bluuebunny/crossref_metadata_2025 huggingface dataset:
Prediction of therapeutic intensity level from automatic multiclass segmentation of traumatic brain injury lesions on CT-scans
Authors: Brossard Clément , Grèze Jules , de Busschère Jules-Arnaud , Attyé Arnaud , Richard Marion , Tornior Florian Dhaussy , Acquitter Clément , Payen Jean-François , Barbier Emmanuel L. , Bouzat Pierre , Lemasson Benjamin
Published: November 2023

The prediction of the therapeutic intensity level (TIL) for severe traumatic brain injury (TBI) patients at the early phase of intensive care unit (ICU) remains challenging. Computed tomography images are still manually quantified and then underexploited. In this study, we develop an artificial intelligence-based tool to segment brain lesions on admission CT-scan and predict TIL within the first week in the ICU. A cohort of 29 head injured patients (87 CT-scans; Dataset1) was used to localize (using a structural atlas), segment (manually or automatically with or without transfer learning) 4 or 7 types of lesions and use these metrics to train classifiers, evaluated with AUC on a nested cross-validation, to predict requirements for TIL sum of 11 points or more during the 8 first days in ICU. The validation of the performances of both segmentation and classification tasks was done with Dice and accuracy scores on a sub-dataset of Dataset1 (internal validation) and an external dataset of 12 TBI patients (12 CT-scans; Dataset2). Automatic 4-class segmentation (without transfer learning) was not able to correctly predict the apparition of a day of extreme TIL (AUC = 60 ± 23%). In contrast, manual quantification of volumes of 7 lesions and their spatial location provided a significantly better prediction power (AUC = 89 ± 17%). Transfer learning significantly improved the automatic 4-class segmentation (DICE scores 0.63 vs 0.34) and trained more efficiently a 7-class convolutional neural network (DICE = 0.64). Both validations showed that segmentations based on transfer learning were able to predict extreme TIL with better or equivalent accuracy (83%) as those made with manual segmentations. Our automatic characterization (volume, type and spatial location) of initial brain lesions observed on CT-scan, publicly available on a dedicated computing platform, could predict requirements for high TIL during the first 8 days after severe TBI. Transfer learning strategies may improve the accuracy of CNN-based segmentation models.

Trial registrations Radiomic-TBI cohort; NCT04058379, first posted: 15 august 2019; Radioxy-TC cohort; Health Data Hub index F20220207212747, first posted: 7 February 2022.
Development of a facile method to compute collagen network pathological anisotropy using AFM imaging
Authors: Khattignavong Emilie , Neshatian Mehrnoosh , Vaez Mina , Guillermin Amaury , Tauer Josephine T. , Odlyha Marianne , Mittal Nimish , Komarova Svetlana V. , Zahouani Hassan , Bozec Laurent
Published: November 2023

Type I collagen, a fundamental extracellular matrix (ECM) component, is pivotal in maintaining tissue integrity and strength. It is also the most prevalent fibrous biopolymer within the ECM, ubiquitous in mammalian organisms. This structural protein provides essential mechanical stability and resilience to various tissues, including tendons, ligaments, skin, bone, and dentin. Collagen has been structurally investigated for several decades, and variation to its ultrastructure by histology has been associated with several pathological conditions. The current study addresses a critical challenge in the field of collagen research by providing a novel method for studying collagen fibril morphology at the nanoscale. It offers a computational approach to quantifying collagen properties, enabling a deeper understanding of how collagen type I can be affected by pathological conditions. The application of Fast Fourier Transform (FFT) coupled with Atomic Force Microscope (AFM) imaging distinguishes not only healthy and diseased skin but also holds potential for automated diagnosis of connective tissue disorders (CTDs), contributing to both clinical diagnostics and fundamental research in this area. Here we studied the changes in the structural parameters of collagen fibrils in Ehlers Danlos Syndrome (EDS). We have used skin extracted from genetically mutant mice that exhibit EDS phenotype as our model system (Col1a1Jrt/+ mice). The collagen fibrils were analyzed by AFM based descriptive-structural parameters, coupled with a 2D Fast Fourier Transform(2D-FFT) approach that automated the analysis of AFM images. In addition, each sample was characterized based on its FFT and power spectral density. Our qualitative data showed morphological differences in collagen fibril clarity (clearness of the collagen fibril edge with their neighbouring fibri), D-banding, orientation, and linearity. We have also demonstrated that FFT could be a new tool for distinguishing healthy from tissues with CTDs by measuring the disorganization of fibrils in the matrix. We have also employed FFT to reveal the orientations of the collagen fibrils, providing clinically relevant phenotypic information on their organization and anisotropy. The result of this study can be used to develop a new automated tool for better diagnosis of CTDs.
Efficient end-to-end long-read sequence mapping using minimap2-fpga integrated with hardware accelerated chaining
Authors: Liyanage Kisaru , Samarakoon Hiruna , Parameswaran Sri , Gamaarachchi Hasindu
Published: November 2023

minimap2is the gold-standard software for reference-based sequence mapping in third-generation long-read sequencing. Whileminimap2is relatively fast, further speedup is desirable, especially when processing a multitude of large datasets. In this work, we presentminimap2-fpga, a hardware-accelerated version ofminimap2that speeds up the mapping process by integrating an FPGA kernel optimised for chaining. Integrating the FPGA kernel intominimap2posed significant challenges that we solved by accurately predicting the processing time on hardware while considering data transfer overheads, mitigating hardware scheduling overheads in a multi-threaded environment, and optimizing memory management for processing large realistic datasets. We demonstrate speed-ups in end-to-end run-time for data from both Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio).minimap2-fpgais up to 79% and 53% faster thanminimap2for$$\sim 30\times $$∼30×ONT and$$\sim 50\times $$∼50×PacBio datasets respectively, when mapping without base-level alignment. When mapping with base-level alignment,minimap2-fpgais up to 62% and 10% faster thanminimap2for$$\sim 30\times $$∼30×ONT and$$\sim 50\times $$∼50×PacBio datasets respectively. The accuracy is near-identical to that of originalminimap2for both ONT and PacBio data, when mapping both with and without base-level alignment.minimap2-fpgais supported on Intel FPGA-based systems (evaluations performed on an on-premise system) and Xilinx FPGA-based systems (evaluations performed on a cloud system). We also provide a well-documented library for the FPGA-accelerated chaining kernel to be used by future researchers developing sequence alignment software with limited hardware background.
Unveiling the effect of strain engineering on the electrochemical properties of hydrothermally grown nanostructured indium doped ZnSeO3 for photoanode applications
Authors: Maswanganye M. W. , Kabongo G. L. , Mathevula L. E. , Mothudi B. M. , Dhlamini M. S.
Published: November 2023

The crucial role of In as a dopant on the structural, optical, and thermogravimetric characteristics of the zinc selenite (ZnSeO3) nanopowders has been investigated in detail using X-Ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), Energy Dispersive Spectroscopy (EDS), Raman spectroscopy, diffuse reflectance spectroscopy (DRS), photoluminescence (PL) spectroscopy, and Thermogravimetric Analysis (TGA). The structural analysis indicates that all patterns are assigned to the ZnSeO3 orthorhombic structure. Also, XRD analysis shows that In3+ ions may have replaced Zn2+ ions, which causes lattice expansion. Both the Debye–Scherrer method, and the Williamson–Hall method have also been applied to study the influence of strain on the calculation of the crystallite size. The crystallite size was observed to increase with an increase in dopant concentration. The FE-SEM corroborated that the prepared samples are orthorhombic, with the EDS and mapping confirming the presence of In as a dopant. Raman spectroscopy results corroborated the XRD results indicating an expansion in the crystal structure of ZnSeO3 with the introduction of dopants. Based on DRS data, the introduction of In decreases the energy band gap of the synthesized ZnSeO3 nanopowder samples from 3.305 to 3.276. PL spectra confirm the presence of indium with the green emission band attributed to dopants dominating the emission. The TGA investigation shows an improvement in the mass loss with the introduction of dopants. EIS results indicated an improvement in the conductivity as the charge transfer resistance decreased from 525.04 to 21.95 kΩ for the undoped ZnSeO3 and 0.75% In–ZnSeO3 thin films showing improvement in charge mobility.
Genome sequence and characterization of Streptomyces phages Vanseggelen and Verabelle, representing two new species within the genus Camvirus
Authors: Ongenae Véronique , Kempff Annabel , van Neer Vera , Shomar Helena , Tesson Florian , Rozen Daniël , Briegel Ariane , Claessen Dennis
Published: November 2023

Despite the rising interest in bacteriophages, little is known about their infection cycle and lifestyle in a multicellular host. Even in the model system Streptomyces, only a small number of phages have been sequenced and well characterized so far. Here, we report the complete characterization and genome sequences of Streptomyces phages Vanseggelen and Verabelle isolated using Streptomyces coelicolor as a host. A wide range of Streptomyces strains could be infected by both phages, but neither of the two phages was able to infect members of the closely related sister genus Kitasatospora. The phages Vanseggelen and Verabelle have a double-stranded DNA genome with lengths of 48,720 and 48,126 bp, respectively. Both phage genomes contain 72 putative genes, and the presence of an integrase encoding protein indicates a lysogenic lifestyle. Characterization of the phages revealed their stability over a wide range of temperatures (30–45 °C) and pH values (4–10). In conclusion, Streptomyces phage Vanseggelen and Streptomyces phage Verabelle are newly isolated phages that can be classified as new species in the genus Camvirus, within the subfamily Arquattrovirinae.
Hypercitratemia is a mortality predictor among patients on continuous venovenous hemodiafiltration and regional citrate anticoagulation
Authors: dos Santos Thais Oliveira Claizoni , dos Santos Ferreira Carlos Eduardo , Mangueira Cristóvão Luis Pitangueira , Ammirati Adriano Luiz , Scherer Patricia Faria , Doher Marisa Petrucelli , Matsui Thais Nemoto , dos Santos Bento Fortunato Cardoso , Pereira Virgílio Gonçalves , Batista Marcelo Costa , Monte Julio Cesar Martins , Santos Oscar Fernando Pavão , de Souza Durão Marcelino
Published: November 2023

The use of regional citrate anticoagulation (RCA) in liver failure (LF) patients can lead to citrate accumulation. We aimed to evaluate serum levels of citrate and correlate them with liver function markers and with the Cat/Cai in patients under intensive care and undergoing continuous venovenous hemodiafiltration with regional citrate anticoagulation (CVVHDF-RCA). A prospective cohort study in an intensive care unit was conducted. We compared survival, clinical, laboratorial and dialysis data between patients with and without LF. Citrate was measured daily. We evaluated 200 patients, 62 (31%) with LF. Citrate was significantly higher in the LF group. Dialysis dose, filter lifespan, systemic ionized calcium and Cat/Cai were similar between groups. There were weak to moderate positive correlations between Citrate and indicators of liver function and Cat/Cai. The LF group had higher mortality (70.5% vs. 51.8%, p = 0.014). Citrate was an independent risk factor for death, OR 11.3 (95% CI 2.74–46.8). In conclusion, hypercitratemia was an independent risk factor for death in individuals undergoing CVVHDF-ARC. The increase in citrate was limited in the LF group, without clinical significance. The correlation between citrate and liver function indicators was weak to moderate.
Ethnic disparity in diagnosing asymptomatic bacterial vaginosis using machine learning
Authors: Celeste Cameron , Ming Dion , Broce Justin , Ojo Diandra P. , Drobina Emma , Louis-Jacques Adetola F. , Gilbert Juan E. , Fang Ruogu , Parker Ivana K.
Published: November 2023

While machine learning (ML) has shown great promise in medical diagnostics, a major challenge is that ML models do not always perform equally well among ethnic groups. This is alarming for women’s health, as there are already existing health disparities that vary by ethnicity. Bacterial Vaginosis (BV) is a common vaginal syndrome among women of reproductive age and has clear diagnostic differences among ethnic groups. Here, we investigate the ability of four ML algorithms to diagnose BV. We determine the fairness in the prediction of asymptomatic BV using 16S rRNA sequencing data from Asian, Black, Hispanic, and white women. General purpose ML model performances vary based on ethnicity. When evaluating the metric of false positive or false negative rate, we find that models perform least effectively for Hispanic and Asian women. Models generally have the highest performance for white women and the lowest for Asian women. These findings demonstrate a need for improved methodologies to increase model fairness for predicting BV.
Topological structure of complex predictions
Authors: Liu Meng , Dey Tamal K. , Gleich David F.
Published: November 2023

Current complex prediction models are the result of fitting deep neural networks, graph convolutional networks or transducers to a set of training data. A key challenge with these models is that they are highly parameterized, which makes describing and interpreting the prediction strategies difficult. We use topological data analysis to transform these complex prediction models into a simplified topological view of the prediction landscape. The result is a map of the predictions that enables inspection of the model results with more specificity than dimensionality-reduction methods such as tSNE and UMAP. The methods scale up to large datasets across different domains. We present a case study of a transformer-based model previously designed to predict expression levels of a piece of DNA in thousands of genomic tracks. When the model is used to study mutations in theBRCA1gene, our topological analysis shows that it is sensitive to the location of a mutation and the exon structure ofBRCA1in ways that cannot be found with tools based on dimensionality reduction. Moreover, the topological framework offers multiple ways to inspect results, including an error estimate that is more accurate than model uncertainty. Further studies show how these ideas produce useful results in graph-based learning and image classification.
Saliency models perform best for women’s and young adults' fixations
Authors: Strauch Christoph , Hoogerbrugge Alex J. , Baer Gregor , Hooge Ignace T. C. , Nijboer Tanja C. W. , Stuit Sjoerd M. , Van der Stigchel Stefan
Published: November 2023

Saliency models seek to predict fixation locations in (human) gaze behaviour. These are typically created to generalize across a wide range of visual scenes but validated using only a few participants. Generalizations across individuals are generally implied. We tested this implied generalization across people, not images, with gaze data of 1600 participants. Using a single, feature-rich image, we found shortcomings in the prediction of fixations across this diverse sample. Models performed optimally for women and participants aged 18-29. Furthermore, model predictions differed in performance from earlier to later fixations. Our findings show that gaze behavior towards low-level visual input varies across participants and reflects dynamic underlying processes. We conclude that modeling and understanding gaze behavior will require an approach which incorporates differences in gaze behavior across participants and fixations; validates generalizability; and has a critical eye to potential biases in training- and testing data.
Cancer Therapy with Carbon Dots
Authors: Zhang Jinyi , Lan Minhuan , Wu Peng
Published: November 2023

Due to the diversity, complexity, and heterogeneity of tumors, effective cancer therapy remains a long-standing challenge. Precise nanomedicine is regarded as a breakthrough in pre-clinical and clinical exploration. Carbon dots (CDs) have aroused extensive interest in various fields, especially in cancer therapy. Their excellent biocompatibility, easy preparation and surface modification, and tunable photochemical and photophysical properties have led to a remarkable application of CDs in nanomedicine. In this chapter, we summarize the progress of research on CDs in cancer therapy, including their use as anticancer drugs and nanocarriers for chemotherapy and phototherapy agents. Furthermore, the advantages, challenges, and prospects they could bring to cancer therapy are also discussed.
Alternatives to PFASs in Molded Fiber Fast Food Packaging
Authors: Teli M. , Maruzzo A. , Bălan S. A.
Published: November 2023

In this chapter, an evaluation of alternatives to perfluoroalkyl and polyfluoroalkyl substances (PFASs) added to molded fiber fast food packaging is conducted. Although these PFASs provide water and oil barrier properties, they are associated with high environmental and human health hazards and are already being phased-out in several states in the U.S. Safer alternatives are therefore needed for molded fibers, and research into their potential hazards is necessary to avoid a regrettable substitute. This chapter evaluates two alternatives, cellulose nanocrystals (CNCs) and lignin, which are materials that can be sourced from within the paper industry. CNCs and lignin are promising alternatives that show potential to be incorporated into the molded fiber production process, can be easily composted, and are less hazardous than the PFASs that have been used in food packaging. In this chapter, the potential technical and hazard profiles of these alternatives are assessed and several future needs are identified, such as evaluating the efficiency of incorporation within the molded fiber matrix, creating common language and transparency with regard to technical information in the food packaging literature, and investing in green chemistry innovation for processing aids.
Strategies for PFAS Removal During Carpet Recycling
Authors: Maruzzo A. , Bălan S. A.
Published: November 2023

The prevalence of perfluoroalkyl and polyfluoroalkyl substances (PFASs) in discarded carpets prohibits safe and effective recycling, limiting progress toward a circular economy. This project aimed to identify safe and effective treatment processes that remove PFASs from carpet fibers prior to recycling. The project team characterized four potential strategies: base hydrolysis with granular activated carbon, base hydrolysis with reverse osmosis and plasma, esterase hydrolysis, and mineralization with enzymatic laccases. These short- and long-term strategies draw inspiration from nature to remove or destroy PFASs from high-value carpet face fibers while considering the technical constraints of carpet recycling facilities.
Ionic Liquids in Air Treatment: VOCs and Other Pollutants
Authors: Carvalho Pedro J. , Soares Bruna F. , Pais Ricardo T. , Marrucho Isabel M.
Published: November 2023

The global population breathes air that exceeds air quality limits which has a negative impact on human health. The degradation of indoor air quality is being attributed to several types of contaminants, like gases (carbon monoxide, carbon dioxide, ozone, nitrogen oxide and sulfur oxide species), particulate matter, but mostly to volatile organic compounds (VOCs), with different volatilities, and other emerging compounds. Despite their degree of maturity, the majority of existent technologies cannot be directly used for the removal of indoor VOCs, often due to downscale design related to feed scale and the pollutants’ partial pressures. However, new methods are being proposed, with new solvents, like ionic liquids, being proposed as materials with outstanding potential for absorption-based capture and separation processes. The distinctive characteristics of ionic liquids, such as their high thermal and chemical stability, non-flammability, non-volatility, high solubility and selectivity, and their designability make them stand out over other technologies, like metal–organic frameworks, photocatalysis, ultraviolet sterilization, ozonation, filtration, non-thermal plasma and phytoremediation. In this chapter, built on a critical literature review, the prospects of ionic liquids for target separations are assessed.
CO2 Capture and Separation Using Ionic Liquids
Authors: Bara Jason E.
Published: November 2023

In the past 20 years, ionic liquids (ILs) have been considered as potential alternatives or replacements for aqueous amine solvents in CO2 capture processes. These efforts have undoubtedly yielded much knowledge and new ideas on the molecular design of liquids for CO2 capture and have influenced other technologies such as polymer membranes and solid adsorbents. Despite these great efforts and enthusiasm, there has not been a major project that has scaled-up ILs for CO2 capture much beyond the laboratory stage. In this chapter, the fundamentals of CO2 capture will be discussed along with considerations on the manufacture of solvents for this application. Then, ILs and “reversible ILs” (RevILs) will be introduced along with the various ways they have been applied to CO2 capture, while framing the discussion in the context of the real technical and economic demands of large scale CO2 capture processes, along with the urgency with which such processes must be deployed. While it is unclear whether ILs or RevILs will ultimately meet these demands, it is clear that they have made an indelible impact on the ways scientists and engineers think about the molecular design of solvents for CO2 capture.
High-throughput (Micro)Extraction of Organic Pollutants from Environmental Liquid Samples Using Ionic Liquids and Derivatives
Authors: González-Martín R. , Trujillo-Rodríguez M. J. , Pino V.
Published: November 2023

Ionic liquids (ILs) and derivatives have been extensively used in analytical sample preparation due to their outstanding solvation and physicochemical properties, together with their synthetic versatility. Recent trends in this field are focused on facilitating the current analytical approaches through the specific design of extraction devices, supporting the IL or IL derivative on different formats, applying magnetic separation tools, and/or incorporating automated systems using robotic equipment or flow-injection. These strategies have been implemented together with the use of liquid-phase microextraction or sorbent-based microextraction. They are considered high-throughput technologies as they contribute to the further miniaturization and simplification of the analytical method while ensuring faster and more sensitive analysis. This book chapter provides an overview of all these high-throughput approaches, with emphasis on the use of ILs, polymeric ILs, magnetic ILs, and IL-based sorbents and/or composites, particularly for the monitoring of organic pollutants, including both persistent and those of emerging concern, present in liquid environmental samples.
Micropollutants’ Extraction from Aqueous Systems Using Ionic Liquids and Deep Eutectic Solvents
Authors: Paul N. , Banerjee T.
Published: November 2023

This chapter reports on Molecular Dynamics (MD) and Quantum Chemical (QC) calculations for micropollutants’ extraction from aqueous environments. Two separate Deep Eutectic Solvents (DESs) consisting of dl-menthol as a hydrogen bond acceptor (HBA) and two carboxylic acids (octanoic acid and dodecanoic acid) as hydrogen bond donors (HBDs) were adopted. The noncovalent interactions, structural properties and hydrogen bonding extent via MD simulation highlighted the enhanced and favorable interactions of the DES components with the pesticides as compared to water. Further, transport properties elaborated the affinity of the pesticides, namely acetamiprid and imidacloprid, towards the DES-rich phase. The distribution coefficients (β) obtained by simulation were 8.67 and 6.25, respectively, and the selectivity (S) values were 102.38 and 71.86, for acetamiprid and imidacloprid systems, respectively. Within QC, the charge transfer (CT) process confirmed the direction of charge transfer from DES to pesticide and the NBO analysis and the HOMO–LUMO studies established the stable character of both the DESs. A slight increase in the O (HBA)⋯H (HBD) distance confirmed the increased interaction between the DES and pesticide. The DES–pesticide optimized clusters confirmed interactions between the pesticide and DES at a distance ranging from 2.896 to 3.77 Å for imidacloprid and from 1.724 to 2.03 Å for acetamiprid, which are validated by MD simulation.
Adsorption of Ionic Liquids from Aqueous Streams on Activated Carbon
Authors: Lemus J. , Palomar J.
Published: November 2023

Adsorption methodology is a fast, economic, non-destructive, and universal technology for water purification and adsorbent recycling. The development of low-cost activated carbons (ACs) has led to the growth of scientific interest in this area. This chapter propounds a guideline for the efficient treatment of ionic liquids (ILs) at low concentrations (<5 mmol L−1) from aqueous solution based on the adsorption onto ACs. Relevant features of adsorption phenomena and successfully applied theoretical models are described with a focus on process design to improve the recovery of both hydrophobic and hydrophilic ILs. The structural and chemical properties of the inexpensive AC adsorbent required to enhance the adsorption thermodynamics and kinetics depending on the IL’s nature are reported in batch and fixed-bed operations. Available procedures for the regeneration of AC after being the adsorbent re-used in successive sorption–desorption cycles and the efficient recovery of IL with high purity are discussed. Adsorption of ILs onto ACs is highlighted as an affordable and versatile technology for wastewater treatment, providing suitable recovery of ILs with remarkably different chemical structures. AC adsorbent is demonstrated to be efficiently regenerated in successive sorption–desorption cycles for continuous treatment of aqueous streams.
In vitro-simulated intestinal flora fermentation of Porphyra haitanensis polysaccharides obtained by different assisted extractions and their fermented products against HT-29 human colon cancer cells
Authors: Shen Shiqi , Hong Tao , Liu Zhiyu , Liu Shuji , Ni Hui , Jiang Zedong , Yang Yuanfan , Zheng Mingjing
Published: November 2023

Herein, the in vitro-simulated intestinal flora fermentation of Porphyra haitanensis polysaccharides with microwave, ultrasonic, ultra-high pressure-assisted extraction and their fermented products against HT-29 human colon cancer cells were studied.
Promising Ce single-atom-dispersed nitrogen-doped graphene catalysts for the hydrogen evolution reaction
Authors: Yadav Sunny , Dao Vandung , Wang Wenmeng , Chen Kai , Kim Chiyeop , Kim Gyu-Cheol , Lee In-Hwan
Published: November 2023

Ce SAs on porous NGr (1Ce/NGr) show potential for HER with low onset overpotential and high stability. The modified environment of Ce SAs and NGr active sites facilitates water adsorption and dissociation, liberating hydrogen.
Phase controlled green synthesis of wurtzite (P63mc) ZnO nanoparticles: interplay of green ligands with precursor anions, anisotropy and photocatalysis
Authors: Verma Lahur Mani , Kumar Ajay , Bashir Aejaz Ul , Gangwar Upanshu , Ingole Pravin P. , Sharma Satyawati
Published: November 2023

Agro-waste based green synthesis of ZnO nanoparticles and sun light driven photocatalysis.
Hybrid AuNPs-3MPS-MTX nanosystem and its evaluation for treating cervical cancer and melanoma
Authors: Hernández-Esparza M. J. , Fratoddi Ilaria , Cerra Sara , Juarez-Moreno K. , Huirache-Acuña R.
Published: November 2023

This research presents an evaluation of a hybrid material based on gold nanoparticles (AuNPs), stabilized with the thiol 3-mercapto-propanesulfonate (3MPS) and loaded with the methotrexate drug (MTX).
Correlating structural changes in thermoresponsive hydrogels to the optical response of embedded plasmonic nanoparticles
Authors: Zygadlo Kamila , Liu Chung-Hao , Bernardo Emmanuel Reynoso , Ai Huayue , Nieh Mu-Ping , Hanson Lindsey A.
Published: November 2023

Hybrid sensors rely on correlation of the optical and structural response. Thermal response of AuNR-hydrogel nanocomposites is examined by DLS, optical spectroscopy and NMR, revealing differences in collapse between the composite core and surface.
Real-time detection of acetone gas molecules at ppt levels in an air atmosphere using a partially suspended graphene surface acoustic wave skin gas sensor
Authors: Zhou Haolong , Ramaraj Sankar Ganesh , Ma Kaijie , Sarker Md Shamim , Liao Zhiqiang , Tang Siyi , Yamahara Hiroyasu , Tabata Hitoshi
Published: November 2023

To improve the quality of modern life in the current society, low-power, highly sensitive, and reliable healthcare technology is necessary to monitor human health in real-time.
Molecular insight into intrinsic-trap-mediated emission from atomically precise copper-based chalcogenide models
Authors: Xu Yi-Lei , Ding Yayun , Zhang Lin-Mei , Ma Hao , Liu Jia-Xing , Zhang Jiaxu , Zhou Rui , Li Dong-Sheng , Yuan Shang-Fu , Wu Tao
Published: November 2023

Component-dependent trap sites in copper-related emission were studied using chalcogenide cluster models. In3+ ions in the clusters induce shallow-delocalized traps while Ga3+ ions are associated with carrier localization at deep-localized traps.
Engineering interfacial architectures toward nitrate electrocatalysis and nitrogen neutral cycle
Authors: Gu Lin , Luo Hongxia , Zhang Yingbing , Cong Yuting , Kuang Min , Yang Jianping
Published: November 2023

Interface engineering is an important way to prepare high performance catalysts. In this review, we discuss synthesis strategies, the main types of interface engineering for efficient nitrate electrocatalysis and their special effects.
Photoredox-catalyzed intramolecular oxy- and aminoacylation of alkenes with acyl oxime esters: facile synthesis of acylated saturated heterocycles
Authors: Mou Xue-Qing , Wang Min , Ren Liang-Chen , Jian Yu-Rui , Fu Xin-Yu , Zheng Hong-Yu , Zha Xiao , Cui Bao-Dong , Zhang Yun , Chen Yong-Zheng
Published: November 2023

A general photoredox-catalyzed intramolecular oxy- and aminoacylation of internal alkenes with acyl oxime esters to access various acyl heterocycles is developed.
A novel modified nano-alumina composite sol for potential application in forest firefighting
Authors: Du Weining , Yan Mingqiang , Yin Chaolu , Zhang Zejiang
Published: November 2023

FES-mAPP@Als sol coated wood showed admirable fire prevention, while the FES-mAPP@Als sol exhibited good fire-extinguishing and anti-reignition capacities.
Development of a cationic polyethyleneimine-poly(lactic-co-glycolic acid) nanoparticle system for enhanced intracellular delivery of biologics
Authors: Tracey Shannon R. , Smyth Peter , Herron Una M. , Burrows James F. , Porter Andrew J. , Barelle Caroline J. , Scott Christopher J.
Published: November 2023

Cationic PEI-PLGA nanoparticles are shown as a superior delivery platform, displaying enhanced intracellular delivery of biologics.
Synthesis, biological evaluation and in silico investigations of benzotriazole derivatives as potential inhibitors of NIMA related kinase
Authors: Qadri Tahir , Aziz Mubashir , Channar Pervaiz Ali , Ejaz Syeda Abida , Hussain Mumtaz , Attaullah Hafiz Muhammad , Ujan Rabail , Hussain Zahid , Zehra Tasneem , Saeed Aamer , Shah M. R. , Ogaly Hanan A. , Al-Zahrani Fatimah A. M.
Published: November 2023

Newly Synthesized bis(3-(2H-benzo[d][1,2,3]triazol-2-yl)-2-(prop-2-yn-1-yloxy)-5-(2,4,4-trimethylpentan-2-yl)phenyl)methane (TAJ1) as potential anti-cancer agent and highly selective NEK-7 inhibitor.
Integration of machine learning in 3D-QSAR CoMSIA models for the identification of lipid antioxidant peptides
Authors: Nha Tran Thi Thanh , Thuan Tran Thi Dieu , Thuy Bui Thi Thu
Published: November 2023

A routine for developing ML-based CoMSIA models using three feature selection methods and twenty-four regression estimators tuned by GridSearchCV.
A fundamental understanding of structure evolution in the synthesis of hard carbon from coal tar pitch for high-performance sodium storage
Authors: Ji Lichang , Zhao Yun , Cao Lijuan , Li Yong , Ma Canliang , Qi Xingguo , Shao Zongping
Published: November 2023

Coal tar pitches (CTPs) can be used to fabricate low-cost hard carbon anodes in sodium-ion batteries (SIBs) via pre-oxidation methods; however, an in-depth analysis of their synthesis processes is still scarce in literature.
Three-component color-tunable room temperature afterglow doped materials through Förster-resonance energy transfer
Authors: Huang Huaiying , Jiang Yitian , Liu Miaochang , Dai Wenbo , Lei Yunxiang , Guan Yan , Ding Qiuping , Wu Huayue , Huang Xiaobo
Published: November 2023

A facile and versatile strategy for the development of room-temperature afterglow doped materials is constructed based on fluorescent molecules containing olefinic bonds by a mechanism of Förster-resonance energy transfer.
Synthesis of titanium carbide coating on Ti15Mo alloy and its tribological behaviour
Authors: Wang Hongxiang , Shen Jiajia , Zhang Weipeng , Xu Zhentao , Zheng Jingjing , Luo Yong
Published: November 2023

Ti15Mo alloy has been regarded as one of the most potential biomedical materials due to its excellent performance. However, the low hardness and poor wear resistance of titanium alloy limit the further application. Therefore, high temperature solid carburising technology was performed on the surface of Ti15Mo alloys to prepare titanium carbide (TiC) coating with graphene (G) as the carburising agent. The microstructure, mechanical properties, and tribological properties of TiC coating under different lubricants were investigated. Results showed that TiC coating was closely bonded to the titanium substrate. The maximum thickness of TiC coating treated with 1150°C was approximately 184.02 μm, and the microhardness of alloys treated with 1100°C can achieve 1221.5 HV. All modified Ti15Mo alloys showed improved tribological performance compared to the original samples. The wear mechanisms of modified Ti15Mo alloys were abrasive wear and adhesive wear under the SBF lubricant, and the TiC coating was slightly peeled off. The overall friction coefficient and wear rate under 25% calf serum lubricant were lower than the SBF lubricant, and surface scratches were almost absent, and slight abrasive wear and adhesive wear occurred on the surface.
A risk‐based driver behaviour model
Authors: Yuan Yuxia , Wang Xinwei , Calvert Simeon , Happee Riender , Wang Meng
Published: November 2023

Current driver behaviour models (DBMs) are primarily designed for the general driver population under specific scenarios, such as car following or lane changing. Hence DBMs capturing individual behaviour under various scenarios are lacking. This paper presents a novel method to quantify individual perceived driving risk in the longitudinal and lateral directions using risk thresholds capturing the time headway and time to line crossing. These are integrated in a risk‐based DBM formulated under a model predictive control (MPC) framework taking into account vehicle dynamics. The DBM assumes drivers to operate as predictive controllers jointly optimising multiple criteria, including driving risk, discomfort, and travel inefficiency. Simulation results in car following and passing a slower vehicle demonstrate that the DBM predicts plausible behaviour under representative driving scenarios, and that the risk thresholds are able to reflect individual driving behaviour. Furthermore, the proposed DBM is verified using empirical driving data collected from a driving simulator, and the results show it is able to accurately generate vehicle longitudinal and lateral control matching individual human drivers. Overall, this model can capture individual risk perception behaviour and can be applied to the design and assessment of intelligent vehicle systems.
Optimal gear ratio selection of linear primary permanent magnet vernier machines for wave energy applications
Authors: Jafari Reza , Asef Pedram , Sarhadi Pouria , Pei Xiaoze
Published: November 2023

Linear permanent magnet vernier generators offer a high capability of force density, making them appealing configurations for wave energy harvesting systems. In absolute terms, the performance of these machines is significantly influenced by the selection of slot/pole combinations based on the magnetic gearing effect. For the first time, this paper aims to investigate the impact of different gear ratios on a wide array of linear primary permanent magnet vernier machines (LPPMVMs) with different slot/pole combinations based on fair criteria to offer a more comprehensive understanding of gear ratio selection. To find the optimal number of slots and poles, the response surface methodology is adopted to obtain a robust design and make a fair comparison among LPPMVMs with optimum design characteristics using a cost‐effective approach for the fast and reliable optimisation process. The higher gear ratios result in higher thrust force capability. This will help establishing a new route toward faster develpment of advanced LPPMVMs. The power loss models of LPPMVMs are studied to predict their steady‐state and transient thermal behaviours, verifying their stability and safety, while a simple external forced convection method can be utilised. To verify the model, finite element analysis is exploited to confirm the electromagnetic and thermal analysis results and provide a more exhaustive investigation.
A multi‐variables loss of excitation protection scheme to improve the factors of accuracy and speed of detection than the conventional impedance relay
Authors: Fatolahi Mohammad Sajad , Hosseini‐Hemati Saman , Rastgou Abdollah , Kharrati Saeed
Published: November 2023

The most common disadvantages of loss of excitation (LOE) impedance relay are its improper operation in the conditions of power system disturbances (PSD) and its long operation time. In this regard, to solve the mentioned problems and as a result improve the accuracy and speed of detecting the LOE events, four key variables including active power (Psg), reactive power (Qsg), load angle (δsg), and terminal voltage (Vsg) are selected, on the basis of an in‐depth theoretical‐ and simulation studies. These variables have been implemented in a precise combinational logic. In the developed LOE detection logic of this paper, the dependent parameters of Vsg and Qsg as well as Psg and δsg parameters are examined simultaneously in an AND logic cluster. If both the defined combinations detect the occurrence of LOE, the generator trip command is issued. Otherwise, the normal operation of the under‐study machine will continue. The results of the performed simulations in MATLAB/Simulink environment (2017b) during the detailed scenarios of LOE and PSD events on the IEEE 39‐bus standard system confirm the efficiency of the outlined LOE protection model. Meanwhile, its superiority from the viewpoints of accuracy, swiftness, and simplicity and cost of implementation are comprehensively compared with the other schemes.
Implementation of UI/UX the Design Thinking Approach Method In Inventory Information System
Authors: Huda Baenil , Manongga Danny , Sediyono Eko , Yulianto Sri , Fauzi Ahmad , Hananto April Lia , Tukino  , Tarmuji
Published: November 2023

CV. Smart Motecare Mandiri (SMM) is a provider of information technology devices and services in the Karawang area. SMM often experiences difficulties in collecting inventory data and integrating it with sales data. To overcome this problem, making an inventory information system is considered the right solution. This article uses the Design Thinking method in designing the UX and UI of an inventory information system. This method involves the Empathize, Define, Ideate, Prototype, and Testing stages. At the Empathize stage, observations and interviews are carried out to understand user needs. The Define stage involves gathering ideas and creating a user persona. The Ideate stage involves brainstorming to generate creative ideas. The Prototype Stage involves building a prototype using the Figma software. The Testing phase involves testing the prototype with users to get feedback. The results show that the SMM inventory information system gets positive ratings from users. The results of the System Usability Scale (SUS) show an average score of 82.5 which has an excellent adjective rating.
Using Data Mining Techniques to Diagnosis of the Covid-19 Effects on the Hospital Readmission
Authors: Zakur Yahya , Mirashrafi Seyed Bagher , Flaih Laith
Published: November 2023

The COVID-19 pandemic led to a substantial increase in the volume, diversity, and output pace of healthcare data. Countries depended on traditional methods to monitor diseases and public health to manage the epidemic, while advanced technology such as artificial intelligence and computation enabled efficient data processing. That datasets are usually enormous, growing exponentially, and comprise a collection of complicated item sets. To extract big, complicated itemsets, robust, straightforward, and computationally efficient techniques are crucial. Based on concepts from computer science, machine learning, and data mining, the Apriori method is a viable approach for supporting the values of database items in this study. There are two distinct implementation methods for Apiori: low confidence and support (Apiori algorithm) and the Apriori property algorithm. In conclusion, the performance of the Apriori property algorithm was superior to that of the traditional Apriori algorithm.
Analysis of Visitor Review Data Using Lexicon Based, Support Vector Machine, Random Forest in Determining The Priority Scale of Building Labuan Bajo Tourism Objects
Authors: Dahur Arnoldus Janssen , Wahyul Syafei Amien , Prahasto Toni
Published: November 2023

Labuan Bajo tourist destination is one of the super priority tourist destinations in Indonesia. The importance of obtaining and analyzing tourists' reviews is to understand their preferences and views on the existing facilities and services. Therefore, this research is conducted to obtain and analyze visitor review data obtained from TripAdvisor and Google Maps. The methods used in analyzing these visitor reviews are Lexicon-Based for labeling, Support Vector Machine (SVM), and Random Forest for classification. The labeling results using the Lexicon-Based method showed 4187 positive reviews, 1796 negative reviews, and 1774 neutral reviews. The classification was performed using SMOTE (Synthetic Minority Over-sampling Technique) and without using SMOTE due to data imbalance. Results using SMOTE with SVM showed an accuracy of 0.89, precision of 0.95, recall of 0.85, and f1-measure of 0.90, with an ROC AUC value of 0.94, with Random Forest showed an accuracy of 0.87, precision of 0.91, recall of 0.86, and f1-measure of 0.88, with an ROC AUC value of 0.93. The determination of priority scale was done by obtaining the top 10 words and the number of sentiments related to development. The frequently occurring positive sentiment words were 'beautiful,' 'natural,' 'exotic,' 'scenic,' 'clean,' 'ancient,' 'amazed,' and 'historical.' The preservation of natural and historical assets must be maintained and continuously preserved.On the other hand, the frequently occurring negative words were 'expensive,' 'cost,' 'guide,' 'road,' 'garbage,' and 'hot.' Based on these words, the development of transportation and infrastructure is undoubtedly needed to enhance the attractiveness of Labuan Bajo as a tourist destination.
Toward Improving the Prediction Accuracy of a Product Recommendation System Based on Word Sequential Using LSTM Embedded
Authors: Jaeni Jaeni , Purwanto Purwanto , Warsito Budi , Wibowo Adi
Published: November 2023

The ability to predict purchases is crucial for e-commerce decision makers when making offers and suggestions to customers. In the development of recommendation models, two common problems often encountered are a lack of personalization and irrelevant recommendations. To address these issues, it is crucial to consider user history data, such as the user's interactions with previous products. This allows the model to learn user preferences from the past and generate more personalized and relevant recommendations. In this study, word2vec is used to provide rating predictions, while document context is enhanced using LSTM capture contextual understanding of product reviews. This study makes use of an actual dataset to test our model using an Amazon Review Dress. The results of our investigation demonstrate that, as 88% of the recommendations are made in accordance with the recommendation system's criteria, it can be considered that it offers reasonably accurate and dependable recommendations with an accuracy of 0.8752
Deep Learning Algorithm as an Alternative of Automatic Classification of Dirty Eggs
Authors: Setiaji Pratomo , Adi Kusworo , Surarso Bayu
Published: November 2023

Egg, which is one of the daily needs, contains important nutrition like protein, vitamins, mineral and 75 Kcal. Thus, one of the external qualities of eggs becomes one of the considerations in determining egg quality. The poor condition of the egg shell can be unhealthy and harmful. The dirty eggs and the clean ones are important elements in an automatic separation system since they relate to the consumer’s economic and health values. An element such as dirt at the eggshell leads the raise of Salmonella bacteria and will be worse if the bacteria get into the eggshell. An automatic method of egg selection is crucial to determining the egg's quality before it reaches the consumer. Selecting and classifying egg manually is inefficient for the seller, due to it takes more time and complicated processes. The development of an automatic method is required to categorize and simplify the manual system. Deep Learning Algorithm become one of remarkable solution in developing the method of classification and qualified egg. The method of deep learning algorithms is a computational process that is simpler than other computing methods. The process of egg selection is conducted based on the dirtiness of the eggs. The result of this system is able to classify accurately and efficiently to the select whether the egg has good or bad quality, in order to simplify the breeders’ job.