Miracle-dz/UniFAR-FTUs huggingface dataset:
Decoding the EEG patterns induced by sequential finger movement for brain-computer interfaces

Objective In recent years, motor imagery-based brain–computer interfaces (MI-BCIs) have developed rapidly due to their great potential in neurological rehabilitation. However, the controllable instruction set limits its application in daily life. To extend the instruction set, we proposed a novel movement-intention encoding paradigm based on sequential finger movement. Approach Ten subjects participated in the offline experiment. During the experiment, they were required to press a key sequentially [i.e., Left→Left (LL), Right→Right (RR), Left→Right (LR), and Right→Left (RL)] using the left or right index finger at about 1 s intervals under an auditory prompt of 1 Hz. The movement-related cortical potential (MRCP) and event-related desynchronization (ERD) features were used to investigate the electroencephalography (EEG) variation induced by the sequential finger movement tasks. Twelve subjects participated in an online experiment to verify the feasibility of the proposed paradigm. Main results As a result, both the MRCP and ERD features showed the specific temporal–spatial EEG patterns of different sequential finger movement tasks. For the offline experiment, the average classification accuracy of the four tasks was 71.69%, with the highest accuracy of 79.26%. For the online experiment, the average accuracies were 83.33% and 82.71% for LL-versus-RR and LR-versus-RL, respectively. Significance This paper demonstrated the feasibility of the proposed sequential finger movement paradigm through offline and online experiments. This study would be helpful for optimizing the encoding method of motor-related EEG information and providing a promising approach to extending the instruction set of the movement intention-based BCIs.
Accelerating Discovery: Mining Unstructured Information for Hypothesis Generation

Unstructured Mining Approaches to Solve Complex Scientific Problems As the volume of scientific data and literature increases exponentially, scientists need more powerful tools and methods to process and synthesize information and to formulate new hypotheses that are most likely to be both true and important. Accelerating Discovery: Mining Unstructured Information for Hypothesis Generation describes a novel approach to scientific research that uses unstructured data analysis as a generative tool for new hypotheses. The author develops a systematic process for leveraging heterogeneous structured and unstructured data sources, data mining, and computational architectures to make the discovery process faster and more effective. This process accelerates human creativity by allowing scientists and inventors to more readily analyze and comprehend the space of possibilities, compare alternatives, and discover entirely new approaches. Encompassing systematic and practical perspectives, the book provides the necessary motivation and strategies as well as a heterogeneous set of comprehensive, illustrative examples. It reveals the importance of heterogeneous data analytics in aiding scientific discoveries and furthers data science as a discipline.
Bone Canopies in Pediatric Renal Osteodystrophy

Pediatric renal osteodystrophy (ROD) is characterized by changes in bone turnover, mineralization, and volume that are brought about by alterations in bone resorption and formation. The resorptive and formative surfaces on the cancellous bone are separated from the marrow cavity by canopies consisting of a layer of flat osteoblastic cells. These canopies have been suggested to play a key role in the recruitment of osteoprogenitors during the process of bone remodeling. This study was performed to address the characteristics of the canopies above bone formation and resorption sites and their association with biochemical and bone histomorphometric parameters in 106 pediatric chronic kidney disease (CKD) patients (stage 2–5) across the spectrum of ROD. Canopies in CKD patients often appeared as thickened multilayered canopies, similar to previous reports in patients with primary hyperparathyroidism. This finding contrasts with the thin appearance reported in healthy individuals with normal kidney function. Furthermore, canopies in pediatric CKD patients showed immunoreactivity to the PTH receptor (PTHR1) as well as to the receptor activator of nuclear factor kappa-B ligand (RANKL). The number of surfaces with visible canopy coverage was associated with plasma parathyroid hormone (PTH) levels, bone formation rate, and the extent of remodeling surfaces. Collectively, these data support the conclusion that canopies respond to the elevated PTH levels in CKD and that they possess the molecular machinery necessary to respond to PTH signaling.
Enhancing Scientific Literacy of Lower Secondary Students through Technological Pedagogical and Content Knowledge Framework

All students today attach a great deal of importance to having access to a technologically-enhanced education. As a result, teachers are pressured to integrate their teaching, material, and technological expertise into their classes. The TPACK framework was utilized to teach science in lower secondary schools as part of this action research. For this investigation, the most significant individuals were 19-year-old students from on school. As study methods, a test of scientific literacy, observations of behavior patterns, and eight TPAC lesson plans were utilized. During the first academic year of 2022, the spiraling action plan was implemented twice. Results indicated that TPACK played a significant role in increasing students' scientific literacy. The first cycle of instruction, the scientific literacy levels of students were high, and after the second cycle, they were very high. There is potential for TPACK implementation in contemporary schools, however, teachers will need to provide instructional materials that are appropriate for student learning and apply instructional approaches that are adapted to the individual academic pursuits of their students
The global impact of the transport sectors on the atmospheric aerosol and the resulting climate effects under the Shared Socioeconomic Pathways (SSPs)

Abstract. A global aerosol–climate model is applied to quantify the impact of the transport sectors (land transport, shipping, and aviation) on aerosol and climate. Global simulations are performed for the present day (2015), based on the emission inventory of the Climate Model Intercomparison Project Phase 6 (CMIP6), and for near-term (2030) and mid-term (2050) future projections, under the Shared Socioeconomic Pathways (SSPs). The results for the present day show that land transport emissions have a large impact on near-surface concentrations of black carbon and aerosol nitrate over the most populated areas of the globe, but with contrasting patterns in terms of relative contributions between developed and developing countries. In spite of the recently introduced regulations to limit the fuel sulfur content in the shipping sector, shipping emissions are still responsible for a considerable impact on aerosol sulfate near-surface concentrations, about 0.5 to 1 µg m−3 in the most travelled regions, with significant effects on continental air pollution and in the northern polar regions as well. Aviation impacts on aerosol mass are found to be quite small, of the order of a few nanograms per cubic metre, while this sector considerably affects particle number concentrations, contributing up to 20 %–30 % of the upper-tropospheric particle number concentration at the northern mid-latitudes. The transport-induced impacts on aerosol mass and number concentrations result in a present-day radiative forcing of −164, −145, and −64 mW m−2 for land transport, shipping, and aviation, respectively, with a dominating contribution by aerosol–cloud interactions. These forcings represent a marked offset to the CO2 warming from the transport sectors and are therefore very relevant for climate policy. The projections under the SSPs show that the impact of the transport sectors on aerosol and climate are generally consistent with the narratives underlying these scenarios: the lowest impacts of transport on both aerosol and climate are simulated under SSP1, especially for the land transport sector, while SSP3 is generally characterized by the largest effects. Notable exceptions to this picture, however, exist, as the emissions of other anthropogenic sectors also contribute to the overall aerosol concentrations and thus modulate the relevance of the transport sectors in the different scenarios, not always consistently with their underlying storyline. On a qualitative level, the results for the present day mostly confirm the findings of our previous assessment for the year 2000, which used a predecessor version of the same model and the CMIP5 emission data. Some important quantitative differences are found, which can mostly be ascribed to the improved representation of aerosol background concentrations in the present study.
ECOLOGICAL AND EXPOSURE ASSESSMENT OF VIBRIO VULNIFICUS AND VIBRIO PARAHAEMOLYTICUS IN AREAS OF IMPORTANCE FOR HUMAN USE IN CHESAPEAKE BAY

Title of Document: ECOLOGICAL AND EXPOSURE ASSESSMENT OF VIBRIO VULNIFICUS AND VIBRIO PARAHAEMOLYTICUS IN AREAS OF IMPORTANCE FOR HUMAN USE IN CHESAPEAKE BAY Kristi Stevens Shaw, Doctor of Philosophy, 2013 Directed By: Dr. Byron C. Crump Associate Professor of Oceanography University of Maryland Center for Environmental Science Current microbial surveillance of water quality in marine and estuarine environments focuses on fecal indicator concentrations to determine suitable conditions for swimming or fishing, including commercial harvest of seafood. However, there are many pathogens in our waters, such as Vibrio vulnificus and V. parahaemolyticus, and it remains unclear how well fecal indicator surveillance protects the public from infection. This dissertation studied V. vulnificus and V. parahaemolyticus at locations in Chesapeake Bay where human contact is likely, in order to quantify dermal transmission to humans, describe the impact of storms on pathogen concentrations in oysters, and quantify antimicrobial resistance. Swim studies at four public beaches in Chesapeake Bay in 2009 and 2011 were the first of their kind to quantify Vibrio exposure by recreating swimmers and to qualify exposure in terms of dermal dose. Estimated exposures correlated with surface water Vibrio concentrations and suggested that the public could be exposed to V. vulnificus and V. parahaemolyticus at rates that may cause illness. To better protect human health, estimates of non-consumption dose-response would be helpful in completing a quantitative microbial risk assessment to calculate relative risk of swimming in waters known to harbor Vibrio bacteria. Oysters, water, and sediment were sampled at an aquaculture facility before and after Hurricane Irene impacted the Chesapeake Bay in 2011. Results indicated no difference in Vibrio uptake between oysters positioned on floats and on bottom sediments, but showed a difference in Vibrio species uptake, with V. parahaemolyticus increasing 1 day post-Irene, unlike V. vulnificus. This study suggests that storm events may increase V. parahaemolyticus in oyster tissue, and that virulent sub-types of both Vibrio species may increase in percent abundance within oysters following a storm event. Antimicrobial susceptibility testing showed that a large percentage of isolates from surface waters in the Chesapeake Bay displayed intermediate resistance to chloramphenicol. Most antimicrobial agents recommended for treatment of Vibrio illness by CDC were effective at controlling growth of V. vulnificus and V. parahaemolyticus. Results suggest treatment of pediatric illness with trimethoprimsulfamethoxazole and the aminoglycoside, gentamicin, which was the only aminoglycoside 100% effective in controlling Vibrio growth in this study. ECOLOGICAL AND EXPOSURE ASSESSMENT OF VIBRIO VULNIFICUS AND VIBRIO PARAHAEMOLYTICUS IN AREAS OF IMPORTANCE FOR HUMAN USE IN CHESAPEAKE BAY
Enabling Switch Memory Management for Distributed Training with In-Network Aggregation

Distributed training (DT) in shared clusters usually deploys a scheduler for resource allocation to multiple concurrent jobs. Meanwhile, a recent acceleration primitive, In-Network Aggregation (INA), introduces switch memory as a new critical resource for DT jobs, out of the prior scheduler's management. Lacking switch memory management leads to inefficient cluster resource usage. We build INAlloc, a switch memory management system for DT job schedulers to improve INA-empowered DT jobs in shared clusters. INAlloc adds a switch memory management layer to organize the physical switch memory, allocate memory to jobs, and provide friendly interfaces to schedulers. INAlloc incorporates switch memory into modeling a job's completion time (JCT) and its resources, which assists the scheduler in deciding the switch memory allocation. INAlloc overcomes the challenges of consistent and nondisruptive runtime switch memory reallocation. Our prototype and evaluation on real-world traces show that INAlloc can reduce the jobs' deadline miss ratio by 75% and JCT by 27%.
The BioGRID Interaction Database: 2011 update

The Biological General Repository for Interaction Datasets (BioGRID) is a public database that archives and disseminates genetic and protein interaction data from model organisms and humans (http://www.thebiogrid.org). BioGRID currently holds 347 966 interactions (170 162 genetic, 177 804 protein) curated from both high-throughput data sets and individual focused studies, as derived from over 23 000 publications in the primary literature. Complete coverage of the entire literature is maintained for budding yeast (Saccharomyces cerevisiae), fission yeast (Schizosaccharomyces pombe) and thale cress (Arabidopsis thaliana), and efforts to expand curation across multiple metazoan species are underway. The BioGRID houses 48 831 human protein interactions that have been curated from 10 247 publications. Current curation drives are focused on particular areas of biology to enable insights into conserved networks and pathways that are relevant to human health. The BioGRID 3.0 web interface contains new search and display features that enable rapid queries across multiple data types and sources. An automated Interaction Management System (IMS) is used to prioritize, coordinate and track curation across international sites and projects. BioGRID provides interaction data to several model organism databases, resources such as Entrez-Gene and other interaction meta-databases. The entire BioGRID 3.0 data collection may be downloaded in multiple file formats, including PSI MI XML. Source code for BioGRID 3.0 is freely available without any restrictions.
Shifts in Facial Impression Structures Across Group Boundaries

Facial impressions have long been argued to be driven by two independent dimensions of trustworthiness and dominance. However, in an intergroup context, we reasoned that these dimensions may shift predictably and become more positively related for ingroup members, yet negatively related for outgroup members, due to dominance signaling outgroup threat and/or ingroup prosociality. In two studies, we examined how the two dimensions shift across minimal group boundaries for White targets. In Study 1, core dimensions of trustworthiness and dominance became intertwined with each other differently for ingroup and outgroup targets. In Study 2, stronger stereotypic beliefs that trustworthiness ≈ dominance for ingroup than outgroup mediated the shifts in facial impression dimensions. This work advances our understanding of facial impression and intergroup bias by showing that the facial impression dimensions are not fixed but may shift across group boundaries and that such shifts occur above and beyond simple ingroup favoritism.
Decision Support System in Healthcare for Predicting Blood Pressure Disorders

Blood pressure problems including hypertension and hypotension are considered common among the elderly population, especially hypertension. Recently it started being common among younger adults due to many factors related to unhealthy lifestyles, stress, or genetic factors. Besides the fact of considering hypertension as a chronic disease, it is also considered a primary or contributing cause of complicated risky health issues such as strokes, heart diseases, and chronic kidney failure. In many cases, hypertensive patients may not be aware of their problem because they don't experience any symptoms or warning signs. For this reason, it is essential to build a decision support system to identify individuals at high risk of blood pressure problems including raised blood pressure and low blood pressure. This paper proposes a decision support model for predicting blood pressure disorders by using input variables such as sex, age, body mass index (BMI), cholesterol level, heart rate, and glucose level. The decision model helps in early warning of the potential risk of hypertension or hypotension. As a result, people under potential risk are advised to measure their blood pressure regularly and take the needed precautions or medications to avoid or control this health issue. The proposed decision support model is based on using supervised machine learning classification algorithms, mainly Random Forest, Decision Tree, and XGBoost. The experimental results show that the model achieved the best performance when implemented using a Random Forest classifier with a 10-fold cross-validation method, with an accuracy of 85.81%.
Extraction and Physicochemical Characterization of Adansonia digitata L. Mucilage

Background: Adansonia digitata leaves are edible and they contain a significant amount of mucilage that has shown potentials for pharmaceutical application. However, there is paucity of information on this mucilage contained in its leaves. Objective: This study aimed to determine a suitable method for extraction of Adansonia digitata mucilage (ADM) as well as to characterize its physicochemical properties. Materials and Methods: ADM was extracted from the aqueous dispersion of the dried leaves powder of Adansonia digitata L. in water via precipitation with ethanol. Physicochemical characterization of ADM was based on viscosity, chemical composition, particle size characterization via QICPIC, Scanning Electron Microscopy (SEM), Attenuated Total Reflectance Fourier Transforms Infrared (ATR -FTIR) spectroscopy, Nuclear magnetic resonance (NMR), moisture content and Dynamic vapor sorption (DVS) studies as well as pH and aqueous solubility tests. In addition to this, colour of the mucilage and thermal analysis via DSC were used as criteria for selection of the most suitable method for extraction of ADM. Results: Colour and thermal analysis revealed a level of purity in the extraction process. The irregular needle like structures of ADM revealed by SEM was found to be mildly acidic with a high viscosity that is concentration dependent in aqueous medium. Thermal characterization revealed a glass transition (Tg) and melting temperatures (Tm) of 55.61 °C and 179.10 °C respectively. Finger prints of functional groups revealed azo aromatic groups and other chemical constituents of sugars including glucose, galactose, arabinose and rhamnose, and sugar acid form of galacturonic acid were identified by NMR. Moisture sorption provided insight into water sorption mechanism, processing, packaging and storage conditions of the mucilage. Conclusion: Extraction of ADM via a heat free method improved its colour and purity based on the technique used. The mucilage was found to be a highly viscous and hydrates rapidly with good gelling properties. In addition its moisture sorption characteristics and glass transition temperature gave insight into its stability, storage and packaging conditions. These properties reveal its potential for many pharmaceutical applications including binding, coating, gelling agents as well as matrix forming.
Spotlight on 5G: Performance, Device Evolution and Challenges from a Mobile Operator Perspective

Fifth Generation (5G) has been acknowledged as a significant shift in cellular networks, expected to run significantly different classes of services and do so with outstanding performance in terms of low latency, high capacity, and extreme reliability. Managing the resulting complexity of mobile network architectures will depend on making efficient decisions at all network levels based on end-user requirements. However, to achieve this, it is critical to first understand the current mobile ecosystem and capture the device heterogeneity, which is one of the major challenges for ensuring the successful exploitation of 5G technologies.In this paper, we conduct a large-scale measurement study of a commercial mobile operator in the UK, focusing on bringing forward a real-world view on the available network resources, as well as how more than 30M end-user devices utilize the mobile network. We focus on the current status of the 5G Non-Standalone (NSA) deployment and the network-level performance and show how it caters to the prominent use cases that 5G promises to support. Finally, we demonstrate that a fine-granular set of requirements is, in fact, necessary to orchestrate the service to the diverse groups of 5G devices, some of which operate in permanent roaming.
Task Offloading Using Multi-Armed Bandit Optimization in Autonomous Mobile Robots

Evolution in ubiquitous and wireless services has enabled the massive adoption of autonomous cyber-physical systems for improving the workflows in dynamic environments. Among other applications, it has been witnessed that these modern technologies with the help of machine learning and high-speed communications can enable optimum and safe utilization of resources to complete various repetitive yet hazardous tasks. The industry 5.0 vision requires a multitude of devices to work with such orchestration that compute-intensive tasks may be offloaded to nearby nodes to enable collaboration for such time-critical yet compute-intensive tasks. In this work, we present a multi-armed bandit-based approach for task offloading in unmanned autonomous robots. Through experimental validation, a proof of concept is given. It has been demonstrated that using the proposed technique we have achieved a higher task delivery rate with reduced average delay.
Recurrent Neural Network for M/EEG Source Localization

This paper presents the solution of the inverse M/EEG problem using machine learning algorithms. The data on which the algorithm was trained were obtained as a result of solving the forward problem. The data are generated using a recurrent model of neuronal brain activity, where the consecutive excitation of sources in time of closely spaced sources is based on a stochastic Markov chain. The dipole activation model is expected to increase the accuracy of the M/EEG inverse problem solution by reducing its indeterminacy by replacing uncorrelated excitations of sources whose magnetic field is statically distributed on the brain surface with their spatially related time series. Machine learning algorithms in this case can recognize the correlation of source excitations and solve the problem in real time. The application of a method for localization of neuronal activity of the brain that takes into account the spatiotemporal character of the excitations can presumably provide a more complete picture of the brain functioning.