bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ventilation and features of the lung environment dynamically alter modeled intrapulmonary aerosol exposure from inhaled electronic cigarettes
Authors: Li Liqiao , Chen Haoxuan , Zhu Yifang , Harui Airi , Roth Michael D. 
Published: December 2024

AbstractElectronic cigarettes (e-cigs) fundamentally differ from tobacco cigarettes in their generation of liquid-based aerosols. Investigating how e-cig aerosols behave when inhaled into the dynamic environment of the lung is important for understanding vaping-related exposure and toxicity. A ventilated artificial lung model was developed to replicate the ventilatory and environmental features of the human lung and study their impact on the characteristics of inhaled e-cig aerosols from simulated vaping scenarios. Compared to static conditions, normal breathing decreased peak particle number concentrations (PNCs) and area under the curve (AUC) by 40% and 70%, respectively, and increased particle decay rates fourfold. However, even with ventilation, intrapulmonary PNC levels exceeded 2 × 106 particles/mL in a 4-puff vaping session. Both respiratory rate and tidal volume modulated e-cig aerosol exposure in a manner inversely proportional to minute ventilation. The modeled lung environment (37 °C, 88% relative humidity) also significantly altered particle size distributions by facilitating aerosol transformations such as hygroscopic growth, which further impacted e-cig aerosol exposure and particle removal. This work highlights the dynamic nature of intrapulmonary exposures and underscores the need to account for lung physiology and environmental factors when assessing inhaled e-cig aerosols.
A stacked CNN and random forest ensemble architecture for complex nursing activity recognition and nurse identification
Authors: Rahman Arafat , Nahid Nazmun , Schuller Björn , Ahad Md Atiqur Rahman 
Published: December 2024

AbstractNursing activity recognition has immense importance in the development of smart healthcare management and is an extremely challenging area of research in human activity recognition. The main reasons are an extreme class-imbalance problem and intra-class variability depending on both the subject and the recipient. In this paper, we apply a unique two-step feature extraction, coupled with an intermediate feature ‘Angle’ and a new feature called mean min max sum to render the features robust against intra-class variation. After intermediate and final feature extraction, we use an ensemble of a random forest classifier and a stacked convolutional neural network (S-CNN) model to detect activities and users. Unlike traditional CNN, the S-CNN takes the input feature channels in separate pathways with equal importance, which makes it robust to intra-class variation and produces accurate results. We apply this method to two benchmark open-source nurse care activity data sets. Our algorithm is robust enough to recognize both activity and user (Nurse) simultaneously. During the recognition process, this algorithm automatically finds the important features in the data set. Using this algorithm, the highest testing accuracies were achieved for activity recognition on the two (publicly available in IEEE DataPort) benchmark data sets: The CARECOM Nurse Care Activity (70.6% accuracy) and the Heiseikai Nurse Care Activity data set (85.7% accuracy). Moreover, the highest accuracy achieved for user identification on Data Set 1 and Data Set 2 is 78.2% and 92.7%, respectively.
Design and implementation of the fractional-order controllers for a real-time nonlinear process using the AGTM optimization technique
Authors: Jayaram Sabavath , Venkatesan Nithya 
Published: December 2024

AbstractSpherical tanks have been predominantly used in process industries due to their large storage capability. The fundamental challenges in process industries require a very efficient controller to control the various process parameters owing to their nonlinear behavior. The current research work in this paper aims to propose the Approximate Generalized Time Moments (AGTM) optimization technique for designing Fractional-Order PI (FOPI) and Fractional-Order PID (FOPID) controllers for the nonlinear Single Spherical Tank Liquid Level System (SSTLLS). This system features a large dead time, and its real-time modeling generally represents a Single Input Single Output (SISO) model. However, in practice, the derived SISO model is often a First Order Plus Dead Time (FOPDT) model, necessitating an effective controller to maintain the tank’s steady-state level. In this research, the proposed AGTM method, based on the conventional Proportional Integral (PI) and Proportional Integral Derivative (PID) controllers, is compared with the FOPI and FOPID controllers for the nonlinear SSTLLS. The performance of these controllers is contrasted using metrics such as Integral Squared Error (ISE) and Integral Absolute Error (IAE), as well as time-domain characteristics containing Rise time, Peak time, Settling time, Peak overshoot, and Steady-state error. The implementation of the aforementioned controllers is done in simulation and real-time employing the MATLAB software environment and the Data Acquisition (DAQ) device National Instrument NI-DAQmx 6211. The simulation and experimental results demonstrate the exceptional performance of the designed Fractional-Order controllers based on the proposed method which offers an increased degree of freedom despite the more complex design process.
Night flight facilitates late breeding catch-up in a long-distance migratory seabird
Authors: Siddiqi-Davies Katrina , Wynn Joe , Padget Oliver , Bond Sarah , Danielsen Jóhannis , Fayet Annette L. , Fisher-Reeves Lewis , Freeman Robin , Gillies Natasha , Kirk Holly , Maurice Lou , Morgan Greg , Syposz Martyna , Shoji Akiko , Guilford Tim 
Published: December 2024

AbstractLong-distance migrants must optimise their timing of breeding to capitalise on resources at both breeding and over-wintering sites. In species with protracted breeding seasons, departing earlier on migration might be advantageous, but is constrained by the ongoing breeding attempt. Here we investigated how breeding timing affects migratory strategies in the Manx shearwater (Puffinus puffinus), a trans-hemispheric migratory seabird with large temporal variation in the onset of breeding. Using a geolocator tracking dataset, we found that that later-laying shearwaters had shorter overall breeding periods, yet still departed later for autumn migration. Earlier laying birds had increased migratory duration, stopped with greater frequency and at sites of higher chlorophyll concentration. Meanwhile, later departing birds flew more at night during migratory stints, and night flight generally increased with moon illumination, which could reflect moonlight providing the light conditions required for visually guided flight. Accordingly, birds that experienced higher levels of moon illumination whilst migrating had shorter migration durations. Here we provide an example of migratory behaviour being adjustable with breeding timing, allowing birds to both complete breeding and capitalise on resource availability at the wintering site.
Challenges in adjusting scoring matrices when comparing functional motifs with non-standard compositions
Authors: Jarnot Patryk 
Published: December 2024

AbstractMethods for scoring matrix adjustment decrease the significance of biased residues to better detect homology between protein sequences. This is because non-homologous proteins often contain fragments with non-standard compositions that are strikingly similar to each other. However, these fragments are also functionally important in proteins and are receiving an increasing attention from the scientific community. In this study, we described why the gold standard method for scoring matrix adjustment is unable to emphasise frequent amino acids. Further, we used BLAST to align collagen-like domains with and without the scoring matrix adjustment and compared the results. We found that the scoring matrices were adjusted in the opposite direction to the optimal state. Therefore, turning off the adjustment improved alignment quality of collagen-like domains, but scoring matrices still need refinement. This study provides a detailed analysis of why the gold standard method fails, and opens doors for new methods to adjust scoring matrices for functional motifs with non-standard compositions.
A novel MAC protocol for power line communication with integrated NFC for smart home applications
Authors: Haridoss G. , Arun Pandian J. , Sivaranjani K. , Thanga Mariappan L. 
Published: December 2024

AbstractOur day-to-day lives have become comfortable and sophisticated with many recent technologies. Likewise, today’s world has been enhanced by new innovative technologies. Everyone is moving towards smart cities and smart homes. Power-Line Communication (PLC) is a breakthrough communication technology that supports cost-effectiveness with maximum speed and reliable communication performance, producing no interference complications for wireless signal transmission. It operates over existing power line cables, avoiding other transmission mediums by using low-cost PLC chipsets. Transmission between two cities is performed using power grid technology. However, with a PLC chipset, data transmission is managed locally within towns and inside homes. To avoid inconveniences such as noise, significant signal reduction, and time period fluctuations, a novel Medium Access Control (MAC) protocol is proposed to improve the stability of the PLC network and data transmission efficiency. Using a Modified Intra-cluster MAC algorithm based on time allocation to enhance the reliability of access nodes in the PLC network is a novel attempt. Moreover, this work ensures enhancements in data transmission speed and security. Finally, the security-enhanced PLC network is integrated with Near Field Communication (NFC) technology for intelligent home applications.
Imaging pathology in archived cornea with Fuchs’ endothelial corneal dystrophy including tissue reprocessing for volume electron microscopy
Authors: Maeno Sayo , Lewis Philip N. , Young Robert D. , Oie Yoshinori , Nishida Kohji , Quantock Andrew J. 
Published: December 2024

AbstractFuchs’ endothelial corneal dystrophy (FECD) is a common sight-threatening condition characterised by pathological changes in the posterior cornea. Here we report observations by light, transmission and volume scanning electron microscopy on changes in the endothelium and matrix associated with the characteristic deformations of Descemet’s membrane, termed guttae. Specimens were archived full-thickness human corneal tissue, removed during graft surgery, that had been fixed, stained and embedded by conventional processing methods for examination by transmission electron microscopy more than 40-years previously. Intact archived samples can be extremely valuable where, as with FECD, new cell-based methods of therapy now avoid excision of the full cornea thickness and any tissue excised is inferior for study. Volume electron microscopy, in particular serial block face scanning electron microscopy (SBF SEM), employing backscatter electron detection from resin-embedded specimens, has become an invaluable technique for 3D imaging of biological samples. However, archived specimens are normally considered unsuitable for imaging as conventional processing methods generate low backscatter electron yield. To overcome this for SBF SEM, we subjected epoxy resin-embedded specimens to de-plastination, then applied additional contrasting agents, uranyl acetate and lead acetate, prior to re-embedding. Selected regions of interest in the new resin blocks were examined in a scanning electron microscope equipped for SBF SEM and serial image datasets acquired. Enhanced contrast enabled 3D reconstruction of endothelium and guttae in Descemet’s membrane over large tissue volumes.
A new band selection framework for hyperspectral remote sensing image classification
Authors: Phaneendra Kumar B. L. N. , Vaddi Radhesyam , Manoharan Prabukumar , Agilandeeswari L. , Sangeetha V. 
Published: December 2024

AbstractDimensionality Reduction (DR) is an indispensable step to enhance classifier accuracy with data redundancy in hyperspectral images (HSI). This paper proposes a framework for DR that combines band selection (BS) and effective spatial features. The conventional clustering methods for BS typically face hard encounters when we have a less data items matched to the dimensionality of the accompanying feature space. So, to fully mine the effective information, BS is established using dual partitioning and ranking. The bands from the dual partitioning have undergone informative band selection via ranking. The reduced band subset is then given to a hemispherical reflectance-based spatial filter. Then, finally, a Convolutional Neural Network (CNN) is used for effective classification by incorporating three-dimensional convolutions. On a set of three hyperspectral datasets - Indian Pines, Salinas, and KSC, the proposed method was tested with different state-of-the-art techniques. The classification results are compared using quantitative and qualitative measures. The reported overall accuracy is 99.92% on Indian Pines, 99.94% on Salinas, and 97.23% on the KSC dataset. Also, the Mean Spectral Divergence values are 42.4, 63.75, and 41.2 on the three datasets respectively, which signifies the effectiveness of band selection. The results have clearly shown the impact of the band selection proposed and can be utilized for a wide variety of applications.
Research on truck-drone collaborative route planning for rural logistics delivery services
Authors: Wang Yong , Yang Suo , Wang Xi Vincent , Wang Lihui 
Published: December 2024

AbstractThis study investigates the implementation of collaborative route planning between trucks and drones within rural logistics to improve distribution efficiency and service quality. The paper commences with an analysis of the unique characteristics and challenges inherent in rural logistics, emphasizing the limitations of traditional methods while highlighting the advantages of integrating truck and drone technologies. It proceeds to review the current state of development for these two technologies and presents case studies that illustrate their application in rural logistics. Building on this analysis, a collaborative path planning method is proposed, establishing a path optimization model and designing an enhanced simulated annealing algorithm. The effectiveness of this approach is validated through simulation experiments, which reveal that the collaborative delivery system for trucks and drones can significantly boost efficiency, lower costs, and improve service quality. In conclusion, the research findings and potential future research directions are discussed to offer theoretical insights and practical guidance for further innovations in rural logistics technology.
Roughness integration across fingers within compared with across the hands
Authors: Roberts Roberta D. 
Published: December 2024

AbstractFeeling a texture typically involves sliding the fingers of a hand across that surface or rubbing the surface between the thumb and another digit. Texture signals appear to be integrated across the digits of a hand with perceived roughness at one finger swayed in the direction of texture touched by another finger of the same hand. To date, one study has reported similar integrative effects when the pairs of digits belong to different hands. This contrasts with observations from studies of tactile detection and tactile attention where the patterns of interactions between the digits depend on whether the digits belong to the same hand or not. The present experiments revisit the question of hand identity on multidigit roughness perception using two interval forced choice (2IFC) discrimination and single interval absolute magnitude estimation (AME). Pairs of tactile gratings were actively touched using the thumb and index fingers from the same or different hands. Attention was directed towards roughness at the thumb and index finger sensations were to be ignored. For both discrimination and ratings tasks, roughness perceived at the thumb varied with the textures touched by the index finger suggesting integration of roughness cues from the two digits. This integration occurred despite differences in the two tasks such as working memory requirements. Notably, roughness signals were integrated when originating in pairs of digits on the same hand but not when from different hands. These findings add to a body of evidence based on experiments using different stimuli and tasks, suggesting that hand identity affects interactions across the digits.
Engagement in moderate-intensity physical activity supports overnight memory retention in older adults
Authors: Chappel-Farley Miranda G. , Berisha Destiny E. , Dave Abhishek , Sanders Rachel M. , Kline Christopher E. , Janecek John T. , Sattari Negin , Lui Kitty K. , Chen Ivy Y. , Neikrug Ariel B. , Benca Ruth M. , Yassa Michael A. , Mander Bryce A. 
Published: December 2024

AbstractPreserving the ability to vividly recall emotionally rich experiences contributes to quality of life in older adulthood. While prior works suggest that moderate-intensity physical activity (MPA) may bolster memory, it is unclear whether this extends to emotionally salient memories consolidated during sleep. In the current study, older adults (mean age = 72.3 ± 5.8) completed an overnight polysomnography assessment with emotional memory tested before and after sleep and a self-report questionnaire assessing habitual PA. Results show that better negative emotional memory consolidation was associated with the frequency and duration of MPA. Statistically replacing 30 min of lower-intensity activity with MPA was associated with better negative emotional memory consolidation. MPA may enhance sleep-dependent consolidation of negative memories in older adults, with modest increases in MPA yielding significant consolidation benefits. Findings may guide interventions and inform public health recommendations by demonstrating that substituting even short durations of low-intensity activity for MPA could produce significant cognitive gains in older adulthood.
The wrapped Rama distribution
Authors: Bell William , Nadarajah Saralees 
Published: December 2024

AbstractA new one-parameter distribution is proposed for circular data based on wrapping. Most distributions constructed via wrapping do not yield elementary expressions for their mathematical properties. Yet the new distribution yields elementary expressions for all of its mathematical properties. Better fits of the new distribution over the three-parameter distribution due to Jones and Pewsey10 and six other wrapped distributions including four that have two parameters each are shown for at least two data sets. Better fits were assessed in terms of probability plots, density plots, values of Akaike information criterion and values of Bayesian information criterion.
Maximum power point tracking in fuel cells an AI controller based on metaheuristic optimisation
Authors: Preethiraj P.M. , J. Belwin Edward 
Published: December 2024

AbstractThe increasing concern about global warming and the depletion of fossil fuel reserves has led to a growing interest in alternative energy sources, particularly fuel cells (FCs). These green energy sources convert chemical energy into electrical energy, offering advantages such as quick initiation, high power density, and efficient operation at low temperatures. However, the performance of FCs is influenced by changes in operating temperature, and optimal efficiency is achieved by operating them at their maximum power point (MPP). This study uses Proton Exchange Membrane Fuel Cells (PEMFCs) to charge electric vehicles (EVs), amplifying the voltage generated by the FC using the Interleaved Boost-Cuk (IBC) converter. The optimal tracking of the maximum power output is achieved using the Improved Mayfly optimized (IMO) Cascaded Adaptive Neuro Fuzzy Inference System (Cascaded ANFIS). The study uses MATLAB to simulate the task in various settings and analyze the relevant performances, demonstrating enhanced efficiency and power tracking outputs. The proposed converter efficiency has improved to 94% with a minimal part count of 2 switched configurations. configuration. The applied control logic, in my opinion, Cascaded ANFIS is capable of operating the BLDC with an operational efficiency of 98.92%, including better output voltage generations of 350 V.
Promotora intervention for metabolic and mental health to reduce type 2 diabetes risk: a pilot randomized controlled trial
Authors: Vargas Maria C. , Katz Joshua B. , Lopez Azucena , Carrillo Arturo , Gregory Dyanna L. , O’Brien Matthew J. 
Published: December 2024

AbstractThe objective of this study was to develop and evaluate a novel behavioral intervention (PRIME2) that integrates evidence-based approaches for reducing diabetes risk and perceived stress. We conducted a pilot randomized controlled trial of the 16-session PRIME2 intervention vs. usual care among 40 Spanish-speaking Latinx adults with prediabetes and body mass index (BMI) ≥ 25 kg/m2. The 3-month change in weight and perceived stress were co-primary outcomes. Secondary outcomes were 3-month changes in hemoglobin A1c (HbA1c), blood pressure, and depressive symptoms. Study participants were women (95%) with mean age 49.9 (± 9.3) years, moderate levels of perceived stress, and risk factors for diabetes, including mean BMI 33.3 (± 5.4) kg/m2 and elevated HbA1c [mean 6.0% (± 0.2)]. PRIME2 participants demonstrated significantly greater 3-month weight loss relative to usual care (− 3.7lbs, p = 0.02). Reductions in perceived stress in both study arms were similar. There were no significant differences in secondary outcomes among PRIME2 participants vs. usual care. This pilot trial demonstrates the feasibility and preliminary weight loss effectiveness of the novel PRIME2 intervention, without observing a significant difference in perceived stress between study arms. Future research should include larger randomized samples that enable a definitive evaluation of intervention effects on both metabolic and mental health endpoints.
In vitro and in silico insights into antimicrobial and anticancer activities of novel imidazo[2,1-b][1,3,4]thiadiazoles
Authors: Dwarakanath Deepika , Nayak Yogeesha N. , Kulal Ananda , Pandey Samyak , Pai K Sreedhara Ranganath , Gaonkar Santosh L. 
Published: December 2024

AbstractThis study explores the design, synthesis, and evaluation of a novel series of isobenzofuran-based imidazo[2,1-b][1,3,4]thiadiazole derivatives, targeting their antimicrobial and anticancer properties. These compounds integrate the pharmacologically significant 1,3,4-thiadiazole and imidazole moieties, which are known for their potential in drug development, although imidazo[2,1-b][1,3,4]thiadiazole-based drugs are not yet available on the market. Therefore, the aim of this study is to develop novel derivatives that could serve as promising candidates for future therapeutic applications. The derivatives were synthesized in two steps and thoroughly characterized using IR, 1H NMR, 13C NMR, and mass spectrometry. All the derivatives had shown fairly good antimicrobial activity against four microorganisms (Escherichia coli, Staphylococcus aureus, Mycobacterium smegmatis and Candida albicans) with minimum inhibition concentration’s ranging from 0.14 to 0.59 mM. The anticancer activity of the compounds against MCF-7 cell lines showed promising activity, where three derivatives, 3a, 3c and 3d exhibited better inhibition than the standard, cisplatin. The highest anticancer activity was shown by the derivative 3c with an IC50 value of 35.81 μM. Molecular docking was studied to determine the docking poses and binding interaction of the derivatives with the protein bearing PDB: 5BNS and 3ZNR; ADME properties of the derivatives are also inferred which gives insights into the bioavailability. The molecular dynamics simulation of the derivative 3c with HDAC7 protien (PDB: 3ZNR) was evalauted to determine the stability of the interaction between the protein and the ligand.