bluuebunny/crossref_metadata_2025 huggingface dataset:
Immunoinformatics approaches in developing a novel multi-epitope chimeric vaccine protective against Saprolegnia parasitica
Authors: Choudhury Abhigyan , Kumar Pawan , Nafidi Hiba-Allah , Almaary Khalid S. , Wondmie Gezahign Fentahun , Kumar Ajit , Bourhia Mohammed
Published: January 2024

Saprolegnia parasitica is responsible for devastating infections in fish and poses a tremendous threat to the global aquaculture industry. Presently, no safe and effective control measures are available, on the contrary, use of banned toxic compounds against the pathogen is affecting humans via biomagnification routes. This pioneering study aims to design an effective multi-epitope multi-target vaccine candidate against S. parasitica by targeting key proteins involved in the infection process. The proteins were analyzed and linear B-cell epitopes, MHC class I, and class II epitopes were predicted. Subsequently, highly antigenic epitopes were selected and fused to a highly immunogenic adjuvant, 50S ribosomal protein L7/L12, to design a multi-epitope chimeric vaccine construct. The structure of the vaccine was generated and validated for its stereochemical quality, physicochemical properties, antigenicity, allergenicity, and virulence traits. Molecular docking analyses demonstrated strong binding interactions between the vaccine and piscine immune receptors (TLR5, MHC I, MHC II). Molecular dynamics simulations and binding energy calculations of the complexes, further, reflected the stability and favorable interactions of the vaccine and predicted its cytosolic stability. Immune simulations predicted robust and consistent kinetics of the immune response elicited by the vaccine. The study posits the vaccine as a promising solution to combat saprolegniasis in the aquaculture industry.
New micro-hole zone catheter reduces residual urine and mucosal microtrauma in a lower urinary tract model
Authors: Schrøder Brit , Tentor Fabio , Miclăuş Teodora , Stærk Kristian , Andersen Thomas Emil , Spinelli Michele , Rendeli Claudia , Del Popolo Giulio , Bagi Per , Nielsen Lene Feldskov
Published: January 2024

Urinary tract infections (UTIs) are the main complication associated with clean intermittent catheterization (CIC) and are facilitated by post-void residual urine and trauma to the mucosa during voiding. The risk of UTI may be diminished by reducing the residual volumes and preventing microtrauma caused by mucosal suction through the eyelets of conventional eyelet catheters (CEC). A new micro-hole zone catheter (MHZC) was developed and tested in an ex vivo porcine lower urinary tract model and in vivo, in pigs, against a CEC. It was shown that, irrespective of the micro-hole diameter, the new catheter ensured increased flowrates and significantly lower residual volumes at the first flow-stop. Furthermore, with a micro-hole diameter of 0.4 mm, mucosal suction was virtually eliminated, regardless of the insertion depth or simulated intra-abdominal pressure mimicking sitting or standing humans. Pressure profile experiments and endoscopy studies indicated that the bladder gradually folds against the drainage tip of the new catheter, without blocking the flow, and, unlike with the CEC, sharp pressure variations and flow-stops did not occur during voiding. The MHZC outperformed the CEC in all tested scenarios and decreased residual volumes, thus potentially decreasing the risk of UTIs.
Power suppression in EEG after the onset of SAH is a significant marker of early brain injury in rat models
Authors: Takasugi Yuji , Hishikawa Tomohito , Shimizu Tomohisa , Murai Satoshi , Haruma Jun , Hiramatsu Masafumi , Tokunaga Koji , Takeda Yoshimasa , Sugiu Kenji , Morimatsu Hiroshi , Date Isao
Published: January 2024

We analyzed the correlation between the duration of electroencephalogram (EEG) recovery and histological outcome in rats in the acute stage of subarachnoid hemorrhage (SAH) to find a new predictor of the subsequent outcome. SAH was induced in eight rats by cisternal blood injection, and the duration of cortical depolarization was measured. EEG power spectrums were given by time frequency analysis, and histology was evaluated. The appropriate frequency band and recovery percentage of EEG (defined as EEG recovery time) to predict the neuronal damage were determined from 25 patterns (5 bands × 5 recovery rates) of receiver operating characteristic (ROC) curves. Probit regression curves were depicted to evaluate the relationships between neuronal injury and duration of depolarization and EEG recovery. The optimal values of the EEG band and the EEG recovery time to predict neuronal damage were 10–15 Hz and 40%, respectively (area under the curve [AUC]: 0.97). There was a close relationship between the percentage of damaged neurons and the duration of depolarization or EEG recovery time. These results suggest that EEG recovery time, under the above frequency band and recovery rate, may be a novel marker to predict the outcome after SAH.
Stability of the retinal image under normal viewing conditions and the implications for neural adaptation
Authors: Manzanera Silvestre , Artal Pablo
Published: January 2024

Previous studies have demonstrated that the visual system adapts to the specific aberration pattern of an individual’s eye. Alterations to this pattern can lead to reduced visual performance, even when the Root Mean Square (RMS) of the wavefront error remains constant. However, it is well-established that ocular aberrations are dynamic and can change with factors such as pupil size and accommodation. This raises an intriguing question: can the neural system adapt to continuously changing aberration patterns? To address this question, we measured the ocular aberrations in four subjects under various natural viewing conditions, which included changes in accommodative state and pupil size. We subsequently computed the associated Point Spread Functions (PSFs). For each subject, we examined the stability in the orientation of the PSFs and analyzed the cross-correlation between different PSFs. These findings were then compared to the characteristics of a distribution featuring PSF shapes akin to random variations. Our results indicate that the changes observed in the PSFs are not substantial enough to produce a PSF shape distribution resembling random variations. This lends support to the notion that neural adaptation is indeed a viable mechanism even in response to continuously changing aberration patterns.
Urinary vanin-1, tubular injury, and graft failure in kidney transplant recipients
Authors: Alkaff Firas F. , Kremer Daan , Niekolaas Tessa M. , van den Born Jacob , Rimbach Gerald , Tseng Tzu-Ling , Berger Stefan P. , Bakker Stephan J. L. , de Borst Martin H.
Published: January 2024

We investigated whether urinary vascular non-inflammatory molecule-1 (vanin-1), a promising early-onset tubular injury marker, correlates with other established tubular injury markers and is associated with graft failure in kidney transplant recipients (KTR). We measured 24 h urinary vanin-1 excretion in 656 KTR (age 53 ± 13 years, 43% female, estimated glomerular filtration rate (eGFR) 53 ± 21 mL/min/1.73 m2) who had undergone kidney transplantation ≥ 1 year. The median 24 h urinary vanin-1 excretion was 145 [51–331] pmol/24 h. 24 h urinary vanin-1 excretion correlated weakly but significantly with other tubular injury markers (ρ = 0.14, p < 0.001 with urinary liver-type fatty acid binding protein, ρ = 0.13, p = 0.001 with urinary post-translationally modified fetuin-A protein, and ρ = 0.10, p = 0.011 with plasma neutrophil gelatinase-associated lipocalin) and with eGFR (ρ = − 0.13, p = 0.001). During a median follow-up of 7.4 [4.9–8.0] years, 94 (14%) KTR developed death-censored graft failure. In multivariable Cox regression analyses, 24 h urinary vanin-1 excretion was not associated with an increased risk of death-censored graft failure (adjusted hazard ratio [95% confidence interval] = 0.96 [0.86–1.07], p = 0.5). In conclusion, our findings do not support the role of urinary vanin-1 as a biomarker of graft failure after kidney transplantation.
A comparative patient-level prediction study in OMOP CDM: applicative potential and insights from synthetic data
Authors: Ahmadi Najia , Nguyen Quang Vu , Sedlmayr Martin , Wolfien Markus
Published: January 2024

The emergence of collaborations, which standardize and combine multiple clinical databases across different regions, provide a wealthy source of data, which is fundamental for clinical prediction models, such as patient-level predictions. With the aid of such large data pools, researchers are able to develop clinical prediction models for improved disease classification, risk assessment, and beyond. To fully utilize this potential, Machine Learning (ML) methods are commonly required to process these large amounts of data on disease-specific patient cohorts. As a consequence, the Observational Health Data Sciences and Informatics (OHDSI) collaborative develops a framework to facilitate the application of ML models for these standardized patient datasets by using the Observational Medical Outcomes Partnership (OMOP) common data model (CDM). In this study, we compare the feasibility of current web-based OHDSI approaches, namely ATLAS and “Patient-level Prediction” (PLP), against a native solution (R based) to conduct such ML-based patient-level prediction analyses in OMOP. This will enable potential users to select the most suitable approach for their investigation. Each of the applied ML solutions was individually utilized to solve the same patient-level prediction task. Both approaches went through an exemplary benchmarking analysis to assess the weaknesses and strengths of the PLP R-Package. In this work, the performance of this package was subsequently compared versus the commonly used native R-package calledMachine Learning in R 3(mlr3), and its sub-packages. The approaches were evaluated on performance, execution time, and ease of model implementation. The results show that the PLP package has shorter execution times, which indicates great scalability, as well as intuitive code implementation, and numerous possibilities for visualization. However, limitations in comparison to native packages were depicted in the implementation of specific ML classifiers (e.g., Lasso), which may result in a decreased performance for real-world prediction problems. The findings here contribute to the overall effort of developing ML-based prediction models on a clinical scale and provide a snapshot for future studies that explicitly aim to develop patient-level prediction models in OMOP CDM.
Biosurfactant and biopolymer producing microorganisms from West Kazakhstan oilfield
Authors: Shaimerdenova Ulzhan , Kaiyrmanova Gulzhan , Lewandowska Wioleta , Bartoszewicz Marek , Swiecicka Izabela , Yernazarova Aliya
Published: January 2024

Microbiological enhanced oil recovery (MEOR) uses indigenous or exogenous microorganisms and nutrients to enhance oil production through synthesis of metabolites reducing oil viscosity and surface tension. In order to find bacteria suitable for MEOR, we studied 26 isolates from wells in the Akingen oilfield in West Kazakhstan. Six of them were selected for further analysis based on their ability to reduce surface tension to less than 40 mN/m, with the A9 isolate exhibiting tension reduction values of 32.76 ± 0.3 mN/m. Based on the morphological features, biochemical activities, and the 16S rRNA gene, the isolates were classified to the Bacillus subtilis group. In the phylogenetic analysis the isolates grouped into two main clusters. Genes encoding the surfactin synthetase subunits were found in A2, A8, A9, A12, PW2, only the PW2 strain had lchAA encoding lichenysin, while sacB encoding levan was noted in A2, A8, A9, and A12. The expression of srfAB, srfAC, and sacB tested with qPCR varied among strains. Nevertheless, whereas temperature moderately affects the expression level, with the highest level recorded at 40 °C, salinity significantly impacts the expression of the genes encoding biosurfactants. B. subtilis strains isolated in the study, especially A9, are promising for microbial-enhanced oil recovery.
A neutrophil extracellular trap-related risk score predicts prognosis and characterizes the tumor microenvironment in multiple myeloma
Authors: Zhao Zhijia , Huo Yuan , Du Yufeng , Huang Yanan , Liu Hongchen , Zhang Chengtao , Yan Jinsong
Published: January 2024

Multiple myeloma (MM) is a distinguished hematologic malignancy, with existing studies elucidating its interaction with neutrophil extracellular traps (NETs), which may potentially facilitate tumor growth. However, systematic investigations into the role of NETs in MM remain limited. Utilizing the single-cell dataset GSE223060, we discerned active NET cell subgroups, namely neutrophils, monocytes, and macrophages. A transcriptional trajectory was subsequently constructed to comprehend the progression of MM. Following this, an analysis of cellular communication in MM was conducted with a particular emphasis on neutrophils, revealing an augmentation in interactions albeit with diminished strength, alongside abnormal communication links between neutrophils and NK cells within MM samples. Through the intersection of differentially expressed genes (DEGs) between NET active/inactive cells and MM versus healthy samples, a total of 316 genes were identified. This led to the development of a 13-gene risk model for prognostic prediction based on overall survival, utilizing transcriptomics dataset GSE136337. The high-risk group manifested altered immune infiltration and heightened sensitivity to chemotherapy. A constructed nomogram for predicting survival probabilities demonstrated encouraging AUCs for 1, 3, and 5-year survival predictions. Collectively, our findings unveil a novel NET-related prognostic signature for MM, thereby providing a potential avenue for therapeutic exploration.
Diagnostic performance of deep learning in ultrasound diagnosis of breast cancer: a systematic review
Authors: Dan Qing , Xu Ziting , Burrows Hannah , Bissram Jennifer , Stringer Jeffrey S. A. , Li Yingjia
Published: January 2024

Deep learning (DL) has been widely investigated in breast ultrasound (US) for distinguishing between benign and malignant breast masses. This systematic review of test diagnosis aims to examine the accuracy of DL, compared to human readers, for the diagnosis of breast cancer in the US under clinical settings. Our literature search included records from databases including PubMed, Embase, Scopus, and Cochrane Library. Test accuracy outcomes were synthesized to compare the diagnostic performance of DL and human readers as well as to evaluate the assistive role of DL to human readers. A total of 16 studies involving 9238 female participants were included. There were no prospective studies comparing the test accuracy of DL versus human readers in clinical workflows. Diagnostic test results varied across the included studies. In 14 studies employing standalone DL systems, DL showed significantly lower sensitivities in 5 studies with comparable specificities and outperformed human readers at higher specificities in another 4 studies; in the remaining studies, DL models and human readers showed equivalent test outcomes. In 12 studies that assessed assistive DL systems, no studies proved the assistive role of DL in the overall diagnostic performance of human readers. Current evidence is insufficient to conclude that DL outperforms human readers or enhances the accuracy of diagnostic breast US in a clinical setting. Standardization of study methodologies is required to improve the reproducibility and generalizability of DL research, which will aid in clinical translation and application.
Spin-order-dependent magneto-elastic coupling in two dimensional antiferromagnetic MnPSe3 observed through Raman spectroscopy
Authors: Gillard Daniel J. , Wolverson Daniel , Hutchings Oscar M. , Tartakovskii Alexander I.
Published: January 2024

Layered antiferromagnetic materials have recently emerged as an intriguing subset of the two-dimensional family providing a highly accessible regime with prospects for layer-number-dependent magnetism. Furthermore, transition metal phosphorus trichalcogenides, MPX3 (M = transition metal; X = chalcogen) provide a platform on which to investigate fundamental interactions between magnetic and lattice degrees of freedom and further explore the developing fields of spintronics and magnonics. Here, we use a combination of temperature dependent Raman spectroscopy and density functional theory to explore magnetic-ordering-dependent interactions between the manganese spin degree of freedom and lattice vibrations of the non-magnetic sub-lattice via a Kramers-Anderson super-exchange pathway in both bulk, and few-layer, manganese phosphorus triselenide (MnPSe3). We observe a nonlinear temperature-dependent shift of phonon modes predominantly associated with the non-magnetic sub-lattice, revealing their non-trivial spin-phonon coupling below the Néel temperature at 74 K, allowing us to extract mode-specific spin-phonon coupling constants.
Defined microbial communities and their soluble products protect mice from Clostridioides difficile infection
Authors: Douchant Katya , He Shu-Mei , Noordhof Curtis , Greenlaw Jill , Vancuren Sarah , Schroeter Kathleen , Allen-Vercoe Emma , Sjaarda Calvin , Vanner Stephen J. , Petrof Elaine O. , Sheth Prameet M. , Guzman Mabel
Published: January 2024

Clostridioides difficileis the leading cause of antibiotic-associated infectious diarrhea. The development ofC.difficileinfection is tied to perturbations of the bacterial community in the gastrointestinal tract, called the gastrointestinal microbiota. Repairing the gastrointestinal microbiota by introducing lab-designed bacterial communities, or defined microbial communities, has recently shown promise as therapeutics againstC.difficileinfection, however, the mechanisms of action of defined microbial communities remain unclear. Using an antibiotic-C.difficilemouse model, we report the ability of an 18-member community and a refined 4-member community to protect mice from two ribotypes ofC.difficile(CD027, CD078;p < 0.05). Furthermore, bacteria-free supernatant delivered orally to mice from the 4-member community proteolyzedC.difficiletoxins in vitro and protected mice fromC.difficileinfection in vivo (p < 0.05). This study demonstrates that bacteria-free supernatant is sufficient to protect mice fromC.difficile; and could be further explored as a therapeutic strategy againstC.difficileinfection.
Enhanced future vegetation growth with elevated carbon dioxide concentrations could increase fire activity
Authors: Allen Robert J. , Gomez James , Horowitz Larry W. , Shevliakova Elena
Published: January 2024

Many regions of the planet have experienced an increase in fire activity in recent decades. Although such increases are consistent with warming and drying under continued climate change, the driving mechanisms remain uncertain. Here, we investigate the effects of increasing atmospheric carbon dioxide concentrations on future fire activity using seven Earth system models. Centered on the time of carbon dioxide doubling, the multi-model mean percent change in fire carbon emissions is 66.4 ± 38.8% (versus 1850 carbon dioxide concentrations, under fixed 1850 land-use conditions). A substantial increase is associated with enhanced vegetation growth due to carbon dioxide biogeochemical impacts at 60.1 ± 46.9%. In contrast, carbon dioxide radiative impacts, including warming and drying, yield a negligible response of fire carbon emissions at 1.7 ± 9.4%. Although model representation of fire processes remains uncertain, our results show the importance of vegetation dynamics to future increases in fire activity under increasing carbon dioxide, with potentially important policy implications.
Deep learning-based approach for high spatial resolution fibre shape sensing
Authors: Manavi Roodsari Samaneh , Freund Sara , Angelmahr Martin , Seppi Carlo , Rauter Georg , Schade Wolfgang , Cattin Philippe C.
Published: January 2024

Fiber optic shape sensing is an innovative technology that has enabled remarkable advances in various navigation and tracking applications. Although the state-of-the-art fiber optic shape sensing mechanisms can provide sub-millimeter spatial resolution for off-axis strain measurement and reconstruct the sensor’s shape with high tip accuracy, their overall cost is very high. The major challenge in more cost-effective fiber sensor alternatives for providing accurate shape measurement is the limited sensing resolution in detecting shape deformations. Here, we present a data-driven technique to overcome this limitation by removing strain measurement, curvature estimation, and shape reconstruction steps. We designed an end-to-end convolutional neural network that is trained to directly predict the sensor’s shape based on its spectrum. Our fiber sensor is based on easy-to-fabricate eccentric fiber Bragg gratings and can be interrogated with a simple and cost-effective readout unit in the spectral domain. We demonstrate that our deep-learning model benefits from undesired bending-induced effects (e.g., cladding mode coupling and polarization), which contain high-resolution shape deformation information. These findings are the preliminary steps toward a low-cost yet accurate fiber shape sensing solution for detecting complex multi-bend deformations.
Crystal structure analysis of helix–turn–helix type motifs in α,γ-hybrid peptides
Authors: Nalawade Sachin A. , Kumar Mothukuri Ganesh , Puneeth Kumar DRGKoppalu R. , Singh Manjeet , Dey Sanjit , Gopi Hosahudya N.
Published: January 2024

Design of helix–turn–helix type mimetics using short α,γ-hybrid peptides as helices and (E)-α,β-unsaturated γ-amino acids as conformationally rigid linkers and their conformations in single crystals are reported.
A high loading nanocarrier for the 5-fluorouracil anticancer drug based on chloromethylated graphene
Authors: Safaiee R. , Aminzadeh H. , Sardarian A. R. , Nasresfahani Sh. , Sheikhi M. H.
Published: January 2024

In the present work, we report a facile and simple strategy to functionalize graphene with the chloromethyl (CH2Cl) functional group as a nanoplatform for effectual loading of the 5-fluorouracil (5-FU) anticancer drug.
A theoretical study of the mechanism of cationic polymerization of isobutylene catalysed by EtAlCl2/t-BuCl with bis(2-chloroethyl)ether in hexanes
Authors: Yan Xinrong , Du Meng , Li Jiwei , Xue Ying , Wu Yibo , Zhang Hao , Wang Xin , Xu Dingguo
Published: January 2024

The mechanism of cationic polymerization of isobutylene catalyzed by t-BuCl/EADC combined with CEE in n-hexane solvent was studied by AIMD based metadynamics (MTD) simulations.
Structure and dynamics of dissociated and undissociated forms of nitric acid and their implications in interfacial mass transfer: insights from molecular dynamics simulations
Authors: Das Arya , Musharaf Ali Sk.
Published: January 2024

Nitric acid (HNO3) is widely used in various chemical and nuclear industries.
Ultrahigh zT from strong electron–phonon interactions and a low-dimensional Fermi surface
Authors: Ranganayakulu V. K. , Wang Te-Hsien , Chen Cheng-Lung , Huang Angus , Ma Ma-Hsuan , Wu Chun-Min , Tsai Wei-Han , Hung Tsu-Lien , Ou Min-Nan , Jeng Horng-Tay , Lee Chih-Hao , Chen Kuei-Hsien , Li Wen-Hsien , Brod Madison K. , Snyder G. Jeffrey , Chen Yang-Yuan
Published: January 2024

An ultrahigh zT of 2.7 at 700 K is achieved in a (Ge0.86Sb0.08Bi0.06)Te single crystal. This outstanding performance is attributed to one-dimensional-like electronic structure and the resulting strong electron–phonon interaction.
High-efficiency nickel recovery from spent electroless nickel plating solution: effective degradation of high-concentration nickel complexes to form a nickel ferrite nanomaterial via Fe3O4 catalytic oxidation
Authors: Lu Kaibin , Qin Jiemin , Hu Meihua , Hu Limeng , Mao Minlin , Li Xiaoqin , Lin Zhang , Liu Weizhen
Published: January 2024

High-concentration organic nickel complexes were efficiently degraded and converted into NiFe2O4 nanomaterials via Fe3O4 catalytic oxidation under alkaline conditions, with high removal efficiency of nickel (99.99%) and TOC (81.94%), respectively.
Direct and indirect oxidation removal of chloride ions from sulfuric acid wastewater using photoactivated PMS/PS: efficiency and mechanism
Authors: Dou Wenyue , Li Dong , Wu Jiaqi , Zhu Kaili , Wu Chenyang , Kong Linghao , Hu Xingyun
Published: January 2024

PMS and PS showed excellent performance in removing Cl(−i) as Cl2 from sulfuric acid wastewater under UV irradiation. Indirect oxidation via ·SO4− radicals played an essential role in removing Cl(−i).
Formation of nitro(so) by-products of concern during the treatment of phenolic compounds by the hydroxylamine-enhanced Fe(ii)/peroxymonosulfate process
Authors: Duan Jiebin , Guan Chaoting , Pang Su-yan , Jiang Jin
Published: January 2024

The transformation pathway of hydroxylamine and the formation mechanism of nitro(so) by-products during the treatment of phenols using the Fe(ii)/peroxymonosulfate/hydroxylamine system are elaborated.
Starch-based edible packaging: rheological, thermal, mechanical, microstructural, and barrier properties – a review
Authors: Santhosh Ravichandran , Ahmed Jasim , Thakur Rahul , Sarkar Preetam
Published: January 2024

The microstructures of starch-based edible films are shown in the following micrographs. A well-defined distribution of edible oils and cellulose nanoparticles on the starch surface can be clearly visualized.
Photocatalyst-free H2O-regulated and regiodivergent multicomponent hydrogenation/bifunctional sulfonylation of alkynes
Authors: Sun Jie , Wang Chaodong , Wu Chunlei , Wang Wenjian , Zeng Yue , Song Shengjie , Chen Zhiwei , Li Jianjun
Published: January 2024

An efficient multicomponent reaction for the preparation of vinyl sulfone was achieved by the hydrogenation/bifunctional sulfonylation of alkynyl esters with N-allyl bromodifluoroacetamide, DABSO, and H2O under visible-light-only conditions.
Cytochrome bd oxidase: an emerging anti-tubercular drug target
Authors: Saha Pallavi , Das Samarpita , Indurthi Harish K. , Kumar Rohit , Roy Arnab , Kalia Nitin Pal , Sharma Deepak K.
Published: January 2024

Cytochrome bd (cyt-bd) oxidase, one of the two terminal oxidases in the Mycobacterium tuberculosis (Mtb) oxidative phosphorylation pathway, plays an indispensable role in maintaining the functionality of the metabolic pathway under stressful conditions.
Hyper strength, high sensitivity integrated wearable signal sensor based on non-covalent interaction of an ionic liquid and bacterial cellulose for human behavior monitoring
Authors: Rong Xuhui , Ding Qijun , Chen Luzheng , Yang Shuo , Lou Jiang , Liu Zhuqing , Li Xia , Jiang Yifei , Wang Xiaolei , Han Wenjia
Published: January 2024

Ion-sensing hydrogels exhibit electrical conductivity, softness, and mechanical and sensory properties akin to human tissue, rendering them an ideal material for mimicking human skin.
Magnetically driven Janus conical vertical array for all-weather freshwater collection
Authors: Zhang Xiangyi , Zhu Mengyao , Chen Junhao , Wang Zongwei , Li Sanchuan , Yang Huiyu , Xu Hongman , He Guang , Deng Ziwei , Gu Shaojin , Liu Xin , Shang Bin
Published: January 2024

The strategy for fabricating a Janus conical vertical array is novel in the integration of solar vapor generation and fog collection, which has great significance for all-weather freshwater production.
Insights into the micellar catalysed efficient oxidation of 2- and 3-pentanol by cerium(iv) in a greener medium of SDS and STS
Authors: Kundu Sandip , Layek Mousumi , Mondal Subhodip , Mitra Mandira , Karmakar Priya , Rahaman Sk Mehebub , Mahali Kalachand , Acharjee Animesh , Saha Bidyut
Published: January 2024

A green envelop of aqueous micellar media generated by anionic surfactants SDS and STS catalyzes the Ce(iv) based oxidation of isomeric pentanols.
A high-sensitivity SnSe/Si heterojunction position-sensitive detector for ultra-low power detection
Authors: Hu Bing , Liu Yunjie , Zhang Bo , Guo Fuhai , Zhang Mingcong , Yu Weizhuo , Li Siqi , Hao Lanzhong
Published: January 2024

A large-area uniform SnSe nanorod array film was fabricated to form a SnSe/Si heterostructure PSD to realize high sensitivity and weak light detection.
Hydrogen production from complete dehydrogenation of hydrazine borane on carbon-doped TiO2-supported NiCr catalysts
Authors: Zhang Xiaolei , Yao Qilu , Wang Yuzhao , Liu Mengying , Lu Zhang-Hui
Published: January 2024

C-doped mesoporous TiO2-supported Cr(OH)3 modified Ni nanoparticles (Ni–Cr/CTO) with different morphologies were synthesized by a simple wet-chemical method and used as efficient catalysts for hydrogen production from hydrazine borane.
Methane steam reforming over Ni/YSZ cermet anode materials synthesized by different methods
Authors: Bassil S. , Caillot T. , Cadete Santos Aires F. J. , Meunier F. C. , Kaddouri A.
Published: January 2024

Ni/YSZ is a benchmark material for solid oxide fuel cells (SOFCs) but suffers from both Ni agglomeration and carbon deposition when hydrocarbons are used as fuels. Sol–gel-prepared catalysts offer superior activities.
A conductive and sodiophilic Ag coating layer regulating Na deposition behaviors for highly reversible sodium metal batteries
Authors: Chen Xiaomin , Zhou Xunzhu , Yang Zhuo , Hao Zhiqiang , Chen Jian , Kuang Wenxi , Shi Xiaoyan , Wu Xingqiao , Li Lin , Chou Shu-Lei
Published: January 2024

A conductive and sodiophilic Ag coating layer induces homogeneous Na+ ion and electric field distribution, guaranteeing uniform Na deposition with ultrahigh and ultrastable Coulombic efficiency of 99.9%.
Interfacial chemistry regulation using functional frameworks for stable metal batteries
Authors: Huang Yaohui , Geng Jiarun , Zhang Tong , Jiang Zhuoliang , Fang Hengyi , Hu Wei , Li Fujun
Published: January 2024

Recent advances on functional framework materials, including PCFs and IOFs, are summarized to regulate interfacial chemistry in metal batteries, which facilitate cation desolvation and metal nucleation for improved electrochemical performance.
Carbon dots derived from metformin by electrochemical synthesis with broad-spectrum antibacterial properties
Authors: Du Xin , Zhang Mengling , Ma Yurong , Zhang Yan , Li Wenwen , Hu Tao , Liu Yang , Huang Hui , Kang Zhenhui
Published: January 2024

Carbon dots (MCDs) with broad-spectrum antibacterial properties were synthesized from metformin by electrochemical oxidation. MCDs mainly affect the transport of nutrients and stimulate oxidative stress in SA and E. coli, respectively.
Tunable mechanochromic luminescence of benzofuran-fused pyrazine: effects of alkyl chain length and branching pattern
Authors: Nakamura Shotaro , Okubo Kohei , Nishii Yuji , Hirano Koji , Tohnai Norimitsu , Miura Masahiro
Published: January 2024

Tuning the alkyl chains of bisbenzofuropyrazines allowed control over the on/off switching of mechanochromic luminescence and self-recovery rates.
Fast-charging anodes for lithium ion batteries: progress and challenges
Authors: Ding Xiaobo , Zhou Qingfeng , Li Xiaodan , Xiong Xunhui
Published: January 2024

This article summarizes the basic principles, current research progress, advanced strategies and challenges of fast-charging anodes.
Last piece of the puzzle: lead-assisted assembly of an arsenopalladate nanostar as an antitumor metallodrug
Authors: Zhao Yue , Li Cheng-Lan , Chen Chao-Qin , Du Jing , Kortz Ulrich , Gong Teng , Yang Peng
Published: January 2024

Access to the long-sought nanostar of arsenopalladate is workable via host–guest assembly. The arsenate functions of such POP metallodrugs drive the ROS-induced apoptosis with great antitumor potential.
Impact of Occlusion Masks on Gender Classification from Iris Texture
Authors: Yáñez Claudio , Tapia Juan E. , Perez Claudio A. , Busch Christoph
Published: January 2024

Gender classification on normalized iris images has been previously attempted with varying degrees of success. In these previous studies, it has been shown that occlusion masks may introduce gender information; occlusion masks are used in iris recognition to remove non‐iris elements. When, the goal is to classify the gender using exclusively the iris texture, the presence of gender information in the masks may result in apparently higher accuracy, thereby not reflecting the actual gender information present in the iris. However, no measures have been taken to eliminate this information while preserving as much iris information as possible. We propose a novel method to assess the gender information present in the iris more accurately by eliminating gender information in the masks. This consists of pairing iris with similar masks and different gender, generating a paired mask using the OR operator, and applying this mask to the iris. Additionally, we manually fix iris segmentation errors to study their impact on the gender classification. Our results show that occlusion masks can account for 6.92% of the gender classification accuracy on average. Therefore, works aiming to perform gender classification using the iris texture from normalized iris images should eliminate this correlation.
The application of distributed autonomous organization governance mechanisms to civic medical data management
Authors: Cunningham James , Davies Nigel , Devaney Sarah , Holm Søren , Harding Mike , Neumann Victoria , Ainsworth John
Published: January 2024

Decentralized autonomous organizations (DAOs) have emerged as a novel governance mechanism that operates through distributed ledgers and smart contracts, enabling members to direct an organization's actions. The widespread adoption of DAOs has occurred in response to their utility in managing emergent semi‐structured projects and has led to the development of various innovative governance mechanisms. The mechanisms employed by DAOs has the potential to be generalized beyond their core financial domain to a wide range of use cases. In the medical field the use of blockchain and DAOs can provide secure and transparent access to medical data, while ensuring patient privacy. Civic access to medical data is a growing area of interest, where individuals have control over their own medical data and can share it with healthcare providers, researchers, and other stakeholders. DAOs can facilitate this civic access, enabling individuals to share their data securely and selectively with authorized parties for research and other purposes. This paper explores the use of DAOs to medical data sharing, with a focus on ownership, governance, and transaction models. An application framework and API that enables the deployment of DAO‐like organizations is derived and this approach is applied to the patient‐centric management of medical data.
A bitcoin service community classification method based on Random Forest and improved KNN algorithm
Authors: Gao Muyun , Lin Shenwen , Tian Xin , He Xi , He Ketai , Chen Shifeng
Published: January 2024

There are service communities with different functions in the Bitcoin transactions system. Identifying community categories helps to further understand the Bitcoin transactions system and facilitates targeted regulation of anonymized Bitcoin transactions. To this end, a Bitcoin service community classification method based on Random Forest and improved K‐Nearest Neighbor (KNN) algorithm is proposed. First, the transaction characteristics of different types of communities are analyzed and summarized, and the corresponding transaction features are extracted from the address and entity levels; then multiple classification algorithms are compared, the optimal model to filter the effective features is selected, and the feature vector of entity addresses is constructed. Finally, a classification model is constructed based on Random Forest and improved KNN algorithm to classify the entities. By constructing different classification models for experimental comparison, the accuracy and stability advantages of the proposed method for classification in service community classification research are verified.
Intelligent algorithms and methodologies for low‐carbon smart manufacturing: Review on past research, recent developments and future research directions
Authors: Joshi Sudhanshu , Sharma Manu
Published: January 2024

Significant attention has been given to low‐carbon smart manufacturing (SM) as a strategy for promoting sustainability and carbon‐free emissions in the manufacturing industry. The implementation of intelligent algorithms and procedures enables the attainment and enhancement of low‐carbon clever manufacturing processes. These algorithms facilitate real‐time monitoring and predictive maintenance, ensuring efficient and sustainable operations and data‐driven decision‐making, increasing resource utilisation, waste reduction, and energy efficiency. The research examines the utilisation of algorithms in the context of low‐carbon smart manufacturing, encompassing machine learning, optimisation algorithms, and predictive analytics. A comprehensive literature evaluation spanning from 2011 to 2023 is conducted to assess the significance of low‐carbon approaches in the context of smart manufacturing. An integrated approach is used using content analysis, network data analysis, bibliometric analysis, and cluster analysis. Based on the published literature the leading contributors to low‐carbon manufacturing research are India, China, United States, United Kingdom, Singapore, and Italy. The results have shown five main themes—Low‐carbon smart manufacturing and applications of Algorithms; Industry 4.0 technologies for low‐carbon manufacturing; low carbon and green manufacturing; Low‐carbon Manufacturing, and Product design and control; Lean Systems and Smart Manufacturing. The purpose of this study is to provide policymakers and researchers with a guide for the academic development of low‐carbon manufacturing by evaluating research efforts in light of identified research deficits.
Partial discharge pattern recognition algorithm of overhead covered conductors based on feature optimization and bidirectional LSTM‐GRU
Authors: Zhang Chungfeng , Chen Mingli , Zhang Yongjun , Deng Wenyang , Gong Yu , Zhang Di
Published: January 2024

Recognition of partial discharge (PD) patterns is essential for insulation diagnosis of covered conductors in overhead lines. Current research has not sufficiently addressed the complex background noise in real environments, and most detection methods depend primarily on feature engineering or deep learning, suggesting potential for improvement in accuracy and efficiency. This has led the authors to propose a PD pattern recognition algorithm that integrates feature selection and deep learning. This algorithm incorporates the design of a discrete wavelet denoising function specifically tailored to the characteristics of PD for data preprocessing. It employs Bayesian optimization algorithms and light gradient boosting machines for characterizing corona discharge features. Furthermore, it develops multi‐scale clustering features and phase‐resolved features for feature fusion, and constructs insightful features based on the light gradient boosting machine. Finally, a novel deep learning model is formulated, demonstrating exceptional detection performance for early faults in covered conductors. Experimental results show that this algorithm attains an Matthews correlation coefficient score of 0.814, a 13.2% improvement over the baseline algorithm's 0.719, and a speed increase of 39.18%. The final accuracy amounts to 97.85%. This algorithm demonstrates exceptional performance in detecting early insulation faults in conductors.
Compact Archimedean spiral antenna with high gain for electrostatic discharge detection
Authors: Wang Tong , Chen Dongwei , Li Mingjie , Liu Weidong , Fang Qingyuan
Published: January 2024

A compact Archimedean spiral antenna with high gain for electrostatic discharge (ESD) detection is investigated. A frustum‐shaped cavity proposed in this work is formed to optimise the maximum antenna gain. Additionally, a helix arm is loaded to enhance antenna gain in lower band. The measured results demonstrate that the proposed antenna, which employs a unidirectional radiation mode, exhibits a maximum antenna gain ranging from −0.74 dBi to 9.46 dBi and a voltage standing wave ratio less than 3 across a frequency range of 0.65–5 GHz. A test system consisted of proposed antenna and a commercial ESD detection antenna is established. The verified results indicate that the proposed antenna has a performance close to the commercial antenna, so that proposed antenna with compact size is preferred to be utilised for ESD detection.
Comparative analysis of three‐phase dual active bridge converter with different transformer topology and modern universal control for DC microgrids
Authors: Bachman Serafin , Turzyński Marek , Jasiński Marek
Published: January 2024

The presented work discusses issues related to the use of modern multiphase topologies of Dual Active Bridge (DAB)‐type converters. Converters of this type are widely used in most DC microgrid applications. The introduction emphasizes a comparative analysis between single‐phase and multi‐phase DAB topologies within high‐power DC microgrids, delving into their respective advantages, drawbacks, design procedures, and considerations based on the latest knowledge. The publication explores the comparison and selection of viable topologies for deployment in high‐power and high‐efficiency DC microgrids. The unified method of controlling 1‐phase and multi‐phase DAB converters was proposed in this design, simplifying the issues of DC microgrid control. All topologies were tested on the same controller concept. The study performs laboratory investigation of DAB 1‐phase and 3‐phase: Star–Star, and Star–Delta topologies. Attention was paid to maintaining uniform operating conditions of the system, contrary to studies known from the literature, all tests were carried out on the same laboratory stand and the same magnetic components in different configurations. Analytical and laboratory analyses of the Zero Voltage Switching (ZVS) region were performed, accounting for non‐linear phenomena. Based on these findings, an assessment of the system's performance in soft switching was carried out. The presented results were implemented in a simulation model and subsequently validated through tests on a constructed laboratory setup to ensure the proper operation of the system. This work meticulously presents and discusses variations in efficiency, dynamic response, phase current harmonic distribution, phase shift distribution, ZVS switching region, and more among the examined topologies. To ensure a fair comparison, the converter configuration for both simulation and laboratory models utilized identical components across all configurations.
Graph‐based spectrum sensing algorithm via nonlinear function regulation
Authors: Wu Shanshan , Hu Guobing
Published: January 2024

To solve the difficulties in threshold selection and poor performance under low signal‐to‐noise ratio (SNR) conditions in existing spectrum sensing algorithms, a graph‐based spectrum sensing algorithm using nonlinear function regulation was proposed. The idea was to add a specific nonlinear transformation between the normalisation and quantization steps of the existing signal‐to‐graph converter (SGC). If the autocorrelation function of the observed signal selected as the input fed to SGC, the nonlinear function has the ability to adjust the uniformity of its probability distribution, increasing the probability of the observed signal being transformed into a complete graph under the alternative hypothesis, whereas remaining a noncomplete graph under the null hypothesis. Thus transformed the graph‐based spectrum sensing into a complete graph‐detection problem. Based on the theory of dispersive ordering, a theoretical analysis of the mechanism by which nonlinear transformations affect graph connectivity was conducted. The simulation results showed that the detection performance of the proposed algorithm was superior to that of existing graph‐based spectrum sensing algorithms. When SNR was −7 dB, the detection probability of the proposed algorithm exceeded 95%. Moreover, among the existing graph‐based spectrum sensing algorithms, the proposed algorithm exhibited the lowest computational complexity apart from the block range‐based method.
An unmanned aerial vehicle light detection and ranging Simultaneous Localisation And Mapping algorithm based on factor graph optimisation for tunnel 3D mapping
Authors: Xie Jian , Wu Zhuoping , Wang Bing , Xu Aoshu , Chen Yunfei , Li Jing
Published: January 2024

The current mature Simultaneous Localisation And Mapping (SLAM) algorithms, when applied to tunnel scenarios with point cloud degradation and poor lighting conditions, often lead to a sharp increase in the estimated attitude error of the unmanned aerial vehicle (UAV), or even prevent the UAV from moving autonomously due to severe feature degradation. To address the above problems, the authors propose a SLAM algorithm based on factor graph optimisation, Iterative Closest Point and Normal Distributions Transform algorithms. A front‐end point cloud registration module and a back‐end construction algorithm based on filtering and graph optimisation are designed. To verify the effectiveness of the proposed algorithm, experiments are conducted on KITTI dataset and real tunnel scenes, and compared with LiDAR Odometry and Mapping (LOAM) and lightweight and ground optimised (LeGO)‐LOAM algorithms. The results show that the average processing time of the proposed method is about 75 ms, which can meet the real‐time requirements of autonomous aerial vehicles. Compared with LOAM and LeGO‐LOAM in the real tunnel experiment, the proposed method shows the tunnel 3D map construction.
Demystifying the Radiography of Age Estimation in Criminal Jurisprudence: A Pictorial Review
Authors: Bhardwaj Vritika , Kumar Ishan , Aggarwal Priyanka , Singh Pramod Kumar , Shukla Ram C. , Verma Ashish
Published: January 2024

Skeletal radiographs along with dental examination are frequently used for age estimation in medicolegal cases where documentary evidence pertaining to age is not available. Wrist and hand radiographs are the most common skeletal radiograph considered for age estimation. Other parts imaged are elbow, shoulder, knee, and hip according to suspected age categories. Age estimation by wrist radiographs is usually done by the Tanner-Whitehouse method where the maturity level of each bone is categorized into stages and a final total score is calculated that is then transformed into the bone age. Careful assessment and interpretation at multiple joints are needed to minimize the error and categorize into age-group. In this article, we aimed to summarize a suitable radiographic examination and interpretation for bone age estimation in living children, adolescents, young adults, and adults for medicolegal purposes.
Shall brands create their own virtual influencers? A comprehensive study of 33 virtual influencers on Instagram
Authors: Shen Zheng
Published: January 2024

Increasing customer-brand engagement on social media has been a focus of brand success for many years. Recently, virtual influencers have gained popularity as a new way for brands to increase customer engagement, but there has been limited analysis of this new phenomenon. As such, by investigating 33 virtual influencers on Instagram, this study explored whether brands should create or collaborate with virtual influencers and how they can increase customer-brand engagement. The findings reveal that non-branded virtual influencers are more engaged than branded virtual influencers. Also, virtual influencers’ communication strategies to increase customer-brand engagement were further discussed in the study to develop a typology of virtual influencers. Thus, this study fills a theoretical gap in the limited analysis of virtual influencers in customer-brand engagement, and suggests that brands collaborate with virtual influencers rather than creating their own virtual influencers in practice.