Miracle-dz/UniFAR-FTUs huggingface dataset:
UNSUR GHARAR DALAM PEMBERIAN VOUCHER PROMO SHOPEE

This research aims to understand the practice of giving promotional vouchers on the Shopee application and identify elements of gharar. The research method used is qualitative, by conducting field research using interviews, documentation and observation. The research results show that the practice of giving promo vouchers through the Shopee application is a program organized by Shopee.id. The mechanism of this program is simple, where Shopee application users who are interested in getting promo vouchers must participate in a prize draw or other promo. Users must fulfill the terms and conditions set by the organizer. These terms and conditions can be easily accessed via the Shopee application or the official Shopee website. However, gharar practices were found related to giving Shopee promotional vouchers among STIS Al-Hilal Sigli students. Some students experienced uncertainty where the vouchers were not provided as promised. For example, even though customers are supposed to get a 20% promotional voucher, the price of the item after the promo does not reflect the promised discount. For example, if the initial price of an item is IDR 10,000, it should be IDR 8,000 after the promo, but in reality this is not the case. This creates uncertainty and ambiguity regarding the benefits that users should obtain through providing promotional vouchers
Cementitious Composites with Cellulose Nanomaterials and Basalt Fiber Pellets: Experimental and Statistical Modeling

The production of high-performance fiber-reinforced cementitious composites (HPFRCCs) as a durable construction material using different types of fibers and nanomaterials critically relies on the synergic effects of the two materials as well as the cementitious composite mixes. In this study, novel HPFRCCs were developed, which comprised high content (50%) slag by mass of the base binder as well as nano-silica (NS) or nano-crystalline cellulose (NCC). In addition, nano-fibrillated cellulose (NFC), and basalt fiber pellets (BFP), representing nano-/micro- and macro-fibers, respectively, were incorporated into the composites. The response surface method was used in this study’s statistical modeling part to evaluate the impact of key factors (NS, NCC, NFC, BFP) on the performance of 15 mixtures. The composites were assessed in terms of setting times, early- and late-age compressive strength, flexural performance, and resistance to freezing-thawing cycles, and the bulk trends were corroborated by fluid absorption, thermogravimetry, and microscopy tests. Incorporating NS/NCC in the slag-based binders catalyzed the reactivity of cement and slag with time, thus maintaining the setting times within an acceptable range (maximum 9 h), achieving high early- (above 33 MPa at 3 days) and later-age (above 70 MPa at 28 days) strength, and resistance to fluid absorption (less than 2.5%) and frost action (DF above 90%) of the composites. In addition, all nano-modified composites with multi-scale fibers showed notable improvement in terms of post-cracking flexural performance (Residual Strength Index above 40%), which qualify them for multiple infrastructure applications (i.e., shear key bridge joints) requiring a balance between high-strength properties, ductility, and durability.
Functional Arm Movement Classification in Stroke Survivors Using Deep Learning with Accelerometry Data

The rehabilitation of the upper extremity (UE) plays a pivotal role in the recovery process for stroke survivors. Despite the routine practice of UE rehabilitation, assessing its effectiveness poses a significant challenge. Evaluation methods vary, ranging from clinical assessments of UE motor skills and patient feedback to the utilization of wearable technology equipped with accelerometer or gyro sensors. In this study, we employed deep learning approaches such as Multi-Layer Perceptron, Long Short-term Memory, and Gated Recurrent Units, in conjunction with data from accelerometry sensors embedded in wrist-wearable Inertial Measurement Units. Our findings demonstrate the successful differentiation between functional and non-functional UE movements in individuals recovering from strokes with 90% and 74% of classification accuracy for intra-subject and inter-subject models, respectively. The methodologies employed in this study exhibit consistency and reliability, providing precise outcomes for individual and acceptable outcomes for cross-participant evaluations. Notably, our advanced models directly utilized raw accelerometry data instead of manual extracted features that was often used in traditional machine learning algorithms. These innovative techniques offer cost-effective and adaptable tools for monitoring UE functionality in real-world settings. The insights derived from this study hold the potential to be transformative in tailoring rehabilitation strategies for adults affected by strokes.
Chapter 3: Climate Change

Key Findings  Since 1960, all but two southern capital cities (Montgomery, AL and Oklahoma City, OK) have experienced a statistically significant increase in average annual temperature (approximately 0.016 C), but none has experienced significant trends in precipitation.  The South is forecasted to experience warmer temperatures for the duration of the 21 century; forecasts are mixed for precipitation changes during the same period.  Climate predictions range from wet and warm (1167 mm/19.06 C) to moderate and warm (1083 mm/19.45 C and 1106 mm/19.27 C) to dry and hot (912 mm/20.22 C).
A Systematic Literature Review to Develop a Conceptual Model to Assess Farmers' Sustainable Agriculture Potential for Organic Use in Rice Cultivation in Sri Lanka

The researcher found a timely need to assess Sri Lankan Rice Farmers' Sustainable Agriculture (SA) potential, their readiness to adopt more organic-centric farming, and the institutional help they receive for such adoption. Since no straightforward assessment method has been found in the literature, this paper describes how the researcher developed a conceptual framework and suggests indicators to explain the constructs for such an assessment. The conceptual model and indicators are drawn through a systematic literature review. The systematic literature review helped to find a set of credible and relevant articles for the study and provided a structured way of extracting usable findings for this study. Resilience theory (RT) properties are the foundation selected to develop the conceptual framework combined with the Rural Livelihood Assessment Framework (RLAF). The previous research findings of SA studies show that capital assets described in RLAF, Human, Social, Financial, Physical, and Natural, would adequately explain farmers' SA potentials. The suggestions from RT on ecosystem resilience can describe the farmer's predictive behaviours in this transitioning ecosystem. The researcher believes this conceptual combination is adequate to explore the unknowns mentioned in the study's objective. Also, the literature provides insights to develop an indicator framework which will guide the development of a research instrument explaining the latent constructs of the model in future research.
Performance Evaluation of Data Imputation Methods for Graph Deep Learning-Based Traffic Prediction

We present five approaches for interpolating missing data in real-world traffic datasets using basic, statistical, and generative model methods. The effectiveness of these approaches is evaluated using graph deep learning models, and the results show that imputing missing data improves the performance of traffic prediction models, especially when dealing with a higher proportion of missing data. The experiments are conducted on two real-world datasets, METR-LA and PEMS-BAY, to evaluate the proposed methods comprehensively. The results demonstrate that imputing missing data significantly enhances the performance of traffic prediction models, particularly when dealing with a higher proportion of missing data.
Body-Centered Dynamically-Tuned Error-State Extended Kalman Filter for Visual Inertial Odometry in GNSS-Denied Environments

The use of visual odometry in autonomous land vehicle and unmanned ground vehicle (UGV) applications has shown to be promising in providing robust and accurate positioning and navigation in GNSS-denied environments. While GNSS is the most accurate and reliable positioning source, it cannot be received indoors. Furthermore, low-cost micro-electro-mechanical system (MEMS)-based inertial sensors are used for positioning but can result in rapid drift during prolonged GNSS outages. To address these limitations, this paper proposes an innovative method of integrating MEMS-based inertial sensors with a vision-based navigation system. The vision-based navigation relies on a visual odometry pipeline modified to replace the feature-matching step with Lucas-Kanade optical flow. While the inertial-based system is derived from the three-dimensional reduced inertial sensor system (3D-RISS) and modified for a body-centered update. The fusion of both systems is based on an Extended Kalman filter (EKF) to estimate the position and heading information and correct for inertial sensor errors. To assess the effectiveness of the proposed method, real indoor and road test trajectories were conducted using both a Husky A200 UGV and a sensor-equipped minivan. The results show that the body-centered inertial system, aided with reliable vision updates, provides a reliable positioning solution for an extended duration in GNSS-denied environments.
The dynamic relationship between foreign direct investment inflows and interest rate in Sri Lanka

Foreign direct investment acts as an engine of rapid growth and development of developing and emerging countries. It helps to promote the host nation’s economic growth, financial inflows and markets, technology, and skills. This study aims to identify the dynamic influence and relationship between Foreign Direct Investment (FDI) and interest rate in Sri Lanka over the period 1978 to 2020. Foreign direct investment inflow has been used as the dependent variable while gross domestic products, interest rate, inflation rate, trade openness and exchange rate are the independent variables. This study used the ARDL model for the analysis. According to the bound test, F statistics is greater than the upper bound value. Therefore, this study confirmed that there is a cointegration relationship between foreign direct investment inflows and other explanatory variables. A negative and significant error correction coefficient of FDI inflows reveals that 128% disequilibrium is corrected each year which implies that FDI moves downward towards long-run equilibrium. This study found that there is no substantial relationship between the interest rate and FDI inflows in the long run and a negative and substantial relationship in the short run. Therefore, this study suggests that the government of Sri Lanka has to reflect on developing a monetary policy and maintaining the balance of interest rate and exchange rate. Because of the currency depreciation, the exchange rate negatively influences FDI.
Design of Center Radiation Slot for Radial Line Slot Antenna

To enhance the degree of freedom for radiation pattern of radial line slot antenna (RLSA), we propose attaching a center radiation slot. In this study, we developed a procedure for attaching a center radiation slot to the RLSA. We created two types of center slots: ring-shaped and cross-shaped. Both exhibited good characteristics in terms of the rotation mode, coupling, and reflection of the radial line in wide bandwidth.
Electroencephalogram based human emotion classification for valence and arousal using machine learning approach

Humans have unique ability to express emotions and electroencephalogram (EEG) signals are one of the sought-after ways to analyze a person’s emotional state. However, extracting proper emotion related features from EEG and finding corresponding emotion is challenging because of complex nature of emotions and underlying brain activities. The objective of this paper is to address this issue for more accurate emotion classification based on EEG. It also compares feature extraction methods namely fast fourier transform (FFT) and discrete wavelet transform (DWT). DEAP dataset is used for classification of human emotions through support vector machine (SVM) and K-nearest neighbor (KNN) algorithms by considering features such as standard deviation, mean, variance, power spectrum density (PSD) for FFT; and energy, entropy for DWT. It is observed that feature extraction from FFT yielded better results than DWT and KNN gave more accuracy of 96.61% for valence and 96.42% for arousal as compared to SVM. The proposed method based on PSD and FFT fared better than other existing ones in terms of accuracy when compared against different features and feature extraction techniques. This approach is expected to help researchers to understand feature extraction from EEG signals and decide proper features and techniques for better implementation.
Research on optimal portfolio trading strategy based on decision to quantification model

The purpose of this paper is to establish a model that is conducive to making decisions, and to analyze decisions from the operational mechanism of events. When it comes to buying and selling decisions in the financial markets, we know how difficult it can be to decide when to buy or sell, because selling too early or buying too late can reduce returns. Therefore, a model composed of decision system and quantitative system is proposed to make daily trading strategy. The decision system is used to decide whether a trade should be made that day, while the quantitative system is used to decide the number of trades to be made when a trade is made that day. Build a model based on the historical price of gold and bitcoin to decide whether to trade on the day. Using the constructed technical indicators and combined with the effective turtle strategy to analyze the designated decision scheme. In addition, to make decisions more robust, polynomial strategies are added to predict future price movements.
OVERACTIVE BLADDER: CORRELATION BETWEEN URODYNAMIC VALUES AND PSYCHO-EMOTIONAL INDICES IN WOMEN

In patients with mild to moderate levels of mood changes, there was a statistically significant difference demonstrating higher maximum detrusor pressure (p<0.05) compared to patients with no psychological disorders (62.95±42.48 cmH 2 O). Patients with detrusor overactivity (DO) confirmed on urodynamic tests were positively correlated (R 2 =0,86) with mild anxiety in 50% of cases and mood changes in 35.7% of cases. Conclusions. The comparison of psychometric parameters with clinical and urodynamic results revealed an association between the GAD-7 and PHQ-9 scores and DO. Mild and moderate degrees of change of mood and anxiety are prevalent in women with OAB and correlate with the severity of OAB symptoms, detrusor overactivity, and lower quality of life.
CALCULATION OF THE MAXIMUM WIND SPEED OF TYPHOON IN THE WESTERN PACIFIC

In this paper, the relationship between the muximum wind speed around the typhoon center on the sea surface and its central pressure are discussed in terms of dynamics. An experimental formula for the maximum wind speed and central pressure of typhoon in the western pacific is derived using the five-year wind velocity and pressure data and resulting a value of 0.9 of correlation coefficient. In addition, the experiment also shows a good agreement with the theoretical curve.
The Impact of Nociception Monitor-Guided Multimodal General Anesthesia on Postoperative Outcomes in Patients Undergoing Laparoscopic Bowel Surgery: A Randomized Controlled Trial

Background: Excess surgical stress responses, caused by heightened nociception, can lead to elevated levels of postoperative inflammation, resulting in an increased incidence of complications after surgery. We hypothesized that utilizing nociception monitor-guided multimodal general anesthesia would exert effects on postoperative outcomes (e.g., serum concentrations of C-reactive protein (CRP) after surgery, postoperative complications). Methods: This single-center, double-blinded, randomized trial enrolled ASA class I/II adult patients with normal preoperative CRP levels, scheduled for laparoscopic bowel surgery. Patients were randomized to receive either standard care (control group) or nociception monitor-guided multimodal general anesthesia using the nociceptive response (NR) index (NR group), where NR index was kept below 0.85 as possible. The co-primary endpoint was serum concentrations of CRP after surgery or rates of 30-day postoperative complications (defined as Clavien–Dindo grades ≥ II). Main Results: One hundred and four patients (control group, n = 52; NR group, n = 52) were enrolled for analysis. The serum CRP level on postoperative day (POD) 1 was significantly lower in the NR group (2.70 mg·dL−1 [95% confidence interval (CI), 2.19–3.20]) than in the control group (3.66 mg·dL−1 [95% CI, 2.98–4.34], p = 0.024). The postoperative complication rate was also significantly lower in the NR group (11.5% [95% CI, 5.4–23.0]) than in the control group (38.5% [95% CI, 26.5–52.0], p = 0.002). Conclusions: Nociception monitor-guided multimodal general anesthesia, which suppressed intraoperative nociception, mitigated serum concentrations of CRP level, and decreased postoperative complications after laparoscopic bowel surgery.
Genetics and Molecular Biology

The transmission of pathogens from wild animals to humans is called “zoonotic spillover”. Most human infectious diseases (60-75%) are derived from pathogens that originally circulated in non-human animal species. This demonstrates that spillover has a fundamental role in the emergence of new human infectious diseases. Understanding the factors that facilitate the transmission of pathogens from wild animals to humans is essential to establish strategies focused on the reduction of the frequency of spillover events. In this context, this article describes the basic aspects of zoonotic spillover and the main factors involved in spillover events, considering the role of the inter-species interactions, phylogenetic distance between host species, environmental drivers, and specific characteristics of the pathogens, animals, and humans. As an example, the factors involved in the emergence of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic are discussed, indicating what can be learned from this public health emergency, and what can be applied to the Brazilian scenario. Finally, this article discusses actions to prevent or reduce the frequency of zoonotic spillover events.
Separation of prestack seismic diffractions using an improved sparse apex-shifted hyperbolic Radon transform

The apex-shifted hyperbolic Radon transform (ASHRT) which is defined as an extension of standard hyperbolic Radon transform (HRT) shifts the apexes of basis functions along the offset. In this paper, we develop an improved sparse ASHRT to separate the diffractions from reflections before stacking. To speed up the calculation, the forward and adjoint operators of ASHRT in the time domain are replaced with the Fourier-kernel Stolt-based modelling and imaging operators. To overcome the limitation of velocity variations, the time axis stretching is implemented. With lower computation cost compared to the time domain operators, we can collapse the apex-shifted events with one velocity. Meanwhile, we introduce a sparsity-promoting inversion by the fast iterative shrinkage thresholding algorithm (FISTA), which produces a sparse Radon panel to separate the diffracted energy easily. Two synthetic examples of variable velocity model show that our proposition is robust and efficient. Another marine data example further demonstrates the effectiveness of this method in separation of prestack seismic diffractions. We have developed an improved sparse apex-shifted hyperbolic Radon transform with Fourier-kernel Stolt-based modelling and imaging operators to separate seismic diffractions from reflections. The time axis is stretched to overcome the limitation of the multi-velocity model and a sparsity-promoting inversion is implemented to enhance the resolution of the Radon panel.
Evaluation of the effects of latanoprost and benzalkonium chloride on the cell viability and nonpolar lipid profile produced by human meibomian gland epithelial cells in culture

Purpose The purpose of this study was to explore the effects of a PGF2α analog, latanoprost, and its preservative, benzalkonium chloride (BAK), on the cell viability and lipidomic expression of immortalized human meibomian gland epithelial cells (HMGECs). Methods Differentiated HMGECs were exposed to latanoprost (0.05 to 50 µg/ml), BAK (0.2 to 200 µg/ml), or combined latanoprost-BAK (0.05–0.2 to 50–200 µg/ml). EP- and FP-type receptors, the cognate receptors of PGE2 and PGF2α, were inhibited, thereby sparing and isolating the function of each receptor to one condition. Cell viability was assessed by ATP quantitation, and lipid extracts were analyzed by ESI-MSMSALL with a Triple TOF 5600 Mass Spectrometer (SCIEX, Framingham, MA) using SCIEX LipidView 1.3. Results Latanoprost and BAK were found to be lethal to HMGECs at the highest concentrations (p < 0.001 for both). The cytotoxicity of latanoprost was mediated through FP- and EP-independent mechanisms. Both latanoprost and BAK significantly modulated the lipidomic expression of several cholesteryl esters (8% and 30%, respectively) and triacylglycerols (10% and 12%, respectively). The combined latanoprost-BAK agent appeared to be no more toxic and to only negligibly alter the lipid profile relative to its individual components. Conclusions The use of latanoprost and BAK in glaucoma may alter the viability of the meibomian glands and their lipid expression in vivo. Sublethal concentrations of BAK appear to modulate meibum lipid expression, particularly in relation to sterol biosynthesis. Non-preserved latanoprost had less cytotoxicity at lower doses and fewer lipidomic effects compared to BAK, further strengthening the argument in favor of BAK-free pharmaceutical preparations.
The ecological suitability area of Cirsium lineare (Thunb.) Sch.‐Bip. under future climate change in China based on MaxEnt modeling

Abstract Many kinds of medicinal ingredients occur in Cirsium lineare that have good clinical efficacy, conferring on this species its high medicinal development value. However, with a rapidly changing global climate, it is increasingly imperative to study the factors affecting the habitat distribution and survival of species. We predicted the current and future distribution areas of suitable habitats for C. lineare, analyzed the importance of environmental variables in influencing habitat shifts, and described the alterations to suitable habitats of C. lineare in different periods (modern, 2050s, and 2070s) and scenarios (RCP2.6, RCP4.5, and RCP8.5). The results show that, under the current climate, the total suitable area of C. lineare is about 2,220,900 km2, of which the highly suitable portion amounts to ca. 292,600 km2. The minimum temperature of the coldest month, annual precipitation, and mean daily temperature range are the chief environmental variables affecting the distribution of habitat for C. lineare. In the same period, with rising greenhouse gas emission concentrations, the total suitable area will increase. In general, under future climate change, the suitable habitat for C. lineare will gradually migrate to the west and north, and its total suitable area will also expand. The results of this experiment can be used for the conservation and management of the wild resources of C. lineare. We can choose suitable growth areas to protect the medicinal resources of C. lineare through in situ conservation and artificial breeding.