Miracle-dz/UniFAR-FTUs huggingface dataset:
Balancing Fairness and Energy Efficiency in SWIPT-based D2D networks: Deep Reinforcement Learning Based Approach

In this study, we propose a method to balance between user fairness and energy efficiency of users in the context of simultaneous wireless information and power transfer (SWIPT)-based device-to-device (D2D) networks. For this purpose, we build an optimization model which determines the subchannel allocation, transmit power level, and power splitting ratio of D2D users, with the purpose of maximizing the objective function which presents the trade-off level between the harvested energy and the average logarithmic data rate of users. To solve this problem, we employ deep reinforcement learning (DRL) which combines deep neural network (DNN) with reinforcement learning (RL). Despite the use of DRL, the dimension of the action space in our work is still very high because it should include subchannel allocation indicator, power splitting ratio, and transmit power of all D2D users. We therefore apply an interior point method to the output of the DNN in DRL to avoid the excess convergent time of DRL. Through the simulations, we compare the performance of our proposed algorithm to that of the conventional iterative algorithms; exhaustive search (ES) and gradient search (GS). Results show that the objective function value remains stable regardless of the change in the maximum transmission power. In addition, it is verified that varying the power splitting ratio has little effect on the system performance, which justifies using a constant power splitting ratio in SWIPT-based D2D networks. Furthermore, it is verified that the proposed DRL achieves near-global-optimal solution compared with conventional algorithms, with lower computational complexity.
ANTENA MIKROSTRIP BENTUK SEGIEMPAT 1,8 GHZ MENGGUNAKAN METODE PARSIAL GROUND UNTUK APLIKASI ANTENA 4G LTE

In this paper, a research about the design of quadrilateral microstrip antenna that works at 1.8 GHz frequency with partial ground method for 4G antenna applications. In order to create a 4G antenna, it is designed an antenna that meets the antenna work standard that is VAWR value, return loss, impedance and wide bandwidth antenna. From the results of the design that is done by using the partial ground method, it is obtained 40.3 mm of patch width, 51.2 mm of patch length and 20 mm of ground cutting length. From the result of measurement and research, it is obtained the value of VSWR 1,4, return loss -19,58 dB, impedance 56 Ω , HPBW 80o and bandwidth width 120 MHz by using partial ground method can increase microstrip antenna bandwidth from 54 MHz to 213 MHz. From the research that has been done, it shows that the antenna can be used for 4G Antenna.
Optimization of Multimodal Nanoparticles Internalization Process in Mesenchymal Stem Cells for Cell Therapy Studies

Considering there are several difficulties and limitations in labeling stem cells using multifunctional nanoparticles (MFNP), the purpose of this study was to determine the optimal conditions for labeling human bone marrow mesenchymal stem cells (hBM-MSC), aiming to monitor these cells in vivo. Thus, this study provides information on hBM-MSC direct labeling using multimodal nanoparticles in terms of concentration, magnetic field, and period of incubation while maintaining these cells’ viability and the homing ability for in vivo experiments. The cell labeling process was assessed using 10, 30, and 50 µg Fe/mL of MFNP, with periods of incubation ranging from 4 to 24 h, with or without a magnetic field, using optical microscopy, near-infrared fluorescence (NIRF), and inductively coupled plasma mass spectrometry (ICP-MS). After the determination of optimal labeling conditions, these cells were applied in vivo 24 h after stroke induction, intending to evaluate cell homing and improve NIRF signal detection. In the presence of a magnetic field and utilizing the maximal concentration of MFNP during cell labeling, the iron load assessed by NIRF and ICP-MS was four times higher than what was achieved before. In addition, considering cell viability higher than 98%, the recommended incubation time was 9 h, which corresponded to a 25.4 pg Fe/cell iron load (86% of the iron load internalized in 24 h). The optimization of cellular labeling for application in the in vivo study promoted an increase in the NIRF signal by 215% at 1 h and 201% at 7 h due to the use of a magnetized field during the cellular labeling process. In the case of BLI, the signal does not depend on cell labeling showing no significant differences between unlabeled or labeled cells (with or without a magnetic field). Therefore, the in vitro cellular optimized labeling process using magnetic fields resulted in a shorter period of incubation with efficient iron load internalization using higher MFNP concentration (50 μgFe/mL), leading to significant improvement in cell detection by NIRF technique without compromising cellular viability in the stroke model.
Protective Effect of Momordica Charantia Against Hepatic Ischemic Reperfusion Injury Model in Rats

Protection from the effects of ischemia and reperfusion injury (I/R) posesa serious problem in liver transplantation and resection surgery. Liver ischemia is a common cause of frequent clinical complications i.e.hepatocytes death, liver failure, and liver graft rejection. Oxidative stress playsa key role in hepatic I/R. Momordica charantia L. (Cucurbitaceae) is a strong antioxidant countering oxidative stress. This study was carried out to evaluate the effect of Momordica Charantia (MC) fruit extract on hepatic ischemic reperfusion injury in rats.Adult Wistar albino rats underwent 45 min of ischemia followed by 3 h of reperfusion by ligation of hepatic artery and left portal vein. Momodica charantia fruit extract was administered once daily for 4 days at a dose of 200mg/kg. Allopurinol (50mg/kg) was used as standard drug. After four days of drug administration, surgery was done on the fifth day and serum and liver samples were collected from all experimental animals after surgery. Biochemical parameters like SGOT (serum glutamic oxaloacetic transaminase), SGPT (serum glutamic-pyruvic transaminase), ALP (alkaline phosphatase), LDH (lactate dehydrogenase) and CRP (C-reactive protein) and various antioxidant parameters were measured from serum sample and liver homogenate, respectively. MC significantly (p<0.05) reversed I/R induced changes in liver damage marker -SGOT, SGPT and ALP, tissue damage marker LDH and inflammatory marker CRP. Changes in level of antioxidant parameters were also reversed by MC.MC possess protective effect against ischemic reperfusion injury that can be attributed to its antioxidant activity.
Arabinogalactan Proteins: Focus on the Role in Cellulose Synthesis and Deposition during Plant Cell Wall Biogenesis

Arabinogalactan proteins (AGPs) belong to a family of glycoproteins that are widely present in plants. AGPs are mostly composed of a protein backbone decorated with complex carbohydrate side chains and are usually anchored to the plasma membrane or secreted extracellularly. A trickle of compelling biochemical and genetic evidence has demonstrated that AGPs make exciting candidates for a multitude of vital activities related to plant growth and development. However, because of the diversity of AGPs, functional redundancy of AGP family members, and blunt-force research tools, the precise functions of AGPs and their mechanisms of action remain elusive. In this review, we put together the current knowledge about the characteristics, classification, and identification of AGPs and make a summary of the biological functions of AGPs in multiple phases of plant reproduction and developmental processes. In addition, we especially discuss deeply the potential mechanisms for AGP action in different biological processes via their impacts on cellulose synthesis and deposition based on previous studies. Particularly, five hypothetical models that may explain the AGP involvement in cellulose synthesis and deposition during plant cell wall biogenesis are proposed. AGPs open a new avenue for understanding cellulose synthesis and deposition in plants.
Phase Offset Tracking for Free Space Digital Coherent Optical Communication System

The coherent receiving method can improve the sensitivity of an optical signal receiver for free space optical communication system effectively. To implement coherent receiving, the phase offset between the local laser in the receiver and the received optical signal through the atmosphere needs to be measured and estimated. The commonly used algorithm is the Viterbi-Viterbi phase offset estimation method (VVPE) and this method always produces great errors especially with low SNR (signal to noise ratio). To improve the estimation performance, we present a new method combing the VVPE with the Kalman filter (VVPE-KF) to estimate the phase offset. This method can lower the estimation error by no less than 60%, when the SNR is low. To verify the performance of this new method, the constant parameter channel and atmosphere turbulence channel are employed to evaluate the algorithm. The impact of the atmosphere turbulence intensity on the tracking error is discussed.
Influence of Aliphatic Chain Length on Structural, Thermal and Electrochemical Properties of n-alkylene Benzyl Alcohols: A Study of the Odd–Even Effect

The century-old, well-known odd–even effect phenomenon is still a very attractive and intriguing topic in supramolecular and nano-scale organic chemistry. As a part of our continuous efforts in the study of supramolecular chemistry, we have prepared three novel aromatic alcohols (1,2-bis[2-(hydroxymethyl)phenoxy]butylene (Do4OH), 1,2-bis[2-(hydroxymethyl)phenoxy]pentylene (Do5OH) and 1,2-bis[2-(hydroxymethyl)phenoxy]hexylene (Do6OH)) and determined their crystal and molecular structures by single-crystal X-ray diffraction. In all compounds, two benzyl alcohol groups are linked by an aliphatic chain of different lengths (CH2)n; n = 4, 5 and 6. The major differences in the molecular structures were found in the overall planarity of the molecules and the conformation of the aliphatic chain. Molecules with an even number of CH2 groups tend to be planar with an all-trans conformation of the aliphatic chain, while the odd-numbered molecule is non-planar, with partial gauche conformation. A direct consequence of these structural differences is visible in the melting points—odd-numbered compounds of a particular series display systematically lower melting points. Crystal and molecular structures were additionally studied by the theoretical calculations and the melting points were correlated with packing density and the number of CH2 groups. The results have shown that the generally accepted rule, higher density = higher stability = higher melting point, could not be applied to these compounds. It was found that the denser packaging causes an increase in the percentage of repulsive H‧‧‧H interactions, thereby reducing the stability of the crystal, and consequently, the melting points. Another interesting consequence of different molecular structures is their electrochemical and antioxidative properties—a non-planar structure displays the highest oxidation peak of hydroxyl groups and moderate antioxidant activity.
Real-Life Application of a Wearable Device towards Injury Prevention in Tennis: A Single-Case Study

The purpose of this article is to present the use of a previously validated wearable sensor device, Armbeep, in a real-life application, to enhance a tennis player’s training by monitoring and analysis of the time, physiological, movement, and tennis-specific workload and recovery indicators, based on fused sensor data acquired by the wearable sensor—a miniature wearable sensor device, designed to be worn on a wrist, that can detect and record movement and biometric information, where the basic signal processing is performed directly on the device, while the more complex signal analysis is performed in the cloud. The inertial measurements and pulse-rate detection of the wearable device were validated previously, showing acceptability for monitoring workload and recovery during tennis practice and matches. This study is one of the first attempts to monitor the daily workload and recovery of tennis players under real conditions. Based on these data, we can instruct the coach and the player to adjust the daily workload. This optimizes the level of an athlete’s training load, increases the effectiveness of training, enables an individual approach, and reduces the possibility of overuse or injuries. This study is a practical example of the use of modern technology in the return of injured athletes to normal training and competition. This information will help tennis coaches and players to objectify their workloads during training and competitions, as this is usually only an intuitive assessment.
A Deep Regression Framework Toward Laboratory Accuracy in the Shop Floor of Microelectronics

Deep learning (DL) has certainly improved industrial inspection, while significant progress has also been achieved in metrology with impressive results reached through their combination. However, it is not easy to deploy metrology sensors in a factory, as they are expensive, and require special acquisition conditions. In this article, we propose a methodology to replace a high-end sensor with a low-cost one introducing a data-driven soft sensor (SS) model. Concretely, a residual architecture (R$^{2}$esNet) is proposed for quality inspection, along with an error-correction scheme to lessen noise impact. Our method is validated in printed circuit board (PCB) manufacturing, through the identification of defects related to glue dispensing before the attachment of silicon dies. Finally, a detection system is developed to localize PCB regions of interest, thus offering flexibility during data acquisition. Our methodology is evaluated under operational conditions achieving promising results, whereas PCB inspection takes a fraction of the time needed by other methods.
A New Method to Analyse the Role of Surface Plasmon Polaritons on Dielectric-Metal Interfaces

At nano-scale new phenomena have been discovered, allowing devices’ miniaturisation, energy harvesting, and power reduction. The Extraordinary Optical Transmission (EOT) phenomenon was reported in 1998 by Ebessen, who stated that the resonant peaks in the response of metallic nano antennas were more intense than predicted by classical diffraction theories. Years later, the main reason for this behaviour was attributed to the Surface Plasmon Polaritons (SPP), at least in ultraviolet and visible regions. In this article, a new method to model the radiation-matter interaction on a dielectric-metal interface is proposed, based on Maxwell’s equations and Fresnel Coefficients in absorbing media. Transmission percentage, angle and propagation length are obtained for a rigorous sweep on incident angle and wavelength. The results taken using some noble metals allow us to observe the presence of surface phenomena at expected SPP resonances. First, it is noticeable huge values of transmission in ultraviolet and visible regions, meaning that the metal does not reflect all the radiation. Also, the transmission angle tends to be higher, meaning huge surface components. Furthermore, the transmission length is on orders of 50-100 nm, meaning that phenomena as EOT only occurs at the nano-scale, since waves should arrive at another interface before being absorbed.
The information geometry of em variants for speech and image processing

Estimation from incomplete data arises in many statistical applications such as speech recognition and Positron Emission Tomography (PET). We analyze the Expectation Maximization (EM) algorithm for solving this problem using the information geometry of Csiszar and Tusnady, in order to understand how it may be extended. Under this framework, the EM algorithm is viewed as the alternating minimization (forward and backward projection) under the Kullback-Leibler divergence between a family of models and another family defined by the observed data. The GEM variant of the EM algorithm corresponds to replacing the backward projection with a step that reduces the divergence rather than minimizing it. The convergence properties of the EM algorithm are retained when the forward projection is similarly extended. The incremental EM algorithm of Neal and Hinton results from such an extension. Applying this algorithm to PET yields a block-iterative algorithm similar to Ordered Subsets EM (OS-EM), Block Iterative EMML (BI-EMML), and the Row Action ML Algorithm (RAMLA). Unlike these algorithms, incremental EM is guaranteed to give maximum likelihood solutions in all cases. Extending the family defined by the data yields robust procedures for estimation from small amounts of data. Applying such a procedure for rapid adaptation of hidden Markov models in speech recognition yields a robust adaptation scheme termed Discounted Likelihood Linear Regression (DLLR), which is a variant of the popular Maximum Likelihood Linear Regression (MLLR) scheme. EM variants based on extensions of the information divergence such as the f-divergences is also examined. In particular, we derive a family of EM variants that provide a continuous family of estimators that range from the maximum likelihood estimator to the chi-squared estimator, which extends some existing estimators to incomplete data problems.
Team Zoom @ AutoMin 2021: Cross-domain Pretraining for Automatic Minuting

This Paper describes Zoom’s submission to the First Shared Task on Automatic Minuting at Interspeech 2021. We participated in Task A: generating abstractive summaries of meetings. For this task, we use a transformer-based summarization model which is first trained on data from a similar domain and then finetuned for domain transfer. In this configuration, our model does not yet produce usable summaries. We theorize that in the choice of pretraining corpus, the target side is more important than the source.
Deepfake Detection Using Machine Learning Algorithms

Deepfake, a new video manipulation technique, has drawn much attention recently. Among the unlawful or nefarious applications, Deepfake has been used for spreading misinformation, fomenting political discord, smearing opponents, or even blackmailing. As the technology becomes more sophisticated and the apps for creating them ever more available, detecting Deepfake has become a challenging task, and accordingly researchers have proposed various deep learning (DL) methods for detection. Though the DL-based approach can achieve good solutions, this paper presents the results of our study indicating that traditional machine learning (ML) techniques alone can obtain superior performance in detecting Deepfake. The ML-based approach is based on the standard methods of feature development and feature selection, followed by training, tuning, and testing an ML classifier. The advantage of the ML approach is that it allows better understandability and interpretability of the model with reduced computational cost. We present results on several Deepfake datasets that are obtained relatively fast with comparable or superior performance to the state-of-the-art DL-based methods: 99.84% accuracy on FaceForecics++, 99.38% accuracy on DFDC, 99.66% accuracy on VDFD, and 99.43% on Celeb-DF datasets. Our results suggest that an effective system for detecting Deepfakes can be built using traditional ML methods.
THE ROLE OF BIOLOGICAL KNOWLEDGE IN THE DEVELOPMENT OF SUSTAINABLE BIOECONOMY – CASE OF POTATO AND ITS BENEFICIAL MICROORGANISMS INTERACTIONS

The primary objective of this paper was threefold. Firstly, the importance and impact of knowledge factors in the economy were reviewed. Secondly, the role of innovations in biological sciences was identified and the role of biological knowledge in the development of bioeconomy was assessed. Finally, the case of potato (Solanum tuberosum) and its beneficial microorganisms interactions, as an example of the knowledge-based innovations in agricultural production in Poland, was presented. Based on the systematic literature review high importance of knowledge factor, considered as the main resource, in the development of economic systems was identified. It is argued that knowledge is a specific resource, not subject to the same laws as the other production factors (land, labor, and capital). Unlike other resources, which are exhausted when used, knowledge can be shared and thrive through its use. From the inception of the bioeconomy concept the knowledge, a part of technology, and renewable biological resources was considered as primary driver. As bioeconomy is assuming the development of an economic system that is sustainable and climate-neutral, especially biological knowledge is crucial in its growth. The diffusion of biological knowledge-based innovations in agricultural production can be achieved by using beneficial microorganisms and their interactions with arable crops. Their interactions with potatoes show positive effects and the diffusion of such innovations in Poland is mostly conditioned by the experiences of farmers, which should be strengthened through network facilitation.
Hybrid Buck Converter With Constant Mode Changing Point and Smooth Mode Transition for High-Frequency Applications

To achieve high frequency and high efficiency over a wide load range, a monolithic voltage-mode dc–dc buck converter with advanced burst mode (ABM) and pulsewidth modulation (PWM) is presented in this paper. The load current is detected by estimating the currents flowing through the high-side and low-side switches, which maintains a near constant mode changing point. A counter-based scheme is used to achieve seamless and smooth transition between PWM and ABM. Both operating modes share the same control blocks, and no additional zero current detecting circuit is needed. In addition, the output stage switch size can be adjusted to reduce switching loss and improve efficiency under ultra-light load conditions. Experimental results show that the integrated 3 MHz converter achieves high efficiency from 80 to 91%, over a wide load range from 0.001 to 5 A. When the switch size is set to 1/3 of the maximum value, an additional 5% efficiency improvement at under 0.1 A load can be realized. The mode changing point is set at 0.4 A with less than 10% variation when the input voltage changes from 3 to 5 V. The load transient response is also improved significantly.
Design of a US28 ORF Deletion Virus in a Temperature-Sensitive Cytomegalovirus Strain Fails to Promote Lytic Replication in Hematopoietic Cells

Human cytomegalovirus (CMV) is a ubiquitous pathogen that latently resides in hematopoietic cells. Latently infected individuals with dysfunctional immune systems often experience CMV reactivation, which can cause devastating disease and mortality. While factors dictating the balance between latency and reactivation are not completely understood, CMV US28 is required for maintaining latent infection, and viral mutants that alter US28 function result in a lytic-like, rather than latent, infection in hematopoietic cells. In turn, viral lytic factors alter the host cell, making it challenging to characterize the US28-specific changes in the cellular milieu. To circumvent this, we generated a temperature-sensitive TB40/E recombinant virus, TB40/EgfpC510G (tsC510G), into which we engineered an amino acid change at position 510 (C510G) of IE2, as previously described in the CMV Towne strain. Using tsC510G, we then deleted the US28 ORF, termed tsC510G-US28Δ. Consistent with previous findings, tsC510G-US28Δ fails to undergo latency in Kasumi-3 cells at the permissive temperature. However, parallel cultures maintained at the non-permissive temperature showed a significant reduction in infectious center frequency, as measured by limiting dilution assay. Thus, we generated a new US28 mutant virus for use as a tool to study US28-specific changes in latently infected hematopoietic cells in the absence of induced lytic replication.