Miracle-dz/UniFAR-FTUs huggingface dataset:
The Use of Two Fingers to Control Virtual Keyboards with Leap Motion Sensor

This research proposes two virtual keyboard designs and two control methods of virtual keyboards. The Keyboard I layout is a $4\times 10$ matrix with QWERTY array keypad and the Keyboard II layout is a $7\times 6$ matrix with alphabet array keypad. Both methods use thumb and index finger only to operate the keyboards. These two fingers can execute five commands: right searching, down searching, selecting character, clearing monitor, and activating the buzzer. The directions of fingers are detected by a Leap Motion sensor. The Algorithm I has two-finger gestures and 4 variables of time. The first gesture is the pointing down index finger with three functions which depends on the time variable: right searching, down searching and clearing monitor. The thumb finger pointing to the inside palm is the second gesture for selecting and activating the buzzer. The algorithm II has one additional gesture, but it has only 3 variables of time. The extra gesture is a slightly left movement of the index finger. This movement is for the down searching. The time variable is only used to clear the monitor and activate the buzzer in the 2nd algorithm. The results showed that algorithm II can reduce the time consumed to type a single character in both keyboard types. The Algorithm I spent approximately 6.7 seconds/character. This number was reduced by Algorithm II into only 5 seconds/character. The Algorithm I had a less finger gesture, but too much time variables confused the user in operating the system. Both algorithms and virtual keyboard designs had successfully been implemented in this research. Those who can just use at least the index finger and thumb would enable them to chat with others by using this application.
Big Data Storage for a Health Predictive System

When referring to health data processing and management quickly comes across the challenge for storing this kind of information. This article resumes the research for a storage model able to support a baby’s multimodal healthcare predictive system, which our team is developing. We have took a structured approach where we define the data characteristics, the systems requirements and most significant operations required to our “business”. Understanding relational and non-relational approaches, and differences between NoSQL database categories we have defined what we consider the best approach for our scenario, is this case a mix of document and extensible record stores. Finally we analyze the different available technologies/platforms on this categories and how they can support our system requirements.
Microbial Diversity in Actively Forming Iron Oxides from Weathered Banded Iron Formation Systems

The surface crust that caps highly weathered banded iron formations (BIFs) supports a unique ecosystem that is a post-mining restoration priority in iron ore areas. Geochemical evidence indicates that biological processes drive the dissolution of iron oxide minerals and contribute to the ongoing evolution of this duricrust. However, limited information is available on present-day biogeochemical processes in these systems, particularly those that contribute to the precipitation of iron oxides and, thus, the cementation and stabilization of duricrusts. Freshly formed iron precipitates in water bodies perched on cangas in Karijini National Park, Western Australia, were sampled for microscopic and molecular analyses to understand currently active microbial contributions to iron precipitation in these areas. Microscopy revealed sheaths and stalks associated with iron-oxidizing bacteria. The iron-oxidizing lineages Sphaerotilus, Sideroxydans, and Pedomicrobium were identified in various samples and Leptothrix was common in four out of five samples. The iron-reducing bacteria Anaeromyxobacter dehalogens and Geobacter lovleyi were identified in the same four samples, with various heterotrophs and diverse cyanobacteria. Given this arid, deeply weathered environment, the driver of contemporary iron cycling in Karijini National Park appears to be iron-reducing bacteria, which may exist in anaerobic niches through associations with aerobic heterotrophs. Overall oxidizing conditions and Leptothrix iron-oxidizers contribute to net iron oxide precipitation in our sampes, rather than a closed biogeochemical cycle, which would result in net iron oxide dissolution as has been suggested for canga caves in Brazil. Enhancements in microbial iron oxide dissolution and subsequent reprecipitation have potential as a surface-crust-ecosystem remediation strategy at mine sites.
Temperature Management With Paracetamol in Acute Stroke Patients: Evidence From Randomized Controlled Trials

Whether or not paracetamol can improve functional outcomes in patients with acute stroke has been examined in several clinical trials. The inconsistent results of these trials have caused great controversy regarding the need for further studies. In the present meta-analysis, we have aimed to address this controversy. The main databases (Medline, Embase, and Cochrane Library) were searched for randomized controlled trials involving the use of paracetamol in acute stroke patients. Pooled relative risks (RRs) or mean differences (MDs) and 95% confidence intervals (CIs) were calculated using a random-effects model. A total of 1,836 patients were pooled from four phase II and two phase III trials. The use of paracetamol resulted in a significant reduction in body temperature after 24 h (MD, −0.21; 95% CI, −0.28 to −0.13; P < 0.001) and mortality rate after 7–14 days (RR, 0.62; 95% CI, 0.41–0.93; P = 0.02) when compared with the placebo group; however, no effect of paracetamol was observed in the modified Rankin Scale score (RR, 1.07; 95% CI, 0.91–1.27; P = 0.40) or Barthel Index score (RR, 0.98; 95% CI, 0.91–1.06; P = 0.63) at 30 or 90 days. No significant differences were observed with respect to serious adverse events between the paracetamol and the placebo groups (P > 0.05). Subgroup analyses were performed to detect the source of the heterogeneity, which showed that ischemic stroke, serious condition at baseline, and late time-to-treatment had adverse impacts on the effect of paracetamol post stroke. In conclusion, temperature management with paracetamol in acute stroke patients is safe. Although paracetamol reduced the mortality rate in the early stage of stroke, it did not appear to affect long-term mortality and functional recovery. It should be noted that this conclusion is based on the results from studies of poor quality. A large clinical trial with a focus on early treatment of patients with acute stroke is warranted.
Work-in-Progress: Fog Computing for Adaptive Human-Robot Collaboration

Fog computing is an emerging technology that enables the design of novel time sensitive industrial applications. This new computing paradigm also opens several new research challenges in different scientific domains, ranging from computer architectures to networks, from robotics to real-time systems. In this paper, we present a use case in the human-robot collaboration domain, and we identify some of the most relevant research challenges.
Encapsulation of S-nitrosoglutathione: a transcriptomic validation

Abstract Objective: S-nitrosogluthatione (GSNO), a S-nitrosothiol, is a commonly used as nitric oxide (NO•) donor. However, its half-life is too short for a direct therapeutic use. To protect and ensure a sustained release of NO•, the encapsulation of GSNO into nanoparticles may be an interesting option. Methods: In this work, we have investigated the early (4 h) and late (24 h) transcriptomic response of THP-1 human monocytes cells to two doses (1.4 and 6 µM) of either free or Eudragit® nano-encapsulated GSNO using RNA microarray. Results: After exposure to free GSNO, genes mainly involved in apoptosis, cell differentiation, immune response and metabolic processes were differentially expressed. Although, cells exposed to free or encapsulated GSNO behave differently, activation of genes involved in blood coagulation, immune response and cell cycle was observed in both conditions. Conclusions: These results suggest that the encapsulation of low doses of GSNO into Eudragit® nanoparticles leads to a progressive release of GSNO making this compound a possible oral therapy for several biomedical applications like inflammatory bowel diseases.
Spatial Frequency Filtering Using Sofm For Image Compression

The aim of the research is to propose a new approach to image coding using SOFM and spatial frequency band-pass filter to investigate the Artificial Neural Network. The approach is based on SOFM which is similar to vector quantization (VQ) and it is adopted the technique to improve the image compression effectively. In the approach has been using the band-pass filter for image compression by SOFM based on vector quantization by components as the original image and the spatial frequency image component, which is derived from the adaptive to the contours of the 2D analysis and synthesis. The calculation of the computational cost is compression based on SOFM. The new approach of image coding using a band-pass filter, where is used as a first stage of proposed method of image encoding and as well as the image decoding has been presented with De-quantization with entropy coding based on arithmetic coder and high pass filter, the evaluation with jpeg format compression shows, that using 16x16 image block of pre-processing in SOFM has given the best compression ratio with small SNR. On the given experiment shows the different pixels presented by Lena.bmp, girl256.bmp and compared with a compression ratio of the Iena.jpeg file.
Overview of Botnet Detection Based on Machine Learning

With the rapid development of the information industry, the applications of Internet of things, cloud computing and artificial intelligence have greatly affected people's life, and the network equipment has increased with a blowout type. At the same time, more complex network environment has also led to a more serious network security problem. The traditional security solution becomes inefficient in the new situation. Therefore, it is an important task for the security industry to seek technical progress and improve the protection detection and protection ability of the security industry. Botnets have been one of the most important issues in many network security problems, especially in the last one or two years, and China has become one of the most endangered countries by botnets, thus the huge impact of botnets in the world has caused its detection problems to reset people's attention. This paper, based on the topic of botnet detection, focuses on the latest research achievements of botnet detection based on machine learning technology. Firstly, it expounds the application process of machine learning technology in the research of network space security, introduces the structure characteristics of botnet, and then introduces the machine learning in botnet detection. The security features of these solutions and the commonly used machine learning algorithms are emphatically analyzed and summarized. Finally, it summarizes the existing problems in the existing solutions, and the future development direction and challenges of machine learning technology in the research of network space security.
Investigation of a Low-Profile Ultra-Wideband Tightly Coupled Dipole Array

The investigation of a low-profile wideband and two-dimensional wide-scanning tightly coupled dipole array (TCDA) is presented. The array is designed without wide angle impedance matching (WAIM) layer for the sake of low-profile and a compact wideband integrated balun is implemented to feed the array aperture. Parasitic rectangular strips etched on each side of the dipole are utilized to improve the radiation performances in the H-plane, which is orthogonal to the current excited direction that is tightly coupled. An $8 \times8$ planar prototype array was fabricated and measured. The array is able to achieve 4:1 impedance bandwidth with active VSWR $\lt/p\gt\lt3.1$ in the E-plane while scanning up to ±60° and active VSWR $\lt/p\gt\lt3.0$ in the H-plane while scanning up to ±45°, respectively. Meanwhile, the measured cross polarization level of the prototype is less than −20 dB along principal planes scanning.
Second to fourth digit ratio and lung function (forced vital capacity): predictors of maximum urinary flow rate after holmium laser enucleation of the prostate

Maximum urinary flow rate (Qmax) is usually increased after holmium laser enucleation of the prostate (HoLEP). However, improvements vary between patients and results regarding potential predictors of Qmax after HoLEP are inconsistent. Thus, we investigated pre‐operative variables including second to fourth digit ratio (digit ratio) and pulmonary function test (PFT) findings as potential predictors of Qmax after HoLEP.
Flood Disaster Warning System on the go

Floods are one of the top natural disaster that affects many regions around the world, harming human lives and lessening economy growth. Therefore, it is crucial to build an early warning system that forecast flow rate and water level to reduce the casualties of flood disaster. The objective of this paper is to design a flood monitoring system which integrates both flow and water level sensor and use two class neural network to predict the flood status from stored data in the database. A laboratory experiment was carried out to simulate the system and a pressure gauge was utilized to measure the pressure of inflowing water. A NodeMCU ESP8266 enables transmission of sensor data to Thingspeak channel for real-time visualization and storing the data in database. Furthermore, two class neural network module built in Microsoft’s Azure Machine Learning (AzureML) was used to predict flood status according to a pre-define rule. The result of the 2-class neural network showed that using 3 hidden layers has the highest accuracy of 98.9% and precision of 100%.
Design Parameters for the Housing of Two-Dimensional Air Flow Sensors

Micro machined sensors for the 2-D measurement of air flow magnitude and direction achieve very high sensitivity. The housing required for the protection of the sensor membrane greatly reduces the sensitivity for low velocities and induces deformation of the angle response. The first critical design factor is the gap height between the sensor surface and the protecting cover plate. The second is the layout of the necessary mounting bars to hold the cover. Several laboratory tests were conducted to evaluate and compare the influence of these factors. Best results were achieved by replacing the initial four needles holding the cover with a high number of wedge-shaped fins. The final sensor with improved housing was able to measure air flow in cooled fruit warehouses between 0.1 and 2 m/s with a 2% deviation from an ideal sinusoid angle dependency.
Development of Freeze-Thaw Tolerant Lactobacillus rhamnosus GG by Adaptive Laboratory Evolution

The industrial application of microorganisms as starters or probiotics requires their preservation to assure viability and metabolic activity. Freezing is routinely used for this purpose, but the cold damage caused by ice crystal formation may result in severe decrease in microbial activity. In this study, adaptive laboratory evolution (ALE) technique was applied to a lactic acid bacterium to select tolerant strains against freezing and thawing stresses. Lactobacillus rhamnosus GG was subjected to freeze-thaw-growth (FTG) for 150 cycles with four replicates. After 150 cycles, FTG-evolved mutants showed improved fitness (survival rates), faster growth rate, and shortened lag phase than those of the ancestor. Genome sequencing analysis of two evolved mutants showed genetic variants at distant loci in six genes and one intergenic space. Loss-of-function mutations were thought to alter the structure of the microbial cell membrane (one insertion in cls), peptidoglycan (two missense mutations in dacA and murQ), and capsular polysaccharides (one missense mutation in wze), resulting in an increase in cellular fluidity. Consequently, L. rhamnosus GG was successfully evolved into stress-tolerant mutants using FTG-ALE in a concerted mode at distal loci of DNA. This study reports for the first time the functioning of dacA and murQ in freeze-thaw sensitivity of cells and demonstrates that simple treatment of ALE designed appropriately can lead to an intelligent genetic changes at multiple target genes in the host microbial cell.
Graphical Computational Tool for Segmentation of Gray and White Matter Regions in Brain MRI Images

There is a need for computational tools for processing medical patient data and extracting clinically relevant information from patient images for providing patient-specific personalized treatment. Tools have been and are actively being developed by software engineers and programmers in the field of bio-medical image processing for assisting doctors, scientists and researchers. This paper presents an independent stand-alone software application that is a graphical computational tool with a user interface for automatic segmentation of brain MRI images. The same software tool subsequently functions as a neurological disease prediction framework for detection of disease, dementia, impairment, injury, lesions, or tumors in brain MRI images. Brain MRI image segmentation techniques have become an important tool for neurologists to detect disease and cure patients in their early stages of the disease so detected. The tool presented in this paper facilitates the user to automatically segment the regions of brain MRI images using an algorithm called adapted fuzzy c-means (FCM). This methodology for segmentation is based on pixel classification technique, in conjunction with connected region analysis.
Photodynamic application of protoporphyrin IX as a photosensitizer encapsulated by silica nanoparticles

Abstract Background Achieved Silica Nanoparticles (SiNPs) to encapsulate the photosensitizer [Protoporphyrin IX (PpIX)] in photodynamic therapy (PDT) application was reported in this research. Materials and Methods Cytotoxicity for five different concentrations of encapsulated and naked PpIX was measured. Optimum concentration and optimum exposure time of encapsulated and naked PpIX that needed to destroy the cells (Osteosarcoma cells) was measured. Results The results showed that the encapsulated PpIX has more efficacy compared to the naked PpIX and the applicability of the encapsulated PpIX-SiNPs was proved on osteosarcoma cells. Conclusion The results established the important in-vitro photodynamic effectiveness of PpIX-SiNP, which may open a new application for PpIX in its clinical and in-vitro studies.
Modeling of Current Conduction during RESET Phase of Pt/Ta2O5/TaOx/Pt Bipolar Resistive RAM Devices

This paper presents an enhanced analytical model for bipolar resistive switching in Pt/Ta2O5/TaOx/Pt Resistive Random-Access Memory (RRAM) devices. The conduction mechanism of the proposed model has been enhanced compared to the existing similar models of the device by taking in to account of the leakage current through the outside conduction filament (OCF) region during the RESET switching. This leakage current will lead to increase the total device current during RESET stage. Two current conduction mechanisms, namely; Trap Assisted Tunneling (TAT) and Generalized TAT(GTAT) have been analyzed to explain the effect of leakage current at OCF on the device characteristics. Moreover, trap density in GTAT is considered as a variable which changes with diffusion of oxygen vacancies to OCF. The simulation results revealed that GTAT approach which depends on density of traps at OCF and material properties of dielectric shows better fitting to the experimental data compared to TAT current mechanism.
Energy-Rich Mesopelagic Fishes Revealed as a Critical Prey Resource for a Deep-Diving Predator Using Quantitative Fatty Acid Signature Analysis

Understanding the diet of deep-diving predators can provide essential insight to the trophic structure of the mesopelagic ecosystem. Comprehensive population-level diet estimates are exceptionally difficult to obtain for elusive marine predators due to the logistical challenges involved in observing their feeding behavior and collecting samples for traditional stomach content or fecal analyses. We used quantitative fatty acid signature analysis (QFASA) to estimate the diet composition of a wide-ranging mesopelagic predator, the northern elephant seal (Mirounga angustirostris), across five years. To implement QFASA, we first compiled a library of prey fatty acid profiles from the mesopelagic eastern North Pacific. Given the scarcity of a priori diet data for northern elephant seals, our prey library was necessarily large to encompass the range of potential prey in their foraging habitat. However, statistical constraints limit the number of prey species that can be included in the prey library to the number of dietary fatty acids in the analysis. Exceeding that limit could produce non-unique diet estimates (i.e. multiple diet estimates fit the data equally well). Consequently, we developed a novel ad-hoc method to identify which prey were unlikely to contribute to diet and could, therefore, be excluded from the final QFASA model. The model results suggest that seals predominantly consumed small mesopelagic fishes, including myctophids (lanternfishes) and bathylagids (deep sea smelts), while non-migrating mesopelagic squids comprised a third of their diet, substantially less than suggested by previous studies. Our results revealed that mesopelagic fishes, particularly energy-rich myctophids, were a critical prey resource, refuting the long-held view that elephant seals are squid specialists.
On Effective Graphene Based Computing

With CMOS feature size heading towards atomic dimensions, unjustifiable static power, reliability, and economic implications are exacerbating, prompting for research on new materials, devices, and/or computation paradigms. Within this context, Graphene Nanoribbons (GNRs), owing to graphene's excellent electronic properties, may serve as basic blocks for carbon-based nanoelectronics. In this paper, we present the two main avenues, i.e., graphene FET- and GNR- based, undertaken towards graphene based computing. The first approach is conservative and focuses on the realization of graphene FET transistor based switches as MOSFET replacements to maintain the state of the art logic Boolean algebra paradigm design methodology. The second one follows a different line of thinking and seeks GNR-based structures able to provide more complex behaviours by making better use of graphene's conduction properties. We first discuss Graphene Nanoribbon (GNR) based field Effect Transistors (GNRFETs) and Tunnelling GNR based Transistors (GNRTFETs) and their utilization as underlying elements for Boolean gate implementations. Subsequently, we present GNR-based structures that can directly compute Boolean functions, e.g., NAND, XOR, by means of one GNR only and a way to complementary arrange them in energy effective gates. To get inside into the potential of the two avenues we consider an inverter as discussion vehicle and evaluate the designs in terms of area and energy consumption. The GNR-based structure outperforms its counterparts by 15× up to 104× and 230× smaller delay and 6 to 7 and 4 orders of magnitude smaller power than the GNRFET-and GNRTFET- based designs, respectively. Moreover, when compared with CMOS 7 nm Boolean gates GNR-based desgns exhibit up to 6× smaller delay, and up to 2 orders of magnitude smaller active area, and total power consumption. Our analysis confirms that the alternative GNR-based design paradigm, which transcends the traditional switch based approach and takes better advantage of graphene intrinsicnproperties, is better suited for future carbon based nanoelectronics.