bluuebunny/crossref_metadata_2025_split huggingface dataset:
RSSI‐Based Indoor Distance Estimation in Wi‐Fi IoT Application Using AI Approaches
Authors: Mane Sarika , Kulkarni Makarand , Gupta Sudha 
Published: February 2025

ABSTRACTIn many Internet of Things (IoT) applications, device tracking is required to improve localization‐based services. Distance estimation is a key component of localization methods. Accurate distance estimation helps Wi‐Fi access point's providers, to provide better quality of services (QoS) to connected devices. Distance estimation determines a device's precise location in an environment. In this work, three different scenarios are considered. The work proposes distance estimation using Euclidean distance and the received signal strength indicator (RSSI). This approach eliminates the need for three access points required in the triangulation method. Euclidean distance is measured between access points and devices spread throughout the indoor environment. Random forest (RF) algorithm, K‐nearest neighbor (KNN), Gaussian process (GP), and ant colony optimization (ACO) are used to train the models to correlate Euclidian distances and the RSSI of connected devices. The performance is measured using mean absolute error (MAE). The optimum performance improvement obtained in Scenario 1, for RF, is 41.87%; in Scenario 2, for KNN, it is 37.98%; and in Scenario 3, for RF, it is 56.97%, with respect to earlier reported work. ACO achieves a 78.61% improvement in Scenario 1, 88.57% in Scenario 2, and 50.90% in Scenario 3 over the reported work.
Multiport MIMO Antenna System With Diverse Radiation Pattern for Sub‐6 GHz Wireless Communications
Authors: Sharma Gouri Shankar , Gupta Anshul , Gangwar Ravi Kumar , Pandey Amit Kr 
Published: February 2025

ABSTRACTThe design and implementation of a six‐port MIMO dielectric resonator (DR) antenna (DRA) featuring diverse radiation patterns for wireless applications has been rendered in this article. The antenna structure is organized as a coplanar waveguide (CPW), fed three cylindrical DRs installed on the upper and lower sides of the substrate to achieve diverse radiation patterns. The proclaimed MIMO antenna exhibits a measured working frequency spectrum from 5.16 to 6.04 GHz (fractional bandwidth = 15.74%) with an isolation exceeding −27 dB at the resonance frequency. The antenna is characterized by its high isolation due to the spatial positioning of DRs at each side on the modified ground plane of the FR‐4 substrate and pattern diversity, which allows the antenna to maintain its MIMO performance parameters like envelope correlation coefficient (ECC), diversity gain (DG), total active reflection coefficient (TARC), and channel capacity loss (CCL) necessary for the MIMO antenna system within optimal limits. The closeness between simulated and experimental results makes the proclaimed antenna suitable for sub‐6 GHz WLAN (5.15–5.925GHz), 5G (n46), and wireless fidelity (Wi‐Fi) supported IoT applications.
Unrecorded Butterfly Species and Potential Local Extinctions: The Role of Citizen Science and Sampling
Authors: Alberti S. , Pollo A. , Cerrato C. , Viterbi R. , Balletto E. , Dapporto L. , Bonelli S. , Piccini I. 
Published: February 2025

ABSTRACTEstimating species extinction risk is crucial to reverse biodiversity loss and to adopt proper conservation measures. Different sources may play a pivotal role in prioritising species conservation. Recently, citizen science demonstrated a substantial role, especially when it comes to butterflies. This study examines species records and richness in Aosta Valley, which represents one of the highest mountain areas in Europe. Through 30,351 data points from 1825 to 2022, the impact and efficiency of three groups of data sources were investigated: literature (i.e., publications and collections), sampling (butterfly experts' recording), and citizen science (open‐source databases). The study also aims to assess the extinction potential of the butterflies in relation to functional traits. The results showed that even if there were significant differences in the number of records between the three sources, there were no significant differences for species recorded. Moreover, 2.9% of the butterfly community risks extinction, and it is related to some response traits. Indeed, extinction risks increase when the altitudinal range decreases and for multivoltines. In conclusion, citizen science has a strong impact on the amount of data and could be exploited to fill data gaps at low/medium altitudes. However, professional sampling is needed to focus on species no longer reported, and in particular on species that are difficult to identify, have specific distributions or particular traits (e.g., limited altitudinal range). Using different data sources, extinction risk estimation, and trait analysis, it is possible to prioritise studies on some species using different efforts (sampling and/or citizen sciences).
Historical reindeer corrals in northern boreal forests reveal divergent post‐disturbance reorganization by forest type
Authors: Manninen Outi H. , Stark Sari 
Published: February 2025

AbstractCurrent theoretical frameworks on alternative ecosystem states in boreal forests do not contain a clear prediction on how the sensitivity for state transitions depends on site productivity, which forms a major gap in understanding how disturbances impact these ecosystems. Here, we compared 26 historical reindeer (Rangifer tarandus L.) corrals used in animal husbandry between the late 1800s and early 1960s with reference forests in the northern boreal forest zone in Finland. We found that mesic forests experienced strong forest reorganization that involved shifts in both tree species composition and density. Sub‐xeric forests were most commonly on a trajectory toward the pre‐disturbance state due to strong self‐thinning. No state transitions in understory vegetation occurred. There was little difference in understory vegetation recovery between mesic and sub‐xeric forests; however, disturbance increased spatial heterogeneity in understory vegetation. These findings from historical sites support a high resilience of northern boreal forests to disturbances. They also indicate that disturbances may more likely induce reorganization in more productive mesic sites. Although stronger environmental constraints in less productive sub‐xeric sites may slow down recovery, they also direct post‐disturbance trajectories toward the pre‐disturbance state.
Are dispersal and dormancy alternative strategies for overcoming environmental variability?
Authors: Slimon Kelley F. , Szojka Megan C. , Germain Rachel M. 
Published: February 2025

AbstractDispersal and dormancy serve as strategies for persistence in varying and uncertain environments and are critical to ecological models of biodiversity maintenance. Theories of specific ecological scenarios that favor dispersal, dormancy, or their covariance are rarely tested empirically, particularly in response to realistically complex patterns of spatiotemporal environmental variation. To resolve these complexities, we collected 20 populations of Vulpia microstachys, an annual grass native to California, from the field and grew them in a greenhouse, and on the offspring generation measured seed dispersal ability and seed dormancy rates. We hypothesized that seed dormancy rates, but not dispersal abilities, would be highest in populations from more productive, temporally variable sites, causing dispersal and dormancy to evolve independently—in other words, we leveraged evolved differences among populations to identify what ecological strategy (i.e., dispersal, dormancy, or both) is most likely to evolve at different parts of a variability gradient. Our data suggest that both dispersal and dormancy evolve to combat different axes and scales of spatial heterogeneity and can evolve independently (thus, they are not forced to covary). Most surprisingly, seed dormancy appears to have evolved as a strategy for overcoming microgeographic heterogeneity, an outcome that to our knowledge has not been considered by theory; we confirm the plausibility of this conclusion with a simulation. In sum, we provide much needed empirical data on the evolution of ecological strategies for coping with environmental variance, as well as a new perspective on the ecological function dormancy provides in heterogeneous landscapes.
Shortened Relative Leukocyte Telomere Length Is Associated With Polycystic Ovary Syndrome and Metabolic Traits
Authors: Chan Raymond N.C. , Huang Chuiguo , Ng Noel Y. H. , Tam Henry C. H. , Tam Claudia H. T. , Cheng Feifei , Wong Kwun Kiu , Shi Mai , Ng Alex C. W. , Tsang Atta Y. T. , Wang Chi Chiu , Cheung Lai Ping , Tam Wing Hung , Joglekar Mugdha V. , Hardikar Anandwardhan A. , Jenkins Alicia J. , Chan Juliana C. N. , Lim Cadmon K. P. , Ma Ronald C. W. 
Published: February 2025

ABSTRACTBackgroundPolycystic ovary syndrome (PCOS) is one of the commonest gyneco‐endocrine disorders amongst women of reproductive age. Whether PCOS and cardiometabolic traits in PCOS patients are associated with shortened telomere length (TL) or relative leukocyte telomere length (rLTL) remains unclear.Methods214 women with PCOS and 214 age‐matched women were recruited. rLTL was measured with an updated quantitative real‐time PCR protocol and reported as ΔΔCt between telomere and a single‐copy gene encoding β‐globin relative to a normalisation control. A two‐way Mendelian randomization analysis using the UK Biobank Resource was performed to assess the causal relationship between rLTL and PCOS.ResultsWomen with PCOS had significantly shortened rLTL (PCOS: 0.5 ± 0.7; control: 0.8 ± 0.6; p < 0.001). Longer rLTL was associated with a lower risk of PCOS after adjusting for age, history of smoking and other cardiometabolic traits (OR: 0.503; 95% CI: 0.342–0.730; p < 0.001). Longer rLTL was associated with reduced risk of dyslidpidemia (OR: 0.563; 95% CI: 0.450–0.968; p = 0.042) in PCOS patients. PCOS subjects with rLTL shorter than mean of the rLTL of control subjects had an elevated risk of dysglycemia (OR: 2.09; 95% CI: 1.04–4.29; p = 0.040). No causal relationships were found between rLTL and PCOS in the Mendelian randomization study.ConclusionsWomen with PCOS have significantly reduced rLTL and shorter LTL may be associated with cardiometabolic risk factors in PCOS subjects. There are no causal relationship between genetically determined PCOS and TL or vice versa.
Towards the Next Generation of Data‐Driven Therapeutics Using Spatially Resolved Single‐Cell Technologies and Generative AI
Authors: Rodov Avital , Baniadam Hosna , Zeiser Robert , Amit Ido , Yosef Nir , Wertheimer Tobias , Ingelfinger Florian 
Published: February 2025

ABSTRACTRecent advances in multi‐omics and spatially resolved single‐cell technologies have revolutionised our ability to profile millions of cellular states, offering unprecedented opportunities to understand the complex molecular landscapes of human tissues in both health and disease. These developments hold immense potential for precision medicine, particularly in the rational design of novel therapeutics for treating inflammatory and autoimmune diseases. However, the vast, high‐dimensional data generated by these technologies present significant analytical challenges, such as distinguishing technical variation from biological variation or defining relevant questions that leverage the added spatial dimension to improve our understanding of tissue organisation. Generative artificial intelligence (AI), specifically variational autoencoder‐ or transformer‐based latent variable models, provides a powerful and flexible approach to addressing these challenges. These models make inferences about a cell's intrinsic state by effectively identifying complex patterns, reducing data dimensionality and modelling the biological variability in single‐cell datasets. This review explores the current landscape of single‐cell and spatial multi‐omics technologies, the application of generative AI in data analysis and modelling and their transformative impact on our understanding of autoimmune diseases. By combining spatial and single‐cell data with advanced AI methodologies, we highlight novel insights into the pathogenesis of autoimmune disorders and outline future directions for leveraging these technologies to achieve the goal of AI‐powered personalised medicine.
Design Guidelines of Free‐Flow Counterflow Gradient Focusing Device for Protein Fractionation
Authors: Courtney Matthew , Carreno‐Molina Oscar Manuel , Ren Carolyn L. 
Published: February 2025

ABSTRACTFree‐flow counterflow gradient focusing (FF‐CGF) is a promising tool for protein fractionation. This work establishes guidelines to construct an FF‐CGF system suitable for preparative applications. Numerical and analytical computations are undertaken to investigate design parameters, including device geometry, flow conditions, sample concentration, and applied voltage. This collective information serves as a design tool to guide the fabrication of the device, which, in this case, is constructed from polymethyl methacrylate (PMMA) and double‐sided tape. Furthermore, the design tool also supports device operation and provides insights into the expected resolution and throughput. Experimental results, which demonstrated the ability of the designed chip to generate a uniform counterflow gradient while separating small molecules and proteins, validated the accuracy of the design guidelines.
3D‐Printed Microfluidic‐Based Cell Culture System With Analysis to Investigate Macrophage Activation
Authors: Selemani Major A. , Kabandana Giraso Keza Monia , Chen Chengpeng , Martin R. Scott 
Published: February 2025

ABSTRACTIn this paper, we describe the development of 3D‐printed microfluidic cell culture devices that can be coupled with a circulation system to study the dynamics of both intracellular and extracellular (release) processes. Key to this approach is the ability to quantitate key analytes on a minutes timescale with either on‐line (in this study, quantitating nitric oxide production using an amperometric flow cell) or off‐line (in this work, quantitating intracellular itaconate production with LC/MS) analytical measurements. To demonstrate the usefulness of this approach, we chose to study macrophage polarization as a function of the extracellular matrix (silk) fiber size, a major area of research in tissue engineering. It was found that the use of larger fibers (1280 nm vs. smaller 512 nm fibers) led to increases in the production of both nitric oxide and itaconate. These findings set the foundation for future research for the creation of finely tuned microfluidic 3D cell culture approaches in areas where flow and the extracellular matrix play a significant role in barrier transport and where integrated analysis of key markers is needed.
Characterizing Recent PDMS Changes in Electrokinetic‐Based Microfluidic Devices’ Performance and Manufacturing for Cell Sorting Applications
Authors: Hyler Alexandra R. , Thomas Dean E. , Kinskie Kyle S. , Brown Kyle M. , Duncan Josie L. , Cemazar Jaka , Schultz Jeff , Brown Simeon , Shiri Farhad , Soper Steven A. , Swami Nathan S. , Davalos Rafael V. 
Published: February 2025

ABSTRACTUnderstanding cells from complex biological samples is vital to understanding cellular biology and medical applications. One evolving tool for cell sorting is the use of microfluidic devices to achieve higher precision and remove the need for labeling cell subpopulations. However, few microfluidic devices have been translated commercially beyond academic research often due to challenges in larger scale fabrication. Here, we initially investigated a compelling label‐free microfluidic device with complex geometries to perform contactless dielectrophoresis (cDEP) for applications in enriching cell subpopulations in oncology, neurology, stem cells, and sample preparation. We began scaling the manufacturing of cDEP devices using Dow Sylgard 184, more commonly referred to as PDMS (polydimethylsiloxane). However, we began observing a new, dynamic bubble formation phenomenon which had significant impacts on device performance. Within just 5 min of exposure at typical experimental values, cell death was nearly 100%. Variables related to manufacturing, environment, equipment, personnel, raw materials sourcing, lithography methods and experimental conditions/parameters were systematically evaluated to find the root cause of the exacerbated bubble formation observed. Further, alternate polymers were sourced for manufacturing and experimental performance comparisons. All variables investigated failed to solve the significant decline in device performance and increase in cell death. Upon completing chemical analysis in this work, we conclude that the decline in device performance was a direct result of changes to the expected PDMS properties and composition. Despite these challenges, our robust quality control combined with experimental protocols to remove bubbles from the cDEP devices achieved consistent experimental performance including 2–3 h run times and >90% cell viability after sorting. These new PDMS behaviors will need to continue to be monitored and controlled to ensure consistency in experimentation, application and commercialization feasibility for a wide variety of microfluidic device designs and applications.
Numerical Simulation Noise and Its Significant Impact on the Dielectrophoretic Motion of Particles Near the Electrode Edges
Authors: Bangaru Aaditya Venkatesha Babu , Williams Stuart. J. 
Published: February 2025

ABSTRACTDielectrophoresis (DEP) has been extensively researched over the years for filtration, separation, detection, and collection of micro/nano/bioparticles. Numerical models have historically been employed to predict particle trajectories in three‐dimensional (3D) DEP systems, but a common issue arises due to inherent noise near the edges of electrodes due to electric potential discontinuity, specifically when calculating electric field and gradient of electric field‐squared, . This noise can be reduced to a certain extent with a finer mesh density but results near the electrode edge still have significant error. Realizing the importance of particle‐electrode edge interactions prevalent in positive DEP systems, analytical solutions given by Sun et al. was incorporated to demonstrate an improved 3D model of interdigitated electrodes. The results of electric field and gradient of electric field‐squared of the numerical model and the improved analytical 3D model were compared, within a simulation space of 50 µm height, 10 µm width, and 50 µm length with interdigitated electrodes of the same width and gap of 10 µm. The DEP particle trajectory error due to the noise was quantified for different particle sizes at various heights above the electrode edge. For example, at 5 Vrms, a trapped 500 nm particles exhibited a velocity error of 104 µm/s (it should have been zero).
Graph Theory and Molecular Behavior: Utilizing Platt Numbers and Polynomial Approximations for Boiling Point Estimations
Authors: Sarika S G , Manjusha R 
Published: February 2025

ABSTRACTAnalyzing complex multivalued datasets, such as boiling points and molecular Platt numbers, poses significant challenges due to the intricate relationships within the data, which traditional methods struggle to capture effectively. To address this, the study introduces Edge Neighborhood Graphs Ne(G), a novel graph‐based approach where the neighborhoods of edges in a graph G = (V, E) are treated as vertices, and an edge is drawn between two vertices if their corresponding neighborhoods intersect. This method is explored using complete, cycle, and wheel graphs, with formulas derived for vertex degrees in Ne(G) to uncover structural patterns. Polynomial approximations are employed to apply Ne(G) to multivalued data, demonstrating its effectiveness in modeling complex relationships and providing accurate predictions. By offering a powerful new tool for analyzing intricate datasets, this study makes a unique contribution to graph theory and data modeling, with promising potential for application to other graph types and diverse datasets in the future.
Classifications and Analysis of Caching Strategies in Information‐Centric Networking for Modern Communication Systems
Authors: Qaiser Firdous , Said Al Harthy Khoula , Hussain Mudassar , Frnda Jaroslav , Amin Rashid , Gantassi Rahma , Zakaria Muhammad D. 
Published: February 2025

ABSTRACTInformation Centric Networking (ICN), a paradigm shift in network design that prioritizes content distribution over host‐centric methods, arises in this dynamic environment. Integrating built‐in network caches into ICN is essential for optimizing content distribution effectiveness, which raises hit rates, boosts throughput, and lowers transmission delays. This paper provides a comprehensive exploration and classification of ICN caching strategies across diverse domains, including the Internet of Things (IoT), Internet of Vehicles (IoV), Mobility ICN, Edge Computing, and Fog Computing. It further offers a detailed analysis of these strategies based on their proposed methods, identifying key trends, strengths, and limitations. Through a balanced performance assessment, the study evaluates these strategies using critical metrics such as cache hit ratio, efficiency, retrieval latency, security, and throughput. Furthermore, the paper highlights open challenges and presents future research directions to advance caching mechanisms, fostering the continued evolution of ICN for scalable and efficient content delivery for users.
Time Series Analysis of Solar Power Generation Based on Machine Learning for Efficient Monitoring
Authors: Farooq Umer , Mushtaq Muhammad Faheem , Ullah Zahid , Ejaz Muhammad Talha , Akram Urooj , Aslam Sheraz 
Published: February 2025

ABSTRACTSolar energy, a renewable resource, is essential for the efficiency of solar photovoltaic (PV) panels. However, meteorological factors, such as solar irradiation, weather patterns, precipitation, and overall climate conditions, pose challenges to the seamless integration of energy production into the power grid. Accurate prediction of PV system power output is necessary to enhance the integration of renewable energy into the grid. The study focuses on utilizing machine learning (ML) methodologies for accurate forecasting of solar power generation, addressing challenges related to integrating renewable energy into the power grid. By analyzing power generation data and employing advanced ML models, the research aims to enhance the efficiency and predictability of solar energy systems. The significance of this study lies in its potential to optimize renewable energy production, improve grid stability, and contribute to the transition towards sustainable energy sources. This study assesses the appropriateness of ML approaches for accurately projecting solar power generation in half‐hourly cycles for the next day. The study consists of many analytical phases, including exploratory data analysis, power generation data analysis, and inverter data analysis, which are carried out on two separate power plants. The following step is to conduct comparative analyses. The data are analyzed using ML models like gradient boosting classifiers and linear regressions. The first power plant produces the best results, with an amazing 0.97% accuracy utilizing the gradient boosting classifier and linear regression classifier. Contrarily, the second power plant achieved an accuracy of 0.61% with the gradient boosting classifier and 0.62% with the linear regression models. This study's techniques and insights can help PV plant operators and electricity market stakeholders make informed decisions to optimize the use of generated PV power, minimize waste, plan for system preservation, reduce costs, and facilitate the widespread integration of PV power into the electricity grid.