evaluating their performance empirically using a Monte Carlo simulation. In relation to significance, we note the existence of measures used to estimate correlation from chi-squared sums, and relate our proposals to these estimates. In relation to confidence, we discuss the advantages of confidence intervals over mere statistical significance. These advantages are particularly pertinent at a time when the Machine Learning community is increasingly concerned about the overuse of repositories of standard datasets – one in twenty experiments may be expected to be significantly better than chance or than any other specific result when significance is evaluated to the 0.05 level, but on the other hand standard correction techniques tend to be overly conservative and represent an explicit bias against later work.
Exploring the Factors Affecting the Adoption of Environmentally Friendly Vehicles

This study investigates the factors that induce the early adoption of environmentally friendly vehicles. We measure the affective, cognitive, and behavioral factors influencing the early adoption intentions of car owners in three countries (the US, Germany, and Japan). We find that affective and cognitive factors are significant, but behavioral factors are only partially significant. Specifically, eco-centric values and knowledge about green technology are significant in explaining early adoption intent. However, of the behavioral factors, usage context is significant, whereas commute distance is not significant in explaining early adoption intent. Finally, between-country differences indicate that any policy to increase the adoption of environmentally friendly vehicles should be fine-tuned for each country.
Implementation of AIoT Edge Cluster System via Distributed Deep Learning Pipeline

Recently, IoT systems are cloud-based, so that continuous and large amounts of data collected from sensor nodes are processed in the data server through the cloud. However, in the centralized configuration of large-scale cloud computing, computational processing must be performed at a physical location where data collection and processing take place, and the need for edge computers to reduce the network load of the cloud system is gradually expanding. In this paper, a cluster system consisting of 6 inexpensive Raspberry Pi boards was constructed to perform fast data processing. And we propose "Kubernetes cluster system(KCS)" for processing large data collection and analysis by model distribution and data pipeline method. To compare the performance of this study, an ensemble model of deep learning was built, and the accuracy, processing performance, and processing time through the proposed KCS system and model distribution were compared and analyzed. As a result, the ensemble model was excellent in accuracy, but the KCS implemented as a data pipeline proved to be superior in processing speed.
On-metal UHF-RFID passive tags based on complementary split-ring resonators

The use of complementary split-ring resonators (CSRRs) as radiating elements in low-profile on-metal ultra-high-frequency radio-frequency identification (UHF-RFID) tags is explored in this study. First, the radiation properties of the edge-coupled and the non-bianisotropic (NB-CSRR) versions of the CSRR are studied. The tag design strategy is then discussed in detail. On that basis, a compact (λ 0/7 × λ 0/7) low-profile (1.27 mm) tag prototype based on the non-bianisotropic version of the CSRR (NB-CSRR) antenna is designed and manufactured to operate in the North-American UHF-RFID band. The experimental results validate the theoretical and simulated behaviour, and exhibit a maximum read range of 6.8 m.