Miracle-dz/UniFAR-FTUs huggingface dataset:
Anti-Inflammatory Effect of Vanillin Protects the Stomach against Ulcer Formation

Gastric ulcer is one of the most frequent gastrointestinal disorders, and there is an increasing search for natural products that can heal ulcers and avoid their recurrence. We aimed to evaluate the gastroprotective activity of vanillin, including the investigation of anti-inflammatory activity and the modulation of gene expression. Wistar rats were orally treated with vehicle, carbenoxolone, or vanillin (25, 50, or 100 mg/kg) and orally received absolute ethanol to develop gastric ulcers. We analyzed the ulcer area, conducted histological analysis, and measured the levels of the inflammatory cytokines TNF-α, IL-6, IL-1β, and IFN-γ, and anti-inflammatory cytokine IL-10 by ELISA. We analyzed mRNA expression for NF-κB, TNF-α, and Il-10. We measured NOx levels using the Griess reaction. Our results showed similar gastroprotection for the three doses. Vanillin increased mucus production and preserved gastric mucosa integrity. The gastroprotective effect was linked to anti-inflammatory activity as a result of decreasing the levels of TNF-α, IL-6, IL-1β, and IFN-γ and increasing IL-10 levels. Vanillin downregulated the mRNA expression of NF-κB and TNF-α, upregulated the mRNA expression of Il-10, and increased NOx levels in the stomach. The gastroprotective activity of vanillin is related to the maintenance of gastric mucus and the local inflammatory response modulation.
Quadratic Pose Estimation Problems: Globally Optimal Solutions, Solvability/Observability Analysis, and Uncertainty Description

Pose estimation problems are fundamental in robotics. Most of these problems are challenging due to the nonconvex nature. This also sets up an obstacle for uncertainty description that is essential for pose integration and quality control. In this article, we show that a large class of related problems can be categorized as the quadratic pose estimation problems (QPEPs) and we propose a general quaternion-based mathematical model to unify these problems. To solve the nonconvex QPEPs, a Gröbner-basis method is investigated to derive their globally optimal and robust solutions. Furthermore, we develop the rules for characterizing the solvability and observability of these solutions. In addition, the uncertainty description, i.e., covariance matrix, as an important piece of information in robotic state estimation frameworks, is analyzed in detail. Theoretical results show that the covariance can be estimated via online optimization, in an efficient and unbiased manner. In this way, both the solution and covariance are guaranteed to be globally optimal. Through simulations and experiments, we show that the proposed QPEP-based solver is not only accurate, robust, and efficient but outperforms the representatives for covariance estimation. The designed algorithms are also assembled as a C++/MATLAB/Octave/ROS library, while these developed interfaces are built for main stream platforms and simultaneous localization and mapping schemes.
A deep network ensemble for segmentation of cervical spinal cord and neural foramina

The automated interpretation of spinal imaging using machine learning has emerged as a promising method for standardizing the assessment and diagnosis of numerous spinal column pathologies. While magnetic resonance images (MRIs) of the lumbar spine have been extensively studied in this context, the cervical spine remains vastly understudied. Our objective was to develop a method for automatically delineating cervical spinal cord and neural foramina on axial MRIs using machine learning. In this study, we train a state-of-the-art algorithm, namely a multiresolution ensemble of deep U-Nets, to delineate cervical spinal cord and neural foramina on 50 axial T2-weighted MRI-series segmented by a team of expert clinicians. We then evaluate algorithm performance against two independent human raters using 50 separate MRI-series. Dice coefficients, Hausdorff coefficients, and average surface distances (ASDs) were computed for this final set between the algorithm and each rater, and between raters, in order to evaluate algorithm performance for each segmentation task. The resulting cervical cord Dice coefficients were 0.76 (auto vs human, average) and 0.87 (human vs human), and the cervical foramina Dice coefficients were 0.57 (auto vs human, average) and 0.59 (human vs human). Hausdorff coefficients and ASDs reflected similar results. We conclude that the algorithm achieved a higher degree of consistency with human raters for cervical cord than for cervical foramina, and that cervical foramina are challenging to segment accurately for both humans and machine. Further technical development in machine learning is necessary to accurately segment the highly anatomically variable neural foramina of the human spine.
A Study on Factors That Influence Monthly Household Expend? Ture amongst Educators in Ser? Iskandar, Perak

In Malaysia, Household Expenditure Survey (HES) was conducted every five years to collect information on the level and pattern of consumption expenditure. Basically, monthly household expenditure represents the total outlay that a household has to make in order to satisfy needs and commitments incurred in a family. In 2011, Seri Iskandar had been chosen as the hub of education and commercial centre for Perak state. In conjunction with that, it is crucial to identify the factors which influence the household expenditure amongst the educators due to the rapid economics in Seri Iskandar. Bayesian Network approach is conducted in this study to analyse the causal relationship in monthly household expenditure and educators. Eight different structural learning algorithm are used in this study, which are Grow-Shrink, Incremental Association Markov Blanket, Fast Incremental Association, Interleaved Incremental Association, Hill-Climbing, Tabu Search, Max-Min Hill-Climbing and General 2-Phase Restricted Maximization. In this study, bnlearn package from R programming language is utilized in order to run all the eight structural learning algorithms. The network scores are used to identify which algorithm gives the best fitted network. Moreover, the arc strength is applied in the final network to determine the most influential relationship in this study. As a result, the network from Tabu Search algorithm is identified as the best final network in this study. Furthermore, the outcome shows that household size is identified as the main factor which influence the monthly expenditure amongst the educators in Seri Iskandar according to their gender.
Positive Health Behavior Changes in Custodial Grandparents and Grandchildren Following an Intervention

Background: Custodial grandparents experience greater physical health declines and higher rates of depression than their same-age peers who do not provide care, and grandchildren in grandfamilies often have behavior problems. However, few researchers have explored the impact of self-care education on decreasing these negative outcomes. Our study examined how a self-care and life-skills intervention influenced health behavior change in a sample of grandparents and grandchildren. Methods: Data were collected during eleven focus groups (and two interviews) with 55 grandparents, and one focus group with five grandchildren, at 6 months after the 6-week intervention detailed in this paper. Grandparents ranged in age from 46 to 84 years old (M = 62.19, SD = 8.24). Participating grandchildren ranged in age from 9 to 12 years old. Focus group transcripts were coded for content related to grandparents’ and grandchildren’s positive behavior changes following the intervention. Findings: Grandparents reported taking more time for themselves, reducing negative self-talk, increasing healthy physical choices, and having better communication skills after participating in the intervention. Similarly, grandchildren reported increased confidence in making friends, making good decisions, and getting along with others. Findings suggest that a self-care and life skills program show promise for improving the health and wellness of grandfamilies.
Education and Revolutions (Why Do Some Revolutions Take up Arms while Others Do Not?)

Despite the existence of numerous hypotheses about what factors influence protesters’ choice of armed vs. unarmed struggle tactics, today there is a dearth of global, quantitative cross-national studies aimed at identifying the reasons why revolutions take a violent vs. a non-violent form. The article attempts to fill one of the gaps existing here by conducting a crossnational analysis of the relationship between education and the type of revolutionary action. Based on the existing literature, the authors document several mechanisms that nudge educated people to choose unarmed forms of protest. In particular, education makes people more receptive to liberal-democratic valu es (including recognition and respect for the rights of others), promotes the development of tolerance and a culture of peaceful discussion, as well as increases human capital, which, on the one hand, makes it possible to successfully use non-violent instruments to defend one’s interests, and, on the other hand, increases the relative costs of participating in armed uprisings. Hence, the authors put forward a hypothesis, according to which in countries with a higher percentage of the population with formal education, the likelihood of armed revolutionary uprisings will decrease. The analysis of 387 revolutionary events that took place in the world from 1950 to 2019 conducted via cross-tabulation, correlation and regression methods, fully confirms the hypothesis: high level of education is indeed a strong and significant predictor of the unarmed nature of revolutionary upheavals. The correlation is especially high between the proportion of revolutionary events and the average number of years of schooling (in the logged form), which approaches the level of functional depen dence. Therefore, the factor of education has the maximum influence on the nature of revolutionary uprisings precisely in the early stages of modernization.
The Role of 3D-Printed Custom-Made Vertebral Body Implants in the Treatment of Spinal Tumors: A Systematic Review

In spinal surgery, 3D prothesis represents a useful instrument for spinal reconstruction after the removal of spinal tumors that require an “en bloc” resection. This represents a complex and demanding procedure, aiming to restore spinal length, alignment and weight-bearing capacity and to provide immediate stability. Thus, in this systematic review the authors searched the literature to investigate and discuss the advantages and limitations of using 3D-printed custom-made vertebral bodies in the treatment of spinal tumors. A systematic literature review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement, with no limits in terms of date of publication. The collected studies were exported to Mendeley. The articles were selected according to the following inclusion criteria: availability of full articles, full articles in English, studies regarding the implant of 3D custom-made prothesis after total or partial vertebral resection, studies regarding patients with a histologically confirmed diagnosis of primary spinal tumor or solitary bone metastasis; studies evaluating the implant of 3d custom-made prothesis in the cervical, thoracic, and lumbar spine. Nineteen published studies were included in this literature review, and include a total of 87 patients, 49 males (56.3%) and 38 females (43.7%). The main tumoral location and primary tumor diagnosis were evaluated. The 3D custom-made prothesis represents a feasible tool after tumor en-bloc resection in spinal reconstruction. This procedure is still evolving, and long-term follow-ups are mandatory to assess its safeness and usefulness.
Cost-Efficient Resources Scheduling for Mobile Edge Computing in Ultra-Dense Networks

With the development of 5G communication technologies and smart mobile devices, various computation-intensive and delay-sensitive tasks continue to increase. The combination of Mobile Edge Computing (MEC) and Ultra-Dense Networks (UDN) increases the network capacity and improves the computing capability of mobile devices, which effectively meets the transmission and computing demands of tasks. However, the ultra-dense deployment of network infrastructures causes energy shortage and channel interference, making it challenging to reduce the system cost. In this paper, we investigate the task offloading and resources scheduling problem in UDN with MEC. In order to minimize the total system cost including delay and energy consumption in the intensive deployment environment of edge servers and base stations (BSs) simultaneously, we design the strategy of task offloading, BS selection and resources scheduling of mobile devices. Because of the complex coupling of decision variables, the original problem is decomposed into two sub-problems. We propose Newton-IPM based Computing Resource Allocation (NICRA) algorithm and Genetic Algorithm based BS Selection and Resources Scheduling (GABSRS) algorithm to solve these two sub-problems, respectively. Then, we prove the number of iterations can be reduced effectively by the GABSRS algorithm while reaching the optimal solution through mathematical analysis. Through experiments analysis, the effectiveness of the GABSRS algorithm is validated.
Not Without You: Role of Shopping Pal and Store Atmospherics in Impulse Buying

Purpose: Retailers acknowledge the importance of the retail store environment as a tool for differentiation and valuable for positioning the retail store. It makes the buyer's decision to visit the store easier. Further, a pleasing retail environment improves the customer's purchasing experience and satisfaction. This paper aims to; investigate the influence of store atmospherics and marketing stimuli on impulse buying behavior among females. Shopping pal provides a boundary condition between the influence of store environment, emotional response, and impulse buying. Methodology: This is a quantitative study and data was collected using surveys. The investigation was conducted using Amos and SPSS version 21, and the results were within an acceptable range. Findings: The findings of the study suggest that an impressive store environment with marketing stimuli such as price discount leads to positive emotions and impulse purchase behavior. Further, a shopping pal provides boundary conditions between the influence. Implications: Retailers must focus on creating a retail experience that is clean, relaxing pleasant, and has an excellent interior design, and they must encourage to group shopping. They might concentrate on promotional activities that encourage group shopping, such as group purchasing coupons and family prizes.
Effect of Co and Ni Doping on the Structure, Magnetic and Magnetocaloric Properties of Fe-Rich (Mn,Fe)2(P,Si) Compounds

The effect of Co and Ni doping on the structure, magnetic and magnetocaloric properties of Fe-rich (Mn,Fe) 2 (P, Si) compounds was studied. With increasing Co and Ni content, both the Curie temperature ( T c ) and the thermal hysteresis ( Δ T hys ) decreased, whereas the hexagonal P-62 m crystal structure was maintained. A pronounced reduction in hysteresis was observed upon Co doping, while a significant reduction in Curie temperature was found upon Ni doping. M ¨ ossbauer spectroscopy measurements and DFT calculations indicated the substitution of Fe at the 3f site for both Co and Ni doping. Rietveld refinement of the X-ray diffraction data showed that Co substitute atoms in the main phase and the impurity phase, while Ni exhibits an affinity to the main phase. Magnetization measurements on the Co doped samples revealed an increase in magnetization for 2 at. % of Co, followed by a decrease for higher concentrations. DFT calculations showed that the magnetic moment on the 3f site is enhanced by Co substitution, whereas an opposite trend was observed for Ni substitution.
Climate Finance Coordination From the Global to the Local: Norm Localization and the Politics of Climate Finance Coordination in Zambia

Abstract The transfer of climate finance to developing countries is a key feature in global climate change agreements. This article examines how conflicting preferences between climate finance donors and domestic actors affect the diffusion of emerging international norms on climate finance coordination at national and subnational levels in Zambia. Informed by literature on norm localization we trace the diverging preferences and interactions among donors and domestic actors over a 12-year period as they shape climate finance coordination in Zambia with support from the global Climate Investment Funds. We find that this has been a highly conflictual and dynamic process with contestation centered on norm application rather than norm validity. While the World Bank have had a strong initial influence on the enactment of coordination norms, domestic actors have over time undermined and reconfigured coordination arrangements to align better with their own preferences. Our findings show (i) how emerging international norms on climate finance coordination may be localized and reshaped as they are enacted in developing countries; (ii) that preferences may differ significantly between and among donors and domestic actors in this regard, and (iii) that past relationships from development cooperation may be carried forward but also challenged in this process.
Population Projection of Pakistan: What is there in 2028?

Pakistan has been facing a massive population upheaval for the past three decades which needs to be tackled through a comprehensive, effective, and resourceful plan. This study aimed to build a methodology for population projection as an imperative step to address the population concerns for an inclusive socioeconomic development planning. The projections made in this study are based on three data sets of Pakistan Demographic and Health Surveys (PDHS) and Pakistan Census Reports (PCR). Cohort Component Method is employed for projecting total population and population aggregates of young, working, and old aged people, women (in childbearing age)and sex ratio, crude birth and crude death rates, and population growth for the period 1998 to 2028. The empirical results exhibited that due to continuous drop-in total fertility rate, proportion of young and old aged group would likely to reduce, whereas working age population appeared to be increased. The percentage of reproductive women is projected to be increased, whereas sex ratio, crude birth and death rates are predicted to be decreased over time. A carefully drawn population policy must be focused on awareness about family planning programs to control an immense population growth in Pakistan.
Multi-modal learning with missing data for cancer diagnosis using histopathological and genomic data

Multi-modal learning (e.g., integrating pathological images with genomic features) tends to improve the accuracy of cancer diagnosis and prognosis as compared to learning with a single modality. However, missing data is a common problem in clinical practice, i.e., not every patient has all modalities available. Most of the previous works directly discarded samples with missing modalities, which might lose information in these data and increase the likelihood of overfitting. In this work, we generalize the multi-modal learning in cancer diagnosis with the capacity of dealing with missing data using histological images and genomic data. Our integrated model can utilize all available data from patients with both complete and partial modalities. The experiments on the public TCGA-GBM and TCGA-LGG datasets show that the data with missing modalities can contribute to multi-modal learning, which improvesthe model performance in grade classification of glioma cancer.
Clinical and Biochemical Potential of Antioxidants in Treating Polycystic Ovary Syndrome

Abstract Polycystic ovary syndrome (PCOS) is the most common cause of infertility in reproductive-age women. Increased reactive oxygen species levels and decreased antioxidant capacity in PCOS patients can lead to metabolic disorders and damage the ovarian tissues, resulting in the occurrence of related symptoms. Antioxidants have been used in the treatment of PCOS and have yielded satisfactory outcomes due to their ability to counter oxidative stress. Many experiments on PCOS patients have proved that antioxidants can not only improve the ovarian environment, promote follicular maturation, and elevate oocyte quantities but can also regulate lipid and glucose metabolism as well as vascular endothelial cell function in PCOS patients, thereby attenuating adiposity and reducing the occurrence rate of chronic complications to ensure that patients can obtain long-term benefits. This review describes the use of antioxidants in PCOS, which have been used in the treatment.
Performance improvement of an ultra-wideband antenna using textile material with a PIN diode

Wearable antennas are critical in on-body wireless communications and have piqued scientists’ curiosity. Because wearable antennas work so near to the human body, the loading effect caused by body tissue breakdown, along with their high dielectric constants and conductivity, makes developing a high-radiation output antenna challenging. The demand that these antennas be lightweight, low-cost, maintenance-free, and require no setup adds to the problem. In recent years, the demand for wearable devices has skyrocketed. Wearable antennas are utilized in a variety of applications, including wireless body area networks (WBANs), in many circumstances. The patch antenna is one of the primary research goals of wearable antennas for WBAN applications due to the extensive ground plane employed in its construction and its relatively high directionality. In addition to directivity, microstrip patch antennas provide other benefits for on-body wearable applications. The wearable textile ultra-wideband (UWB) antenna’s frequency of changes versus gain is set. The maximum gain is about 17.2 dB at 16 GHz. The gain of the frequency of 14 GHz is obtained at 16.8 dB, with a high performance rate. The design in the present paper has low power consumption as a result of achieving gain results of less than 5 dB in most frequency bands from 2 to 16 GHz. While the reflector is present, the size of the reflector is chosen to make the antenna resistant to frequency detuning. When the distance between the monopole and the reflector is lowered, the antenna’s frequency decreases. The greater power, the closer the reflector is to the inductive near zone. However, the reflector was chosen to boost the front-to-back ratio of the proposed monopole. The reflection coefficient is derived by varying different resonating frequencies between −19.45 and −16.45 dB. The highest directivity is 11.5 dB and the lowest is 9.8 dB while varying different resonant frequencies. Gain is achieved by varying different resonant frequencies, with a maximum of 17.2 dB and a minimum of 16.2 dB. The effectiveness of varying different resonant frequencies is 45% maximum and 40% minimum. The suggested UWB antenna was found to have an acceptable agreement between models and tests, demonstrating its potential for microwave applications. The directivity, gain, Voltage Standing Wave Ratio (VSWR), and radiation pattern are all performance metrics that are measured.