Miracle-dz/UniFAR-FTUs huggingface dataset:
Wolff-type integral system including m equations

Abstract In this paper, we are concerned with the positive solutions of the Wolﬀtype integral system where n ≥ 1, γ > 1, β > 0, βγ ̸= n and Ci (x) (i = 1, · · ·, m) are double bounded functions. Discussed in three situations of the integral system, a special iteration scheme in integral form as well as in diﬀerential form are applied. By some critical asymptotic analysis with Wolﬀ potential integral estimates, we study the existence and nonexistence of the positive solutions, especially the radial solutions. Furthermore we obtain the asymptotic rates and the integrability of positive solutions.
Estimating creditworthiness using profit modeling

The recent advancements in machine learning have enabled retail credit issuers to predict a customer’s net present value using behavioral and profit-based modeling approaches. The objective of this research is to curtail the risk associated with credit card underwriting which typically helps in deciding whether an applicant is creditworthy or not. It attempts to estimate the creditworthiness of a customer instead of evaluating the chances of them going delinquent on a debt. Credit issuers can utilize these models to identify potentially risky as well as profitable customers. Using a machine learning approach, profit-based models are constructed in this research using information acquired at the time of customer-acquisition.
Yield of cherry tomatoes as a function of water salinity and irrigation frequency

The use of brackish water in agriculture can cause salinization of soils and reduce plant yield. This problem can be minimized by hydroponic cultivation, which improves plant development. The aim of this study was to evaluate the yield of cherry tomatoes grown in hydroponic system with substrate under salinity levels of the nutrient solution (NS), exposure time to salinity and irrigation frequency. The experiment was conducted in a greenhouse, in a randomized complete block design, in a 6 x 2 x 2 factorial scheme with five replicates: six salinity levels of NS prepared with brackish water (3.01; 4.51; 5.94; 7.34; 8.71 and 10.40 dS m-1); two exposure times to NS (60 and 105 days) and two irrigation frequencies (one irrigation per day and irrigation every two days). Yield and production components of cherry tomatoes cv. 'Rita' were evaluated. NS salinity affected plant yield, reducing fruit production, which was more significant when plants were subjected to a longer time of exposure to salinity. There was no difference between NS applications on fruit production, when these applications were performed once a day or once every two days.
Junk Food Consumption, Physical Activity and Nutritional Status of Adolescent School Children: A Case Study in Ratnapura District of Sri Lanka

Junk food is rich in fats, sugar, salt and calories. Excess consumption of junk food leads to various health and nutritional disorders. The association between junk food consumption at breakfast of adolescent school children, their physical activity level and BMI status was determined in the current study. A cross-sectional study involving 300 male and female students of the age range 12-14 years from four randomly selected schools in Ratnapura district was conducted in 2019. Information regarding physical activity, breakfast practices and energy expenditure was obtained using a pre-tested questionnaire filled by the students. The criteria recommended by the International Obesity Task Force (IOTF) were used to define obesity and overweight, sex and age-specific body mass index (BMI). The study showed, although 72% of the students were aware of the unhealthiness of the junk food habit and more than 85% of the parents were aware and students were knowledgeable about the nutrient facts, 72.7% consumed it due to the taste and the rest due to its convenience. The frequent junk food consumers at breakfast were more obese (p = 0.002) and overweight (p =0.003) compared to occasional junk food eaters most of whom were in the normal BMI range. A negative correlation was observed between BMI and energy expenditure (Pearson correlation = -0.49) where a higher BMI was observed with inactive (p < 0.001) or minimally active (p < 0.002) students. A nutritional transition from underweight to overweight and obesity related to junk food consumption is evident from the above data in Ratnapura district of Sri Lanka.
Approaching high performance PVDF‐HFP based solid composite electrolytes with LLTO nanorods for solid‐state lithium‐ion batteries

Solid polymer electrolytes have the merits of high safety and energy density and lightweight. Therefore, they gradually become the key for commercial applications of solid batteries. In this study, a new type of solid composite electrolyte (SCE) is prepared by adding Li0.33La0.557TiO3 (LLTO) nanorods into poly(vinylidene fluoride‐co‐hexafluoropropylene (PVDF‐HFP) matrix. This SCE displays excellent thermal stability and mechanical properties. Moreover, it has been proven to sufficiently suppress the growth of lithium dendrites. When the content of the LLTO nanorods reaches 10 wt%, the SCE delivers a high ionic conductivity of 1.21 × 10−4 S cm−1 at 25°C and the electrochemical stability window of 4.7 V (vs Li+/Li). The NCM622/SCE/Li solid‐state lithium‐ion battery delivers a discharge specific capacity of 129.1 mAh g−1 at 0.5 C after 100 cycles, and the coulombic efficiency reaches 99%. These results demonstrate that the SCE is expected to become one of the most hopeful candidates for the coming generation of solid‐state lithium‐ion batteries.
Magnetic Tracking of Wireless Capsule Endoscope in Mobile Setup Based on Differential Signals

Magnetic localization methods have been widely studied to provide accurate position and orientation information of intra-body objects, such as wireless capsule endoscopes. However, these methods cannot work well in dynamic scenarios when using a wearable sensor array due to the geomagnetic field. In this article, we propose a novel approach for tracking wireless capsules in a mobile setup based on differential signals from adjacent magnetic sensors. Compared to the previous passive magnetic localization framework, the proposed algorithm can eliminate the interference of the geomagnetic field without introducing other compensation sensors or signals. As a result, the proposed method can be used for wearable sensor arrays without knowing the distribution of the geomagnetic field and the movement of the sensor array in advance. Moreover, the relationship between position error and distance of adjacent sensors has been studied to improve tracking accuracy. Finally, a sensor array with 16 three-axis magnetic sensors is developed. Both static and dynamic experiments have been carried out, whose results verified the effectiveness of the proposed method.
Antimicrobial peptides-An alternative candidates to antibiotics against Staphylococcus aureus and its antibiotic-resistant strains

Staphylococcus aureus and its antibiotic-resistant strains are the cause of so t tissue infections representing some severe life-threatening infections. Thesesituationshavecausedgreat concern for its treatment worldwide. Thus, the need to introduce new antibiotics or an alternative to antibiotics markedly increasing. Antimicrobial peptides (AMPs) have been shown to have various properties and uses in the biological system since their discovery. This review is based on the increasing concern for S. aureus, its resistant strains, the associated infections, pathogenicity, and themechanismof resistance to antibiotics. Lastly, theoverall significanceofAMPsagainstS. aureus showed that they can be ideal candidates as an alternative to antibiotics with high potential for future therapeutics.
Revisión del concepto clase política y análogos en la historia del departamento de Caldas

Objetivo: artículo de Revisión que busca identificar los desarrollos teóricos del concepto clase política y análogos; además, se trata de captar las diferencias de dichos conceptos para mostrar las transformaciones de la clase política en Caldas. Metodología: se aplicó meta-análisis cualitativo de los hallazgos, los cuales derivaron de la búsqueda documental en torno a los mencionados conceptos clave, a partir de determinadas categorías y criterios de análisis. Resultados: se llegó a una definición de clase política, entendida como grupo de individuos que tienen una relación orgánica con la política e intereses directos puestos en ella; esto, independientemente de que ejerzan o no un cargo público, aunque sí el poder político, como la posibilidad de tomar decisiones que afecten a los miembros de la esfera pública. Con esta categorización se hizo un análisis de los usos de estos conceptos en Colombia y Caldas. Allí se muestran las continuidades y transformaciones de su clase política desde la Colonización Antioqueña hasta la época actual, y se especifica su configuración como grupo dirigente y el papel desempeñado en el devenir del departamento. Conclusiones: se destaca el carácter cíclico de la política caldense en relación con sus élites políticas a la luz de los acontecimientos recientes. También se confirma la afirmación clásica en cuanto a la teoría de las élites, la cual asume estas agrupaciones como una constante en todas las democracias.
Uncertainty Handling in Structural Damage Detection via Non-Probabilistic Meta-Models and Interval Mathematics, a Data-Analytics Approach

Recent advancements in sensor technology have resulted in the collection of massive amounts of measured data from the structures that are being monitored. However, these data include inherent measurement errors that often cause the assessment of quantitative damage to be ill-conditioned. Attempts to incorporate a probabilistic method into a model have provided promising solutions to this problem by considering the uncertainties as random variables, mostly modeled with Gaussian probability distribution. However, the success of probabilistic methods is limited due the lack of adequate information required to obtain an unbiased probabilistic distribution of uncertainties. Moreover, the probabilistic surrogate models involve complicated and expensive computations, especially when generating output data. In this study, a non-probabilistic surrogate model based on wavelet weighted least squares support vector machine (WWLS-SVM) is proposed to address the problem of uncertainty in vibration-based damage detection. The input data for WWLS-SVM consists of selected wavelet packet decomposition (WPD) features of the structural response signals, and the output is the Young’s modulus of structural elements. This method calculates the changes in the lower and upper boundaries of Young’s modulus based on an interval analysis method. Considering the uncertainties in the input parameters, the surrogate model is used to predict this interval-bound output. The proposed approach is applied to detect simulated damage in the four-story benchmark structure of the IASC-ASCE SHM group. The results show that the performance of the proposed method is superior to that of the direct finite element model in the uncertainty-based damage detection of structures and requires less computational effort.
Twenty Years of Change in Unintended Births

Although unintended childbearing has declined in recent years (Finer and Zolna, 2016; Jones and Jerman, 2017), reducing unintended childbearing remains a public health goal in the U.S. due to its links to poorer outcomes for mothers, children, and families (Healthy People 2030). In this profile, we investigate trends in birth intendedness among women 15-44 between 1997 and 2018 using the 2002, 2006-10, 2011-15, and 2015-19 cycles of the National Survey of Family Growth1. Birth intendedness is based on a series of questions in which women were asked to characterize each birth as on time, mistimed (wanted but occurring earlier than desired), or unwanted (the respondent did not want any births at all or no additional births). When births were reported as mistimed, women were asked how much earlier than desired the birth occurred, and we categorize mistimed births into two groups: slightly mistimed (less than two years earlier than desired) or seriously mistimed (two or more years too early). This profile is an update of FP-17-08 and is the first in a three-part series on unintended fertility in the U.S.
Effects of Functionalization and Stress on Graphene Electronic Properties: Focusing on Bandgap

Graphene is considered as a promising base material for nanodevices due to the excellent mechanical, thermal and electronic properties. However, when developing 2D semiconductor device such as a field-effect transistor, one obstacle we are facing is the zero bandgap of pure graphene, which makes it hard to apply to the semiconductor field. In this study, we verify the feasibility of opening the bandgap by functionalizing a graphene and adding stresses based on first principle calculations, where hydroxyl and epoxy groups are used. The effect on bandgap is also observed in the calculations after adding stress about a few GPa. The results show that bandgaps of 1~2 eV in functionalized graphene were opened and stresses of 1GPa induced slightly variations of bandgaps. The electron density differences indicate that the loaded functional groups take away the charge of graphene, making it a betatopic system. Our study may provide a potential method to modify the electronic properties of two-dimensional materials.
Machine Learning in Electromagnetics With Applications to Biomedical Imaging: A Review

Biomedical imaging is a relevant noninvasive technique aimed at generating an image of the biological structure under analysis. The arising visual representation of the characteristics of the object is affected by both the measurement process and reconstruction algorithm. This procedure can be considered as a hybridization of data information, measurement physics, and prior information.
Applying Knowledge Inference on Event-Conjunction for Automatic Control in Smart Building

Smart building, one of IoT-based emerging applications is where energy-efficiency, human comfort, automation, security could be managed even better. However, at the current stage, a unified and practical framework for knowledge inference inside the smart building is still lacking. In this paper, we present a practical proposal of knowledge extraction on event-conjunction for automatic control in smart buildings. The proposal consists of a unified API design, ontology model, inference engine for knowledge extraction. Two types of models: finite state machine(FSMs) and bayesian network (BN) have been used for capturing the state transition and sensor data fusion. In particular, to solve the problem that the size of time interval observations between two correlated events was too small to be approximated for estimation, we utilized the Markov Chain Monte Carlo (MCMC) sampling method to optimize the sampling on time intervals. The proposal has been put into use in a real smart building environment. 78-days data collection of the light states and elevator states has been conducted for evaluation. Several events have been inferred in the evaluation, such as room occupancy, elevator moving, as well as the event conjunction of both. The inference on the users’ waiting time of elevator-using revealed the potentials and effectiveness of the automatic control on the elevator.
Emergency Food Product Packaging by Pectin-Based Antimicrobial Coatings Functionalized by Pomegranate Peel Extracts

Emergency food products (EFPs) or energy bars are used in critical situations, such as natural disasters, to promote crisis management. EFPs require sophisticated packaging strategies. Edible coatings incorporated with natural antimicrobial agents could be considered as active packaging materials for increasing EFP safety. In this study, pectin-based coatings incorporated with pomegranate peel extracts were used to protect energy bars. Initially, total phenolic contents and antimicrobial and antioxidant properties of aqueous and ethanolic pomegranate peel extracts (PPEs) were determined. Also, PPEs were analyzed by HPLC. In the next step, the extracts were incorporated into the matrix of edible coatings as active substances. The sensory properties and microbial contamination of coated energy bars were investigated during 30 days of storage. Sixteen phenolic substances were detected in the extracts with gallic acid, ellagic acid, caffeic acid, coumaric acid, and quercetin as major ingredients. The ethanolic extract exhibited higher concentrations for all phenolic compounds. The results indicated that the ethanolic extract showed inhibitory effects on S. aureus and E. coli at concentrations of 30 and 50 mg/ml, and the aqueous extract's inhibitory effects were observed at concentrations of 50 and 80 mg/ml, respectively. The antioxidant and antibacterial effects could be attributed to high phenolic content and a combination of different substances. Microbial and sensory tests performed on coated energy bars showed that the active coatings were able to control and reduce the population of microorganisms during storage without adversely affecting sensory properties.
Novel and Emerging Capabilities that Can Provide a Holistic Understanding of the Plant Root Microbiome

In recent years, the root microbiome (i.e., microorganisms growing inside, on, or in close proximity to plant roots) has been shown to play an important role in plant health and productivity. Despite its importance, the root microbiome is challenging to study because of its complexity, heterogeneity, and subterranean location. Fortunately, root microbiome research has seen a tremendous influx of novel technologies (e.g., imaging tools, robotics, and molecular analyses), experimental platforms (e.g., micro- and mesocosms), and data integration, modeling, and prediction tools in the past decade that have greatly increased our ability to dissect the complex network of interactions between above- and belowground environmental parameters, plants, bacteria, and fungi that dictate soil and broader ecosystem health. Herein, we discuss methods that are currently used in root microbiome research and that can be expanded to phytobiome research in general ranging from laboratory studies to mesocosm-scale studies and, finally, to field studies; evaluate their relevance to ecosystem studies; and discuss future root microbiome research directions.
Increased rainfall variability and nitrogen deposition accelerate succession along a common sere

Ongoing climate change is increasing rainfall variability in many parts of the world; in particular, the heaviest rainfall events are becoming heavier. In terrestrial ecosystems, nitrogen deposition is increasing as a result of emissions from fossil fuel burning and volatilization of nitrogen-based fertilizers. These changes in the timing and rate of resource inputs can impact plant communities by altering competitive dynamics, succession, and community composition. In many systems, these are occurring alongside successional dynamics, making it difficult to tease apart mechanisms. Here, we resampled a nitrogen by rainfall variability manipulation experiment in a restored tallgrass prairie to examine the relative role of background community dynamics and treatment effects on plant diversity. During the treatment period, nitrogen addition and increased rainfall variability reduced diversity. Here, four and five years after the treatments were halted, we found similarly low levels of diversity across all treatments—an effect driven by dominance of a tall, fast-growing, clonal forb, Solidago canadensis. The convergence of plots toward a low diversity state suggests that all experimental communities were gradually becoming dominated by S. canadensis, including in the absence of rainfall or nitrogen treatments. In contrast to short-term findings from the same experiment, we conclude that our treatments accelerated succession toward a tall, clonal forb-dominated community along an existing sere, but did not fundamentally alter longer-term community composition—a result that was only apparent several years after the conclusion of the experiment. These findings reinforce the need to interpret the results from short-term experimental manipulations within the context of long-term successional change.
Non-equilibrium transitions in colloidal glasses and gels

Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva. nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible.
Synthesis and Thermal Characterization of p-Coumaric Acid Complexes of CoII, NiII, CuII and ZnII Metal Cations and Biological Applications

The phenolic compound used in this study is p-coumaric acid, which is the derivative of phenolic acid playing a role in giving color, odor and taste to the plants. The p-coumaric acid is an organic compound derived from cinnamic acid. There are three isomers of this structure as ortho-, meta- and para- depending on the location of the hydroxyl group within the structure. In this study, metal complexes of p-coumaric acid ligand with the transition metal cations (CoII, NiII, CuII, ZnII) were synthesized. The structure of the synthesized complexes was studied via elemental analysis, infrared and ultraviolet-visible spectroscopy, magnetic measurements, melting point and thermal analysis. Furthermore the biological properties of these new molecules were studied. Keywords: p-coumaric acid, metal complexes, thermal analysis, spectroscopy, biological activity DOI: 10.17350/HJSE19030000027 Full Text:
Generative Learning for Postprocessing Semantic Segmentation Predictions: A Lightweight Conditional Generative Adversarial Network Based on Pix2pix to Improve the Extraction of Road Surface Areas

Remote sensing experts have been actively using deep neural networks to solve extraction tasks in high-resolution aerial imagery by means of supervised semantic segmentation operations. However, the extraction operation is imperfect, due to the complex nature of geospatial objects, limitations of sensing resolution, or occlusions present in the scenes. In this work, we tackle the challenge of postprocessing semantic segmentation predictions of road surface areas obtained with a state-of-the-art segmentation model and present a technique based on generative learning and image-to-image translations concepts to improve these initial segmentation predictions. The proposed model is a conditional Generative Adversarial Network based on Pix2pix, heavily modified for computational efficiency (92.4% decrease in the number of parameters in the generator network and 61.3% decrease in the discriminator network). The model is trained to learn the distribution of the road network present in official cartography, using a novel dataset containing 6784 tiles of 256 × 256 pixels in size, covering representative areas of Spain. Afterwards, we conduct a metrical comparison using the Intersection over Union (IoU) score (measuring the ratio between the overlap and union areas) on a novel testing set containing 1696 tiles (unseen during training) and observe a maximum increase of 11.6% in the IoU score (from 0.6726 to 0.7515). In the end, we conduct a qualitative comparison to visually assess the effectiveness of the technique and observe great improvements with respect to the initial semantic segmentation predictions.