Miracle-dz/UniFAR-FTUs huggingface dataset:
Integration of SIW Directional Coupler and E-Plane Tee for Wideband Antenna using Hybrid of Rectangular and Ring Slots for X-Band Applications

Advances in microwave technology and telecommunications have led to compact circuits, reduction of costs, masses and losses. The integration between microwave devices and components are considered a trend for microwave applications. The substrate integrated waveguide (SIW) technology has a great agreement with this trend in these applications. In this paper an integration of $-3 \mathrm{~dB}$ SIW directional coupler operating from 8.31 to 9.67 GHz with 90 degree phase shift at output ports and two series (E-plane) tee junctions operating from 8.8 to 12 GHz with 180 degree phase shift at output ports have been designed to feed 4-array of wideband hybrid slots including rectangular and ring shape slots with phase shifted signals. The wideband arrays consist of 64 rings and 16 rectangular slots distributed equally for the four arrays. The proposed integrated system has been designed and implemented using Roggers 4350 of $\varepsilon_{\mathrm{r}}=3.6, \tan \delta=0.004$ and thickness $h_{R}=1.5 \mathrm{~mm}$. The proposed integrated system has been simulated using HFSS simulator. The SIW system has an agreement between simulated and measured results. The SIW system introduces $S_{11}=-51 \mathrm{~dB}$, gain of 7.5 dB, radiation efficiency of 81.1% and fractional bandwidth of 15.7% for airborne insight, observation and surveillance (AISR) applications.
Aromatic Polyketides from the Deep-Sea Cold-Seep Mussel Associated Endozoic Fungus Talaromyces minioluteus CS-138

Five new aromatic polyketides, including a unique benzofuran derivative, talarominine A (1), and four chromone analogs talamins A–D (2–5), along with one known related metabolite, 5-hydroxy-7-methoxy-2,3-dimethylchromone (6), were isolated and identified from the Talaromyces minioluteus CS-138, an endozoic fungus obtained from the deep-sea cold seep mussel Gigantidas platifrons. Their chemical structures were elucidated by detailed analysis of their NMR spectra, HRESIMS and X-ray crystallographic data, and by comparison with literature data as well. The antibacterial and DPPH scavenging activities of compounds 1–6 were evaluated. Compounds 1–3 showed inhibitory activity against some of the tested bacteria whereas compounds 2 and 5 showed potent DPPH radical scavenging activities, which were better than that of the positive control butylated hydroxytoluene (BHT). This work is likely the first report on marine natural products of mussel-derived fungus living in cold seep environments.
Therapeutic potential of Seabuckthorn (Hippophae rhamnoides L.) in medical sciences

Seabuckthorn has multiple-use properties. This review explores the medicinal applications of Hippophae rhamnoides in healing ailments. The plant is being used in different parts of the world for its nutritional and medicinal properties. Sea buckthorn-based preparations have been extensively exploited in folklore treatment of slow digestion, stomach malfunctioning, cardiovascular problems, liver injury, tendon and ligament injuries, skin diseases and ulcers. In recent years, the medicinal and pharmacological activities of Seabuckthorn have been well investigated using limited clinical trials. Homeopathy is a well-respected modality to assist wellness. Traditional and modern medicinal experts have been applied this plant to treat various diseases. Seabuckthorn is an important plant because of its immense medicinal and therapeutic potential. However, several knowledge gaps identified in this paper would give impetus to new academic and R&D activities, especially for the development of Sea buckthorn-based herbal medicine and nutraceuticals. Its full application in dermatology may be attributed to the presence of a variety of flavonoids, vitamins, and unsaturated fatty acids. Great use of the plant in the traditional system for dermatological aspects, demands further comprehensive phytochemical work based on its actual use by the traditional population. Anti-inflammation is the most important applicable ingredient of this miracle berry. Article info Received: 18 Oct 2021 Revised: 29 Dec 2021 Accepted: 30 Jan 2022 Use your device to scan and read the article online
TNF-α Affects Signature Cytokines of Th1 and Th17 T Cell Subsets through Differential Actions on TNFR1 and TNFR2

Tumor necrosis factor (TNF)-α is a pleiotropic cytokine implicated in the etiology of several autoimmune diseases, including rheumatoid arthritis (RA). TNF-α regulates diverse effector functions through the activation of TNF-α receptor (TNFR)1 and TNFR2. Although the detrimental role of this cytokine has been addressed in distinct disease settings, the effects of TNF-α on cytokine production by isolated CD4+ T helper type 1 (Th1) and Th17 cells, two T cell subpopulations that contribute to the pathogenesis of RA, have not been completely elucidated. Here, we show that TNF-α promotes a reduction and expansion in the frequency of both T cell subsets producing IFN-γ and IL-17, respectively. Selective blockade of TNFR1 or TNFR2 on Th1 and Th17 cells revealed that TNFR2 mediates the decrease in IFN-γ production, while signaling through both receptors augments IL-17 production. We also demonstrate that Th1, but not Th17 cells from RA patients present lower levels of TNFR1 compared to healthy controls, whereas TNFR2 expression on both T cell types is similar between patients and controls. Since TNF-α receptors levels in RA patients are not significantly changed by the therapeutic blockade of TNF-α, we propose that targeting TNFR2 may represent an alternative strategy to normalize the levels of key cytokines that contribute to RA pathogenesis.
Approach to Predict Land Subsidence in Old Goafs considering the Influence of Engineering Noise

Considering that it is easily disturbed by various engineering factors such as weather, hydrology, and construction during engineering monitoring, the collected subsidence data contain various noises. In order to reduce the influence of engineering noise on the accuracy of subsidence prediction, it is proposed to use the Daubechies (DB) wavelet to decompose the original subsidence time series; the items with the low-frequency trend, after decomposition, are predicted using long short-term memory (LSTM) model, items with high-frequency noise used the autoregressive (AR) time series model to make predictions, and the prediction results of the low-frequency trend term and the high-frequency noise term are summed to obtain the total time series predicted value. Combining the actual engineering subsidence monitoring data of the old goaf, compared with the prediction results of the LSTM and RNN models without DB wavelet decomposition and the gray model GM (1,1), the results show that the DB wavelet has an obvious improvement effect in reducing the influence of measurement data noise on prediction error. Compared with the single prediction model LSTM, RNN, and GM (1,1), the proposed prediction model has higher prediction accuracy, smaller error, and better trend. It can be used as a calculation method to improve the prediction accuracy of surface subsidence in old goaf.
Biochemical and Bioinformatic Characterization of Patients with a Sodium Taurocholate Cotransporting Polypeptide Mutation

Background: SLC10A1 codes for the sodium taurocholate cotransporting polypeptide (NTCP). The SLC10A1S267F mutation is associated with loss of function of bile acid (BA) uptake and defined as a new type of hypercholanemia. This kind of hypercholanemia is characterized by high levels of serum BA. However, limited studies have been conducted on this topic. Objectives: This study aimed to describe the biochemical and bioinformatic characterization of patients with an SLC10A1S267F mutation, as well as to dissect pathogenesis in hypercholanemia. Methods: In this study, a total of 12 individuals (including 5 homozygous, 3 heterozygous, and 4 wild-type individuals) were recruited. Whole-genome sequencing (WGS) and Sanger sequencing were used to confirm the genotype. Tests of liver function, renal function, and serum lipid level, in addition to routine blood tests, were performed to evaluate the clinical consequences of patients with an SLC10A1S267F mutation. The ClinVar website and protein prediction tools were used to analyze other cholesterol and BAs related gene mutations in SLC10A1S267F patients, as well as to evaluate their possible effects on serum BA levels of patients. Results: All SLC10A1S267F homozygous patients displayed high levels of BAs. Liver and renal functions were generally normal. According to previous reports, homozygous patients are prone to vitamin D deficiency and deviated blood lipids. However, all homozygous individuals had normal levels of blood lipids, thyroid hormones, and vitamin D (25(OH)D). Moreover, except for the SLC10A1S267F mutation, according to the WGS results, multiple gene mutations were found in 5 homozygous and might affect the level of BAs, but the SLC10A1S267F mutation still is the most important reason resulting in a high level of BAs. Conclusions: This study provided a more detailed description of the SLC10A1S267F mutation-induced hypercholanemia, delivering a new idea that there might be some mutations in SLC10A1S267F homozygotes, probably influencing BA metabolism.
A correlation filter tracking algorithm with adaptive model update strategy

For the traditional correlation filtering algorithm, the model tracking performance degrades when dealing with deformation, rotation and fast movement, etc. In this paper, we propose a tracking algorithm with multi-feature fusion and adaptive model updating strategy. The algorithm fuses fHOG features and color features with their respective response percentages as weighted features, and adaptively adjusts the learning rate of the model for the next frame according to the magnitude of the mean square difference between the current frame and the previous 5 frames of history. The experimental results show that the average overlap rate and the average accuracy of the algorithm in this paper are high. On the OTB100 dataset, the algorithm in this paper improves 6.3% over Staple in terms of accuracy, 10.5% over SAMF, 11.9% over DCF_CA, and 13.9% over fDSST. it improves 5.9% over Staple, 11.2% over SAMF, 19.7% over DCF_CA, and 11.2% over fDSST in terms of overlap rate. It can be seen that this strategy of adaptively updating the model can improve the performance of the algorithm,to some extent,can solve the problems caused by deformation, in-plane rotation out-of-plane rotation, and fast movement to some extent.
Development of EOG-Based Human Computer Interface (HCI) System Using Piecewise Linear Approximation (PLA) and Support Vector Regression (SVR)

Electrooculogram (EOG)-based human-computer interfaces (HCIs) are widely researched and considered to be a good HCI option for disabled people. However, conventional systems can only detect eye direction or blinking action. In this paper, we developed a bio-signal-based HCI that can quantitatively estimate the horizontal position of eyeball. A designed bio-signal acquisition system can measure EOG and temporalis electromyogram (EMG) signals simultaneously without additional electrodes. For real-time processing for practical application, modified sliding window algorithms are designed and applied for piecewise linear approximation (PLA). To find the eyeball position, support vector regression (SVR) is applied as a curve-fitting model. The average tracking error for target circle with a diameter of 3 cm showed only 1.4 cm difference on the screen with a width of 51 cm. A developed HCI system can perform operations similar to dragging and dropping used in a mouse interface in less than 5 s with only eyeball movement and bite action. Compare to conventional EOG-based HCI that detects the position of the eyeball only in 0 and 1 levels, a developed system can continuously track the eyeball position in less than 0.2 s. In addition, compared to conventional EOG-based HCI, the reduced number of electrodes can enhance the interface usability.
Building A Mathematical Model for Allocation Problem of an Attack-Defense Game

In the attack defense game, the attacker (player1) moves and allocates his resources (targets) to attack valuable assets for the defender, then the defender (player2) moves and allocates his resources (killing elements) to protect his defended assets. The task of the defender to allocate his killing elements is not far from to be trivial. A functional overview of the defender’s decision support system will be shown in this work. Once the threat value for each target (resources of player1) has been estimated by the defender’s decision support system, the defender’s allocation problems appear. In this paper, a bi-objective model for the defender’s allocation problem is proposed. The first objective is to minimize the estimated total value of surviving targets and the second objective is to minimize the cost of the defender’s killing elements. The important features of the proposed model are:•Using the method of weights to identify a set of nondominated (efficient) solutions for the defender’s allocation problem. •It helps the decision maker to interact with the allocation problem by varying the weights until getting a suitable solution from his point of view.A numerical example is presented in this paper to show that the single objective model is a special case of the proposed bi-objective one.
Users’ needs for a digital smoking cessation application and how to address them: A mixed-methods study

Background Despite their increasing prevalence and potential, eHealth applications for behavior change suffer from a lack of adherence and from dropout. Advances in virtual coach technology provide new opportunities to improve this. However, these applications still do not always offer what people need. We, therefore, need a better understanding of people’s needs and how to address these, based on both actual experiences of users and their reflections on envisioned scenarios. Methods We conducted a longitudinal study in which 671 smokers interacted with a virtual coach in five sessions. The virtual coach assigned them a new preparatory activity for quitting smoking or increasing physical activity in each session. Participants provided feedback on the activity in the next session. After the five sessions, participants were asked to describe barriers and motivators for doing their activities. In addition, they provided their views on videos of scenarios such as receiving motivational messages. To understand users’ needs, we took a mixed-methods approach. This approach triangulated findings from qualitative data, quantitative data, and the literature. Results We identified 14 main themes that describe people’s views of their current and future behaviors concerning an eHealth application. These themes relate to the behaviors themselves, the users, other parties involved in a behavior, and the environment. The most prevalent theme was the perceived usefulness of behaviors, especially whether they were informative, helpful, motivating, or encouraging. The timing and intensity of behaviors also mattered. With regards to the users, their perceived importance of and motivation to change, autonomy, and personal characteristics were major themes. Another important role was played by other parties that may be involved in a behavior, such as general practitioners or virtual coaches. Here, the themes of companionableness, accountability, and nature of the other party (i.e., human vs AI) were relevant. The last set of main themes was related to the environment in which a behavior is performed. Prevalent themes were the availability of sufficient time, the presence of prompts and triggers, support from one’s social environment, and the diversity of other environmental factors. We provide recommendations for addressing each theme. Conclusions The integrated method of experience-based and envisioning-based needs acquisition with a triangulate analysis provided a comprehensive needs classification (empirically and theoretically grounded). We expect that our themes and recommendations for addressing them will be helpful for designing applications for health behavior change that meet people’s needs. Designers should especially focus on the perceived usefulness of application components. To aid future work, we publish our dataset with user characteristics and 5,074 free-text responses from 671 people.
FinTech and MSMEs: The role of product knowledge

Purpose − This study examines the effect of knowledge of Islamic FinTech products and services of Micro, Small, and Medium Enterprises (MSMEs) actors on their intentions to adopt it.Methodology − This study adopts a quantitative method with primary data filled in by MSME actors in Indonesia. A hundred samples were analyzed using Partial Least Square - Structural Equation Modeling (SEM) by SmartPLS software.Findings − The results showed that both Perceived Usefulness (PU) and Perceived Ease of Use (PEoU) were empirically proven to have a significant positive effect on Attitude Toward Behavior (ATB). Likewise, ATB has been proven to be significant in mediating PU and PEoU on the Behavioral Intention (BI) of MSME actors in using Islamic FinTech products and services. The main finding in this research is that the higher the knowledge/literacy of Islamic finance by MSMEs, the more they will strengthen their intention to use FinTech products.Implications − The socialization of Islamic FinTech products and services by regulators such as the Financial Services Authority through collaboration with the industry players, professional and expertise organizations, the Muslim entrepreneur community, and universities need to be continuously to improve Islamic financial literacy. In addition, amid ongoing business digitalization, MSMEs are expected to be integrated with FinTech, not only in terms of funding but also in improving business performance regarding operations, finance, human resources, and others.Originality − This paper extends TAM with product knowledge on objects in the form of MSME actors in Indonesia.
The influence of the parameter h and a new modified method of Homotopy analysis method for initial value problems

In this paper, we pay more attention to the embedding parameter h, which has an influence on the convergence region of solution series in Homotopy analysis method (HAM). We use some theorems to give the concrete influence and proof. Then we introduce a new modified method of HAM called the Piecewise homotopy analysis method (PHAM). Futhermore, examples such as NLS equation, Ricatti equation and Duffing equation are presented to illustrate the main results.
A Spectrum of Intraoperative and Postoperative Complications of Cochlear Implants: A Critical Review

A cochlear implant is a neuroprosthetic, electrical device that is developed for the treatment of patients who have sensory hearing loss. It directly stimulates the hearing nerve by bypassing the injured or damaged sensory receptors, the hair cells. This implant is directly placed in the inner ear. It is an electronic device which is proved to be very useful in patients with sensorineural hearing loss. This implant consists of a speech processor (externally present), which takes up the sound; transforms it into digital signals, and then internal components take it to convert it into electrical energy, which stimulates auditory nerves, and the brain perceives it and hears it as a sound. This is one of the most successful surgeries, which happens very frequently nowadays. Although, many complications are mostly associated with this implant. This paper deals with the preoperative, operative, and postoperative complications associated with cochlear implant surgery. That includes tinnitus, Meniere's disease, unilateral hearing loss, musical ear syndrome, infections; flap necrosis, facial nerve palsy, improper electrode placement, magnet displacement; failure and re-implantation, cholesteatoma, and pneumocephalus. These are just a few of the complications; there are much more complications which are associated with cochlear implants.
SAMS Acceleration Measurements on Mir From January to May 1997 (NASA Increment 4)

During NASA Increment 4 (January to May 1997), about 5 gigabytes of acceleration data were collected by the Space Acceleration Measurements System (SAMS) onboard the Russian Space Station, Mir. The data were recorded on 28 optical disks which were returned to Earth on STS-84. During this increment, SAMS data were collected in the Priroda module to support the Mir Structural Dynamics Experiment (MiSDE), the Binary Colloidal Alloy Tests (BCAT), Angular Liquid Bridge (ALB), Candle Flames in Microgravity (CFM), Diffusion Controlled Apparatus Module (DCAM), Enhanced Dynamic Load Sensors (EDLS), Forced Flow Flame Spreading Test (FFFT), Liquid Metal Diffusion (LMD), Protein Crystal Growth in Dewar (PCG/Dewar), Queen's University Experiments in Liquid Diffusion (QUELD), and Technical Evaluation of MIM (TEM). This report points out some of the salient features of the microgravity environment to which these experiments were exposed. Also documented are mission events of interest such as the docked phase of STS-84 operations, a Progress engine burn, Soyuz vehicle docking and undocking, and Progress vehicle docking. This report presents an overview of the SAMS acceleration measurements recorded by 10 Hz and 100 Hz sensor heads. The analyses included herein complement those presented in previous summary reports prepared by the Principal Investigator Microgravity Services (PIMS) group.
Decentralized State Estimation of Interconnected DC MGs with Constant Power Loads

Advances in power electronics and avoiding the pollution of fossil fuel sources is leading to a propensity towards distributed generations in direct current microgrids (DC MGs). Emerging more complicated power grids requires advanced state-estimation methods to enhance the control and monitoring purposes. This work forces the issue of decentralized state estimation in a typical nonlinear interconnected DC MG. It is considered that each of the DC MGs contains a source and resistive and nonlinear constant power loads (CPLs) and connects to the other grids via a tie line. The state estimation is done for the case that the information of the other grids is not available. In this regard, a dual-extended Kalman filter (dual-EKF) is utilized to estimate the DC MG states and the effect of other grids. Since the dual-EKF is robust against stochastic noisy measurements and can be implemented with a low computational burden, it is a practical and effective approach for practice. To show the accuracy of the dual-EKF, it is applied on a two-area DC MG with resistive loads, CPLs, and buck converters.