Miracle-dz/UniFAR-FTUs huggingface dataset:
Photocatalytic reduction of GO/ZnO to achieve GNRs for optoelectronic applications

Photocatalytic behaviour of ZnO nanowires (NWs) is employed to prepare graphene nanoribbons (GNRs) embedded in graphene oxide (GO) sheets. For this purpose, GO sheets are dispersed on the base of ZnO NWs grown between interdigital electrodes by a novel plasma-assisted procedure. Raman spectroscopy, optical absorption and electrical measurements indicate the emergence of reduced graphene oxide regions as a consequence of UV-irradiation. The optoelectronic characteristic of the fabricated GNR-based photodetector shows high responsivity value and low dark current. Our results open up a promising, technology-compatible approach to achieve efficient GNR-based devices without involving nanolithography complications.
Independency of Coding for Affective Similarities and for Word Co-occurrences in Temporal Perisylvian Neocortex

Abstract Word valence is one of the principal dimensions in the organization of word meaning. Co-occurrence-based similarities calculated by predictive natural language processing models are relatively poor at representing affective content, but very powerful in their own way. Here, we determined how these two canonical but distinct ways of representing word meaning relate to each other in the human brain both functionally and neuroanatomically. We re-analysed an fMRI study of word valence. A co-occurrence-based model was used and the correlation with the similarity of brain activity patterns was compared to that of affective similarities. The correlation between affective and co-occurrence-based similarities was low (r = 0.065), confirming that affect was captured poorly by co-occurrence modelling. In a whole-brain representational similarity analysis, word embedding similarities correlated significantly with the similarity between activity patterns in a region confined to the superior temporal sulcus to the left, and to a lesser degree to the right. Affective word similarities correlated with the similarity in activity patterns in this same region, confirming previous findings. The affective similarity effect extended more widely beyond the superior temporal cortex than the effect of co-occurrence-based similarities did. The effect of co-occurrence-based similarities remained unaltered after partialling out the effect of affective similarities (and vice versa). To conclude, different aspects of word meaning, derived from affective judgements or from word co-occurrences, are represented in superior temporal language cortex in a neuroanatomically overlapping but functionally independent manner.
Efficient CFI Enforcement for Embedded Systems Using ARM TrustZone-M

Embedded systems are deployed in many fields, from industrial applications to personal products. However, there are growing concerns regarding the security of these embedded systems as the number of attacks targeting them has increased. Control flow integrity (CFI) is a well-known security solution against these attacks. However, according to our analysis, existing CFI methods cannot be widely used in embedded systems one or more of the following reasons. (1) They require special hardware features that are not available in embedded systems, (2) they require that the developer recompile the source code with their compiler toolchain and (3) they incur considerable performance overhead to ensure CFI at runtime. In this paper, we propose CEST, a new scheme to ensure CFI on embedded systems using ARM TrustZone-M, a security extension for embedded ARM processors. For better compatibility, we designed CEST to be binary compatible. The evaluation results show that CEST can effectively enforce CFI compared to the existing studies using SVC.
Bioactive Profile of Distilled Solid By-Products of Rosemary, Greek Sage and Spearmint as Affected by Distillation Methods

By-products of essential oils (EOs) in the industry represent an exploitable material for natural and safe antioxidant production. One representative group of such by-products is distilled solid residues, whose composition is properly modulated by the distillation method applied for the recovery of EOs. Recently, in terms of Green Chemistry principles, conventional extraction and distillation processes are considered outdated and tend to be replaced by more environmentally friendly ones. In the present study, microwave-assisted hydro-distillation (MAHD) was employed as a novel and green method for the recovery of EOs from three aromatic plants (rosemary, Greek sage and spearmint). The method was compared to conventional ones, hydro-distillation (HD) and steam-distillation (SD), in terms of phytochemical composition of distilled solid residues, which was estimated by spectrophotometric and chromatographic methods. Total phenolic content (TPC), total flavonoid content (TFC) and antioxidant activity (ABTS, DPPH and FRAP) results highlighted the distilled solid residues as good sources of antioxidants. Moreover, higher antioxidant activity was achieved for MAHD extracts of solid residues in comparison to HD and SD extracts. A metabolomics approach was carried out on the methanolic extracts of solid residues obtained by different distillation methods using LC-MS analysis followed by multivariate data analysis. A total of 29 specialized metabolites were detected, and 26 of them were identified and quantified, presenting a similar phenolic profile among different treatments, whereas differences were observed among different species. Rosmarinic acid was the most abundant phenolic compound in all extracts, being higher in MAHD extracts. In rosemary and Greek sage extracts, carnosol and carnosic acid were quantified in significant amounts, while trimers and tetramers of caffeic acid (salvianolic acids isomers) were identified and quantified in spearmint extracts, being higher in MAHD extracts. The obtained results pointed out that MAHD extracts of distilled solid by-products could be a good source of bioactives with potential application in the food, pharmaceutical and cosmetic industries, contributing to the circular economy.
MbICE1 Confers Drought and Cold Tolerance through Up-Regulating Antioxidant Capacity and Stress-Resistant Genes in Arabidopsis thaliana

Malus baccata (L.) Borkh is an apple rootstock with good drought and cold resistance. The ICE gene is a key factor in the molecular mechanisms of plant drought and cold resistance. In the present research, the function of drought- and cold-induced MbICE1 of Malus baccata was investigated in Arabidopsis. According to GFP fluorescence images, MbICE1 was determined to be a nuclear protein. The MbICE1 was transferred to Arabidopsis, showing enhanced tolerance to drought and cold stresses. Under drought and cold treatments, the transgenic Arabidopsis had higher chlorophyll content and free proline content than WT plants, but the Malondialdehyde (MDA) content and electrolyte leakage (EL) were lower than those of WT plants. In addition, drought and cold led to a large accumulation of ROS (H2O2 and O2−) content in Arabidopsis, while overexpression of MbICE1 enhanced the antioxidant enzyme activity in Arabidopsis and improved the plant’s resistance to stresses. Moreover, the accumulation of MbICE1 promoted the expression of AtCBF1, AtCBF2, AtCBF3, AtCOR15a, AtCOR47 and AtKIN1 genes in Arabidopsis. These data indicate that MbICE1 is a key regulator of drought and cold and can be used as a backup gene for breeding Malus rootstocks.
Estimation of Genetic Parameters of Heat Tolerance for Production Traits in Canadian Holsteins Cattle

Simple Summary Heat stress is a major problem in dairy cattle and has several negative consequences for both health and production. The possibility to genetically select for more heat tolerant animals can provide additional means for the dairy industry to address this environmental issue. When production records and environmental information, such as temperature and relative humidity are available, the genetic merit of the animals for milk production can be estimated under different environmental conditions. Animals can then be ranked according to their genetic merit under heat stress conditions for selection purpose. Therefore, the objective of this study was to estimate the genetic parameters for heat tolerance of milk, fat, and protein yields in Canadian Holstein cows. The correlation between the estimated genetic merit under thermal comfort and heat stress conditions indicated an antagonistic relationship between the level of production and heat tolerance. However, this correlation was moderate, which suggests that there exist animals with a high genetic merit for production that are also heat tolerant, which allows for genetic selection for heat tolerance to be carried out while still improving production. The results of this study support genetic selection for heat tolerance contributing towards the goal of improving the overall heat tolerance of Canadian Holstein cattle. Abstract Understanding how cows respond to heat stress has helped to provide effective herd management practices to tackle this environmental challenge. The possibility of selecting animals that are genetically more heat tolerant may provide additional means to maintain or even improve the productivity of the Canadian dairy industry, which is facing a shifting environment due to climate changes. The objective of this study was to estimate the genetic parameters for heat tolerance of milk, fat, and protein yields in Canadian Holstein cows. A total of 1.3 million test-day records from 195,448 first-parity cows were available. A repeatability test-day model fitting a reaction norm on the temperature-humidity index (THI) was used to estimate the genetic parameters. The estimated genetic correlations between additive genetic effect for production and for heat tolerance ranged from −0.13 to −0.21, indicating an antagonistic relationship between the level of production and heat tolerance. Heritability increased marginally as THI increased above its threshold for milk yield (0.20 to 0.23) and protein yield (0.14 to 0.16) and remained constant for fat yield (0.17). A Spearman rank correlation between the estimated breeding values under thermal comfort and under heat stress showed a potential genotype by environmental interaction. The existence of a genetic variability for heat tolerance allows for the selection of more heat tolerant cows.
Sparse Direction of Arrival Estimation using Real Acoustic Data

Sparse direction of arrival (DOA) estimation has become highly popular especially after the development of compressive sensing (CS) theory. For hardware and/or software efficiency, i t i s h ighly c ommon t o u ndersample s ensor array measurements using measurement matrices, and perform the DOA estimation on the compressed sensor array data. This paper demonstrates a comparative analysis among sparse DOA estimation, Multiple Signal Classification (MUSIC) and Capon's beamformer using a real acoustic data corpus. Although it operates on the compressed data, the sparse DOA estimation algorithm outperforms MUSIC and Capon's beamformer when an adaptive measurement matrix is used. The findings o f this study verify the feasibility of sparse DOA estimation algorithms in practical applications.
Predicting Common-Mode Inductor Current Using a Time-Domain Hysteresis Model

Common-mode inductors are generally designed so that their operation is in the magnetically linear region of the core material. Very few works consider magnetic hysteresis in the core in terms of the common-mode current waveform. This work validates one such magnetic model and enhances it by including the common-mode current due to the inductor's parasitic capacitance. This work establishes the considerable impact of hysteresis and parasitic capacitance on the ability of a common-mode inductor to reduce the common-mode current. (Statement A: Approved for Release. Distribution is unlimited #2022-0227)
Bi-Objective Incentive Mechanism for Mobile Crowdsensing With Budget/Cost Constraint

In recent years, mobile crowdsensing (MCS) has been widely adopted as an efficient method for large-scale data collection. In MCS systems, insufficient participation and unstable data quality have become two crucial issues that prevent crowdsensing from further development. Thus designing a valid incentive mechanism is essentially significant. Most of the existing works on incentive mechanism design focus on single-objective optimization with various constraints. However, in the real-world crowdsensing, it is common that several objectives to be optimized exist. Furthermore, constraints on budget or cost are often seen in MCS systems as the feasibility of implementing incentive mechanism is indispensable. This paper studies a bi-objective optimization scenario of MCS to simultaneously optimize total value function and coverage function with budget/cost constraint through a set of problem transformations. Then a budget- or cost-feasible bi-objective incentive mechanism is further proposed to solve the aforementioned bi-objective optimization problem through the combination of binary search and greedy heuristic solution under budget or cost constraint, respectively. Through both rigorous theoretical analysis and extensive simulations, the obtained results demonstrate that the mechanisms achieve computation efficiency, individual rationality, truthfulness, and budget or cost feasibility, while one mechanism obtains an approximation.
Automatic Calibration between Multi-Lines LiDAR and Visible Light Camera Based on Edge Refinement and Virtual Mask Matching

To assist in the implementation of a fine 3D terrain reconstruction of the scene in remote sensing applications, an automatic joint calibration method between light detection and ranging (LiDAR) and visible light camera based on edge points refinement and virtual mask matching is proposed in this paper. The proposed method is used to solve the problem of inaccurate edge estimation of LiDAR with different horizontal angle resolutions and low calibration efficiency. First, we design a novel calibration target, adding four hollow rectangles for fully automatic locating of the calibration target and increasing the number of corner points. Second, an edge refinement strategy based on background point clouds is proposed to estimate the target edge more accurately. Third, a two-step method of automatically matching between the calibration target in 3D point clouds and the 2D image is proposed. Through this method, i.e., locating firstly and then fine processing, corner points can be automatically obtained, which can greatly reduce the manual operation. Finally, a joint optimization equation is established to optimize the camera’s intrinsic and extrinsic parameters of LiDAR and camera. According to our experiments, we prove the accuracy and robustness of the proposed method through projection and data consistency verifications. The accuracy can be improved by at least 15.0% when testing on the comparable traditional methods. The final results verify that our method is applicable to LiDAR with large horizontal angle resolutions.
TIME SERIES IMPUTATION USING VAR-IM (CASE STUDY: WEATHER DATA IN METEOROLOGICAL STATION OF CITEKO)

Univariate imputation methods are defined as imputation methods that only use the information of the target variable to estimate missing values. While univariate imputation methods are convenient and flexible since no other variable is required, multivariate imputation methods can potentially improve imputation accuracy given that the other variables are relevant to the target variable. Many multivariate imputation methods have been proposed, one of which is Vector Autoregression Imputation Method (VAR-IM). This study aims to compare imputation results of VAR-IM-based methods and univariate imputation methods on time series data, specifically on long lag seasonal data such as daily weather data. Three modified VAR-IM methods were studied using simulations with three steps: deletion, imputation, and evaluation. The deletion step was conducted using six different schemes with six missing proportions. The simulations were conducted on secondary daily weather data collected from meteorological station of Citeko from January 1, 1991, to June 22, 2013. Nine weather variables were examined, that is the minimum, maximum, and average temperatures, average humidity, rainfall rate, duration of solar radiation, maximum and average wind speed, as well as wind direction at maximum speed. The simulation results show that the three modified VAR-IM methods can improve the accuracies in around 75% of cases. The simulation results also show that imputation results of VAR-IM-based methods tend to be more stable in accuracy as the missing proportion increase compared to the imputation results of univariate imputation methods.
Proteomic repository data submission, dissemination, and reuse: key messages

ABSTRACT Introduction The creation of ProteomeXchange data workflows in 2012 transformed the field of proteomics, consisting of the standardization of data submission and dissemination and enabling the widespread reanalysis of public MS proteomics data worldwide. ProteomeXchange has triggered a growing trend toward public dissemination of proteomics data, facilitating the assessment, reuse, comparative analyses, and extraction of new findings from public datasets. By 2022, the consortium is integrated by PRIDE, PeptideAtlas, MassIVE, jPOST, iProX, and Panorama Public. Areas covered Here, we review and discuss the current ecosystem of resources, guidelines, and file formats for proteomics data dissemination and reanalysis. Special attention is drawn to new exciting quantitative and post-translational modification-oriented resources. The challenges and future directions on data depositions including the lack of metadata and cloud-based and high-performance software solutions for fast and reproducible reanalysis of the available data are discussed. Expert opinion The success of ProteomeXchange and the amount of proteomics data available in the public domain have triggered the creation and/or growth of other protein knowledgebase resources. Data reuse is a leading, active, and evolving field; supporting the creation of new formats, tools, and workflows to rediscover and reshape the public proteomics data.
Bayesian Model Choice: Asymptotics and Exact Calculations

Abstract : Model determination is a fundamental data analytic task. Here we consider the problem of choosing amongst a finite (with loss of generality we assume two) set of models. After briefly reviewing classical and Bayesian model choice strategies we present a general predictive density which includes all proposed Bayesian approaches we are aware of. Using Laplace approximations we can conveniently assess and compare asymptotic behavior of these approaches. Concern regarding the accuracy of these approximation for small to moderate sample sizes encourages the use of Monte Carlo techniques to carry out exact calculations. A data set fit with nested non linear models enables comparison between proposals and between exact and asymptotic values.
Stable Rotation of a Parametrically Excited Double Pendulum

The rotation of a double pendulum with a dc motor installed between the first and second pendulums was investigated using both experiments and numerical computations. The first pendulum can rotate freely around one end, while the second pendulum is attached to the other end of the first pendulum; the dc motor attached to the first pendulum causes the second pendulum to rotate with respect to the first pendulum. We clarify that there is a range of rotation speed in which the first pendulum rotates at the same, one-half, or one-third of the rotation speed of the second pendulum, and the stable rotation of the double pendulum is realized without torque control of the dc motor and report the characteristics of the stable rotation of the double pendulum.
An ultra-precision error estimation for a multi-axes grating encoder using quadrant photodetectors

For expanding the measurement range and improvement of accuracy of multi-axes grating encoder, a mathematical model of measurement angle and diffraction spot with QPD was established. We proposed a light spot position calculation method with consideration of both the optimized composite algorithm of laser beam feature of Gaussian distribution and the QPD diagonal algorithm. In this method, we use the piecewise polynomial fitting method to fit and solved the parameters of the traditional Infinite integral algorithm and the Boltzmann function fitting algorithm. Meanwhile, we introduce a weight factor and use the Composite algorithm to compensate the spot position error. Based on the given QPD model and the basic parameters of the laser beam, simulation works are carried out and results show that the maximum error of the spot position can reduce to be an order of 10-6 mm within the 2 mm measurement range using piecewise cubic polynomial fitting, around 10% of the traditional methods.
Fast algorithm for iteratively solving the nonlinear static Poisson equation in semiconductors

The method for iteratively solving the nonlinear static Poisson equation in semiconductors is based on a recent paper of Mayergoyz [J. Appl. Phys. 59, 195 (1986)]. It is the most efficient in computer time. All the advantages of his method, especially low memory requirement and applicability to 1‐, 2‐, and 3‐ dimensional problems are also valid for that presented here. A decrease of computer time by more than a factor of 2 without loss of accuracy of the final result is achieved mainly by decreasing the accuracy of a single step of iteration.
MUBot: Learning to Test Large-Scale Commercial Android Apps like a Human

Automated GUI testing has been playing a key role to uncover crashes to ensure the stability and robustness of Android apps. Recent research has proposed random, search-based and model-based testing techniques for GUI event generation. In industrial practices, different companies have developed various GUI exploration tools such as Facebook Sapienz, WeChat WeTest and ByteDance Fastbot to test their products. However, these tools are bound to their predefined GUI exploration strategies and lack of the ability to generate human-like actions to test meaningful scenarios. To address these challenges, Humanoid is the first Android testing tool that utilises deep learning to imitate human behaviours and achieves promising results over current model-based methods. However, we find some challenges when applying Humanoid to test our sophisticated commercial apps such as infinite loops and low test coverage. To this end, we performed the first case study on the performance of deep learning techniques using commercial apps to understand the underlying reason of the current weakness of this promising method. Based on our findings, we propose MUBot (Multi-modal User Bot) for human-like Android testing. Our empirical evaluation reveals that MUBot has better performance over Humanoid and Fastbot, our in-house testing tool on coverage achieved and bug-fixing rate on commercial apps.