Miracle-dz/UniFAR-FTUs huggingface dataset:
High LET-Like Radiation Tracks at the Distal Side of Accelerated Proton Bragg Peak

Proton therapy is a type of hadron radiotherapy used for treating solid tumors. Unlike heavy charged elements, proton radiation is considered to be low LET (Linear Energy Transfer) radiation, like X-rays. However, the clinical SOBP (Spread Out Bragg Peak) proton radiation is considered to be higher in relative biological effectiveness (RBE) than both X-ray and their own entrance region. The RBE is estimated to be 1.1–1.2, which can be attributed to the higher LET at the SOBP region than at the entrance region. In order to clarify the nature of higher LET near the Bragg peak of proton radiation and its potential cytotoxic effects, we utilized a horizontal irradiation system with CHO cells. Additionally, we examined DNA repair mutants, analyzed cytotoxicity with colony formation, and assessed DNA damage and its repair with γ-H2AX foci assay in a high-resolution microscopic scale analysis along with the Bragg peak. Besides confirming that the most cytotoxic effects occurred at the Bragg peak, extended cytotoxicity was observed a few millimeters after the Bragg peak. γ-H2AX foci numbers reached a maximum at the Bragg peak and reduced dramatically after the Bragg peak. However, in the post-Bragg peak region, particle track-like structures were sporadically observed. This region contains foci that are more difficult to repair. The peak and post-Bragg peak regions contain rare high LET-like radiation tracks and can cause cellular lethality. This may have caused unwanted side effects and complexities of outputs for the proton therapy treatment.
Resistance Mutations to BTK Inhibitors Originate From the NF-κB but Not From the PI3K-RAS-MAPK Arm of the B Cell Receptor Signaling Pathway

Since the first clinical report in 2013, inhibitors of the intracellular kinase BTK (BTKi) have profoundly altered the treatment paradigm of B cell malignancies, replacing chemotherapy with targeted agents in patients with chronic lymphocytic leukemia (CLL), mantle cell lymphoma (MCL), and Waldenström’s macroglobulinemia. There are over 20 BTKi, both irreversible and reversible, in clinical development. While loss-of-function (LoF) mutations in the BTK gene cause the immunodeficiency X-linked agammaglobulinemia, neither inherited, nor somatic BTK driver mutations are known. Instead, BTKi-sensitive malignancies are addicted to BTK. BTK is activated by upstream surface receptors, especially the B cell receptor (BCR) but also by chemokine receptors, and adhesion molecules regulating B cell homing. Consequently, BTKi therapy abrogates BCR-driven proliferation and the tissue homing capacity of the malignant cells, which are being redistributed into peripheral blood. BTKi resistance can develop over time, especially in MCL and high-risk CLL patients. Frequently, resistance mutations affect the BTKi binding-site, cysteine 481, thereby reducing drug binding. Less common are gain-of-function (GoF) mutations in downstream signaling components, including phospholipase Cγ2 (PLCγ2). In a subset of patients, mechanisms outside of the BCR pathway, related e.g. to resistance to apoptosis were described. BCR signaling depends on many proteins including SYK, BTK, PI3K; still based on the resistance pattern, BTKi therapy only selects GoF alterations in the NF-κB arm, whereas an inhibitor of the p110δ subunit of PI3K instead selects resistance mutations in the RAS-MAP kinase pathway. BTK and PLCγ2 resistance mutations highlight BTK’s non-redundant role in BCR-mediated NF-κB activation. Of note, mutations affecting BTK tend to generate clone sizes larger than alterations in PLCγ2. This infers that BTK signaling may go beyond the PLCγ2-regulated NF-κB and NFAT arms. Collectively, when comparing the primary and acquired mutation spectrum in BTKi-sensitive malignancies with the phenotype of the corresponding germline alterations, we find that certain observations do not readily fit with the existing models of BCR signaling.
The Research on Organizational Justice in Scopus Indexed Journals: A Bibliometric Analysis of Seven Decades

The organizational justice terminology has had a long journey to become one of the significant contributors to organizational success. Recently, an intense global upsurge in the use of organizational justice terms in publications has forced us for this bibliometric analysis in order to look at the overall publications on organizational justice. The objective of the current research is to advance knowledge about organizational justice research trends using Scopus database and bibliometric analysis research. The analysis was performed to see the publication trends between the years 1941 and 2018; it used authors, journals, countries, academic discipline, research institutes/universities, and various keywords related to organizational justice as search words. After careful consideration and using multiple checkpoints for eliminating irrelevant studies, 5,650 research articles were analyzed. In the realm of organizational justice, procedural justice was the most frequently occurred among other dimensions. Moreover, variables such as organizational trust, job satisfaction, organizational commitment, citizenship behavior, ethics, and turnover are major concepts that occurred within organizational justice research. Some variables with infrequent occurrences, along with future recommendations and study limitations, are also discussed.
Quality of and Recommendations for Relevant Clinical Practice Guidelines for COVID-19 Management: A Systematic Review and Critical Appraisal

Background: The morbidity and mortality of coronavirus disease 2019 (COVID-19) are still increasing. This study aimed to assess the quality of relevant COVID-19 clinical practice guidelines (CPGs) and to compare the similarities and differences between recommendations. Methods: A comprehensive search was conducted using electronic databases (PubMed, Embase, and Web of Science) and representative guidelines repositories from December 1, 2019, to August 11, 2020 (updated to April 5, 2021), to obtain eligible CPGs. The Appraisal of Guidelines for Research and Evaluation (AGREE II) tool was used to evaluate the quality of CPGs. Four authors extracted relevant information and completed data extraction forms. All data were analyzed using R version 3.6. 0 software. Results: In total, 39 CPGs were identified and the quality was not encouragingly high. The median score (interquartile range, IQR) of every domain from AGREE II for evidence-based CPGs (EB-CPGs) versus (vs.) consensus-based CPG (CB-CPGs) was 81.94% (75.00–84.72) vs. 58.33% (52.78–68.06) in scope and purpose, 59.72% (38.89–75.00) vs. 36.11% (33.33–36.11) in stakeholder involvement, 64.58% (32.29–71.88) vs. 22.92% (16.67–26.56) in rigor of development, 75.00% (52.78–86.81) vs. 52.78% (50.00–63.89) in clarity of presentation, 40.63% (22.40–62.50) vs. 20.83% (13.54–25.00) in applicability, and 58.33% (50.00–100.00) vs. 50.00% (50.00–77.08) in editorial independence, respectively. The methodological quality of EB-CPGs were significantly superior to the CB-CPGs in the majority of domains (P < 0.05). There was no agreement on diagnosis criteria of COVID-19. But a few guidelines show Remdesivir may be beneficial for the patients, hydroxychloroquine +/– azithromycin may not, and there were more consistent suggestions regarding discharge management. For instance, after discharge, isolation management and health status monitoring may be continued. Conclusions: In general, the methodological quality of EB-CPGs is greater than CB-CPGs. However, it is still required to be further improved. Besides, the consistency of COVID-19 recommendations on topics such as diagnosis criteria is different. Of them, hydroxychloroquine +/– azithromycin may be not beneficial to treat patients with COVID-19, but remdesivir may be a favorable risk-benefit in severe COVID-19 infection; isolation management and health status monitoring after discharge may be still necessary. Chemoprophylaxis, including SARS-CoV 2 vaccines and antiviral drugs of COVID-19, still require more trials to confirm this.
Decentralized Covert and Collaborative Radio Surveillance on a Group of Mobile Ground Nodes by a UAV Swarm

This paper considers using UAVs to carry out an active and collaborative radio surveillance on a group of ground mobile nodes while disguising the surveillance intention. The UAVs try to prevent being visually noticed by the nodes, and a new measure to quantify the disguising performance is proposed. We formulate a new trajectory optimization problem to maximize the new disguising metric subject to an uninterrupted surveillance requirement and the collision avoidance and the aeronautic maneuverability of the UAVs. A model predictive control (MPC) based scheme is developed. Computer simulations and comparisons with a random trajectory construction method show that the proposed method guarantees the surveillance performance without the loss of disguising performance.
Joint Waveform Design and Passive Beamforming for RIS-Assisted Dual-Functional Radar-Communication System

Dual-functional radar-communication (DFRC) technique has been viewed as a promising component in the emerging platforms. When synthesizing the desired transmit beampattern, the degrees of freedom of waveform design is limited, which introduces high multi-user interference (MUI), thus degrading the communication performance. Inspired by the applications of the Reconfigurable Intelligent Surface (RIS) in mitigating MUI, in this paper, we investigate joint waveform design and passive beamforming in RIS-assisted DFRC system. We first study the minimization of MUI under the strict beampattern constraint by jointly optimizing DFRC waveform and RIS phase shift matrix. To deal with the coupled variables, we propose an alternating algorithm based on manifold optimization. Subsequently, the trade-off between radar and communication performances is investigated. Simulation results show that for both cases of strict beampattern and trade-off design, with the help of RIS, the system throughput can be significantly improved. Moreover, compared with the scenario where no RIS is employed, the obtained beampattern matches with the target transmit beampattern better.
Quantifying Power System Operational and Infrastructural Resilience Under Extreme Conditions Within a Water-Energy Nexus Framework

This work presents definitions and metrics developed for quantifying the resilience of the electric power system (EPS) while considering its interdependent operation with the water distribution system (WDS). These metrics are used within a novel formulation in order to determine EPS resilience during the coordinated real-time operation of the two systems where both the operational state and infrastructural configuration of the EPS is taken into account. Coordinated operation of the two systems involves simulations that account for the main dependencies of each system on the other (cooling water for thermoelectric generation and electric power for WDS pumps). The results for several test cases are used within this work to demonstrate how system resilience in the EPS is captured for simulations that emulate the critical conditions of long-term power outages (in the EPS) and severe drought (in the WDS).
Methylene Blue Adsorption from Aqueous Solution by Magnetic Cellulose/Graphene Oxide Composite: Equilibrium, Kinetics, and Thermodynamics

In the present study, magnetic cellulose/graphene oxide composite (MCGO) was prepared as a novel adsorbent to dispose of dye wastewater. The morphology and chemical structure of the MCGO composite were characterized by the Fourier transform infrared spectrometer (FT-IR), X-ray diffraction (XRD), and scanning electronic microscope (SEM). The adsorption of methylene blue (MB) onto MCGO was studied in relation to initial concentration of MB, contact time, adsorbent dose, and pH value of solution. Adsorption kinetics and the equilibrium adsorption isotherm were fitted by a pseudo-second-order kinetic model and Langmuir isotherm, respectively. The thermodynamic parameters indicated that the adsorption was spontaneous, favorable, and exothermic in nature. Furthermore, MCGO was very stable and can easily be recycled. The adsorption efficiency of MCGO was still over 89% after recycling for five times.
A hybrid seagull optimization algorithm for chemical descriptors classification

Cheminformatics is a field of research that explores the correlation between chemical compound data sets and the metrics used in drug design to evaluate the similarity. Many classical-methods are applie-d in prediction the drug design that aren’t efficient and not effective. This article introduces a classification approach called SOA-SVM that is an hybrid version of the Seagull optimization Algorithm (SOA) combined with Support Vector Machines (SVM) and designed to select the descriptors for chemical compound tasks. The operators used in the exploration and exploitation phases in SOA are modified in order to identify the desired features that permits to enhance the accuracy. For experimental purposes they are employed two data sets, namely, MonoAmine Oxidase (MAO) and QSAR Biodegradation. The results helps to venfy that good performance of the proposal, that is able to find the best solutions to the feature selection problem with a high accuracy in comparison with other methodologies.
Empirical Exploration of Machine Learning Techniques for Detection of Anomalies Based on NIDS

Computer crimes and attacks on data networks have increased significantly, so it has become necessary to implement techniques that detect these threats and safeguard the information of organizations. Network Intrusion Detection Systems (NIDS) allow detecting anomalies and attacks in real time, by analyzing the local and outgoing traffic of the network. At present, to improve its performance, it has been chosen to use Machine Learning (ML) techniques that automate these processes and improve the detection of an anomaly. This paper implements ML techniques through the use of datasets, in the context of a NIDS, for the detection and prediction of anomalies on networks. Tests were performed with non-supervised and supervised learning algorithms on NSL-KDD and UNSW-NB15 datasets. An exploratory analysis of data together with dimensionality reduction techniques allowed us to understand the nature of the data, prior to the modeling. The results show that the methodology can be extrapolated for real scenarios with different network configurations.
Hyperspectral Imaging Assessment of Systemic Sclerosis Using the Soft Abundance Score and Band Selection

Hyperspectral imaging (HSI) is an optical remote sensing technology that has the advantages of high spatial and spectral resolution. Aside from its use in geographical research, HSI has been widely used in medical diagnosis. Systemic sclerosis (SSc) is a multiorgan autoimmune disease that leads to skin tightness, thickness, and fibrosis. Internal organ involvement and mortality in this progressive disease are strongly correlated with the extent and severity of abnormal skin thickness. Our prior study demonstrated that HSI can outperform conventional assessment tools as a diagnostic modality to evaluate the severity of skin sclerosis in SSc patients. However, the analysis algorithm has its limitations. This study aimed to investigate a novel soft abundance score for HSI. We also explored the influence of band selection on the HSI analysis of SSc patients. In total, we enrolled 30 SSc patients (male: 10; female: 20, median age±range, 49.9±17.0 years) and 24 healthy controls (male: 12; female: 12, median age±range, 37.0±11.0 years). We found that most of the spectral bands generated by different band selection methods were similar. Moreover, in the task of distinguishing SSc patients from healthy controls, the soft abundance scores calculated from these bands exhibited greater discriminative power than a spectral angle mapper (SAM), skin scores determined by clinical assessments, or skin thickness determined through ultrasonography. Our results suggest that the spectral bands selected in this study should be taken into consideration to guide future hardware improvement. The analytic algorithm can also be applied as a new clinical method.
Full-Duplex Amplify-and-Forward MIMO Relaying: Design and Performance Analysis Under Erroneous CSI and Hardware Impairments

Full-duplex amplify-and-forward multiple-input multiple-output relaying has been the focus of several recent studies, due to the potential for achieving a higher spectral efficiency and lower latency, together with inherent processing simplicity. However, when the impact of hardware distortions are considered, such relays suffer from a distortion-amplification loop, due to the inter-dependent nature of the relay transmit signal covariance and the residual self-interference covariance. The aforementioned behavior leads to a significant performance degradation for a system with a low or medium hardware accuracy. In this work, we analyze the relay transfer function as well as the mean squared-error performance of a full-duplex amplify-and-forward multiple-input multiple-output relaying communication, under the impact of collective sources of additive and multiplicative transmit and receive impairments. Building on the performed analysis, an optimization problem is devised to minimize the communication Mean Squared-Error and solved by employing the recently proposed penalty dual-decomposition framework. The proposed solution converges to a stationary point of the original problem via a sequence of convex quadratic programs, thereby enjoying an acceptable arithmetic complexity as the problem dimensions grow large. Numerical simulations verify the significance of the proposed distortion-aware design and analysis, compared to the common simplified approaches, as the hardware accuracy degrades.
Pulsed Measurements Based Investigation of Trap Capture and Emission Processes in CNTFETs

A study of reversible capture and emission processes of fabrication-caused interface and oxide traps in a carbon nanotube <sc>fet</sc> (<sc>cntfet</sc>) is performed. Drain-current-based measurement techniques are employed to investigate both the trap-related capture (fill) and the emission (release) process of these emerging three-terminal devices. The measurement techniques are based upon the systematic evaluation of the trap-induced time-dependent lateral shifts of periodically measured complete <inline-formula><tex-math notation="LaTeX">$I_{\text{d}}$</tex-math></inline-formula>-<inline-formula><tex-math notation="LaTeX">$V_{\text{gs}}$</tex-math></inline-formula> curves. The techniques are conceptually demonstrated by means of transient simulations with a <sc>cntfet</sc> compact model containing a trap modeling adjunct network with different time constants for capture and emission of traps. Subsequently, the techniques are experimentally applied to a <sc>cntfet</sc> using dedicated laboratory equipment.
Swing compensation of a payload suspended to a planar copter

A switching strategy to compensate copter’s sus- pended payload swing is presented. In order to prevent the payload swing during the maneuvers, the inverse input shaper is placed to the feedback loop. This approach prevents payload swinging in presence of set-point change or control input disturbance. Nevertheless, in case of external disturbance acting on payload directly, input shaper is powerless. Therefore, a nonlinear time-delayed algorithm is involved to suppress the residual swing by up and down motion of the copter. However, there’s no need to use that algorithm all the time. Thus, a switching strategy between the two algorithms is developed and presented. A simulation based verification is performed to validate the results.
The Relationship Between Cybersickness, Sense of Presence, and the Users’ Expectancy and Perceived Similarity Between Virtual and Real Places

This paper aims to explore the impact of sense of presence and cybersickness on the users’ expectancy and perceived similarity between virtual and the corresponding real environments. Two virtual reality setups were tested (non-immersive and immersive) to achieve further conclusions. This research encompassed a quantitative analysis using data collection based on questionnaires, applied to a sample of 45 participants. A virtual experience was conducted (to explore users’ cybersickness and sense of presence), followed by a visit to the actual real sites (to determine the degree of perceived similarity between the virtual and the corresponding real environment and if their expectations were fulfilled). Our results show a positive correlation between the global sense of presence and perceived similarity and users’ expectancy for the non-immersive VR setup. A positive correlation was also found between the global cybersickness on both perceived similarity and users’ expectancy for the immersive VR setup. Implications of such results for virtual tourism are discussed.
Classification of undesirable events in oil well operation

Various algorithms are compared for the automatic classification of undesirable events during the operation of oil wells. The 3W database compiled by Petrobras is used to compare classifiers and some aspects of the workflow. Classification is performed during the transient phase of the event, and with the aim to help operators identify which of seven classes of unwanted events is occurring. Features are calculated based on sensor data within a time window. Feature selection is found to not significantly affect classification accuracy. Out of the examined classifiers a decision tree performs best: most events are classified at 90+% accuracy, with an Fl-score of 85%. This decision tree has similar performance to a more complex random forest developed in prior work, likely due to choices in the feature engineering.
Speech Emotion Recognition with a ResNet-CNN-Transformer Parallel Neural Network

As a challenging pattern recognition task, speech emotion recognition has attracted more and more attention in recent years and is widely used in medical, Affective Computing, and other fields. In this paper, we proposed a parallel network of ResNet-CNN-Transformer Encoder. The Res-Net is used to alleviate the problems caused by the deepening of the network. The CNN calculates the fewer parameters to increase the fitting expression ability of the network. Due to the traditional recurrent neural network, with a long-term dependence on the feature extraction of speech and text sequences and sequence attributes not capturing long-distance features, the multi attention mechanism of the transformer coding layer is used to parallelize the sequence, improve the processing speed and extract the emotional semantic information in the sequence. Experiments are carried out on the RAVDESS dataset. Our results demonstrate the effectiveness of the proposed method and make a significant improvement compared with the previous results.
On-orbit Gyroscope Bias Compensation to Improve Satellite Attitude Control Performance

MEMS (micro-electro-mechanical-system) based gyroscopes are commonly used for satellite's attitude determination and control system in recent years. They are packaged in a small form factor and have lower power consumption. They provide a low-cost solution to the emerging new space industry. MEMS gyroscopes exhibit some time-zero null bias with variation over temperature. To overcome this problem, this paper proposes a novel on-orbit gyroscope bias compensation technique based on the model predictive control approach. The proposed method can estimate the gyroscope bias instantly which is not feasible with the typical method. Furthermore, the proposed method has a low computational requirement as compared to the typical EKF (Extended Kalman Filter) method. While the proposed method allows on-orbit compensation of gyroscope bias, the satellite's attitude is required to be nearly at steady state. This limitation will be addressed in detail.