Miracle-dz/UniFAR-FTUs huggingface dataset:
Bulletin of Electrical Engineering and Informatics

Received Jun 20, 2022 Revised Jul 14, 2022 Accepted Sep 30, 2022 Multi-core processors support all modern electronic devices nowadays. However, temperature and performance management are one of the most critical issues in the design of today’s microprocessors. In this paper, we propose a framework by using an optimal control method based on fan speed and frequency control of the multi-core processor. The goal is to optimize performance and at the same time avoid violating an expected temperature. Our proposed method uses a high-precision thermal and power model for multi-core processors. This method is validated on asymmetric ODROIDXU4 multi-core processor. The experimental results show the ability of the proposed method to achieve the adequate trade-off between performance and temperature control.
Bulletin of Electrical Engineering and Informatics

Received Aug 12, 2021 Revised Oct 29, 2021 Accepted Dec 10, 2021 Wireless sensor network (WSN) is one of the most promising technologies due to its size, cost-effective nature, and its ability to easily deploy in the target environment, as well as for its entry into many sensitive applications. However, making the most of the potential of this network is very difficult due to many issues, including the data received from the sensor nodes contains a huge amount of data redundant that negatively affects the overall network performance. Recent years have witnessed an increasing interest in data aggregation technology intending to eliminate redundant data from neighboring sensor nodes before transferring to the base station, thus improve performance efficiency and increasing the wireless sensor networks lifespan. This paper focused on applying three intelligent algorithms (SOM, HAC, and RBF) and describing the impact of data aggregation strategy on WSNs through the results obtained. As well as, an accurate description of the literature that applied these algorithms. A Competitive classification accuracy has been achieved when the proposed work is implemented and tested via the intel berkeley research lab dataset.
Bulletin of Electrical Engineering and Informatics

Received Jan 9, 2022 Revised May 9, 2022 Accepted Jul 11, 2022 This research paper proposes a novel bridgeless single-stage isolated converter with power factor correction and load voltage control. The proposed converter reduces the input diode bridge requirement with reduced passive components and provides a unidirectional flow of power to the load. The single-stage design reduces the use of an electrolytic capacitor, which improves reliability and reduces the size of the converter. The proposed control method is based on a single proportional integral (PI) controller to achieve both power factor correction and input current control. The proposed bridgeless converter is suitable for electric vehicle (EV) charging. A simulation study is performed on the MATLAB/Simulink to verify the effectiveness of the proposed converter. The converter is implemented in the laboratory to obtain the experimental results using typhoon hardware in the loop (HIL) based real time simulator.
Bulletin of Electrical Engineering and Informatics

Received Jan 27, 2022 Revised May 30, 2022 Accepted Jun 21, 2022 This paper represents a modified version (called T-AMPD) of automatic multiscale-based peak detection (AMPD) algorithm, which was previously proposed by Scholkmann et al for peak detection of noisy periodic and quasi-periodic signals, in order to optimize the algorithm and reduce the computational cost while still maintaining the accuracy. By introducing a new parameter n0, the number of expected peaks, to the algorithm and some other adjustments, the T-AMPD outperforms the original algorithm in processing time. Experiments in some real-world signals show that the modified algorithm is greatly better than the original one in runtime performance. Specifically, our T-AMPD was compared to the newest version of AMPD which is available in an R programming package. As a result, our T-AMPD is 2 to 25 times faster than the original AMPD in computational time, while the accuracy remains unchanged.
Bulletin of Electrical Engineering and Informatics

Received Jun 28, 2021 Revised Oct 10, 2021 Accepted Dec 21, 2021 The accessibility of cloud storage over the internet as a result of cloud computing technology provides the opportunity to store, share and upload data online with the use of digital devices which can be accessed anytime and anywhere. These benefits can also be exploited by the cybercriminals to perform various criminal activities including storing and exchanging of illegal materials on cloud storage platforms. The logs of malicious usages can be obtained from the cloud service providers for forensic investigations but the privacy issue among other factors make it difficult for such logs to be shared. Therefore, there is a need to perform client-side forensics to be able to carry out forensic investigation on digital devices as related to the activities on cloud storage. This study identifies relevant artifacts that can be forensically extracted from the registry of a window 10 device that accessed iDrive cloud storage. The study explores different experimental setups for the forensic analysis and adopted an integrated conceptual digital forensic framework in the investigation process to detect relevant forensic artifacts from the registry of a windows 10 device. This study increases the knowledge of cloud storage forensics and the significance of registry analysis during digital investigations.
Modeling and Projecting Mortality. A New Model of Heterogeneity and Selection in Survivorship

The demographic and epidemiological literature offers abundant examples of a range of shortcomings of statistical modeling to describe mortality by sex, age, time/cohort, and cause-of-death. Statistical modeling of mortality operating with implicitly homogenous sub-groupings exposed to mortality risk fails to consider latent biological heterogeneity at the level of individuals, and thereby important biological and social selection of survivorship. Defined on the state space of the simple life model, this study presents a proportional hazard model that makes up for such drawbacks as far as latent biological heterogeneity is concerned. The model describes heterogeneity and selection in individual survivorship by iterative stochastic micro simulation using cohort-based population mortality as an empirical benchmark. The model offers efficient linkage between past assorted mortality, on one hand, and informed anticipation of future heterogeneous survivorship, on the other hand. The combination of stochastic micro-simulation and log-linear modeling of the period effect or trend uncovered under the model makes the new Heterogeneity and Selection Model a powerful analytic and predictive tool of survivorship. Postulating a trend independent of age makes the popular Lee-Carter model (1992) unfit for professional demographic and actuarial use. Moreover, by sweeping latent biological heterogeneity under the rug, mortality analysis and projection based on central rates such as the Lee-Carter model (1992) underrates mortality in the mature and elderly ages. This is demonstrated by comparing current official mortality projections of Sweden, Denmark, and England & Wales to a set of alternative mortality projections under the Heterogeneity and Selection Model.
UNIVERSIDADE FEDERAL DO PARANÁ

The mineral nutrients presents in vinasse support algae and plant growth. The algal culture produces some organic and inorganic compounds to the liquid culture medium. These substances can be useful to formulate a plant tissue culture medium, whereas tissue culture medium is constituted of organic and inorganic components. Therefore, the aim of this study was to develop a plant culture medium using the vinasse used for Haematococcus pluvialis culture, to investigate possible beneficial effects of biocompounds presents in algal filtrate on micropropagation of Nidularium procerum (Bromeliaceae), quercetin accumulation and to evaluate the citotoxicity of this vinasse by bioassay with Artemia salina. The vinasse originated from H. pluvialis culture can be used to formulate plant tissue culture using 3% concentration and Its mineral nutrients can support in vitro growth of the plants, but some nutrients must be supplemented. An efficient protocol for micropropagation was developed for N. procerum. The micropropagated plants were suitable transferred to the greenhouse (acclimatized). This culture medium represents a reuse of this waste water and a rational alternative to vinasse disposal, adding value to what is currently considered an undesired residue. Moreover, this process can reduce the production costs of clonal seedlings and/or bioactive compounds in biofactories. There was not a biostimulant effect of the algal filtrate on the morphogenesis, on the other hand, this algal filtrate increase the quercetin production. The culture of H. pluvialis in the vinasse decreases the citotoxicity and phenolic compounds content, avoiding the explant tissue necrosis.
Macrophage Proinflammatory Activation The Exonuclease Trex1 Restrains

The three-prime repair exonuclease 1 (TREX1) is the most abundant exonuclease in mammalian cells. Mutations in Trex1 gene are being linked to the development of Aicardi–Goutie`res syndrome, an inﬂammatory disease of the brain, and systemic lupus erythematosus. In clinical cases and in a Trex1 -deﬁcient murine model, chronic production of type I IFN plays a pathogenic role. In this study, we demonstrate that Trex1 2 / 2 mice present inﬂammatory signatures in many different organs, including the brain. Trex1 is highly induced in macrophages in response to proinﬂammatory stimuli, including TLR7 and TLR9 ligands. Our ﬁndings show that, in the absence of Trex1, macrophages displayed an exacerbate proinﬂammatory response. More speciﬁcally, following proinﬂammatory stimulation, Trex1 2 / 2 macrophages exhibited an increased TNF- a and IFN- a production, higher levels of CD86, and increased Ag presentation to CD4 + T cells, as well as an impaired apoptotic T cell clearance. These results evidence an unrevealed function of the Trex1 as a negative regulator of macrophage inﬂammatory activation and demonstrate that macrophages play a major role in diseases associated with Trex1 mutations, which contributes to the understanding of inﬂammatory signature in these diseases. The Journal of Immunology , 2013, 191: 000–000.
Near Infrared Reflectance Spectroscopy Phenomic and Genomic Prediction of Maize Agronomic and Composition Traits Across Environments

For nearly two decades, genomic selection has supported efforts to increase genetic gains in plant and animal improvement programs. However, novel phenomic strategies helping to predict complex traits in maize have proven beneficial when integrated into across– and within-environment genomic prediction models. One phenomic data modality is near infrared spectroscopy (NIRS), which records reflectance values of biological samples (e.g., maize kernels) based on chemical composition. Predictions of seven maize agronomic traits and three kernel composition traits across two years (2011-2012) and two management conditions (water stressed and well-watered) were conducted using combinations of NIRS and genomic data within four different cross-validation prediction scenarios. In aggregate, models incorporating NIRS data alongside genomic data improved predictive ability over models using only genomic data in 5 of 28 trait/cross-validation scenarios for across-environment prediction and 15 of 28 trait/environment scenarios for within-environment prediction, while the model with NIRS data alone had the highest prediction ability in only 1 of 28 scenarios for within-environment prediction. Potential causes of the surprisingly lower phenomic than genomic prediction power in this study are discussed, including sample size, sample homogenization, and low G×E. A genome-wide association study (GWAS) implicated known (i.e., MADS69, ZCN8, sh1, wx1, du1) and unknown candidate genes linked to plant height and flowering-related agronomic traits as well as compositional traits such as kernel protein and starch content. This study demonstrated that including NIRS with genomic markers is a viable method to predict multiple complex traits with improved predictive ability and elucidate underlying biological causes. Key message Genomic and NIRS data from a maize diversity panel were used for prediction of agronomic and kernel composition traits while uncovering candidate genes for kernel protein and starch content.
ACCI could be a poor prognostic indicator for the in-hospital mortality of patients with SFTS

Abstract This study aims to evaluate the predictive role of age-adjusted Charlson comorbidity index (ACCI) scores for in-hospital prognosis of severe fever in thrombocytopenia syndrome (SFTS) patients. A total of 192 patients diagnosed with SFTS were selected as the study subjects. Clinical data were retrospectively collected. Receiver operating characteristic curves were used to evaluate the diagnostic value of ACCI for the mortality of SFTS patients, and Cox regression models were used to assess the association between predictive factors and prognosis. The 192 SFTS patients were divided into two groups according to the clinical endpoints (survivors/non-survivors). The results showed that the mortality of the 192 hospitalized SFTS patients was 26.6%. The ACCI score of the survivor group was significantly lower than that of the non-survivor group. Multivariate Cox regression analysis showed that the increased ACCI score was a significant predictor of poor prognosis in SFTS. Kaplan–Meier survival analysis showed that SFTS patients with an ACCI >2.5 had shorter mean survival times, indicating a poor prognosis. Our findings suggest that ACCI, as an easy-to-use clinical indicator, may offer a simple and feasible approach for clinicians to determine the severity of SFTS.
2.5D Forward And Inverse Modeling of Elastic Full-waveform Seismic Data

We discuss two-and-half-dimensional (2.5D) forward and inversion modeling for interpreting elastic full-waveform data. The geological structure model is two-dimensional (2D) and point monopoles or dipoles are employed as the sources. The elastic wave equation is transformed into the wavenumber domain and is then discretized on finite-difference staggered grids. After computing the displacement fields for each wavenumber, an inverse transform is then performed to compute the fields. We developed an efficient and adaptive numerical integration scheme to carry out the inverse transform. We also discuss the application of this 2.5D modeling approach for the elastic full-waveform inversion to reconstruct the compressional wave velocity, shear wave velocity, and mass density distributions of the subsurface.
Supplemental Materials : Part Detector Discovery in Deep Convolutional Neural Networks

Current fine-grained classification approaches often rely on a robust localization of object parts to extract localized feature representations suitable for discrimination. However, part localization is a challenging task due to the large variation of appearance and pose. In this paper, we show how pre-trained convolutional neural networks can be used for robust and efficient object part discovery and localization without the necessity to actually train the network on the current dataset. Our approach called “part detector discovery” (PDD) is based on analyzing the gradient maps of the network outputs and finding activation centers spatially related to annotated semantic parts or bounding boxes. This allows us not just to obtain excellent performance on the CUB2002011 dataset, but in contrast to previous approaches also to perform detection and bird classification jointly without requiring a given bounding box annotation during testing and ground-truth parts during training. The code is available at http://www.inf-cv.uni-jena.de/part_ discovery and https://github.com/cvjena/PartDetectorDisovery.
Frailty in Older Patients with Transthyretin Cardiac Amyloidosis

Background—Transthyretin cardiac amyloidosis (ATTR-CA) prevalence increases with age. The interplay between frailty and heart failure has been increasingly recognized. The objective of this study is to compare clinical, biological, and transthoracic echocardiography (TTE) characteristics of older ATTR-CA patients according to the G8 frailty screening tool. Methods—Patients over 75 years old with a confirmed diagnosis of ATTR-CA were included between January 2020 and April 2021. All patients underwent a routine blood test, TTE, and a functional assessment with a six-minute walking distance test (6MWD) or cardiopulmonary exercise testing (CPET), and the G8 score was calculated. Results—Fifty-two patients were included. Thirty-nine (75%) patients were frail and their mean NYHA stage was more severe (2.2 vs. 1.7; p = 0.004); 62% of them had a Gilmore stage of 2 or 3 (p = 0.05). Global left ventricular strain (GLS) was lower (−11.7% vs. −14.9%; p = 0.014) and the interventricular septum was thicker (18 ± 2 mm vs. 17 ± 2 mm; p = 0.033) in frail patients. There were no significant differences according to functional tests. Conclusion—The majority of older patients with ATTR-CA are frail according to the G8 score. They are more symptomatic and have an increased cardiac involvement and a poorer prognosis, requiring more personalized cardiac management.
Hyperspectral Image Classification Based on the Gabor Feature with Correlation Information

Abstract Gabor filter is widely used to extract spatial texture features of hyperspectral images (HSI) for HSI classification; however, a single Gabor filter cannot obtain the complete image features. In the paper, we propose an HSI classification method that combines the Gabor filter (GF) and domain-transformation standard convolution (DTNC) filter. First, we use the Gabor filter to extract spatial texture features from the first two principal components of the dimensionality-reduction HSI with PCA. Second, we use the DTNC filter to extract spatial correlation features from HSI in all bands. Finally, the Large Margin Distribution Machine (LDM) uses the linear fusion of the two kinds of spatial features to classify HSI. The experimental results show that the classification accuracy of Indian Pines, Pavia University, and Kennedy Space Center data sets is 96.64, 98.23, and 98.95% with only 4, 3, and 6% training samples, respectively; and these accuracies are 2–20% higher than the other tested methods. Compared with the hyperspectral information based on SVM, EPF, IFRF, PCA-EPFs, LDM-FL, and GFDN method, the proposed method, GFDTNCLDM, significantly improves the accuracy of HSI classification.
Haptic Cushion: Automatic Generation of Vibro-tactile Feedback Based on Audio Signal for Immersive Interaction with Multimedia

: This paper presents a haptic display providing audio-based vibrotactile feedback to enhance the immersive feeling of the user who interacts with multimedia content. The newly developed display has two main features , i) an automatic transformation algorithm a nd ii) a vibrotactile actuator. The proposed algorithm automatically transforms auditory signals into vibrotactile patterns in real-time by extracting principal frequencies from acoustic unit sequence s and superposing vibration waves. The actuator was designed based on the structure of the voice coil linear motor to operate effectively over a wide range of vibration frequencies. Experiments were carried out to evaluate characteristics of the implemented system and demonstrate the effectiveness of the proposed approach (cid:17)
Interplay of Sphingolipid Metabolism in Predicting Prognosis of GBM Patients: Towards Precision Immunotherapy

Background: In spite of numerous existing bio-surveillance systems for predicting glioma (GBM) prognosis, enhancing the efficacy of immunotherapy remains an ongoing conundrum. The continual scrutiny of the dynamic interplay between the sphingolipid metabolic pathway and tumor immunophenotypes has unveiled potential implications. However, the intricate orchestration of functional and regulatory mechanisms by long non-coding RNAs (lncRNAs) in GBM, particularly in the context of sphingolipid metabolism, remains cryptic. Methods: We harnessed established R packages to intersect gene expression profiles of GBM patients within the The Cancer Genome Atlas (TCGA) database with the compilation of sphingolipid metabolism genes from GeneCards. This enabled us to discern markedly distinct lncRNAs, which were subsequently deployed to construct a robust prognostic model utilizing Lasso-Cox regression analysis. We then scrutinized the immune microenvironment across various risk strata using the ssGSEA and CIBERSORT algorithms. To evaluate mutation patterns and drug resistance profiles within patient subgroups, we devised the "Prophytic" and "Maftools" packages, respectively. Results: Our investigation scrutinized lncRNAs linked to sphingolipid metabolism, utilizing glioma specimens from TCGA. We meticulously curated 1224 sphingolipid-associated genes gleaned from GeneCards and pinpointed 272 differentially expressed mRNAs via transcriptomic analysis. Enrichment analyses underscored their significance in sphingolipid processes. A prognostic model founded on 17 meticulously selected lncRNAs was systematically constructed and validated. This model adeptly stratified GBM patients into high- and low-risk categories, yielding highly precise prognostic insights. We also discerned correlations between immune cell infiltration and genetic mutation discrepancies, along with distinct therapeutic responses through drug sensitivity analysis. Notably, computational findings were corroborated through experimental validation by RT-PCR. Conclusion: In summation, our exhaustive inquiry underscores the multifaceted utility of the sphingolipid metabolic pathway as an autonomous diagnostic and prognostic indicator for glioma patients. Furthermore, we amalgamate a profusion of substantiated evidence concerning immune infiltration and gene mutations, thereby reinforcing the proposition that sphingolipid metabolism may function as a pivotal determinant in the panorama of immunotherapeutic interventions.
Whole-Body Teleoperation Control of Dual-Arm Robot Using Sensor Fusion

As human–robot interaction and teleoperation technologies advance, anthropomorphic control of humanoid arms has garnered increasing attention. However, accurately translating sensor-detected arm motions to the multi-degree freedom of a humanoid robotic arm is challenging, primarily due to occlusion issues with single-sensor setups, which reduce recognition accuracy. To overcome this problem, we propose a human-like arm control strategy based on multi-sensor fusion. We defined the finger bending angle to represent finger posture and employed a depth camera to capture arm movement. Consequently, we developed an arm movement tracking system and achieved anthropomorphic control of the imitation human arm. Finally, we verified our proposed method’s effectiveness through a series of experiments, evaluating the system’s robustness and real-time performance. The experimental results show that this control strategy can control the motion of the humanoid arm stably, and maintain a high recognition accuracy in the face of complex situations such as occlusion.