bluuebunny/crossref_metadata_2025_split huggingface dataset:
Containerized deep learning in agriculture: Orchestrating GoogleNet with Kubernetes on high performance computing
Authors: Hasan Syed Humaid , Hasan Syeda Huyam , Khan Usman Ali , Hasan Syed Hamid 
Published: April 2024

SummarySmart Farming has become a cornerstone of modern agriculture, offering data‐driven insights and automation that optimize resource utilization and increase crop yields. The use of cutting‐edge technologies in agriculture has given rise to Smart Farming, which has transformed traditional farming practices into efficient, data‐driven operations. This paper explores the synergy between high‐performance computing (HPC) systems, Kubernetes orchestration, GoogleNet architecture, and containerization to redefine the future of farming. At the heart of this transformation lies the GoogleNet architecture, a deep learning powerhouse recognized for its efficiency and accuracy in image recognition tasks. The orchestration capabilities of Kubernetes, a versatile tool for managing containerized workloads efficiently on HPC clusters. Hence, in this work, we investigate the intricacies of deploying GoogleNet‐based deep learning models within containerized environments orchestrated by Kubernetes on HPC infrastructure. It explores resource optimization, scalability, security, and adaptability, all tailored to the unique demands of the agricultural domain to evaluate the effectiveness of the given technique it is compared with the existing techniques namely Hermes, Horus, CYBELE, and RZ‐SHAN. The attained ranges of proposed method of various measures of accuracy, precision, recall, and F1‐score are 98.65%, 97.45%, 97.87%, and 98.12% for the Pilot Wheat Ear dataset. Also, the processing time for the proposed approach is 181.50 and 120.2 m for the Pilot Wheat Ear Dataset and the Pilot soya bean farming dataset. The latency of the proposed approach attains a lower value of 1.5 and 1.1 s pilot soya bean farming dataset and Pilot Wheat Ear dataset. The experimental outcome demonstrates the efficiency of the proposed approaches to improve Smart Farming agriculture.
Hollow Polyethyleneimine Nanoparticles with Drug Loaded DNA for Chemotherapeutic Applications
Authors: Rucci Brendan , Boyle Brian , Byrne Mark 
Published: April 2024

AbstractThe next generation of anticancer agents are emerging from rationally designed nanostructured materials. This work involved the synthesis and characterization of novel hollow DNA‐conjugated gold nanoparticles (DNA−AuNPs) for controlled drug delivery. Polyethyleneimine (PEI) was bound to AuNPs, forming polymer‐shell nanoparticles. Dissolution of the gold core via iodine formed hollow core polymeric nanoparticles (HCPNPs) and a high density (85 molecules/particle) of DNA intercalated with daunorubicin was conjugated. Particles were spherical with an average diameter of 105.7±17.3 nm and zeta potential of 20.4±3.54 mV. We hypothesize the DNA backbone electrostatically condensed to the primary amines on the surface of the particle toroidally, weaving itself within the polymer shell. During the DNA intercalation process, increasing the ionic concentration and decreasing the amine/phosphate ratio 10‐fold increased drug intercalation 64 % and 61 %, respectively, allowing us to determine the optimal method of particle synthesis. As intercalation sites increased with increasing DNA strand length, drug loading increased. An average of 874±40.1 daunorubicin molecules were loaded per HCPNP. HCPNPs with drug intercalated DNA have strong potential to be clinically efficacious drug delivery vehicles due to the versatility of DNA and high drug loading capacities.
Cover Feature: Direct Conjugation of Gallium‐(III)‐Corroles to Short Interfering RNA(siRNA) Providing Real‐Time siRNA Imaging and Gene Silencing (ChemPlusChem 6/2024)
Authors: Giorgees Ifrodet , Affe Vanessa , Williams Islah , Desaulniers Jean‐Paul 
Published: April 2024

AbstractInvited for this month′s cover is the group of Jean‐Paul Desaulniers at Ontario Tech University. The cover picture shows the successful conjugation of a GaIII‐corrole to an siRNA to enable live cell imaging. Read the full text of the article at 10.1002/cplu.202400084.
In Vitro Assessment of Cardiac Fibroblast Activation at Physiologic Stiffness
Authors: Goldsmith Robert S. , Tsan Yao‐Chang , Scissors Rachel E. , Helms Adam S. , Brody Matthew J. 
Published: April 2024

AbstractCardiac fibroblasts (CF) are an essential cell type in cardiac physiology, playing diverse roles in maintaining structural integrity, extracellular matrix (ECM) synthesis, and tissue repair. Under normal conditions, these cells reside in the interstitium in a quiescent state poised to sense and respond to injury by synthesizing and secreting collagen, vimentin, hyaluronan, and other ECM components. In response to mechanical and chemical stimuli, these “resident” fibroblasts can undergo a transformation through a continuum of activation states into what is commonly known as a “myofibroblast,” in a process critical for injury response. Despite progress in understanding the contribution of fibroblasts to cardiac health and disease, much remains unknown about the signaling mediating this activation, in part owing to technical challenges in evaluating CF function and activation status in vitro. Given their role in monitoring the ECM, CFs are acutely sensitive to stiffness and pressure. High basal activation of isolated CFs is common due to the super‐physiologic stiffness of traditional cell culture substrates, making assays dependent on quiescent cells challenging. To overcome this problem, cell culture parameters must be tightly controlled, and the use of dishes coated with biocompatible reduced‐stiffness substrates, such as 8‐kPa polydimethylsiloxane (PDMS), has shown promise in reducing basal activation of fibroblasts. Here, we describe cell culture protocol for maintaining CF quiescence in vitro to enable a dynamic range for the assessment of activation status in response to fibrogenic stimuli using PDMS‐coated coverslips. Our protocol provides a cost‐effective tool to study fibroblast signaling and activity, allowing researchers to better understand the underlying mechanisms involved in cardiac fibrosis. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC.Basic Protocol 1: Generation of 8‐kPa polydimethylsiloxane (PDMS)/gelatin‐coated coverslips for cardiac fibroblast cell cultureBasic Protocol 2: Isolation of adult cardiac fibroblasts and plating onto PDMS coverslipsBasic Protocol 3: Assessment of cardiac fibroblast activation by α smooth muscle actin (αSMA) immunocytochemistry
Strategic mapping of the environmental social governance landscape in finance – A bibliometric exploration through concepts and themes
Authors: Thomas Ann Susan , Jayachandran Ambili , Biju Ajithakumari Vijayappan Nair 
Published: April 2024

AbstractA substantial surge in ESG research has triggered a wide dissemination of research across various domains, underscoring the need for an extensive appraisal of the complex ESG landscape. This paper is grounded on a sample of 441 documents retrieved from the Web of Science database, spanning from 2007 to 2023. A strategic mapping was performed to decipher the cumulative scientific knowledge by delving into the interrelations among various facets and identifying the field's basic, motor, niche, and emerging themes. We also explore the thematic evolution of ESG across distinct time frames, indicating ESG's inception, evolution, and diversification over the years. Our findings contribute to the ESG literature by categorizing it into seven clusters across the four quadrants. Results of the strategic mapping accentuate the obsolete, over‐researched and promising areas of ESG research. We find research on CSR, socially responsible investments and ESG‐firm performance themes as over‐researched areas calling for diversification. Promising research areas include the emerging association of ESG with portfolio construction, green innovations and investments, ESG controversies, information asymmetry, divergence, greenwashing, AI‐enabled universal rating mechanism, up‐grading and down‐grading of ESG scores and ESG‐linked compensations. Thematic evolution of ESG research underpins the culmination of its interplay with sustainability, emerging markets, board diversity, and green financing. Finally, the factorial analysis strengthens the reliability of the main findings, making it robust. This paper stands as the initial venture providing a comprehensive exploration of conceptual linkages, shifts, and advancements within the ESG literature through thematic mapping, offering an original contribution to the field.
A switched‐capacitor‐based active‐interleaved‐network with high voltage gain
Authors: Han Chongyang , Wu Yimin , Dai Zhien , Wu Weibin , Zhang Fangren , Lai Rongguang , Tang Luxin , Hou Junwei 
Published: April 2024

SummaryA high voltage gain characteristic is required in fuel cell vehicles to achieve voltage matching between low voltage fuel cell and high voltage DC‐link bus. In this paper, a non‐isolated high step‐up dc‐dc converter based on switched‐capacitor (SC) and active‐interleaved‐network (AIN) is proposed. The main advantages of the converter are the high voltage gain without an extremely high duty cycle, automatic current sharing of inductors, reduced input current ripple, and lower device voltage stress when compared to the topologies with the same output voltage. Detailed descriptions of the converter including operation principle and dynamic response analysis are presented in this manuscript. To verify the correctness of the theoretical analysis and feasibility of the proposed converter, a 500 W prototype with 20 V input and 200 V output is built.
Host genetics and larval host plant modulate microbiome structure and evolution underlying the intimate insect–microbe–plant interactions in Parnassius species on the Qinghai‐Tibet Plateau
Authors: Su Chengyong , Xie Tingting , Jiang Lijun , Wang Yunliang , Wang Ying , Nie Ruie , Zhao Youjie , He Bo , Ma Junye , Yang Qun , Hao Jiasheng 
Published: April 2024

AbstractInsects harbor a remarkable diversity of gut microbiomes critical for host survival, health, and fitness, but the mechanism of this structured symbiotic community remains poorly known, especially for the insect group consisting of many closely related species that inhabit the Qinghai‐Tibet Plateau. Here, we firstly analyzed population‐level 16S rRNA microbial dataset, comprising 11 Parnassius species covering 5 subgenera, from 14 populations mostly sampled in mountainous regions across northwestern‐to‐southeastern China, and meanwhile clarified the relative importance of multiple factors on gut microbial community structure and evolution. Our findings indicated that both host genetics and larval host plant modulated gut microbial diversity and community structure. Moreover, the effect analysis of host genetics and larval diet on gut microbiomes showed that host genetics played a critical role in governing the gut microbial beta diversity and the symbiotic community structure, while larval host plant remarkably influenced the functional evolution of gut microbiomes. These findings of the intimate insect–microbe–plant interactions jointly provide some new insights into the correlation among the host genetic background, larval host plant, the structure and evolution of gut microbiome, as well as the mechanisms of high‐altitude adaptation in closely related species of this alpine butterfly group.
Beauty in the beast – Placozoan biodiversity explored through molluscan predator genomics
Authors: Eitel Michael , Osigus Hans‐Jürgen , Brenzinger Bastian , Wörheide Gert 
Published: April 2024

AbstractThe marine animal phylum Placozoa is characterized by a poorly explored cryptic biodiversity combined with very limited knowledge of their ecology. While placozoans are typically found as part of the epibenthos of coastal waters, known placozoan predators, namely small, shell‐less sea slugs belonging to the family Rhodopidae (Mollusca: Gastropoda: Heterobranchia), inhabit the interstitium of seafloor sediment. In order to gain further insights into this predator–prey relationship and to expand our understanding of placozoan ecological niches, we screened publicly available whole‐body metagenomic data from two rhodopid specimens collected from coastal sediments. Our analysis not only revealed the signatures of three previously unknown placozoan lineages in these sea slug samples but also enabled the assembly of three complete and two partial mitochondrial chromosomes belonging to four previously described placozoan genera, substantially extending the picture of placozoan biodiversity. Our findings further refine the molecular phylogeny of the Placozoa, corroborate the recently established taxonomic ranks in this phylum, and provide molecular support that known placozoan clades should be referred to as genera. We finally discuss the main finding of our study – the presence of placozoans in the sea floor sediment interstitium – in the context of their ecological, biological, and natural history implications.
Hypocapnia is associated with increased in‐hospital mortality and 1 year mortality in acute heart failure patients
Authors: Zhang Lei , Sun Yiwu , Sui Xin , Zhang Jian , Zhao Jingshun , Zhou Runfeng , Xu Wenjia , Yin Chengke , He Zhaoyi , Sun Yufei , Liu Chang , Song Ailing , Han Fei 
Published: April 2024

AbstractAimsBoth hypercapnia and hypocapnia are common in patients with acute heart failure (AHF), but the association between partial pressure of arterial carbon dioxide (PaCO2) and AHF prognosis remains unclear. The objective of this study was to investigate the connection between PaCO2 within 24 h after admission to the intensive care unit (ICU) and mortality during hospitalization and at 1 year in AHF patients.Methods and resultsAHF patients were enrolled from the Medical Information Mart for Intensive Care IV database. The patients were divided into three groups by PaCO2 values of <35, 35–45, and >45 mmHg. The primary outcome was to investigate the connection between PaCO2 and in‐hospital mortality and 1 year mortality in AHF patients. The secondary outcome was to assess the prediction value of PaCO2 in predicting in‐hospital mortality and 1 year mortality in AHF patients. A total of 2374 patients were included in this study, including 457 patients in the PaCO2 < 35 mmHg group, 1072 patients in the PaCO2 = 35–45 mmHg group, and 845 patients in the PaCO2 > 45 mmHg group. The in‐hospital mortality was 19.5%, and the 1 year mortality was 23.9% in the PaCO2 < 35 mmHg group. Multivariate logistic regression analysis showed that the PaCO2 < 35 mmHg group was associated with an increased risk of in‐hospital mortality [hazard ratio (HR) 1.398, 95% confidence interval (CI) 1.039–1.882, P = 0.027] and 1 year mortality (HR 1.327, 95% CI 1.020–1.728, P = 0.035) than the PaCO2 = 35–45 mmHg group. The PaCO2 > 45 mmHg group was associated with an increased risk of in‐hospital mortality (HR 1.387, 95% CI 1.050–1.832, P = 0.021); the 1 year mortality showed no significant difference (HR 1.286, 95% CI 0.995–1.662, P = 0.055) compared with the PaCO2 = 35–45 mmHg group. The Kaplan–Meier survival curves showed that the PaCO2 < 35 mmHg group had a significantly lower 1 year survival rate. The area under the receiver operating characteristic curve for predicting in‐hospital mortality was 0.591 (95% CI 0.526–0.656), and the 1 year mortality was 0.566 (95% CI 0.505–0.627) in the PaCO2 < 35 mmHg group.ConclusionsIn AHF patients, hypocapnia within 24 h after admission to the ICU was associated with increased in‐hospital mortality and 1 year mortality. However, the increase in 1 year mortality may be influenced by hospitalization mortality. Hypercapnia was associated with increased in‐hospital mortality.
Are national or regional surveys useful for nowcasting regional jobseekers? The case of the French region of Pays‐de‐la‐Loire
Authors: Cariou Clément , Charles Amélie , Darné Olivier 
Published: April 2024

AbstactIn this paper we develop nowcasting models for the Pays‐de‐la‐Loire's jobseekers, a dynamic French regional economy. We ask whether these regional nowcasts are more accurate by only using the regional data or by combining the national and regional data. For this purpose, we use penalized regressions, random forest, and dynamic factor models as well as dimension reduction approaches. The best nowcasting performance is provided by the DFM estimated on the regional and regional‐national databases as well as the Elastic‐Net model with a prior screening step for which the national data are the most frequently selected data. For the latter, it appears that the Change in foreign orders in the industry sector, the OECD Composite leading indicator, and the BdF Business sentiment indicator are among the major predictors.
Forecasting healthcare service volumes with machine learning algorithms
Authors: Yang Dong‐Hui , Zhu Ke‐Hui , Wang Ruo‐Nan 
Published: April 2024

AbstractAs an efficacious solution to remedying the imbalance of medical resources, the online medical platform has burgeoned expeditiously. Apt allotment of medical resources on the medical platform can facilitate patients in reasonably selecting physicians and time slots, coordinating doctors' clinical arrangements, and generating precise projections of medical platform service volume to enhance patient satisfaction and alleviate physicians' workload. To this end, grounded in the data‐driven method, this paper assembles an exhaustive feature set encompassing hospital features, physician features, and patient features. Through feature selection, appropriate features are screened, and machine learning algorithms are leveraged to accurately forecast doctors' online consultation volume. Subsequently, to glean the influence relationship between online medical services and offline medical services, this paper introduces features of offline medical services such as hospital registration volume and regional gross domestic product (GDP) to solve the prediction of offline medical service volume using online medical information. The findings signify that online data feature prediction can pinpoint superior machine learning models for online medical platform service volume (with the optimal accuracy up to 96.89%). Online features exert a positive effect on predicting offline medical service volume, but the accuracy declines to some degree (the optimal accuracy is 73%). Physicians with favorable reputations on the online platform are more susceptible to attain higher offline appointment volumes when online consultation volume is a vital feature impacting offline appointment volume.
The novel hierarchical clustering approach using self‐organizing map with optimum dimension selection
Authors: Tripathi Kshitij 
Published: April 2024

AbstractIntroductionData clustering is an important field of machine learning that has applicability in wide areas, like, business analysis, manufacturing, energy, healthcare, traveling, and logistics. A variety of clustering applications have already been developed. Data clustering approaches based on self‐organizing map (SOM) generally use the map dimensions (of the grid) ranging from 2 × 2 to 8 × 8 (4–64 neurons [microclusters]) without any explicit reason for using the particular dimension, and therefore optimized results are not obtained. These algorithms use some secondary approaches to map these microclusters into the lower dimension (actual number of clusters), like, 2, 3, or 4, as the case may be, based on the optimum number of clusters in the specific data set. The secondary approach, observed in most of the works, is not SOM and is an algorithm, like, cut tree or the other.MethodsIn this work, the proposed approach will give an idea of how to select the most optimal higher dimension of SOM for the given data set, and this dimension is again clustered into the lower actual dimension. Primary and secondary, both utilize the SOM to cluster the data and discover that the weight matrix of the SOM is very meaningful. The optimized two‐dimensional configuration of SOM is not the same for every data set, and this work also tries to discover this configuration.ResultsThe adjusted randomized index obtained on the Iris, Wine, Wisconsin diagnostic breast cancer, New Thyroid, Seeds, A1, Imbalance, Dermatology, Ecoli, and Ionosphere is, respectively, 0.7173, 0.9134, 0.7543, 0.8041, 0.7781, 0.8907, 0.8755, 0.7543, 0.5013, and 0.1728, which outperforms all other results available on the web and when no reduction of attributes is done in this work.ConclusionsIt is found that SOM is superior to or on par with other clustering approaches, like, k‐means or the other, and could be used successfully to cluster all types of data sets. Ten benchmark data sets from diverse domains like medical, biological, and chemical are tested in this work, including the synthetic data sets.
Enhancing cooling performance and economic analysis of a vertical earth air heat exchanger (VEAHE) through geometric shape optimization
Authors: Rostami Mohammadreza Hasandust 
Published: April 2024

AbstractThe cooling and heating sector is responsible for the highest energy consumption in the building sector, comprising approximately 30% of the total. Extensive research has been conducted to address this issue and minimize energy consumption through the implementation of innovative technologies. Among these technologies, the passive earth‐air heat exchanger (EAHE) has proven highly effective in reducing energy usage in the cooling and heating sector. This research focused on optimizing U‐shaped EAHE systems and examined their functional and thermal‐fluidic parameters through numerical analysis. The simulation employed COMSOL Multiphysics software, and the results obtained were in excellent agreement with experimental data. The study investigated a base case, as well as five optimized cases with varying inlet velocities, to evaluate performance. The findings revealed that increasing the working fluid's inlet velocity led to a decrease in the system's thermal efficiency. However, at higher velocities, the economic parameters for energy production showed improvements. Specifically, the system generated a maximum energy output of 9132 W in the fifth case, operating at a velocity of 2 m/s. Additionally, the system achieved an impressive performance coefficient of approximately 5.13 in the same case, with an inlet velocity of 0.46 m/s. Notably, the lowest recorded output temperature of the system was 22°C at the specified inlet velocity.
Enhanced physics‐informed neural networks for efficient modelling of hydrodynamics in river networks
Authors: Luo Xiao , Yuan Saiyu , Tang Hongwu , Xu Dong , Ran Qihua , Cen Yuhao , Liang Dongfang 
Published: April 2024

AbstractThis article enhances the physics‐informed neural networks (PINNs) method to effectively model the hydrodynamics of real‐world river networks with irregular cross‐sections. First, we pre‐process hydraulic parameters to optimize training speed without compromising accuracy, achieving a 91.67% acceleration compared with traditional methods. To address the vanishing gradient problem, layer normalization is also incorporated into the architecture. We also introduce novel physical constraints—water level range and junction node equations—to ensure effective training convergence and enrich the model with additional physical insights. Two practical case studies using HEC‐RAS benchmarks demonstrate that our improved PINN method can predict river network hydrodynamics with less data and is less sensitive to time step size, allowing for longer computational time steps. Incorporating physical knowledge, our enhanced PINN methodology emerges as an efficient and promising avenue for modelling the complexities of hydrodynamic processes in natural river networks.