ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
K-Means-Based Multigroup Differential Evolution Optimization Framework for Design of MIMO Antenna With Decoupling Elements

A novel differential evolution optimization method named K-means-based multigroup differential evolution (KMGDE) framework is proposed for multiple-input-multiple-output (MIMO) antenna design. The KMGDE algorithm divides the population into three groups using the K-means clustering method in each iteration. Different mutation strategies are assigned for each group according to its average fitness value. The groups are then combined for crossover operation with an adaptive crossover rate value to balance local search and global search. The fitness function considering the characteristics of the MIMO antenna is proposed to speed up optimization. The performance of the proposed method is evaluated and compared with six state-of-the-art optimization algorithms using an MIMO antenna example. The optimization results and the experimental results show that the proposed KMGDE algorithm has a faster convergence than state-of-the-art optimization algorithms, which significantly improves the efficiency of MIMO antenna design.
Art-Science Collaborative Competencies: A Mixed-Methods Pilot Study for Improving Problem Solving for Sustainability Challenges

The complexity and interconnectedness of sustainability issues has led to the joining of disciplines. This effort has been primarily within the sciences with minimal attention given to the relationship between science and art. The exclusion of art is problematic since sustainability challenges are not only scientific and technical; they are also cultural, so the arts, as shapers of culture, are critical components that warrant representation. Hence, it stands to reason that understanding art-science integration will benefit sustainability's focus on use-inspired basic research. In this paper, we focus on artist-scientist team dynamics and the impact of those team dynamics on the quality of their outputs, in service of gleaning insight into how interdisciplinary teams can better work together to address sustainability challenges. In other words, we ask the question "How do art-science teams reason together, validate ideas, and produce robust outcomes when facing a task related to complex socio-ecological systems, which sit at the crux of sustainability challenges?" To address this question, we conducted a small-group pilot study of artist-scientist teams tasked with developing interpretive signage for the Tres Rios wetland site. We collected survey and ethnographic data to account for intra- and interpersonal interactions in teams. Specifically, this study focuses on variables we call barriers or carriers, which aid or hinder the collaborative interactions of deeply diverse teams. We found that successful art-science collaborations appear to result in improved communication skills, better problem articulation, more creative problem solving, and the questioning of personal and disciplinary mental models.
Enhanced conversion of induced neuronal cells (iN cells) from human fibroblasts: Utility in uncovering cellular deficits in mental illness-associated chromosomal abnormalities

The novel technology of induced neuronal cells (iN cells) is promising for translational neuroscience, as it allows the conversion of human fibroblasts into cells with postmitotic neuronal traits. However, a major technical barrier is the low conversion rate. To overcome this problem, we optimized the conversion media. Using our improved formulation, we studied how major mental illness-associated chromosomal abnormalities may impact the characteristics of iN cells. We demonstrated that our new iN cell culture protocol enabled us to obtain more precise measurement of neuronal cellular phenotypes than previous iN cell methods. Thus, this iN cell culture provides a platform to efficiently obtain possible cellular phenotypes caused by genetic differences, which can be more thoroughly studied in research using other human cell models such as induced pluripotent stem cells.
Six years of continuous mechanical circulatory support following chronic graft failure

Re-transplantation is the preferred treatment for patients with chronic heart transplant failure. If re-transplantation is not a viable option due to the patient's comorbidities, left ventricle assist device can be used as the destination treatment. An interdisciplinary approach with thorough follow-up can help in the early detection and treatment of complications associated with LVAD.
ParIS plus : Data Series Indexing on Multi-Core Architectures

Data series similarity search is a core operation for several data series analysis applications across many different domains. Nevertheless, even state-of-the-art techniques cannot provide the time performance required for large data series collections. We propose ParIS and ParIS+, the first disk-based data series indices carefully designed to inherently take advantage of multi-core architectures, in order to accelerate similarity search processing times. Our experiments demonstrate that ParIS+ completely removes the CPU latency during index construction for disk-resident data, and for exact query answering is up to 1 order of magnitude faster than the current state of the art index scan method, and up to 3 orders of magnitude faster than the optimized serial scan method. ParIS+ (which is an evolution of the ADS+ index) owes its efficiency to the effective use of multi-core and multi-socket architectures, in order to distribute and execute in parallel both index construction and query answering, and to the exploitation of the Single Instruction Multiple Data (SIMD) capabilities of modern CPUs, in order to further parallelize the execution of instructions inside each core.
Medical evacuations out of the U.S. Central and U.S. Africa Commands, active and reserve components, U.S. Armed Forces, 2021

The proportions of evacuations out of USCENTCOM that were due to battle injuries declined substantially in 2021. For USCENTCOM, evacuations for mental health disorders were the most common, followed by non-battle injury and poisoning, and signs, symptoms, and ill-defined conditions. For USAFRICOM, evacuations for non-battle injury and poisoning were most common, followed by disorders of the digestive system and mental health disorders.
The weights initialization methodology of unsupervised neural networks to improve clustering stability

A study on initialization of connection weights of neural networks is expected to be needed because various deep neural networks based on deep learning have attracted much attention recently. However, studies on the relation between the output value of the active function and the learning performance of the neural network with respect to the connection weight value have been conducted mainly on the supervised learning model. This paper focused on improving the efficiency of autonomous neural network model by studying the connection weight initialization as the neural network model of supervised learning. Adaptive resonance theory (ART) is a major model of autonomous neural network that tries to solve the stability-plasticity dilemma by using bottom-up weights and top-down weights. The conventional weights initialization method of ART was to uniformly set all weights, but the proposed method is to initialize by using pre-trained weights. Experiments show that the ART, which initializes the connectivity weights through the proposed method, performs clustering more reliably.
[Bowel Sounds Detection Method and Experiment Based on Multi-feature Combination]

Bowel sounds is an important indicator to monitor and reflect intestinal motor function, and traditional manual auscultation requires high professional knowledge and rich clinical experience of doctors. In addition, long-time auscultation is time-consuming and laborious， which may lead to misjudgment caused by subjective error. To solve the problem, firstly, the wavelet transform is used to preprocess the bowel sounds signal for noise reduction and enhancement. Secondly, three typical features of intestinal sound were extracted. According to the combination of these features, a three-stage decision was designed to carry out multi-parameter and multi-feature joint threshold detection. This algorithm realized the detection of bowel sound signal and the location of its start and end points, making it possible that the complete bowel sound signal was extracted effectively. In this study, a large number of clinical data and label of bowel sounds were collected, and a new effective evaluation method was proposed to verify the proposed method. The accuracy rate is 83.51%. Results of this study will provide systematic support and theoretical guarantee for the diagnosis of intestinal diseases and the monitoring of postoperative intestinal function recovery of patients.
Place-Sensing through Haptic Interfaces: Proposing an Alternative to Modern Sustainability Education

In this article we address the issue of how an instrumental approach to sustainability education has dominated the scientific debate of the last 20 years. By conducting interviews and focus group interviews, we have investigated a community arts initiative in the Flemish city of Antwerp in which artists together with local inhabitants engaged in activities around two art installations and address the sustainability of a particular living environment. Our empirical study of this place-based initiative that we call a 'critical zone observatory' has been enriched by the work of Bruno Latour, Richard Sennett and Hans Schildermans. We conclude that a temporal and spatial shift in sustainability education (research) is needed from (1) development (a steady movement towards a planned future) and (2) human stewardship (the capability of people to shape their passive living environments) to (1) what we call co-sperity (a collective hope in the present) and (2) inhabitation (an attached and undetermined engagement with the dynamic of one's habitat). By proposing a collective study pedagogy as an alternative to individual training, we suggest a need for future research on critical zone observatories.
Stable Overlapping Replicator Dynamics for Brain Community Detection

A fundamental means for understanding the brain's organizational structure is to group its spatially disparate regions into functional subnetworks based on their interactions. Most community detection techniques are designed for generating partitions, but certain brain regions are known to interact with multiple subnetworks. Thus, the brain's underlying subnetworks necessarily overlap. In this paper, we propose a technique for identifying overlapping subnetworks from weighted graphs with statistical control over false node inclusion. Our technique improves upon the replicator dynamics formulation by incorporating a graph augmentation strategy to enable subnetwork overlaps, and a graph incrementation scheme for merging subnetworks that might be falsely split by replicator dynamics due to its stringent mutual similarity criterion in defining subnetworks. To statistically control for inclusion of false nodes into the detected subnetworks, we further present a procedure for integrating stability selection into our subnetwork identification technique. We refer to the resulting technique as stable overlapping replicator dynamics (SORD). Our experiments on synthetic data show significantly higher accuracy in subnetwork identification with SORD than several state-of-the-art techniques. We also demonstrate higher test-retest reliability in multiple network measures on the Human Connectome Project data. Further, we illustrate that SORD enables identification of neuroanatomically-meaningful subnetworks and network hubs.
Detection and characterisation of microplastics and microfibres in fishmeal and soybean meal

Aquaculture is an increasingly important source of nutrition for global food security, which is reliant on animal- and plant-based feeds. Anthropogenic particles, including microplastics and semi-synthetic cellulosic fibres, are prolific marine pollutants that are readily consumed by marine organisms, including small pelagic fish commonly used in fishmeal. Conversely, there is no indication plants can accumulate anthropogenic microparticles. We explore whether aquaculture feed presents a route of contamination for farmed fish. Commercially-sourced aquaculture feedstocks, including fishmeals and soybean meal, were processed (KOH digestion and ZnCl2 density separation) and anthropogenic particles characterised using microscopy and spectroscopic methods. Both fishmeal and soybean meals contained anthropogenic particles, with concentrations ranging 1070-2000 particles kg-1. The prevalence of anthropogenic particles in plant-based feeds indicates that the majority of contamination occurs post-harvest. Based on our findings, farmed Atlantic salmon may be exposed to a minimum of 1788-3013 anthropogenic particles from aquaculture feed across their commercial lifespan.
VPS-SLAM: Visual Planar Semantic SLAM for Aerial Robotic Systems

Indoor environments have abundant presence of high-level semantic information which can provide a better understanding of the environment for robots to improve the uncertainty in their pose estimate. Although semantic information has proved to be useful, there are several challenges faced by the research community to accurately perceive, extract and utilize such semantic information from the environment. In order to address these challenges, in this paper we present a lightweight and real-time visual semantic SLAM framework running on board aerial robotic platforms. This novel method combines low-level visual/visual-inertial odometry (VO/VIO) along with geometrical information corresponding to planar surfaces extracted from detected semantic objects. Extracting the planar surfaces from selected semantic objects provides enhanced robustness and makes it possible to precisely improve the metric estimates rapidly, simultaneously generalizing to several object instances irrespective of their shape and size. Our graph-based approach can integrate several state of the art VO/VIO algorithms along with the state of the art object detectors in order to estimate the complete 6DoF pose of the robot while simultaneously creating a sparse semantic map of the environment. No prior knowledge of the objects is required, which is a significant advantage over other works. We test our approach on a standard RGB-D dataset comparing its performance with the state of the art SLAM algorithms. We also perform several challenging indoor experiments validating our approach in presence of distinct environmental conditions and furthermore test it on board an aerial robot. Video: https://vimeo.com/368217703 Released Code: https://bitbucket.org/hridaybavle/semantic_slam.git
Structure of the BoNT/A1--receptor complex

Botulinum neurotoxin A causes botulism but is also used for medical and cosmetic applications. A detailed molecular understanding of BoNT/A--host receptor interactions is therefore fundamental for improving current clinical applications and for developing new medical strategies targeting human disorders. Towards this end, we recently solved an X-ray crystal structure of BoNT/A1 in complex with its neuronal protein receptor SV2C. Based on our findings, we discuss the potential implications for BoNT/A function.
High Performance Adaptive Algorithms for Single-Group Multicast Beamforming

The single-group multicast beamforming problem is NP-hard, and the available approximations do not always achieve favorable performance-complexity tradeoffs. This paper introduces a new class of adaptive multicast beamforming algorithms that features guaranteed convergence and state-of-the-art performance at low complexity. Each update takes a step in the direction of an inverse signal-to-noise ratio (SNR) weighted linear combination of the SNR-gradient vectors of all the users. Convergence of this update to a Karush-Kuhn-Tucker (KKT) point of proportionally fair beamforming is established. Simulations show that the proposed approach can enable better performance than the prior state-of-art in terms of multicast rate, at considerably lower complexity. This reveals an interesting link between max-min-fair and proportionally fair multicast beamforming formulations. For cases where there is no initial channel state information at the transmitter, an online algorithm is developed that simultaneously learns the user channel correlation matrices and adapts the beamforming vector to maximize the minimum (long-term average) SNR among the users, using only periodic binary SNR feedback from each receiver. The online algorithm uses the analytic center cutting plane method to quickly learn the user correlation matrices with limited signaling overhead.
Quality of Life and Conformity to Gender Norms in Women Receiving Assisted Reproductive Technologies as a Potential Indicator of Mental Health

The prevalence of depression, stress, or anxiety in people receiving assisted reproductive technologies (ART) has been demonstrated. However, knowledge about the influence of gender norms on quality of life (QofL) during infertility treatment is limited. The main objective of this study was to confirm that patients undergoing ART present a vulnerable mental state, which may be an indicator of risk. For this purpose, a quasi-experimental cross-sectional study was carried out in the Assisted Reproduction Unit of the Hospital Politecnico Universitario de la Fe (Spain) in which a total of 438 women participated: 256 in pre-treatment and 182 in treatment. Two questionnaires were administered, FertiQol and CFNI-23, assessing self-perceived QofL and conformity to gender norms, respectively. The results showed significant differences between the pre-treatment and treatment groups on the FertiQol and its subscales. Significant associations were also found between the CFNI-23 factors and the FertiQol subscales. The results suggest that gender norms and ART interfere with women's mental health and QofL and should be considered as possible risk indicators by professionals preventively before the prenatal or perinatal stages. Future research should design prospective studies aimed at estimating the impact of clinical and sociodemographic variables on women and other groups receiving ART.
Deep Multi-View Feature Learning for Person Re-Identification

Person re-identification aims to identify the same pedestrians across different camera views at different locations. This important yet difficult intelligent video analysis problem remains a vigorous area of research due to demands for performance improvements. Person re-identification involves two main steps: feature representation and metric learning. Handcrafted features, such as color and texture histograms, are frequently used for person re-identification, but most handcrafted features are limited by not being directly applicable to practical problems. Deep learning methods have obtained the state-of-the-art performance in a wide variety of applications, including image annotation, face recognition, and speech recognition. However, deep learning features are heavily dependent on large-scale labeling of samples. In this paper, by utilizing the Cross-view Quadratic Discriminant Analysis (XQDA) metric learning, we propose a novel scheme called deep multi-view feature learning (DMVFL), which exploits the collaboration between handcrafted and deep learning features in a simple but effective way. Furthermore, we prove that the XQDA is a robust algorithm. Extensive experiments on two challenging person re-identification data sets (VIPeR and GRID) demonstrate that DMVFL improves on current state-of-the-art methods.
Parallel evolution of semicircular canal form and sensitivity in subterranean mammals

The vertebrate vestibular system is crucial for balance and navigation, and the evolution of its form and function in relation to species' lifestyle and mode of locomotion has been the focus of considerable recent study. Most research, however, has concentrated on aboveground mammals, with much less published on subterranean fauna. Here, we explored variation in anatomy and sensitivity of the semicircular canals among 91 mammal species, including both subterranean and non-subterranean representatives. Quantitative phylogenetically informed analyses showed significant widening of the canals relative to radius of curvature in subterranean species. A relative canal width above 0.166 indicates with 95% certainty that a species is subterranean. Fluid-structure interaction modelling predicted that canal widening leads to a substantial increase in canal sensitivity; a reasonably good estimation of the absolute sensitivity is possible based on the absolute internal canal width alone. In addition, phylogenetic comparative modelling and functional landscape exploration revealed repeated independent evolution of increased relative canal width and anterior canal sensitivity associated with the transition to a subterranean lifestyle, providing evidence of parallel adaptation. Our results suggest that living in dark, subterranean tunnels requires good balance and/or navigation skills which may be facilitated by more sensitive semicircular canals.
Art Market Investment Bubble during COVID-19-Case Study of the Rare Books Market in Poland

The literature provides information on the impact of the pandemic on various markets. Many articles suggest the necessity of diversifying investment portfolios during financial market turmoil. The article in a unique way analyzed the rare books market in Poland during the COVID-19 period. The results of rare books auctions (the largest players with 85% of the market share were taken into account) over the 2018-2022 period were considered. The authors presented the hypothesis that COVID-19 triggered an investment bubble which may burst quite suddenly after the pandemic period. The data presented confirm the hypothesis, showing sharp increases (up to 50%) in sales revenues and a subsequent collapse. Less spectacular results were obtained when analyzing the number of books offered and sold in a given period. The authors used descriptive measures as well as statistical tests. A simple model calculating possible revenue in conjunction with the WPDI (World Pandemics Discussion Index) indicator was also presented.
Software Architecture Reconstruction: A Process-Oriented Taxonomy

To maintain and understand large applications, it is important to know their architecture. The first problem is that unlike classes and packages, architecture is not explicitly represented in the code. The second problem is that successful applications evolve over time, so their architecture inevitably drifts. Reconstructing the architecture and checking whether it is still valid is therefore an important aid. While there is a plethora of approaches and techniques supporting architecture reconstruction, there is no comprehensive software architecture reconstruction state of the art and it is often difficult to compare the approaches. This paper presents a state of the art in software architecture reconstruction approaches.