bluuebunny/crossref_metadata_2025 huggingface dataset:
First‐order Sobolev spaces, self‐similar energies and energy measures on the Sierpiński carpet
Authors: Murugan Mathav , Shimizu Ryosuke
Published: February 2025

For any , we construct ‐energies and the corresponding ‐energy measures on the Sierpiński carpet. A salient feature of our Sobolev space is the self‐similarity of energy. An important motivation for the construction of self‐similar energy and energy measures is to determine whether or not the Ahlfors regular conformal dimension is attained on the Sierpiński carpet. If the Ahlfors regular conformal dimension is attained, we show that any optimal Ahlfors regular measure attaining the Ahlfors regular conformal dimension must necessarily be a bounded perturbation of the ‐energy measure of some function in our Sobolev space, where is the Ahlfors regular conformal dimension. Under the attainment of the Ahlfors regular conformal dimension, the ‐Newtonian Sobolev space corresponding to any optimal Ahlfors regular metric and measure is shown to coincide with our Sobolev space with comparable norms, where is the Ahlfors regular conformal dimension.
Two‐Dimensional Conductive Metal‐Organic Frameworks: Promising Materials for Advanced Energy Storage
Authors: Zhang Guang , Chen Long
Published: February 2025

With the rapid development of science and technology and for a sustainable future, the main energy resources in the world are transitioning from fossil fuels to electricity which is conceived to play a predominant role in the future. Therefore, it is essential to develop high‐performance energy‐storage devices such as supercapacitors and rechargeable batteries; even though they are commercialized, intense research efforts are still devoted to further improve the device performances, e.g. energy density, safety, durability and the charging rate. In this respect, exploring new advanced materials for better devices is a promising approach. The recently emerged two‐dimensional conductive metal‐organic frameworks (2D c‐MOFs) with their inherent electrical conductivities and porosity, rich redox active sites and tailor‐made architectures and functions have attracted considerable attention among energy‐storage community. The initial research results reveal 2D c‐MOFs are superb and advantageous electrode materials for advanced energy storage.
Exploring the Potential of Quantum Dot‐Sensitized Solar Cells: Innovation and Insights.
Authors: Singh Jyoti , Thareja Rakhi , Malik Pragati
Published: February 2025

Photovoltaic technologies have garnered significant attention towards generating renewable and clean energy from solar power. Quantum‐dot‐sensitized solar cells represent a promising third‐generation photovoltaic technology that offers alternatives to conventional silicon‐based solar cells due to their unique properties, their favourable optoelectronic properties for photovoltaic applications including simplified manufacturing, lower processing temperatures, enhanced flexibility, semi‐transparent design, and a theoretical efficiency up to 44%. The unique characteristic of tailoring the size and composition of quantum dots makes them valuable absorber materials capable of efficiently harnessing a broader range of the solar spectrum. The potential of quantum dot‐sensitized solar cells to revolutionize the field of photovoltaic technology is a cause for optimism. However, the major limitation of the overall power conversion efficiency lies in their inability to absorb ultraviolet and near‐infrared. Therefore, a photovoltaic technology that can effectively harness the entire solar spectrum becomes imperative. This review discusses the synthesis and light conversion mechanisms of these solar cells. Additionally, this review offers an overview of the various advancements made in quantum dot‐sensitized solar cells for enhancement in the efficiency of energy conversion. It focuses on the light‐absorbing materials used, their efficiency, and the advantages and drawbacks of quantum dot solar cell technology.
Computational Studies of Enzymes for C–F Bond Degradation and Functionalization
Authors: Cunningham Kendra M. , Shin Wook , Yang Zhongyue J.
Published: February 2025

Organofluorine compounds have revolutionized chemical and pharmaceutical industries, serving as essential components in numerous applications and aspects of modern life. However, their bioaccumulation and resistance to degradation have resulted in environmental pollution, posing significant risks to human and animal health. The exceptionally strong C–F bond in these compounds makes their degradation challenging, with current methods often requiring extreme experimental conditions. Therefore, the development of eco‐friendly approaches that operate under milder conditions is crucial, with enzyme‐mediated C–F bond cleavage strategies emerging as a particularly promising solution. In this review, we present an overview of how computational approaches, including molecular docking, molecular dynamics simulations, quantum mechanics/molecular mechanics calculations, and bioinformatics, have been utilized to investigate the mechanisms underlying enzymatic C–F bond degradation and functionalization. This review highlights how these computational approaches provide critical insights into the atomic‐level interactions and energetics underlying enzymatic processes, offering a foundation for the rational design and engineering of enzymes capable of addressing the challenges posed by fluorinated compounds. This review covers several types of enzymes including: fluoroacetate dehalogenases, cysteine dioxygenase, L‐2‐haloacid dehalogenase, cytochrome P450, fluorinase and tyrosine hydroxylase.
Enhancing Reproducibility in Chemotaxis Assays for Caenorhabditis elegans
Authors: Cesar Leona , Morud Julia
Published: February 2025

The ability of Caenorhabditis elegans (C. elegans) to navigate complex environments is essential for their survival. This natural behavior is commonly used in chemotaxis assays, which are important tools for studying the function of sensory neurons and neural circuits. Chemotaxis has been essential for discovering fundamental functions in neuronal signaling during the past decades. However, a lack of thoroughly optimized and standardized procedures can lead to variable results that can be difficult to interpret. To improve reproducibility, we optimized several aspects of chemotaxis protocols by testing different odorant concentrations, numbers of worms, and assay durations, as well as the preparation of chemotaxis plates and the washing procedures of worms. The usage of a 2‐choice or a 4‐choice assay was also evaluated. Our new protocol improves the clarity of results and simplifies worm counting. The protocol optimization is condensed into a 5‐day step‐by‐step protocol that increases the reproducibility of chemotaxis in C. elegans. Compared to previously published chemotaxis protocols, the revised method reduces day‐to‐day variability using an improved and standardized assay design that ensures clear and reliable results. Several key components in the assay preparation and during the assay have been evaluated based on previous protocols, such as odor concentration, worm density, and assay length. By considering multiple factors that influence the worm's behavior, our optimized protocol enhances the reproducibility of chemotaxis assays in C. elegans, making them more reliable and accessible for studying phenotypes related to olfaction and neural circuit behavior. © 2025 The Author(s). Current Protocols published by Wiley Periodicals LLC.

Basic Protocol: Optimized Chemotaxis Assay for C. elegans
Redefining Cell Culture Using a 3D Flipwell Co‐culture System: A Mimetic for Gut Architecture and Dynamics In Vitro
Authors: Beamer Maria A. , Furuta Saori
Published: February 2025

Gut mucosae are composed of stratified layers of microbes, a selectively permeable mucus, an epithelial lining, and connective tissue homing immune cells. Studying cellular and chemical interactions between the gut mucosal components has been limited without a good model system. We have engineered a three‐dimensional (3D) multi‐cellular co‐culture system we coined “3D Flipwell system” using cell culture inserts stacked against each other. This system allows an assessment of the impact of a gut mucosal environmental change on interactions between gut bacteria, epithelia, and immune cells. As such, this system can be utilized in examining the effects of exogenous stimuli, such as dietary nutrients, bacterial infection, and drugs, on the gut mucosa that could predetermine how these stimuli might influence the rest of body. Here, we describe the methods of construction and application of the new 3D Flipwell system we utilized previously in assessing the crosstalk between the gut mucosa and macrophage polarization. We demonstrate the physiological responses of different components of the co‐cultures to Sepiapterin (SEP), the precursor of the nitric oxide synthase cofactor tetrahydrobiopterin (BH4). We reported previously that SEP induces a pro‐immunogenic shift of macrophages having acquired an immune suppressive phenotype. We also showed that SEP induces a defense mechanism of commensal gut bacteria. The protocol describing the assembly and use of the 3D Flipwell co‐culture system herein would grant its utility in evaluating the concurrent effects of pharmacologic and microbiologic stimuli on gut mucosal components. © 2025 The Author(s). Current Protocols published by Wiley Periodicals LLC.

Basic Protocol 1: 3D Flipwell construction, assembly, and collagen coating

Basic Protocol 2: Flipwell cell seeding and cell culture

Basic Protocol 3: Addition of bacterial culture to the Flipwell system

Basic Protocol 4: Flipwell disassembly for scanning electron microscopy (SEM) studies

Basic Protocol 5: Immunofluorescence antibody staining for confocal microscopy
RSSI‐Based Indoor Distance Estimation in Wi‐Fi IoT Application Using AI Approaches
Authors: Mane Sarika , Kulkarni Makarand , Gupta Sudha
Published: February 2025

In 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

The 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

Estimating 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

Current 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

Dispersal 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

Background

Polycystic 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.

Methods

214 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.

Results

Women 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.

Conclusions

Women 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

Recent 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.
Photocatalytic Nitrogen Fixation Materials and Mechanistic Features: State of the Art and Future Perspectives
Authors: Malavasi Lorenzo , Tedesco Costanza , Giovilli Giulia
Published: February 2025

The essential role ammonia occupies, considering both its major industrial use for nitrogen‐rich fertilizers and the possibility of being utilized as medium for energy storage, clashes with the environmental drawbacks of the Haber‐Bosch process, its main production method. This review investigates the potential of photocatalytic nitrogen fixation (PNF) as an eco‐friendly approach, driven by solar energy and inspired by natural nitrogenase enzymes. It traces the historical development of nitrogen fixation methods and focuses on recent advancements in photocatalytic materials, including metal oxides, sulfides, and composite systems. Strategies to enhance catalytic performance ‐ including doping, defect engineering, and heterojunction construction – are showcased, thus providing a way to mitigate low conversion efficiency and electron‐hole recombination. The work concludes by presenting emergent materials that could revolutionize the field, offering new paths for sustainable ammonia production.
Synthesis of 4H‐imidazol‐4‐ones from α‐Amino acids, and their Antioxidant Activities using Electron Transfer Methods
Authors: Okram Mukherjee Singh , Devi Mayanglambam Maneeta , Devi Keisham Subharani , Devi Thangjam Linda , Singh Thokchom Prasanta
Published: February 2025

A novel series of 4H‐imidazol‐4‐ones, designated as compounds 5(a–l), were synthesized from phenylalanine and tyrosine. This synthesis involved derivatization of amino acids with phenylacetyl chloride, followed by cyclocondensation with aromatic amines using PCl3 and a deep eutectic solvent as both catalyst and solvent. Amide intermediates were first produced via the Schotten‐Baumann reaction. These intermediates then underwent cyclization in the presence of PCl3 and deep‐eutectic solvent, leading to the formation of imidazolones as potent antioxidants. The newly synthesized compounds were evaluated for their antioxidant activities using several electron transfer‐based assays, including DPPH, ABTS, FRAP, and CUPRAC. Among the tested compounds, compound 5i exhibited excellent antioxidant activity compared to the standard reference drugs, ascorbic acid and Trolox.
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

Free‐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

In 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

Understanding 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

Dielectrophoresis (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

Analyzing 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

Information 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

Solar 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.
“Assessing Predictability of Environmental Time Series With Statistical and Machine Learning Models”
Authors: Newlands Nathaniel K. , Lyubchich Vyacheslav
Published: February 2025

The relative merits of machine learning and statistical methods are discussed recently by Bonas et al. 2004, who raise important open questions for the statistical community regarding the value‐added benefits of data science and the future role of environmental statistics. Specifically, they identify three major knowledge gaps where statistics is seen as crucial to strengthening inference in machine learning (ML): to provide an ML model‐based framework amenable to explainability, to determine the best approach for quantifying uncertainty in relation to complex environmental dynamics, and to comprehensively identify ML's value‐added benefits. We continue this discussion by exploring these general questions and sharing our perspective and insights from our modeling of marine and terrestrial ecosystem dynamics. We propose several lines of inquiry where environmental statisticians and data scientists could collaboratively advance predictive analytics.
Hypermotor temporal lobe epilepsy revisited‐expanding the spectrum
Authors: Rudrabhatla Pavan Kumar , Nandana Jayakumari , Vilanilam George C. , Thomas Bejoy , Kesavadas Chandrasekharan , Radhakrishnan Ashalatha , Menon Ramshekhar N.
Published: February 2025

Objective

Typical semiology with associated 5As (Antecedent, Aura, Arrest, Automatisms, and Amnesia) is the prototypical seizure manifestation of temporal lobe epilepsy (TLE) in adults. However, patients with TLE can present with seizure phenomenology referred to as “hypermotor TLE” (HMS‐TLE) which can mimic extratemporal focal epilepsy. We studied the clinico‐electrographic, imaging profile, and surgical outcomes of drug‐resistant HMS‐TLE patients.

Methods

We analyzed the ictal signs and electroencephalography data of HMS‐TLE patients who became seizure free following epilepsy surgery with a minimum follow‐up of 1 year and compared them with patients with typical TLE (1:2 ratio). Hypermotor seizures were identified based on the presence of stereotyped ictal complex movements at seizure onset including body rocking, pelvic thrusting, and bimanual and bipedal movements with/without affective components.

Results

Out of 684 patients with drug‐resistant TLE who underwent surgery during 2010–2020, 16 patients (2.34%) met criteria for HMS‐TLE and were compared with 32 patients with typical TLE. Predominant “hypermotor” components included motor agitation, bicycling/kicking movements, body gyration followed by violent automatisms, and pelvic thrusting. Age at epilepsy onset, age at video telemetry recording, age at surgery, duration of epilepsy at surgery, gender distribution, and presence of aura were comparable between the two groups. History of febrile seizures (31.3% vs. 65.6%; p‐value = .024), presence of hippocampal sclerosis on MRI, and histopathology (56.3% vs. 90.6%; p‐value = .006 and 50% vs. 96.9%; p‐value = .002, respectively) were lower, and temporal neocortical abnormalities on MRI (87.5% vs. 59.4%, p‐value = .048), dysplasia on histopathological evaluation (25% vs. 0%, p‐value = .003) were higher in HMS‐TLE. HMS‐TLE group had lower number of patients with focal‐onset ictal patterns (37.5% vs. 90.6%, p‐value = .0001).

Significance

Hypermotor seizures are rare in TLE, however it does not preclude non‐invasive selection for surgery. Hence, physicians should be aware of this subtype of drug‐resistant TLE as early intervention can have good surgical outcomes, akin to typical TLE.
Integrating Deep Learning Into an Energy Framework for Rapid Regional Damage Assessment and Fragility Analysis Under Mainshock‐Aftershock Sequences
Authors: Tao Yuxuan , Xu Zhao‐Dong , Wei Yaxin , Liu Xin‐Yu , Dong Yao‐Rong , Dai Jun
Published: February 2025

This study introduces an energy‐based framework for rapidly assessing damage and fragility in regional buildings under mainshock‐aftershock sequences. First, the ultimate energy of the structure is derived from the relationship between two time‐varying parameters: effective intrinsic energy and input energy. An energy‐based damage index is then defined, with uncertainties of the structure and earthquake quantified through Latin hypercube sampling. Subsequently, a Gaussian Process model, enhanced with K‐Means clustering and Bayesian optimization, is employed to predict the structural ultimate energy. A Convolutional Neural Network‐Long Short‐Term Memory Network with an Attention mechanism model with a weighted loss function is developed to capture the structural energy time‐history responses, integrating correlation analysis and hyperparameter optimization. A comparative analysis is performed with previous machine learning models. The framework's effectiveness is validated through a comparative study with the inter‐story drift ratio (IDR) index. Finally, the framework is applied to Zeytinburnu in Istanbul, Turkey. The results indicate that reducing the dimensionality of the database through correlation analysis effectively decreases data dimensions while maintaining accuracy. In rapid damage assessment tasks, energy is a superior damage indicator compared to IDR, as it correlates positively with critical parameters such as building height and peak ground acceleration (PGA). It enables a tenfold reduction in response data, enhancing training efficiency by 7.4 times. PGAas/PGAms of 1.0 is recommended for analyzing mainshock‐aftershock effects, providing a more comprehensive perspective on structural performance and ensuring a conservative estimate of regional structural fragility.
An Optimized Dual Generative Hyperbolic Graph Adversarial Network With Multi‐Factor Random Permutation Pseudo Algorithm Based Encryption for Secured Industrial Healthcare Data Transferring
Authors: Muthulakshmi R. Mahesh , Anithaashri T. P
Published: February 2025

The industrial healthcare system suffers from severe security threats while sharing sensitive medical information. Inefficiency in processing data and misclassification of data are some of the problems faced by the system. This paper introduces a new framework, Deep Greylag‐GHGAN, to address these problems. The proposed framework consists of a Dual Generative Hyperbolic Attention Graph Adversarial Network (DG‐HGAN) with a Multi‐Factor Random Permutation Pseudo Algorithm‐based encryption system to monitor the health and ensure secure and efficient data transfer. The healthcare data undergo authentication based on a Multi‐Factor Role‐Based Access Control (MFRBAC). Then encryption with Improved Secure Encryption with Energy Optimization using Random Permutation Pseudo Algorithm (ISEEO‐RPPA) is implemented to secure the data transfer. To clean it, pre‐processing by Grid Constrained Data Cleansing Methods improves the data quality concerning noise reduction and normalization data besides redundancy removals. Classification is conducted with optimized DG‐HGAN through an application of Greylag Goose Optimization (GGO) to achieve high‐accuracy results with efficiency in execution. Experimental results show that there is a 93% improvement in security and a 99.9% accuracy in data classification compared to the existing methodologies. This comprehensive approach ensures the secure handling of sensitive medical data while maintaining processing efficiency and accuracy, making it a promising solution for real‐world industrial healthcare applications.
Lattice Homomorphic Assisted Privacy Preserving Electronic Health Records Data Transmission in Internet of Medical Things Using Blockchain
Authors: Vijay Anand R. , Alagiri I. , Jayalakshmi P. , Brahmam Madala Guru , Abdullah Azween Bin
Published: February 2025

The advancement of healthcare technology, along with the integration of the Internet of Medical Things (IoMT), has improved the efficacy of patient treatment. However, IoMT devices continue to encounter significant security challenges, such as data tampering and unauthorized modifications during data sharing, which threaten the privacy and integrity of medical records. Although advancements in blockchain and cryptography technologies, current methodologies inadequately address these difficulties. To address these security gaps, this paper introduces a novel security framework that leverages lightweight cryptography and blockchain technology for safeguarding Electronic Health Records (EHRs). The proposed approach employs an Improved Merkel Tree (IMM) hashing technique for data integrity, an Amended Elliptic Scheme (AES) for secure key generation, and Lattice Homomorphic Re‐encryption (LHoRe) for data encryption. Key optimization is performed using the Opposition‐Based Coati Optimization technique, while encrypted data is stored in the Inter Planetary File System (IPFS). Performance evaluation shows that the proposed framework achieves a high average trust value of 98.75% and reduces data retrieval latency to 2.24%, offering a more secure and efficient solution for IoMT data sharing compared to existing methods.
Jaw‐Muscle Structure and Function in Primates: Insights Into Muscle Performance and Feeding‐System Behaviors
Authors: Taylor Andrea B. , Holmes Megan A. , Laird Myra F. , Terhune Claire E.
Published: February 2025

The jaw‐adductor muscles drive the movements and forces associated with primate feeding behaviors such as biting and chewing as well as social signaling behaviors such as wide‐mouth canine display. The past several decades have seen a rise in research aimed at the anatomy and physiology of primate chewing muscles to better understand the functional and evolutionary significance of the primate masticatory apparatus. This review summarizes variation in jaw‐adductor fiber types and muscle architecture in primates, focusing on physiological, architectural, and behavioral performance variables such as specific tension, fatigue resistance, muscle and bite force, and muscle stretch and gape. Paranthropus and Australopithecus are used as one paleontological example to showcase the importance of these data for addressing paleobiological questions. The high degree of morphological variation related to sex, age, muscle, and species suggests future research should bracket ranges of performance variables rather than focus on single estimates of performance.
Continuous Adjoint to Proudman's Formula for Aeroacoustic Shape Optimization
Authors: Farhikhteh M. Erfan , Papoutsis Kiachagias E. M. , Giannakoglou K. C.
Published: February 2025

This paper presents an approach for aeroacoustic optimization through the reduction of acoustic sources, based on the integration of Proudman's formula into a continuous adjoint framework coupled with the Reynolds‐averaged Navier–Stokes equations, for the first‐time. The development includes the adjoint to the turbulence model. Here, Proudman's formula is used to compute acoustic emissions of turbulent flows around aerodynamic bodies using the turbulent kinetic energy and specific rate of dissipation. Broadband noise generation through Proudman's formula is initially validated for a case including the flow around an isolated airfoil. Subsequently, the sensitivity derivatives of an objective function quantifying acoustic sources are verified against finite differences, with optimizations of two isolated airfoils and the MEXICO wind turbine following. Optimizations are conducted by extending the tool in OpenFOAM, developed and made publicly available by the group. During the optimization, constraints on the lift force, the drag force, the pitching moment coefficient, the torque, the trailing edge thickness, and airfoil volume are imposed, depending on the case. The geometries and grids are parameterized using PARSEC and morphing boxes based on volumetric B‐Splines. The optimizations result in shapes with reduced acoustic sources while preserving aerodynamic efficiency, highlighting the effectiveness of the proposed method and programmed software.