ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Sexualization of Children or Human Rights? Attitudes Toward Addressing Sexual-Orientation Diversity in School

Lesbian, gay, and bisexual (LGB) adolescents are more likely to experience mental health problems than their heterosexual peers because they are victimized more often or fear discrimination. Governmental plans to improve this situation by addressing sexual diversity in German schools have been accompanied by public resistance and misinformation, e.g., that they aim to sexualize children. The present study assessed how widespread negative attitudes toward such plans really are and how they can be explained. A random sample of 2,013 German residents was surveyed by phone. Only 10% opposed promoting acceptance of LGB in school. Approval of such plans was predominantly predicted by respondents' beliefs about sexual orientation and the plans' aim, and only marginally by societal values. Respondents who knew that the plans' aim was to promote acceptance of LGB and not to sexualize children and that children with same-sex parents are just as well off as those with heterosexual parents showed higher approval, whereas respondents who believed that homosexuality is affected by socialization showed higher opposition.
Plasma-Assisted Reforming of Methane

Methane (CH4 ) is inexpensive, high in heating value, relatively low in carbon footprint compared to coal, and thus a promising energy resource. However, the locations of natural gas production sites are typically far from industrial areas. Therefore, transportation is needed, which could considerably increase the sale price of natural gas. Thus, the development of distributed, clean, affordable processes for the efficient conversion of CH4 has increasingly attracted people's attention. Among them are plasma technology with the advantages of mild operating conditions, low space need, and quick generation of energetic and chemically active species, which allows the reaction to occur far from the thermodynamic equilibrium and at a reasonable cost. Significant progress in plasma-assisted reforming of methane (PARM) is achieved and reviewed in this paper from the perspectives of reactor development, thermal and nonthermal PARM routes, and catalysis. The factors affecting the conversion of reactants and the selectivity of products are studied. The findings from the past works and the insight into the existing challenges in this work should benefit the further development of reactors, high-performance catalysts, and PARM routes.
Ultrasensitive nanomechanical photonic sensor based on horizontal slot-waveguide resonator

A novel integrated nanomechanical optical sensor with ultrahigh sensitivity is presented. The photonic device consists of a silicon-based disk resonator formed by a horizontal slot-waveguide acting as a circular cantilever. The overall sensitivity results from the product of the sensitivities of the slot-waveguide and the disk resonator. Calculations indicate a sensitivity as high as 33 nm(-1), which is four orders of magnitude larger than that of state-of-the-art microcantilever sensors.
Write Pattern Format Algorithm for Reliable NAND-Based SSDs

This brief presents and evaluates a pre-coding algorithm to reduce power consumption and improve data retention in NAND-based solid-state drives. Compared to the state-of-the-art asymmetric coding and stripe pattern elimination algorithm, the proposed write pattern format algorithm (WPFA) achieves better data retention while consuming less power. The hardware for WPFA is simpler and requires less circuitry. The performance of WPFA is evaluated by both computer simulations and field-programmable gate array implementation.
Endpoints for geroscience clinical trials: health outcomes, biomarkers, and biologic age

Treatments that target fundamental processes of aging are expected to delay several aging-related conditions simultaneously. Testing the efficacy of these treatments for potential anti-aging benefits will require clinical trials with endpoints that reflect the potential benefits of slowing processes of aging. There are several potential types of endpoints to capture the benefits of slowing a process of aging, and a consensus is needed to standardize and compare the results of these trials and to guide the analysis of observational data to support trial planning. Using biomarkers instead of clinical outcomes would substantially reduce the size and the duration of clinical trials. This requires validation of surrogate markers showing that treatment induced change in the marker reliably predicts the magnitude of change in the clinical outcome. The surrogate marker must also reflect the biological mechanism for the effect of treatment on the clinical outcome. "Biological age" is a superficially attractive marker for such trials. However, it is essential to establish that treatment induced change in biological age reliably predict the magnitude of benefits in the clinical outcome. Reaching consensus on clinical outcomes for geroscience trials and then validating potential surrogate biomarkers requires time, effort, and coordination that will be worthwhile to develop surrogate outcomes that can be trusted to efficiently test the value of many anti-aging treatments under development.
Online Design of Optimal Precoders for High Dimensional Signal Detection

In this paper, we propose a novel methodology to design optimal precoders for distributed detection of high-dimensional signals. We consider a wireless sensor network (WSN) that consists of multiple sensors that are spatially distributed in a region of interest and a fusion center (FC). The sensors observe an unknown high-dimensional signal and forward their observations to the FCafter precoding. The sensors collect data over both temporal and spatial domains. The FC performs a binary hypothesis test based on the data received fromthe sensors over noisy channels. In this setup, we present a technique to design optimal online linear precoding strategies with transmit power constraints. We show analytically that the error exponents achieved by the proposed precoders are independent of the signal dimension. In contrast, the error exponents of the state-of-the-art precoding strategies deteriorate with the increase in signal dimension. We verify our analysis via numerical simulations and show that the proposed precoders achieve better detection performance compared to those of other state-of-the-art techniques known in the literature.
Theory Integration for Examining Health Care Discrimination among Minoritized Older Adults with Chronic Illness

Prevalence of chronic illnesses, including type 2 diabetes (T2DM), is increasing disproportionately among Latinx adults in the United States. Health care inequities such as health care discrimination contribute to the disparities in this population. Academic and clinical nurses must address health care discrimination from a strong theoretical framework. In this article, we integrate the minority stress theory and ecosocial theory of disease distribution to offer a whole-person model that identifies the concepts most relevant to Latinx older adults who function at multiple levels of intersectionality. This paper uses T2DM as an exemplar of chronic illness. The integrated model depicts possible pathways of physiological and psychological embodiment of lived experiences of minoritized older persons managing chronic illness who are living in a society deeply embedded with structural racism and oppression. This model may guide future research aimed at elucidating the social and structural determinants that impact health-related outcomes among Latinx older adults.
Unveiling the "Proton Lubricant" Chemistry in Aqueous Zinc-MoS2 Batteries

Proton insertion chemistry in aqueous zinc-ion batteries (AZIBs) is becoming a research hotspot owing to its fast kinetics and additional capacities. However, H+ storage mechanism has not been deciphered in the popular MoS2 -based AZIBs. Herein, we innovatively prepared a MoS2 /poly(3,4-ethylenedioxythiophene) (MoS2 /PEDOT) hybrid, where the intercalated PEDOT not only increases the interlayer spacing (from 0.62 to 1.29 nm) and electronic conductivity of MoS2 , but also activates the proton insertion chemistry. Thus, highly efficient and reversible H+ /Zn2+ co-insertion/extraction behaviors are demonstrated for the first time in aqueous Zn-MoS2 batteries. More intriguingly, the co-inserted protons can act as lubricants to effectively shield the electrostatic interactions between MoS2 /PEDOT host and divalent Zn2+ , enabling the accelerated ion-diffusion kinetics and exceptional rate performance. This work proposes a new concept of "proton lubricant" driving Zn2+ transport and broadens the horizons of Zn-MoS2 batteries.
Consensus Head Acceleration Measurement Practices (CHAMP): Study Design and Statistical Analysis

Head impact measurement devices enable opportunities to collect impact data directly from humans to study topics like concussion biomechanics, head impact exposure and its effects, and concussion risk reduction techniques in sports when paired with other relevant data. With recent advances in head impact measurement devices and cost-effective price points, more and more investigators are using them to study brain health questions. However, as the field's literature grows, the variance in study quality is apparent. This brief paper aims to provide a high-level set of key considerations for the design and analysis of head impact measurement studies that can help avoid flaws introduced by sampling biases, false data, missing data, and confounding factors. We discuss key points through four overarching themes: study design, operational management, data quality, and data analysis.
DYNAMIC PLANNING AND DESIGN OF URBAN WATERFRONT LANDSCAPE BASED ON TIME SCALE

With the acceleration of the process of city and industrialization, human beings are facing greater challenges in landscaping activities aimed at environmental transformation and ecological protection. Urban architecture and landscape environment art are a complex. With the rapid development of social civilization, urban landscape environment art becomes more and more important. This paper makes a preliminary study on the related issues of urban waterfront landscape dynamic planning and design, introduces the specific conception of urban waterfront landscape dynamic planning and design from three aspects: spatial scale, time scale and plant arrangement, and shows the specific form of urban waterfront landscape planning and design. This paper puts forward the idea of landscape dynamic planning and overall planning of urban waterfront, and achieves the purpose of sustainable utilization by rationally developing urban waterfront.
Attribution Analysis of Foodborne Disease Outbreaks Related to Meat and Meat Products in China, 2002-2017

This study aimed to understand the epidemiological characteristics of foodborne disease outbreaks related to meat and meat products in China from 2002 to 2017. Data collected from the National Foodborne Diseases Surveillance System and searched databases were analyzed. From 2002 to 2017, China reported 2815 outbreaks caused by foodborne diseases related to meat and meat products, resulting in 52,122 illnesses and 25,361 hospitalizations, and 96 deaths. Outbreaks were markedly seasonal and concentrated from May to September, accounting for 66.93%. Outbreaks were concentrated mainly in China's eastern coastal and southern regions. Unidimensional attribution analysis revealed that livestock meat was the most commonly implicated food category causing the outbreaks, accounting for 28.67%. Bacteria were the most common pathogenic cause of outbreaks, accounting for 51.94%. Clostridium botulinum was the most common pathogenic cause of death, accounting for 34.38%. Improper processing was the most common contributing factor, accounting for 27.89%. Households were the most common food preparation location causing the outbreak, accounting for 34.39%. Two-dimensional and multidimensional attribution analysis found that Salmonella contamination occurred in different locations and regions, mainly caused by various contributing factors and improper processing. Nitrite poisoning is caused by improper processing in households in East China. Bacterial causes were the commonest agents associated with foodborne diseases related to meat and meat products, and improving the safety and quality of meat and meat product should be a priority.
Fast interactive stereo image segmentation

The paper presents an approach to cutting out the same target object from a pair of stereo images interactively. With this approach, a user labels parts of the object and background in either of the images with strokes. The approach generates a segmentation result immediately. In case it is not satisfying, the result can be improved by interactively drawing more strokes, or using an alternative interaction way called adding corresponding points, which is first presented in this paper. The proposed segmentation approach is capable of providing feedback fast after each interaction. The fast computation is performed in the framework of graph cut. First, the labeled parts are used to learn foreground and background color models. Next, an energy function is built by formulating the similarities between unlabeled pixels and the foreground/background color models, color difference between neighbor pixels, and stereo correspondences obtained by SIFT feature matching. At last, graph cut is utilized to find the optimum of the energy function and obtain a segmentation result. Different from state-of-the-art methods, our segmentation approach formulates sparse correspondences rather than dense matches as stereo constraints in the energy function. Experimental results demonstrate that our method is faster in computation. In the meanwhile, it generates comparable results with state-of-the-art methods.
Modulation Classification via Gibbs Sampling Based on a Latent Dirichlet Bayesian Network

A novel Bayesian modulation classification scheme is proposed for a single-antenna system over frequency-selective fading channels. The method is based on Gibbs sampling as applied to a latent Dirichlet Bayesian network (BN). The use of the proposed latent Dirichlet BN provides a systematic solution to the convergence problem encountered by the conventional Gibbs sampling approach for modulation classification. The method generalizes, and is shown to improve upon, the state of the art.
Nonlinear Load Harmonic Mitigation Strategies in Microgrids: State of the Art

A generational shift has led to the evolution of distributed generation (DG) and microgrids. The loads connected in a microgrid can be both linear and nonlinear. Nonlinear loads deteriorate the power quality by drawing harmonic currents. Drawing of nonlinear currents will make the voltage waveform nonsinusoidal, which may prove harmful for other loads connected to the system. Previously, passive and active filters were used to mitigate harmonics, but they are expensive and bulky. The state of the art is to exploit multifunctional capabilities of inverter-interfaced DGs to deal with the power quality issues. This article presents the typical sources of generation of the harmonics, their deleterious effects, available standards, and detection techniques. Harmonic mitigation strategies for both grid-connected and islanded microgrids have been discussed in detail. The aim is to review and categorize the various strategies adopted by authors in recent times to mitigate harmonics and bring out the research gaps so that further avenues can be explored in this area.
Prototype of a pigments color chart for the digital conservation of ancient murals

Digital imaging has become a very important technique in the conservation of cultural art relics because it can nondestructively acquire the color and spectral image of cultural art relics for different applications. Imaging accuracy is one of the key factors in digital protection of cultural art relics. In order to improve the color and spectral accuracy for digital imaging of cultural art relics, the idea of making the specific color charts for different kinds of artworks is presented. Taking ancient Chinese Dunhuang murals as the specific object of study, a prototype pigments color chart of the Dunhuang murals (DCC), containing a six-step grayscale and 30 colored pigment samples, is made to investigate its pigment types and painting techniques. Under the premise of considering the difference in the number of samples in color charts, the DCC is tested and compared with the classic and widely used standard Macbeth colorchecker (CC) in two aspects: color correction for RGB imaging and spectral reconstruction for spectral imaging. The results show that the prototype pigments color chart is more effective and exhibits superior performance to the CC in both aspects for digital conservation of the Dunhuang murals. (C) 2017 SPIE and IS&T
Single-Antenna Coherent Detection of Collided FM0 RFID Signals

This work derives and evaluates single-antenna detection schemes for collided radio frequency identification (RFID) signals, i.e. simultaneous transmission of two RFID tags, following FM0 (biphase-space) encoding. In sharp contrast to prior art, the proposed detection algorithms take explicitly into account the FM0 encoding characteristics, including its inherent memory. The detection algorithms are derived when error at either or only one out of two tags is considered. It is shown that careful design of one-bit-memory two-tag detection can improve bit-error-rate (BER) performance by 3dB, compared to its memoryless counterpart, on par with existing art for single-tag detection. Furthermore, this work calculates the total tag population inventory delay, i.e. how much time is saved when two-tag detection is utilized, as opposed to conventional, single-tag methods. It is found that two-tag detection could lead to significant inventory time reduction (in some cases on the order of 40%) for basic framed-Aloha access schemes. Analytic calculation of inventory time is confirmed by simulation. This work could augment detection software of existing commercial RFID readers, including single-antenna portable versions, without major modification of their RF front ends.
Remove Cosine Window From Correlation Filter-Based Visual Trackers: When and How

Correlation filters (CFs) have been continuously advancing the state-of-the-art tracking performance and have been extensively studied in the recent few years. Nonetheless, the existing CF trackers adopt a cosine window to spatially reweight base image to alleviate boundary discontinuity. However, cosine window emphasizes more on the central regions of base image and has the risk of contaminating negative training samples during model learning. On the other hand, spatial regularization deployed in many recent CF trackers plays a similar role as cosine window by enforcing spatial penalty on CF coefficients. Therefore, we in this paper investigate the feasibility to remove cosine window from CF trackers with spatial regularization. When simply removing cosine window, CF with spatial regularization still suffers from small degree of boundary discontinuity. To tackle this issue, binary and Gaussian shaped mask functions are further introduced for eliminating boundary discontinuity while reweighting the estimation error of each training sample, and can be incorporated with multiple CF trackers with spatial regularization. In comparison to the baseline methods with cosine window, our methods are effective in handling boundary discontinuity and sample contamination, thereby benefiting tracking performance. Extensive experiments on four benchmarks show that our methods perform favorably against the state-of-the-art trackers using either handcrafted or deep CNN features.
Driver attention prediction based on convolution and transformers

In recent years, studying how drivers allocate their attention while driving is critical in achieving human-like cognitive ability for autonomous vehicles. And it has been an active topic in the community of human-machine augmented intelligence for self-driving. However, existing state-of-the-art methods for driver attention prediction are mainly built upon convolutional neural network (CNN) with local receptive field which has a limitation to capture the long-range dependencies. In this work, we propose a novel Attention prediction method based on CNN and Transformer which is termed as ACT-Net. In particular, CNN and Transformer are combined as a block which is further stacked to form the deep model. Through this design, both local and long-range dependencies are captured that both are crucial for driver attention prediction. Exhaustive comparison experiments over other state-of-the-art techniques conducted on widely used dataset of BDD-A and private collected data on BDD-X validate the effectiveness of the proposed ACT-Net.
PSW statistical LSB image steganalysis

Steganography is the art and science of producing covert communications by concealing secret messages in apparently innocent media, while steganalysis is the art and science of detecting the existence of these. This manuscript proposes a novel blind statistical steganalysis technique to detect Least Significant Bit (LSB) flipping image steganography. It shows that the technique has a number of major advantages. First, a novel method of pixel color correlativity analysis in Pixel Similarity Weight (PSW). Second, filtering out image pixels according to their statistically detected suspiciousness, thereby excluding neutral pixels from the steganalysis process. Third, ranking suspicious pixels according to their statistically detected suspiciousness and determining the influence of such pixels based on the level of detected anomalies. Fourth, the capability to classify and analyze pixels in three pixel classes of flat, smooth and edgy, thereby enhancing the sensitivity of the steganalysis. Fifth, achieving an extremely high efficiency level of 98.049% in detecting 0.25bpp stego images with only a single dimension analysis.
Paradoxical self-sustained dynamics emerge from orchestrated excitatory and inhibitory homeostatic plasticity rules

Self-sustained neural activity maintained through local recurrent connections is of fundamental importance to cortical function. Converging theoretical and experimental evidence indicates that cortical circuits generating self-sustained dynamics operate in an inhibition-stabilized regime. Theoretical work has established that four sets of weights (WE←E, WE←I, WI←E, and WI←I) must obey specific relationships to produce inhibition-stabilized dynamics, but it is not known how the brain can appropriately set the values of all four weight classes in an unsupervised manner to be in the inhibition-stabilized regime. We prove that standard homeostatic plasticity rules are generally unable to generate inhibition-stabilized dynamics and that their instability is caused by a signature property of inhibition-stabilized networks: the paradoxical effect. In contrast, we show that a family of "cross-homeostatic" rules overcome the paradoxical effect and robustly lead to the emergence of stable dynamics. This work provides a model of how-beginning from a silent network-self-sustained inhibition-stabilized dynamics can emerge from learning rules governing all four synaptic weight classes in an orchestrated manner.