Miracle-dz/UniFAR-FTUs huggingface dataset:
RSSI-Based MAC-Layer Spoofing Detection: Deep Learning Approach

In some wireless networks Received Signal Strength Indicator (RSSI) based device profiling may be the only viable approach to combating MAC-layer spoofing attacks, while in others it can be used as a valuable complement to the existing defenses. Unfortunately, the previous research works on the use of RSSI-based profiling as a means of detecting MAC-layer spoofing attacks are largely theoretical and thus fall short of providing insights and result that could be applied in the real world. Our work aims to fill this gap and examine the use of RSSI-based device profiling in dynamic real-world environments/networks with moving objects. The main contributions of our work and this paper are two-fold. First, we demonstrate that in dynamic real-world networks with moving objects, RSSI readings corresponding to one fixed transmitting node are neither stationary nor i.i. d. , as generally has been assumed in the previous literature. This implies that in such networks, building an RSSI-based profile of a wireless device using a single statistical/ML model is likely to yield inaccurate results and, consequently, suboptimal detection performance against adversaries. Second, we propose a novel approach to MAC-layer spoofing detection based on RSSI profiling using multi-model Long Short-Term Memory (LSTM) autoencoder—a form of deep recurrent neural network. Through real-world experimentation we prove the performance superiority of this approach over some other solutions previously proposed in the literature. Furthermore, we demonstrate that a real-world defense system using our approach has a built-in ability to self-adjust (i.e., to deal with unpredictable changes in the environment) in an automated and adaptive manner.
A 3D Food Printing Process for the New Normal Era: A Review

Owing to COVID-19, the world has advanced faster in the era of the Fourth Industrial Revolution, along with the 3D printing technology that has achieved innovation in personalized manufacturing. Three-dimensional printing technology has been utilized across various fields such as environmental fields, medical systems, and military materials. Recently, the 3D food printer global market has shown a high annual growth rate and is a huge industry of approximately one billion dollars. Three-dimensional food printing technology can be applied to various food ranges based on the advantages of designing existing food to suit one’s taste and purpose. Currently, many countries worldwide produce various 3D food printers, developing special foods such as combat food, space food, restaurants, floating food, and elderly food. Many people are unaware of the utilization of the 3D food printing technology industry as it is in its early stages. There are various cases using 3D food printing technology in various parts of the world. Three-dimensional food printing technology is expected to become a new trend in the new normal era after COVID-19. Compared to other 3D printing industries, food 3D printing technology has a relatively small overall 3D printing utilization and industry size because of problems such as insufficient institutionalization and limitation of standardized food materials for 3D food printing. In this review, the current industrial status of 3D food printing technology was investigated with suggestions for the improvement of the food 3D printing market in the new normal era.
Integrated Analysis of microRNAs and Metabolomics in Rat’s Serum Reveals Multi-action Modes of Qingfei Paidu Decoction for COVID-19 Treatment

Background During the fight against coronavirus disease 2019 (COVID-19) in China, Qingfei Paidu decoction (QFPDD) has been widely applied to treat COVID-19 patients. Retrospective studies showed that QFPDD could improve clinical outcomes of COVID-19. Thus, it is necessary and interesting to explore the action mode of QFPDD for further application and development.MethodsSprague-Dawley (SD) rats were randomly divided into two groups, QFPDD (n=9) and control (n=10) groups. They were parallelly treated for 12 days with QFPDD and warm distilled water, respectively. At the endpoint, the microRNA (miRNA or miR) profiles in serum were detected to identify differently expressed miRNAs (DEMs). Then, the action mode of QFPDD were explored via review of potential roles of DEMs and functional enrichment analysis of their targets (e.g., GO enrichment and KEGG pathway analysis), especially focusing on the aspects of immunity, inflammation, virus infection and pulmonary fibrosis. Core genes were identified based on KEGG pathway analysis. Metabolomics were detected in serum and significantly changed metabolites (SCMs), especially the metabolic substrates and products of enzyme of core gene were identified as biomarkers to validate the regulation of DEMs to enzyme activity of core gene through metabolomic analysis and linear correlation analysis between SCMs and DEMs. Results23 DEMs were identified in the serum between QFPDD and control groups, with 1636 predicted genes. Reported evidence has showed that both the DEMs and their target genes involve regulation of immunity, inflammation, virus infection and pulmonary fibrosis. Phospholipase C, gamma 1 (Plcg1) was identified as a core gene and predicted to be upregulated attributed to downregulation of novel-89-mature. The levels of three SCMs, PC(P-18:1(11Z)/22:5(4Z,7Z,10Z,13Z,16Z)), PC(22:5(4Z,7Z,10Z,13Z,16Z)/P-18:0) and PC(16:1(9Z)/16:1(9Z)), which were the metabolic substrates of phospholipase C, were significantly reduced in QFPDD group, in addition, PC(P-18:1(11Z)/22:5(4Z,7Z,10Z,13Z,16Z)) and PC(22:5(4Z,7Z,10Z,13Z,16Z)/P-18:0) presented positively linear correlation with the expression level of novel-89-mature. The level of phosphorylcholine, a product of PCs metabolized by phospholipase C, was significantly elevated in QFPDD group. Conclusion QFPDD can induce modification of miRNAs profile, and subsequently multi-regulate the immunity, inflammation, virus infection and pulmonary fibrosis in vivo, playing an important role for the positive outcomes of COVID-19 patients treated by QFPDD in China.
Occurrence of Sturnira tildae De La Torre, 1959 (Chiroptera: Phyllostomidae) in the state of Maranhão, Brazil

The bat genus Sturnira is widely distributed in the Neotropical region, from northwestern Mexico to northern Argentina, and four species occur in Brazil: Sturnira lilium, Sturnira giannae, Sturnira magna, and Sturnira tildae. The present study is the first to record Sturnira tildae in the state of Maranhão, Brazil, based on morphological and molecular diagnoses. The specimen was identified based on its cranial and morphometric traits. The diagnostic traits include discreetly bilobed inner upper incisors with a broad base, lower first and second molars with lingual cusps separated by shallow grooves, and forearm longer than 45 mm. The molecular sequences of Cytochrome C Oxidase Subunit 1 (COI) and 16S rRNA genes confirmed the morphological identification and thus the occurrence of Sturnira tildae in the Amazon biome of Maranhão. This record represents an eastward extension of the known distribution of the species in the Amazonia, to Cândido Mendes, Maranhão, within an area dominated by dense rainforest and influenced by tides.
Evaluating Levels of Automation in Human–Robot Collaboration at Different Workload Levels

This study explored how levels of automation (LOA) influence human robot collaboration when operating at different levels of workload. Two LOA modes were designed, implemented, and evaluated in an experimental collaborative assembly task setup for four levels of workload composed of a secondary task and task complexity. A user study conducted involving 80 participants was assessed through two constructs especially designed for the evaluation (quality of task execution and usability) and user preferences regarding the LOA modes. Results revealed that the quality of task execution and usability was better at high LOA for low workload. Most of participants also preferred high LOA when the workload increases. However, when complexity existed within the workload, most of the participants preferred the low LOA. The results reveal the benefits of high and low LOA in different workload situations. This study provides insights related to shared control designs and reveals the importance of considering different levels of workload as influenced by secondary tasks and task complexity when designing LOA in human–robot collaborations.
Digital communication as a global challenge for trade unions: lessons from Brazil and Portugal

As a cross-cutting issue encompassing all of society, the evolution of digital technologies is particularly challenging to traditional labour actors that are accustomed to physical work and face-to-face contact. This article discusses the distinctive possibilities of social media use as a forward-thinking global strategy for organised labour. After an initial clarification of useful concepts to consider in the digital age, we focus on the social media presence of trade unions, identifying the pros and cons associated with platform-based communication. Finally, the article debates the implications of social media platforms as tools to strengthen networks with nonunion actors and contribute to the amplification of the labour agenda. Through a comparative analysis of four trade union confederations, two from Brazil and two from Portugal, we argue that, despite the possibilities for outreach and interaction enabled by the new communication and information technologies, trade union confederations maintain constricted networks and an outdated top-down communications model.
A CRT Analysis of Policy Making in Nevada: A Case Study for Social Work Education

ABSTRACT This study uses critical race theory (CRT) to uncover racialized interactions that influence legislative processes. The transcripts from public hearings from the 2017 Nevada State legislative session were included in the data analysis. Results demonstrate the utility of CRT as an analytic tool to examine the policy-making process, identify narratives that sustain and protect white supremacy imbedded in policies governing high school education, and uncover racist testimony throughout the policy-making process. More specifically, results demonstrate the ways that subtle racialized interactions drive conversations where White constituents protect white interests at the expense of racial equity. Conversely, findings highlight how People of Color issue a counternarrative by providing necessary historical context and perspectives to work toward racial justice goals.
Effect of Pharmacist Intervention on Quality of Life of Patients with Major Depressive Disorder in Distressed North East Nigeria

Background: Quality of life is considered as an integral component and outcome indicator of mental illness and while pharmacist interventions have been proven to be effective in improving quality of life, no attention has been given to patients with depression in distressed North East Nigeria.Objectives: To explore the effect of pharmacist intervention on quality of life of patients with major depressive disorder.Materials and Methods: A longitudinal prospective randomized controlled trial approved by Ahmadu Bello University Ethics Committee on use of Human Subjects for Research (approval number: ABUCUHSR/2020/018) was conducted on 101 patients with major depressive disorder between April 2019 and March 2020 at a tertiary Neuro-Psychiatric Hospital in Maiduguri, Nigeria. Consenting patients were randomized into Usual Care or Intervention groups using a computer-generated list. The intervention consisted of pharmacist-delivered educational counseling sessions of between 15-30 minutes. Data were collected at baseline, 3 months and 6 months using the World Health Organization Quality of life Bref scale.Results: After the intervention, significant improvements (p <0.001) in mean scores of participants in the intervention group were observed in all of the quality-of-life domains including; physical health [42.49 (SD=11.48) vs 72.25 (SD=15.82], psychological health [45.15 (SD=15.24) vs 85.57 (SD=12.95)], social relationship [40.47 (SD=21.24) vs 78.20 (SD=18.23)] and environment [40.94 (SD=14.09) vs 87.74 (SD=9.78)]. Significant improvements (p <0.001) were also observed in the general health [38.77 (SD=27.51) vs 86.53 (SD=21.27)] and overall quality of life [52.55 (SD=19.26) vs 76.92 (SD=25.16)] in the intervention group.Conclusion: Pharmacist’s intervention significantly improved quality of life in patients with major depressive disorder in this study.
Trace Metal Evidence for Deglacial Ventilation of the Abyssal Pacific and Southern Oceans

The deep ocean has long been recognized as the reservoir that stores the carbon dioxide (CO2) removed from the atmosphere during Pleistocene glacial periods. The removal of glacial atmospheric CO2 into the ocean is likely modulated by an increase in the degree of utilization of macronutrients at the sea surface and enhanced storage of respired CO2 in the deep ocean, known as enhanced efficiency of the biological pump. Enhanced biological pump efficiency during glacial periods is most easily documented in the deep ocean using proxies for oxygen concentrations, which are directly linked to respiratory CO2 levels. We document the enhanced storage of respired CO2 during the Last Glacial Maximum (LGM) in the Pacific Southern Ocean and deepest Equatorial Pacific using records of deglacial authigenic manganese, which form as relict peaks during increases in bottom water oxygen (BWO) concentration. These peaks are found at depths and regions where other oxygenation histories have been ambiguous, due to diagenetic alteration of authigenic uranium, another proxy for BWO. Our results require that the entirety of the abyssal Pacific below approximately 1,000 m was enriched in respired CO2 and depleted in oxygen during the LGM. The presence of authigenic Mn enrichment in the deep Equatorial Pacific for each of the last five deglaciations suggests that the storage of respired CO2 in the deep ocean is a ubiquitous feature of late‐Pleistocene ice ages.
Random fields representation over manifolds via isometric feature mapping‐based dimension reduction

Generating random fields over irregular geometries (e.g., two‐dimensional (2D) manifolds embedded in the three‐dimensional (3D) Euclidean space) is a great challenge because the geometry structure is complex and the correlation function can hardly be derived, thus the traditional methods, for example, spatial discretization methods or series expansion methods, cannot be directly adopted. To solve this issue, the present paper develops a two‐stage strategy to simulate random fields over manifolds. The core idea is to map the manifolds into the 2D Euclidean space through Isometric feature mapping (Isomap), with which the geodesic distance between points in the mapped 2D Euclidean space and the original manifold space is kept as the same. Therefore, the correlation function can be readily derived and the conventional methods can be directly employed to generate random fields. To validate the proposed method, several different types of manifolds are dimensionally reduced to 2D planar domains, and the stochastic harmonic function method is used to generate the random fields. Finally, case studies on the stochastic finite element analysis of two different structures are also performed to demonstrate the applicability and efficiency of the proposed method.
Correlation between Large-Scale Streamwise Velocity Features and the Height of Coherent Vortices in a Turbulent Boundary Layer

The preferential organisation of coherent vortices in a turbulent boundary layer in relation to local large-scale streamwise velocity features was investigated. Coherent vortices were identified in the wake region using the Triple Decomposition Method (originally proposed by Kolář) from 2D particle image velocimetry (PIV) data of a canonical turbulent boundary layer. Two different approaches, based on conditional averaging and quantitative statistical analysis, were used to analyze the data. The large-scale streamwise velocity field was first conditionally averaged on the height of the detected coherent vortices and a change in the sign of the average large scale streamwise fluctuating velocity was seen depending on the height of the vortex core. A correlation coefficient was then defined to quantify this relationship between the height of coherent vortices and local large-scale streamwise fluctuating velocity. Both of these results indicated a strong negative correlation in the wake region of the boundary layer between vortex height and large-scale velocity. The relationship between vortex height and full large-scale velocity isocontours was also studied and a conceptual model based on the findings of the study was proposed. The results served to relate the hairpin vortex model of Adrian et al. to the scale interaction results reported by Mathis et al., and Chung and McKeon.
A Novel Approach to Pre-Impact Measurement from Impact Investing Using Random Forest and Deep Neural Networks

Impacting investing is fast becoming an up-and-coming area in the finance industry. With the massive projection of investment that would go into this sector, there are present predicaments with the measurement of impact from impact investing, which casts doubts on the prospect of this concept. However, it is tagged as being characteristic of the future of investment. The challenge involves defining what to measure when to measure, and at what phase of investment. In this study, a combination of machine learning and deep learning models is used on the intended community to measure the pre-impact factors suitable to generating confidence for the full granting of funds for impact investing. The first phase employed a survey of the impact community to gather features useful for the pre-impact assessment using redundant feature elimination with random forest. A deep neural network is then used to predict the various classes chosen for the classification problem. The results indicate that this new approach creates confidence in the next phase of impact measurement. Thus, the critical features for measuring the impact outcomes are not humanly generated or biased towards individuals but have a mathematical model that selects these features and the accuracy, precision, and recall for all three models are very significant. The deep learning and machine learning models had a unique advantage in resolving pre-impact measurement from impact investing and proved promising for other investment phases with minimal human effort, cost-effectiveness and timeliness.
Which Anxiety Symptoms are Associated with Perceived Ethnic Discrimination in Adolescents With an Immigrant Background?

This study assesses the specific anxiety symptoms that are present in the context of perceived ethnic discrimination in 696 (M age = 13.3, σ = .77, 57% girls) seventh and eighth-grade students with immigrant backgrounds from four different Canadian high schools. Multiple hierarchical linear regressions were conducted to determine the association between perceived ethnic discrimination and specific anxiety symptoms. Results demonstrate that perceived ethnic discrimination is significantly associated with more anxiety symptoms, such as panic/somatic, generalized anxiety, social phobia, and school phobia. Findings provide a better understanding of the association between perceived ethnic discrimination and anxiety symptoms reported by adolescents with an immigrant background. These findings could help school-based mental health professionals in the implementation of prevention and intervention measures aimed at reducing specific anxiety symptoms that are often present in the context of perceived ethnic discrimination.
MHD‐Test Particles Simulations of Moderate CME and CIR‐Driven Geomagnetic Storms at Solar Minimum

As part of the Whole Heliosphere and Planetary Interactions initiative, contrasting drivers of radiation belt electron response at solar minimum have been investigated with MHD‐test particle simulations for the May 13–14, 2019 Coronal Mass Ejection (CME)‐shock event and the August 30–September 3, 2019 high speed solar wind interval. Both solar wind drivers produced moderate geomagnetic storms characterized by a minimum Dst = −65 nT and −52 nT, respectively, with the August ‐ September event accompanied by prolonged substorm activity. The latter, with characteristic features of a Corotating Interaction Region (CIR)‐driven storm, produced the hardest relativistic electron spectrum observed by Van Allen Probes during the last two years of the mission, ending in October 2019. MHD simulations were performed using both the Lyon‐Fedder‐Mobarry global MHD code and recently developed GAMERA model coupled to the Rice Convection Model, run with measured L1 solar wind input for both events studied, and coupled with test particle simulations, including an initial trapped and injected population. Initial electron Phase Space Density (PSD) profiles used measurements from the Relativistic Electron Proton Telescope and MagEIS energetic particle instruments on Van Allen Probes for test particle weighting and updating of the injected population at apogee. Results were compared directly with measurements and found to reproduce magnetopause loss for the CME‐shock event and increased PSD for the CIR event. The two classes of events are contrasted for their impact on outer zone relativistic electrons near the end of Solar Cycle 24.
Circulating S100A4 as a prognostic biomarker for patients with nonparoxysmal atrial fibrillation after catheter ablation

Background Atrial fibrosis is involved in non-paroxysmal atrial fibrillation (NPAF) and is mainly mediated by the calcium-binding protein S100A4. This study aimed to verify the role of circulating S100A4 in the diagnosis of atrial fibrosis and the prognosis of NPAF. Methods Consecutive NPAF patients undergoing catheter ablation were selected. Patients with low voltage amplitudes (<0.40 mV) in the left atrium (LA), defined as low voltage zones (LVZs), were grouped in the scar group by electroanatomic mapping (EAM). Circulating S100A4 was detected by a human enzyme-linked immunosorbent assay (ELISA). The role of S100A4 in atrial fibrosis was further evaluated by Masson’s trichrome staining and immunochemistry (IHC) in NPAF (atrial pacing) and control dogs. The prognostic value of the circulating S100A4 was evaluated by Cox regression analyses, the Kaplan-Meier (KM) method, and receiver operating characteristic (ROC) curves. Results We enrolled a total of 101 NPAF patients (age 60±8 years) who underwent EAM, including 53 patients with scars and 48 patients without scars at 1-year follow-up. The scar group showed a higher serum level of S100A4 (3.4±1.7 vs. 2.5±1.4 ng/mL, P<0.001) than the non-scar group. In the canine model, scar size matched the larger location of interstitial fibrosis in the NPAF group determined by Masson’s trichrome staining. The expression of α-SMA and S100A4 was elevated in the NPAF group as determined by IHC compared to the control group (P<0.001). The clinical recurrence rate was markedly elevated in the scar group (27.1% vs. 8.9%, P<0.001), and the area under the ROC curve was high (0.865, 95% CI: 0.750–0.981) in predicting clinical recurrence of NPAF with the circulating S100A4 model. Conclusions Circulating S100A4 plays a role in atrial fibrosis in NPAF patients following ablation. The level of serum S100A4 can predict the clinical recurrence of NPAF.