Miracle-dz/UniFAR-FTUs huggingface dataset:
In Situ Learning in Hardware Compatible Multilayer Memristive Spiking Neural Network

As one of the most promising methods in the next generation of neuromorphic systems, memristor-based spiking neural networks (SNNs) show great advantages in terms of power efficiency, integration density, and biological plausibility. However, because of the nondifferentiability of discrete spikes, it is difficult to train SNNs with gradient descent and error backpropagation online. In this article, we propose an improved training algorithm for multilayer memristive SNN (MSNN) with three methods spontaneously, supporting in situ learning in hardware: 1) temporal order encoding is applied to generate different pulse trains in neurons; 2) a simplified homeostasis is realized by the activation state and refractory period to regulate hidden neurons spontaneously; and 3) spiking-timing-dependent plasticity (STDP) in memristive synapses is adopted to update weights in situ. Correspondingly, we provide a circuitry example and verify it in LTSPICE. Then the MSNN is benchmarked with the MNIST data set and analyzed with visualization methods, showing better recognition accuracy (95.15%) than existing SNNs with comparable scales and bio-inspired learning rules. We also consider some nonideal effects in memristor crossbar array and peripheral circuits. Evaluation results show that the proposed MSNN is robust to finite resolution, circuit noise and writing noise; and larger network scale will help the MSNN alleviate the negative impacts of other nonideal factors, including yield and device-to-device variation. Moreover, the energy efficiency of a MSNN system is estimated to achieve 7.6TOPS/W, showing great potential in low-power applications.
Glycidyl alkoxysilane reactivities towards simple nucleophiles in organic media for improved molecular structure definition in hybrid materials

For hybrid materials, the relationship between the macroscopic properties and the molecular structures and dynamics at the microscopic between the organic and inorganic components level is crucial. The characterization of these components as well as their reactivity have to be emphasized in order to design and synthesize improved hybrids. We report herein the first comprehensive study of the reactivity in organic media of (3-glycidyloxypropyl)trialkoxysilanes, widely used as precursors in sol–gel hybrid synthesis, towards common nucleophiles. Thorough investigations of the reactions allowed us to draw clear conclusions about the reactivities of both epoxide and alkoxysilane functions. Furthermore, the nucleophile properties and the method of activations have a great influence on the reaction outcomes, and unexpected results were found in some cases. The importance of the nature of the alkoxy residues (methoxy, ethoxy…) was also investigated highlighting chemoselective reactions on glycidyl silane derivatives (GPTMS, GPTES and PECS) and opening a new library of original sol–gel precursors.
Revising NoC in Future Multicore-Based Consumer Electronics for Performance

With the increasing demand for efficient data processing, the latest consumer electronics are powered by multicore processors. Many applications that run on these devices expect minimum data access latency, where network-on-chip (NoC) plays a crucial role. However, NoC is found to be responsible for 60%–75% of miss penalty. The objective of this article is to present a revised NoC communication framework that can possibly reduce the miss penalty. The proposed architecture orchestrates buffers of NoC routers as a victim cache to store evicted cache blocks. Future references to such stored blocks are replied from NoC routers, which significantly reduces miss penalty and improves system performance. Minimizing miss penalty by maximizing NoC resource utilization in these devices is a win–win situation.
The Role of Young Adult Children’s Income in the Relationship between Single Mothers’ Poverty and Their Young Adult Children’s Depression

This study examined the relationship between single mother’s poverty and their young adult children’s depression, and it examined the mediating effect of young adults’ income on the relationship. The National Longitudinal Survey of Youth 1979 (NLSY79) and the National Longitudinal Survey of Youth 79 for Children and Young Adults (NLSY79 CY) were used. A total of 4,224 dyads were selected and the mediation model was conducted. Single mother’s poverty was related to low income and depression among their young adult children. The relationship between mother’s poverty and their young adult children’s depression was partially mediated by their young adult children’s income. Poverty prevention or reduction programs for female-headed households should be provided to single mothers to reduce future levels of depression among their young adults. Improving inequality in intergenerational economic mobility is one way to address depressive symptoms among the young adult children of single mothers in poverty.
Image-based Visual Servoing Control of a Quadcopter Air Vehicle

ABSTRACT This article proposes two optimal controllers that stabilize the altitude, attitude, heading, and position of the quadcopter in space. The first optimal control method consists of computing the proportional controller gain by minimizing the error between measured and the desired feature vector values, as opposed to the classical visual servoing (VS) scheme that requires a known value of the proportional controller gain which is often chosen empirically. The second control method relies on the optimal tuning of PD controller gains applied on the altitude, attitude, and position loops. This, however, may lead to a convergence problem, which is overcome by the use of an optimization technique, the bat algorithm (BA). This latter is an efficient metaheuristic algorithm that has been successfully used in many optimization problems. Several simulations are run in MATLAB, in which visual servo control of the Vertical Takeof and Landing (VTOL) type of Unmanned Aerial Vehicles (UAVs), known as the quadcopter, is implemented. The obtained results show that the Bat algorithm renders the classical PD controller adaptive and provides a high flexibility in tracking the UAV trajectory even in the presence of disturbances. It also guarantees the stability and accuracy of the optimal, minimal time, vision-based controller.
Trends and outcomes of mechanically ventilated cirrhotic patients in the United States from 2005–2014

Purpose Cirrhotic patients in organ failure are frequently admitted to intensive care units (ICUs) to receive invasive mechanical ventilation (IMV). We evaluated the trends of hospitalizations, in-hospital mortality, hospital costs, and hospital length of stay (LOS) of IMV patients with cirrhosis. Methods We analyzed the United States National Inpatient Sample from 2005–2014. We selected discharges of IMV adult (≥18 years) patients with cirrhosis using the International Classification of Diseases, 9th Edition, Clinical Modification codes. Trends were assessed using linear regression and joinpoint regression. Results Between 2005 and 2014, there were approximately 9,441,605 hospitalizations of IMV adult patients, of which 4.7% had cirrhosis. There was an increasing trend in the total number of IMV cirrhotic patient hospitalizations (annual percent change [APC] 7.0%, 95% confidence interval [CI] 6.4%; 7.6%, Ptrend < 0.001). The in-hospital case-fatality ratio declined between 2005–2011 (APC –2.9%, 95% CI, –3.4%; –2.4%, Ptrend < 0.001); however, it remained similar between 2011–2014 (Ptrend = 0.58). The total annual hospital costs of all IMV cirrhotic patients increased from approximately $1.2 billion USD in 2005 to $2.7 billion USD in 2014 (Ptrend < 0.001). The mean hospital costs per patient and mean LOS declined between 2005 and 2014 (Ptrend < 0.001 and Ptrend = 0.01 respectively). Conclusions The total number of hospitalizations and total annual costs of IMV patients with cirrhosis have been increasing over time. However, past hesitancy around admitting cirrhotic patients to the ICU may need to be tempered by the improving mortality trends in this patient population.
A Modeling Framework: To Analyze the Relationship between Accessibility, Land Use and Densities in Urban Areas

The study proposes a framework to model the three-dimensional relationship among density, land use, and accessibility in urban areas constructively contributing to overcome the limitations noted in the domains of urban planning and transport planning. First, most of the existing studies have focused on the topological characteristics in capturing the accessibility, but a limited attention has been given on measuring the accessibility by considering both topological and roadway characteristics. Second, the existing research studies have acknowledged the relationship among density, land use, and accessibility while a limited attention has been given to develop a modeling framework to capture the three-dimensional relationship. The modelling framework was tested in three urban areas in Sri Lanka. The research first analyzed the three-dimensional relationship among density, land use, and accessibility in the case studies. Then, the study developed a set of regression models to capture the density from the land use and accesability. The proposed model recorded a satisfactory level of accuracy (i.e., R2 > 0.70) on a par with internationally accepted standards. The relationship was further elaborated through a decision tree analysis and 4D plot diagrams. Findings of the study can be utilized to model the density of a given land use and the correspondent accessibility scenarios. The proposed model is capable of quantifying the impact of the changes in the density correspondent to the accessibility and land use. Therefore, the study concludes that this will be an effective tool for decision-makers in the fields of land-use planning and transport planning for scenario building, impact analysis, and the formulation of land use zoning and urban development plans aiming at the overarching sustainability of future cities.
Habitat‐specific survival of golden‐winged warblers
 Vermivora chrysoptera
 during the non‐breeding season in an agricultural landscape

tangles and dead hanging leaves, which corresponds to habitat features associated with abundance in prior studies at this same site. Our findings contribute to our existing knowledge about the potential impacts of winter-season events on Nearctic-Neotropical migratory bird populations, and also informs potential conservation strategies for wintering golden-winged warblers.
Potential of Chitosan (Chemically Modified Chitin) for Extraction of Lead-Arsenate Contaminated Soils

ABSTRACT Arsenic, lead, and phosphorous contamination in soils represents a health risk. Chitosan (poly-N-acetyl glucosamine) inexpensive by-product derived from chitin has been used as a metals adsorbent. Objectives of this research were to evaluate the effectiveness of chitosan solution for arsenic, lead, and phosphorous extraction from lead-arsenate contaminated soils, and evaluate the effectiveness of protonated chitosan flakes (PCF) and ferric hydroxide chitosan beads (Fe(III)-CB) for water-soluble As removal from these soils. Percentage of arsenic, lead, and phosphorous removed from the soils by chitosan solution ranged from 0.96% to 17%, 1.80% to 31%, and 0.66% to 11%, respectively. Percentage of water-soluble arsenic removed by PCF and by Fe (III)-CB ranged from 12% to 47% and 36% to 77%, respectively. Averaged over soils, Fe (III)-CB removed slightly more arsenic (As) (42 mg kg−1) compared to Mehlich III (40 mg kg−1) extractant. Results indicate potential for the use of chitosan as an extraction for lead-arsenate contaminated soils.
Decentralized Coordinated Cyberattack Detection and Mitigation Strategy in DC Microgrids Based on Artificial Neural Networks

DC microgrids can be considered as cyber-physical systems (CPSs) and they are vulnerable to cyberattacks. Therefore, it is highly recommended to have effective plans to detect and remove cyberattacks in dc microgrids. This article shows how artificial neural networks can help to detect and mitigate coordinated false data injection attacks (FDIAs) on current measurements as a type of cyberattacks in dc microgrids. FDIAs try to inject the false data into the system to disrupt the control application, which can make the dc microgrid shutdown. The proposed method to mitigate FDIAs is a decentralized approach and it has the capability to estimate the value of the false injected data. In addition, the proposed strategy can remove the FDIAs even for unfair attacks with high domains on all units at the same time. The proposed method is tested on a detailed simulated dc microgrid using the MATLAB/Simulink environment. Finally, real-time simulations by OPAL-RT on the simulated dc microgrid are implemented to evaluate the proposed strategy.
Improvement of Small Signal Equivalent Simulations for Power and Efficiency Matching of GaN HEMTs

In high-frequency power-amplifier design, it is common practice to approach the design of reactive matching networks using linear simulators and targeting a reflection loss limit (referenced to the target impedance). It is well known that this is only a first-pass design technique, since output power or efficiency contours do not correspond to mismatch circles. This paper presents a method to improve the accuracy of this approach in the case of matching network design for power amplifiers based on gallium nitride (GaN) technology. Equivalent mismatch circles, which lay within the power or efficiency contours targeted by the design, are analytically obtained thanks to geometrical considerations. A summary table providing the parameters to use for typical contours is provided. The technique is demonstrated on two examples of power-amplifier design on the 6–12 GHz band using the non-linear large-signal model of a GaN High Electron Mobility Transistor (HEMT).
Bi-selection in the high-dimensional additive hazards regression model

In this article, we consider a class of regularized regression under the additive hazards model with censored survival data and propose a novel approach to achieve simultaneous group selection, variable selection, and parameter estimation for high-dimensional censored data, by combining the composite penalty and the pseudoscore. We develop a local coordinate descent (LCD) algorithm for efficient computation and subsequently establish the theoretical properties for the proposed selection methods. As a result, the selectors possess both group selection oracle property and variable selection oracle property, and thus enable us to simultaneously identify important groups and important variables within selected groups with high probability. Simulation studies demonstrate that the proposed method and LCD algorithm perform well. A real data example is provided for illustration.
Food Marketing, Eating Behaviors and Gender Among Children and Adolescents: A Scoping Review

Background Pervasive marketing of unhealthy foods is one of the main drivers behind the global epidemic of childhood and adolescent overweight and obesity. Sex and gender differences come into play in the design of and responses to these marketing strategies, contributing to the perpetuation of stereotyped behavior and generating disparities in food choices and health among boys and girls. The purpose of this paper is to review the current the literature regarding gender differences in food marketing design and perception among children and adolescents to facilitate evidence-based policy dialogues to address gender-based health disparities in NCD prevention.Methods Scoping review of articles published in scientific journals in English and Spanish from 2003 on that addressed the influence of food marketing on eating behaviors among children and adolescents including a gender perspective. The methodological quality of each article was assessed following criteria specific to each study design.Results A total of 40 articles (42 studies) were included in the review. 19 were experimental and 23 had descriptive, cross-sectional designs. 22 studies were found to have low methodological quality, while 11 and 9 were of medium and high quality, respectively. 24 studies found gender-based differences with regards to the effect of food marketing on food intake, food choice and preferences, responses to specific marketing strategies and techniques, perceptions and attitudes towards food marketing and the need for its regulation, and advertising content and exposure. Conclusions The evidence suggests that food marketing has a similar impact on the consumption of unhealthy foods on both boys and girls, but boys were found to be exposed to food advertising more intensively and their preferences to be more affected by this exposure, in line with a mostly male-dominant advertising content. Limitations of these studies include taking gender as an unproblematic construct equivalent to biological sex and the lack of studies focused on developing countries. As gender is a cross-sectional dimension that interacts with other factors driving health disparities, an integrated gender perspective is needed in order to develop effective, evidence-based policies to control food marketing and tackle the childhood overweight pandemic.
The effects of kefir in mixed meals on appetite and food intake: a randomized cross-over trial

ABSTRACT Objective The natural probiotic kefir is fermented milk, and may have effects on satiety and voluntary energy intake. This randomized crossover trial aimed to determine whether kefir, consumed with low- or high-glycemic index meals, affects appetite and subsequent food intake. Methods Twenty four healthy females aged 21-24 years, were recruited from Erciyes University and the surrounding community. The participants were submitted to three different breakfasts: a low glycemic index accompanied by milk, a low glycemic index with kefir, and a high glycemic index with kefir, with a 1-week washout period between meals. At 0, 15, 30, 60, 90, 120, 150, and 180 minutes after the meal, appetite ratings were measured by the visual analog scale, and then ad libitum lunch was served. Results No differences in appetite scores and voluntary energy intake were detected between the test meals. Furthermore, palatability ratings were similar between test meals, except for the higher score of high glycemic index kefir for overall palatability. Conclusion This study demonstrated that adding kefir to a high glycemic index meal may prevent increases in appetite and food intake, resulting in postprandial responses similar to those of a low glycemic index meal. These findings might enable the development of novel dietary strategies based on appetite regulation to treat or prevent obesity, particularly for Western societies. This trial was registered at ClinicalTrials.gov under the process NCT03636217.
Hexavalent Chromium Reduction by Anoxybacillus rupiensis isolated From Hot Water Spring of Dhapoli, Maharashtra, India

A novel thermophile, aerobic, Gram positive, spore forming bacterium isolated from hot water springs of Dhapoli, Maharashtra; India. The isolate was able to ferment a wide spectrum of sugars, polyols, and polysaccharides like xylan, glycogen and starch. Optimal growth was observed at 60°C, and pH at 6.5. Phylogenetic analysis of the whole 16S rRNA gene sequence clustered the strain T4 with the representatives of the genus Anoxybacillus and with Geobacillus tepidamans. The G+C content of the genomic DNA was 41.7%. Fatty acid profile (major fatty acids iso-C15:0 and iso-C17:0) confirmed the affiliation of the strain to the genus Anoxybacillus. Anoxybacillus rupiensis showed 75% of Cr (VI) reduction after 24 hours of incubation. Our results have confirmed that Anoxybacillus rupiensis is one of the most prominent thermophiles that could be exploited for the treatment of chromium bearing effluents.
A systematic mapping study on agent mining

ABSTRACT Over the past two decades, many studies have been published in diverse fields of application combining agent abilities (knowledge processing, communication, learning, mobility, etc.) and data mining approaches (clustering, decision trees, ontologies, etc.). We performed a systematic mapping study to quantitatively analyse these contributions about agent mining. We determined that most of the publications were in the field of data mining using agent systems to collect or mine the data. Some used data mining solutions to improve agent behaviour, and very few publications integrated both agent and mining approaches. In the latter case, most were published in the last few years, which highlights the advances in research integrating agents and mining approaches.
Impacts of Coping Mechanisms on Nursing Students’ Mental Health during COVID-19 Lockdown: A Cross-Sectional Survey

The COVID-19 pandemic and consequent lockdown have precipitated significant disruption in the educational system. Nursing students are known to have higher levels of stress and anxiety than other non-nursing students, but there is a dearth of evidence regarding the impacts of the COVID-19 lockdown on their mental health and coping mechanisms. Purpose: The aim of this study was to explore the influence of coping mechanisms as predictors of stress, anxiety, and depression among nursing students during the COVID-19 lockdown. Methods: A cross-sectional online survey was conducted from 20 April to 10 May 2020 among 173 nursing students at a private university in Southern California, USA. Results: Self-reported stress, anxiety, and depression were significantly higher during the lockdown compared to the pre-lockdown period (p < 0.001). Almost a quarter of participants reported high stress, while more than half reported moderate-to-severe symptoms of anxiety and depression. High resilience was negatively associated with high stress (Odds Ratio (OR) = 0.46; 95% Confidence Interval (CI) = 0.22–0.98; p = 0.045), moderate-to-severe anxiety (OR = 0.47; 95%CI = 0.25–0.90; p = 0.022), and moderate-to-severe depression (OR = 0.50; 95%CI = 0.26–0.95; p = 0.036). Similarly, high family functioning was negatively associated with high stress (OR = 0.41; 95%CI = 0.20–0.86; p = 0.018), moderate-to-severe anxiety (OR = 0.41; 95%CI = 0.21–0.80; p = 0.009), and moderate-to-severe depression (OR = 0.41; 95%CI = 0.20–0.81; p = 0.011). High spiritual support was negatively associated with moderate-to-severe depression (OR = 0.48; 95%CI = 0.24–0.95; p = 0.035). Conclusions: During the COVID-19 lockdown, nursing students experienced remarkable levels of poor mental health. High levels of resilience and family functioning were associated with 2- to 2.4-fold lower risk of stress, anxiety, and depression, whereas high spiritual support was associated with 2-fold lower risk of depression. As the pandemic evolves, fostering these coping mechanisms may help students to maintain their psychological wellbeing.
A Re-Look at Poverty through the Lens of the Sustainable Development Goals in Ghana: Drivers and Policy Implications

ABSTRACT The study aims a re-examination of the poverty situation in Ghana using the Alkire-Foster multidimensional poverty framework. Using cross-sectional data from the Ghana Living Standards Survey (GLSS7) collected on 14,009 households, the results revealed that multidimensional poverty was higher for the Northern belt followed by the Middle belt and then the Coastal belt. The results further indicated that different socioeconomic and supply-side factors significantly affect multidimensional poverty in different locations of the country. The heterogeneous effect of socio-economic variables on multidimensional poverty across belts implies that designing “best-fits” policy strategy may be more effective than “one size fits all” policy strategy.