Miracle-dz/UniFAR-FTUs huggingface dataset:
Boundary Shape-Preserving Model for Building Mapping From High-Resolution Remote Sensing Images

The building is a critical urban element for developing smart cities. Building extraction research has experienced rapid development due to abundant remote sensing image data availability. Building extraction methods have recently focused on enhancing regional accuracy without obtaining refined building boundaries and do not fit the concave–convex shape of the building and have significant sawtooth noise. This article proposes a building shape-preserving framework to solve the imperfect boundary problem. To delineate the concave and convex building boundaries from remote sensing images, we combine coarse- and fine-grained information with the instance segmentation method, Mask region-based convolutional neural network (R-CNN), which can further refine the indeterminate boundary points, and perform inward fitting of the building boundary. A regular boundary network is designed to learn the features of the edges and footprints to calculate the boundary loss that can smoothen the boundary noise, considering the imperfect boundaries and sawtooth noise in the extracted building boundary. Furthermore, to enhance the ability to evaluate the extracted building shape, a building footprint information evaluation algorithm, the footprint distance metric, is proposed to calculate the footprint distance between each building boundary. Comparing the building extraction results indicates that the proposed method achieves excellent quantitative evaluation scores. Experiments performed using Spacenet and Wuhan University (WHU) datasets to verify the effectiveness of the proposed method are detailed. The experimental results indicate that the algorithm utilizes the powerful edge extraction capability of the method to exhibit excellent performance in terms of extracting building edges from complex remote sensing scenes. Code is available at https://github.com/AnnaCUG/Boundary-shape-preserving-model.
Identifying Effective Feature Selection Methods for Alzheimer’s Disease Biomarker Gene Detection Using Machine Learning

Alzheimer’s disease (AD) is a complex genetic disorder that affects the brain and has been the focus of many bioinformatics research studies. The primary objective of these studies is to identify and classify genes involved in the progression of AD and to explore the function of these risk genes in the disease process. The aim of this research is to identify the most effective model for detecting biomarker genes associated with AD using several feature selection methods. We compared the efficiency of feature selection methods with an SVM classifier, including mRMR, CFS, the Chi-Square Test, F-score, and GA. We calculated the accuracy of the SVM classifier using validation methods such as 10-fold cross-validation. We applied these feature selection methods with SVM to a benchmark AD gene expression dataset consisting of 696 samples and 200 genes. The results indicate that the mRMR and F-score feature selection methods with SVM classifier achieved a high accuracy of around 84%, with a number of genes between 20 and 40. Furthermore, the mRMR and F-score feature selection methods with SVM classifier outperformed the GA, Chi-Square Test, and CFS methods. Overall, these findings suggest that the mRMR and F-score feature selection methods with SVM classifier are effective in identifying biomarker genes related to AD and could potentially lead to more accurate diagnosis and treatment of the disease.
The Associations between Kidney Function and Sexual Dysfunction among Males and Females with Type 2 Diabetes Mellitus

Background and Objectives: Diabetic kidney disease (DKD), expressed either as albuminuria, low estimated glomerular filtration rate (eGFR) or both, and sexual dysfunction (SD), are common complications among type 2 diabetes mellitus (T2DM) patients. This study aims to assess whether an association exists between DKD and SD, erectile dysfunction (ED) or female sexual dysfunction (FSD) in a T2DM population. Materials and Methods: A cross-sectional study was designed and conducted among T2DM patients. The presence of SD was assessed using the International Index of Erectile Function and the Female Sexual Function Index questionnaires for males and females, respectively, and patients were evaluated for DKD. Results: Overall, 80 patients, 50 males and 30 females, agreed to participate. Sexual dysfunction was present in 80% of the study population. Among the participants, 45% had DKD, 38.5% had albuminuria and/or proteinuria and 24.1% had an eGFR below 60 mL/min/1.73 m2. The eGFR was associated with SD, ED and FSD. Moreover, SD and ED were proven as significant determinants for lower eGFR values in multiple linear regression analyses. DKD was associated with lower lubrication scores and eGFR was associated with lower desire, arousal, lubrication and total scores; however, the multivariate linear regression analyses showed no significant associations between them. Older age resulted in significantly lower arousal, lubrication, orgasm and total FSFI scores. Conclusions: SD is commonly encountered in older T2DM patients and DKD affects almost half of them. The eGFR has been significantly associated with SD, ED and FSD, while SD and ED were proven to be significant determinants for the eGFR levels.
The Relationship between Character Traits and In Vivo Cerebral Serotonin Transporter Availability in Healthy Subjects: A High-Resolution PET Study with C-11 DASB

To elucidate the potential roles of serotonergic activity in human character traits (i.e., self-directedness, cooperativeness, and self-transcendence), we investigated the relationship between these character traits and serotonin transporter (5-HTT) in healthy subjects. Twenty-four participants underwent High-Resolution Research Tomograph–positron emission tomography scans with [11C]DASB. To quantify 5-HTT availability, binding potential (BPND) of [11C]DASB was obtained using the simplified reference tissue model. The Temperament and Character Inventory was used to assess subjects’ levels of three character traits. There were no significant correlations between the three character traits. Self-directedness was significantly positively correlated with [11C]DASB BPND in the left hippocampus, left middle occipital gyrus, bilateral superior parietal gyrus, left inferior parietal gyrus, left middle temporal gyrus (MTG), and left inferior temporal gyrus (ITG). Cooperativeness was significantly negatively correlated with [11C]DASB BPND in the median raphe nucleus. Self-transcendence was significantly negatively correlated with [11C]DASB BPND in the right MTG and right ITG. Our results show significant correlations between the three character traits and 5-HTT availability in specific brain regions. In particular, self-directedness was significantly positively correlated with 5-HTT availability, suggesting that a goal-oriented, self-confident, and resourceful character may be related to higher serotonergic neurotransmission.
Forward and backward filtering based on backward stochastic differential equations

In this paper we explore the problem of reconstruction of blurred and noisy images. The idea presented here provides a new methodology based on advanced tools of stochastic analysis which can be successfully used to solve the inverse problem. In order to solve this problem we use backward stochastic differential equations. The reconstructed image is characterized by smoothing noisy pixels and at the same time enhancing and sharpening edges. Our experiments show that the new approach gives very good results and compares favourably with deterministic partial differential equation methods.
Changes in the Secretion of Melatonin and Selected Adipokines during the Progression of Parkinson’s Disease—Preliminary Studies

Parkinson’s disease (PD) is one of the most common neurodegenerative diseases affecting elderly people. Considering the gap in the literature on melatonin and adipokine levels in PD patients at various stages of the disease, we conducted a study to investigate the levels of selected parameters in PD patients at the disease’s early (ES) and advanced (AS) stages. Melatonin, leptin, adiponectin, and resistin concentrations were measured in the blood serum of 20 PD patients without dyskinesia (ES), 24 PD patients with dyskinesia (AS), and 20 healthy volunteers as a control group (CG). The data were analyzed using ANOVA. Melatonin was significantly lower in ES (p < 0.05) and higher in AS patients (p < 0.05) compared to CG. The level of leptin was increased both in ES (p < 0.001) and AS (p < 0.001) versus CG, while resistin was increased only in patients with dyskinesia (p < 0.05). Higher melatonin (p < 0.001) and resistin (p < 0.05) and lower leptin (p < 0.05) levels were found in AS versus ES. The main findings of the study include the changes in inflammatory markers’ levels during PD and a surprising increase in melatonin level in dyskinesia patients. Further research is necessary, which will be aimed at modulating the secretion of melatonin and adipokines as a treatment target for PD.
Design of an angular acceleration boundary observer

Aimed at the nonlinear friction,disturbance torque and load variation in typical mechanical-electrical servo systems,by considering discrete-time output signals of angular position sensor as the observer input,the design of an angular acceleration boundary observer design is presented using sliding-mode control method.Based on the analysis of the structure of the boundary observer,we propose a method to determine the control input by designing the sliding mode.Furthermore,the key performance parameters and the design principle are provided.For a practical mechanics system,the designed angular acceleration observer exhibits the excellent dynamical performance under the nonlinear disturbance.
A Review on Active Heat Transfer Enhancement Techniques within Latent Heat Thermal Energy Storage Systems

Renewable energy resources require energy storage techniques to curb problems with intermittency. One potential solution is the use of phase change materials (PCMs) in latent heat thermal energy storage (LHTES) systems. Despite the high energy storage density of PCMs, their thermal response rate is restricted by low thermal conductivity. The topic of heat transfer enhancement techniques for increasing thermal performance of LHTES systems has mainly focused on passive heat transfer enhancement techniques with less attention towards active methods. Active heat transfer enhancement techniques require external power supplied to the system. In this paper, recent advances in active heat transfer enhancement techniques within LHTES systems are reviewed, including mechanical aids, vibration, jet impingement, injection, and external fields. The pertinent findings related to the field are summarized in relation to the charging and discharging processes of PCMs. Suggestions for future research are proposed, and the importance of additional energy input for storage is discussed.
Effect of Surface Roughness on Boiling Heat Transfer

The reason why the data on the boiling coefficient of heat transfer disperse, is owing to the surface phenomena that the number of vapour columns would be influenced by both surface conditions of the heating surface and properties of liquid, and that the heat transfer to boiling liquid is closely related to the column number. The column number should depend on various factors such as heat flux, surface roughness, surface contamination, properties of liquid, etc. In this paper the effect of surface roughness on heat transfer in nucleate boiling is treated. The experiments were carried out with distilled water boiler on the horizontal heating surfaces, fitted with various shapes of concentrica grooves of triangle cross section. It was found that the mean height was adequate to the scale of roughness on the boiling heat transfer. Further, by introducing a new "coefficient of foaming" which represents the foaming ability of both heating surface and boiling liquid, experimental formulae on the heat transfer in the nucleate boiling were derived.
Distance estimation of colored objects in image

Detecting objects with a specific color is one of the topics in computer vision. An object captured by the camera will be represented as an image. Closer objects will appear larger in the image and vice versa; however, size changes do not give the actual distance of the object to the camera. This paper proposes a method to estimate the distance between the colored object to the camera by processing the image captured. First, image color filtering based on HSV color model performed and then to detect the colored object in the image, object detection applied to labeling nearest eight connected components with the same value with Moore-neighborhood tracing algorithm. The distance of the object estimates with the single point projection principle of the object height in the image, represented by the bounding rectangle, and the actual object’s height. An experiment conducted using objects with different color and object distance variance in centimeters. Results show that the method can estimate the real distan.
Data-Driven Method to Quantify Correlated Uncertainties

Polynomial chaos (PC) has been proven to be an efficient method for uncertainty quantification, but its applicability is limited by two strong assumptions: the mutual independence of random variables and the requirement of exact knowledge about the distribution of the random variables. We describe a new data-driven method for dealing with correlated multivariate random variables for uncertainty quantification that requires only observed data of the random variables. It is based on the transformation of correlated random variables into independent random variables. We use singular value decomposition as a transformation strategy that does not require information about the probability distribution. For the transformed random variables, we can construct the PC basis to build a surrogate model. This approach provides an additional benefit of quantifying high-dimensional uncertainties by combining our method with the analysis-of-variance (ANOVA) method. We demonstrate in several numerical examples that our proposed approach leads to accurate solutions with a much smaller number of simulations compared to the Monte Carlo method.
Changes in body composition and metabolic rate during a very low-calorie ketogenic diet and their association with weight loss

Introduction: The purpose of this research was to examine how a very low-calorie ketogenic diet affected body composition and metabolic rate in overweight and obese people. Method: A 12-week weight loss program using a ketogenic diet that had only a few hundred calories per day was offered to 80 participants at the Hayatabad Medical Complex in Peshawar. Participants consumed 800-1000 calories each day and were instructed to maintain a state of ketosis. Data on body composition, metabolic rate, and appetite hormones were collected at baseline and after the intervention. Results: The study's findings demonstrated that a ketogenic diet consisting of extremely few calories helped people lose a considerable amount of weight and improved their body composition and metabolic rate. Participants' weight, fat percentage, and circumference of the waist all decreased, as well as an increase in lean body mass. The metabolic rate of the participants increased, suggesting that the ketogenic diet may help with weight reduction and metabolic health. Conclusion: This research shows that an exceedingly low-calorie ketogenic diet may help those who are overweight or obese, especially those who have insulin resistance, lose weight.
Adsorption of Direct Orange 46 and phosphate ions on waste tomato stem ash used as a bio‐based adsorbent

It is necessary to use adsorbents derived from natural materials, such as biosorbents since they do not any damage to the environment, for pollutants elimination that contaminate the aquatic environment. Since the deficiency in the literature was determined in the study in which only the adsorbent obtained from tomato stem was used before this study, a waste material with tomato stem was selected as a natural adsorbent without chemical compounds. Phosphate and dye contaminants, which are often present in industrial and municipal wastewater, were eliminated by adsorption process. Fourier transform infrared (FTIR), x‐ray fluorescence (XRF), Zeta potential, and scanning electron microscope (SEM) with energy dispersive x‐ray (EDX) spectroscopy were used to analyze tomato stems. The tomato stems resulted in high pollutants removal efficiencies recording 99.31% of phosphate ( PO43− ) removal efficiency and 99.44% of Direct Orange 46 (DO 46) dye removal efficiency at optimal conditions. The best conditions for phosphate adsorption were pH = 6.0, 2.0 g/L of tomato stems concentration, 50 mg/L initial phosphate concentration and a contact time of 120 min resulting in an adsorption capacity of 4.24 mg/g. The best conditions for DO 46 adsorption were pH = 12, 2.0 g/L of adsorbent concentration, initial dye of 200 mg/L concentration and a contact time of 120 min (q = 13.76 mg/g). For both adsorption experiments, the adsorbent size was chosen to be less than 500 μm. As a result of the adsorption experiments on tomato stem ash, Freundlich isotherm (R2: 0.94) was suitable for DO 46 dye adsorption and Temkin isotherm (R2: 0.99) was suitable for PO43− elimination. The phosphate (R2: 0.97) and dye (R2: 1) adsorption onto tomato stem ash was explained using a pseudo‐second‐order kinetic model. It has been determined that the adsorption of the DO 46 dye with the tomato stem ash is an exothermic (∆H: −3.174) process that occurs naturally due to its nature (∆G: −8.41). On the other hand, it was determined that the adsorption of PO43− was spontaneous (∆G: −5.21) and endothermic (∆H: 5.7357). According to the adsorption results obtained in this study, it is appropriate to use the tomato stem, which is generated as waste, as an adsorbent. It is thought that obtaining from bio‐based waste in the removal of pollutants such as dye and phosphate in water will both reduce the cost and create an advantage in terms of biodegradability.
Privacy-Preserving Truth Discovery for Collaborative-Cloud Encryption in Mobile Crowdsensing

In mobile crowdsensing (MCS) system, a variety of sensors are required to operate together to glean and upload sensory data to clouds for processing. In real practice, truth discovery has been widely explored to find reliable information from various mobile devices. Under the requirement of MCS security, privacy-preserving truth discovery (PPTD) causes wide concern, which refers to discovering truthful information from these unreliable uploaded data while protecting users' private information. Although many PPTD mechanisms have been proposed, they can either not guarantee low communication from users to the cloud, or fail to realize fully strong privacy protect including sensing data privacy, weight privacy, intermediate privacy, and estimated truth privacy. This study designs a collaborative cloud encryption architecture. In this framework, we propose a new system architecture that adapts a two-cloud peer model while leveraging garbled circuit (GC). Users only need to transfer data to two clouds once, which realizes low users workloads while supporting dynamic users. The two cloud terminals cooperate to complete the weight update through the GC but execute the truth discovery algorithm, respectively. In this way, the noninteractive comes true and the users' workloads are transferred to the cloud server side. The weight update finish and weight privacy are fulfilled through GC, meanwhile the other intermediate values maintain strong privacy. At the same time, because the collaborative cloud architecture of the two clouds ensures the confidentiality of the truth value in the cloud and the homomorphism at the inquiry side, it ensures the strong privacy of the whole process from users to inquiries. The performance of this proposed method is verified through extensive evaluations.
Yield- and protein-neutral reduction in fertilizer rate for wheat, maize and rice can reduce the release of reactive nitrogen and greenhouse gas emissions in China

Seeking food security, contemporary Chinese agriculture has followed a trajectory of overfertilization and associated environmental problems, hence the need for nitrogen-balancing practices that do not compromise yield and quality. Here we present a national meta-analysis using 224 studies with 1972 comparisons to quantify the potential to reduce nitrogen (N) fertilization to improve environmental outcomes while maintaining yield and grain protein. We calculated a nitrogen reduction ratio (NRR), as 100 × (N C − N T)/N C; where N is N fertilizer rate and subscripts indicate farmer practice (C) and reduced N rate treatment (T). Our meta-analysis showed that the NRR that maintained yield and grain protein content at the level of current practice was up to 10% in wheat and up to 30% in maize and rice. Larger yield-neutral NRR could be achieved in more fertile, heavier-textured soils, and with practices including enhanced-efficiency N fertilizer, combined application of organic and inorganic N fertilizer, and incorporated straw. Assuming a reduction in N fertilizer usage by 10% for wheat and by 30% for maize and rice in the current cropping area, there is a potential to save 5.7 Mt N yr−1; reduce loss of reactive nitrogen by 1.26 Mt N yr−1, equivalent to 63% of annual total Nr losses for rice in China, reduce N-related greenhouse emissions by 75.2 Mt CO2-eq yr−1, equivalent to 14.5%–25% of the emissions associated with the N fertilizer chain in China; and improve N use efficiency by 23%. Our results highlight the feasibility of maintaining yield and grain protein, and achieving substantial environmental benefits with reduced fertilization rate, and the environmental and agronomic scenarios where these outcomes are more likely.
A Statistical Approach for Reconstructing Continuous Series of Mortality by Cause of Death

Data on cause-specic mortality have been collected over several decades for dierent countries. Nevertheless this valuable source of information has not been suciently explored. This is mainly due to frequent revisions of the International Classication of Diseases which lead to discontinuities and disruptions in cause-specic mortality trends. This paper proposes a new methodology for reconstructing continuous series of mortality by cause of death. The model assumes death counts as realizations from a Poisson distribution. The expected values of this distribution is a composed mean which embodies the continuous series and the redistribution pattern among causes of death. An algorithm allows the simultaneous estimation of these two components. The known associations among causes is used as input and the only assumption that is made about the underlying continuous series is smoothness. We present a simulation study and an application for demonstrating the performance of our model and its practical characteristics.