bluuebunny/crossref_metadata_2025_split huggingface dataset:
An effective hybrid feature selection using entropy weight method for automatic sleep staging
Authors: Wang Weibo , Li Junwen , Fang Yu , Zheng Yongkang , You Fang 
Published: October 2023

Abstract

Objective. Sleep staging is the basis for sleep quality assessment and diagnosis of sleep-related disorders. In response to the inadequacy of traditional manual judgement of sleep stages, using machine learning techniques for automatic sleep staging has become a hot topic. To improve the performance of sleep staging, numerous studies have extracted a large number of sleep-related characteristics. However, there are redundant and irrelevant features in the high-dimensional features that reduce the classification accuracy. To address this issue, an effective hybrid feature selection method based on the entropy weight method is proposed in this paper for automatic sleep staging. Approach. Firstly, we preprocess the four modal polysomnography (PSG) signals, including electroencephalogram (EEG), electrooculogram (EOG), electrocardiogram (ECG) and electromyogram (EMG). Secondly, the time domain, frequency domain and nonlinear features are extracted from the preprocessed signals, with a total of 185 features. Then, in order to acquire characteristics of the multi-modal signals that are highly correlated with the sleep stages, the proposed hybrid feature selection method is applied to choose effective features. This method is divided into two stages. In stage I, the entropy weight method is employed to combine two filter methods to build a subset of features. This stage evaluates features based on information theory and distance metrics, which can quickly obtain a subset of features and retain the relevant features. In stage II, Sequential Forward Selection is used to evaluate the subset of features and eliminate redundant features. Further more, to achieve better performance of classification, an ensemble model based on support vector machine, K-nearest neighbor, random forest and multilayer perceptron is finally constructed for classifying sleep stages. Main results. The experiment using the Cyclic Alternating Pattern (CAP) sleep database is performed to assess the performance of the method proposed in this paper. The proposed hybrid feature selection method chooses only 30 features highly correlated to sleep stages. The accuracy, F1 score and Kappa coefficient of 6 class sleep staging reach 88.86%, 83.15% and 0.8531%, respectively. Significance. Experimental results show the effectiveness of the proposed method compared to the existing state-of-the-art studies. It greatly reduces the number of features required while achieving outstanding auto-sleep staging results.
Pretreatment strategies of precursors for efficient photocatalytic hydrogen production of graphitic phase carbon nitride
Authors: Niu Fengyan , He Qisheng , Wu Shiran , Lei Weiyan , Shen Yi 
Published: October 2023

Abstract
The most sustainable preparation method for nanostructured materials must be urgently determined. In particular, the influence of different precursor pretreatment strategies on the structure and photocatalytic performance of highly attractive Graphitic carbon nitride photocatalyst is necessary to determine the most effective precursor pretreatment strategy. In this paper, three different precursor pretreatment methods were used to prepare g-C3N4 materials, so namely direct mixing (CN-C), freeze-drying, hydrothermal (CN-H) with thermal condensation polymerization two-step method processed urea, melamine and NH4Cl precursor mixtures. The results showed that NH4Cl, as a template, would not destroy the integrity of the tristriazine structural units in the product, and the CN-H sample had a lamellar structure, and the specific surface area and pore volume of the sample increased, which could provide more active reaction sites for photocatalytic H2 production, had the highest and most stable H2 evolution rate, up to 118.4 μmol g−1, about 1.7 times CN-C’s. This strategy provides a new idea for the design of g-C3N4 photocatalyst.
Rapid de-stiffening of multilayer transparent structures using controlled thermoplastic softening
Authors: Charaklias Dimitrios , Qiang Dayuan , Dorey Robert , Mohagheghian Iman 
Published: October 2023

Abstract
Thermoplastic softening is one of the most desirable de-stiffening methods because of its reversibility, scalability, and applicability in many of current multi-layered structures without compromising structural performance. Despite the advantages, long activation times and high activation power requirements are generally considered as the main drawbacks for this method which can potentially limit its application in scenarios where fast de-stiffening is required. The aim of this study is to identify the key design requirements of heating element to minimise the de-stiffening response time using thermoplastic softening while maximising transparency. The focus of this study is on multilayer transparent structures, with low heating element content. A systematic investigation, including experimental and numerical investigation, is performed to study the effect of the fill factor and the heating element’s length scale on the response time of de-stiffening. Melting of the polymer and melting or electrical breakdown of the heating element are observed as practical limitations and are introduced as constraints to the design maps. The fill factor is found to have considerable influence on improving the response time, especially at low fill factors (i.e. below 10%). For the material combinations investigated here, the design maps show that heating elements with wire diameters up to 7 μm, at maximum transparency of 2% fill factor and up to 12 μm at 20% fill factor can achieve sub-second response times for temperature increase of 30 °C. This new understanding will accelerate the technology readiness level of active structural control technology to be used in the future multi-functional and smart structures with a wide range of application in robotics, shape morphing, active damping, and active impact protection.
Improved photo sensing behavior of the terbium doped In2S3 thin films synthesized by spray pyrolysis technique
Authors: Alkallas Fatemah H , Ben Gouider Trabelsi Amira , Shkir Mohd , Gupta Manish 
Published: October 2023

Abstract
In the present work, undoped In2S3 and varying concentrations of Tb (1, 2, 3, 4 & 5 wt%) doped In2S3 thin films were synthesized using the economical and nebulizer spray pyrolysis technique for studying their photosensing characteristics at room temperature. X-ray diffractometer (XRD) studies showed the diffraction pattern of the thin films exhibits a cubic- β phase of In2S3 and the highest crystallite size of 61 nm was observed for the In2S3: Tb (2wt%) thin film. The morphological & elemental analysis showed that all the synthesized thin films exhibit homogeneous non-uniform crystal morphology with the existence of S, In, Tb compounds with their distinct composition. From Ultraviolet-Visible(UV–vis) studies, all the prepared thin films exhibit maximum absorption in the UV region with a minimum bandgap of 2.85 eV for the In2S3:Tb (2wt%) thin film. Also, the Photoluminescence (PL) spectra showed that the In2S3:Tb (2wt%) thin film possesses two large intense emission peaks around ∼470 nm (Yellow) and 530 nm (green). Finally, the photo sensing studies showed that the fabricated In2S3:Tb (2wt%) thin film photodetector showed better photocurrent, Responsivity (R), Detectivity (D*), External Quantum Efficiency (EQE), Response/Recovery time values of ∼12.5 μA, 0.296 AW−1, 7.75 × 1010 Jones, 60%, 2.9/3.6 s, respectively indicating the device better suitable for commercial photodetector application.
Time-dependent magnetotransport in a hybrid graphene/single magnetic molecule structure
Authors: Javadpour Ayat , Hessami Pilehrood Saeid 
Published: October 2023

Abstract
Graphene is potentially attractive for spintronics due to its weak intrinsic spin–orbit effect leading to a long spin relaxation length. This study examines molecular couplings on a planar graphene sheet, where one molecule is attached to the graphene sheet connected to ferromagnetic leads. The effects of a strong magnetic field, gate voltage, and time-dependent bias voltage on spin and charge tunneling currents are investigated. It is indicated that the studied model system could be controlled by applying a gate voltage and a time-dependent bias voltage. Regarding the interaction between graphene, magnetic molecule, and magnetic field, it is observed that the mutual interaction coefficients between them have the most considerable effect on the transport current. Applying the magnetic field shows that the response time of the current is very short, and the current is not zero in the steady state. In this situation, the current passes through the system continuously.
Exploring the role of heterogeneity in quorum sensing cells: a discrete state stochastic approach
Authors: Jangid Pankaj , Chaudhury Srabanti 
Published: October 2023

Abstract
Quorum sensing (QS) is a bacterial communication process in which cells detect the local environment and collectively activate gene expression. The transcriptional activator (LuxR) for these genes has a specific allosteric site where the autoinducer (AHL) can bind and turn the activator on. At the single-cell level, the response to gene activation is poorly coordinated among the QS population which leads to heterogeneity in an isogenic population. The paper presents a theoretical framework that uses a discrete-state stochastic approach to address the issue of heterogeneity in the context of DNA target search processes in QS cells and takes into account the relevant physical–chemical processes. This minimal model allows us to understand the molecular mechanisms of the protein target search in the context of cell heterogeneity, and evaluate the analytical expression of the protein search times for the targets on DNA. Specifically, it is shown that the variations in AHL and LuxR binding rates, arising from the asymmetric distribution of LuxR proteins in the cell population, can cause significant variations in the target search on the DNA. It is also found that the intrinsic heterogeneity in the cell population can affect the average search time and this in turn depends on the AHL and LuxR binding rates. Proposed theoretical results have been validated by Monte Carlo simulations.
Examining spatial and temporal drivers of pollinator nutritional resources: evidence from five decades of honey bee colony productivity data
Authors: Quinlan Gabriela M , Miller David A W , Grozinger Christina M 
Published: October 2023

Abstract
Pollinators are an essential component of terrestrial food webs and agricultural systems but are threatened by insufficient access to floral resources. Managed honey bees, as generalist foragers that hoard nectar as honey, can act as bioindicators of floral resources available to pollinators in a given landscape through their accumulation of honey. Honey yields across the United States have decreased appreciably since the 1990s, concurrent with shifts in climate, land-use, and large-scale pesticide application. While many factors can affect honey accumulation, this suggests that anthropogenic stressors may be having large-scale impacts on the floral resources that pollinators depend on for their nutrition. We used hierarchical partitioning on five decades of state-level data to parse the most important environmental factors and likely mechanisms associated with spatial and temporal variation in honey yields across the US. Climatic conditions and soil productivity were among the most important variables for estimating honey yields, with states in warm or cool regions with productive soils having the highest honey yields per colony. These findings suggest that foundational factors constrain pollinator habitat suitability and define ecoregions of low or high honey production. The most important temporally varying factors were change in herbicide use, land use (i.e. increase in intensive agriculture and reduction in land conservation programs that support pollinators) and annual weather anomalies. This study provides insights into the interplay between broad abiotic conditions and fine temporal variation on habitat suitability for honey bees and other pollinators. Our results also provide a baseline for investigating how these factors influence floral resource availability, which is essential to developing strategies for resilient plant–pollinator communities in the face of global change.
Examining water risk perception and evaluation in the corporate and financial sector: a mixed methods study in Ontario, Canada
Authors: Sandhu Guneet , Weber Olaf , Wood Michael O , Rus Horatiu A , Thistlethwaite Jason 
Published: October 2023

Abstract
As primary users of a socially, economically, and environmentally significant yet increasingly stressed resource like water, the corporate and financial sectors have an important role in sustainable water management. However, extant literature reveals a gap in the empirical assessment of water risk perception and its influence on water risk evaluation and decision-making in the corporate and financial sectors. Our explanatory sequential mixed methods study examined the relationship between water risk perception and risk evaluation (risk ratings), addressing these gaps. We employed a cross-sectional survey (N = 25) followed by semi-structured interviews (N = 22), with a purposive expert sample of analysts, practitioners, and decision-makers in the corporate and financial sector in Ontario, Canada. Our study finds multi-dimensional risk perception factors, including knowledge, professional experience, perceived controllability, values, trust, location, and gender, that influence water risk ratings and vary with the type of risk. Moreover, the in-depth follow-up interviews reveal multiple drivers of different risk ratings, such as proximity bias, sector differences, trust in various institutions, as well as the influence of tacit knowledge, exposure, the role of regulations, media, and financial materiality. Our study empirically concludes that the water risk perception of analysts, practitioners, and decision-makers in the corporate and financial sectors is highly nuanced and impacts the evaluation of different water risks, and should be systematically integrated into risk assessment and decision-making frameworks. Our study advances knowledge in the fields of risk analysis and sustainable water management and contributes by empirically examining and explaining the complex and underexplored relationship between water risk perception factors and evaluation using novel interdisciplinary Risk Theory and mixed methods approaches. Finally, the study’s findings can help integrate sector and location-specific preferences and priorities with analytical data to design contextually-attuned decision support tools for sustainable water management strategies, policies, and practices.
Thin film absorbers for tandem solar cells: an industrial perspective
Authors: Yu Ming L , Los Andrei , Xiong Gang 
Published: October 2023

Abstract
Tandem solar cells have received a lot attention from academia and industrial researchers as the potential next-generation PV technology, with higher efficiency above the limit of single-junction solar cells. Thin-film/thin-film (TF/TF) tandems are attractive due to similar toolset and processes producing the top and bottom cells, which improve scalability and promote cost reduction compared to TF/wafer tandem technologies. TF/TF/tandems additionally offer more absorber bandgap flexibility that promotes photovoltaic conversion efficiency optimization. Many materials not suitable for single junction solar cells can be explored as tandem top or bottom cells. To assess the practical efficiency potential of tandem solar cells limited by non-ideal material and device quality, we present a Shockley–Queisser-like efficiency calculation for tandem devices consisting of non-ideal top and bottom cells and with a range of absorber band gaps. The non-ideality is introduced through an experimentally measurable external radiative quantum efficiency (ERE). We find that a range of top and bottom cell band gaps enabling the highest tandem efficiency shifts from the ideal Shockley–Queisser case and depends on the top and bottom cell ERE. Furthermore, tandem cell efficiency greater than 37% can be achieved with very modest top/bottom cell EREs, for example of only 0.008%/0.5% which is typical for CdTe/CIS cells. Our results indicate that high efficiency tandem solar cells have good probability to be manufactured at high volume within a foreseeable future, despite non-ideal material and device quality due to early stages of development or constraint by manufacturing requirements. Finally, we review a number of mature and emerging thin film absorber material candidates for tandem applications. We discuss properties of these materials and the corresponding device performance as well as the associated technological challenges. We concludes on the promise of each of these materials for tandem applications that is expected to provide guidance to the photovoltaic research community.
Double diamond approach helping multidisciplinary health research team to mitigate infrastructure limitations
Authors: Abensur Silvia Itzcovici , Carlstron Regina , Hoehne Juliana Lopes , Bachiega Helder Luiz , Schor Paulo 
Published: October 2023

Abstract
The Double Diamond approach was used to creatively solve problems during bioengineering projects and facilitate a bioprinting protocol in the hard science. Users reported difficulties while using a inkjet bioprinter for specific research involving overprint and predefined location in ophthalmic applications. Given the laboratory’s limited financial resources, the pursuit of low-cost solutions became essential, and the Double Diamond approach played a pivotal role in finding them. This method was chosen due to its high resolubility focused on generating creative solutions. Moreover, its emphasis on User Experience made it an accessible design process, benefiting both designers and non-designers and enhancing user adherence while enabling easy application within the multidisciplinary team. As a result of this research, a solution was discovered, allowing researchers to overcome the problem and continue their ongoing work while also facilitating the development of new projects. In conclusion, the successful implementation of the Double Diamond approach in this specific application highlights its potential to address challenges across diverse projects. Its adaptable nature and emphasis on user-centricity make it a valuable tool in tackling complex issues in various fields of development.
Enhancement of power quality in grid-connected HRES using UPQC with self-improved Battle Royal Optimizer in IEEE 14 bus system
Authors: Ranjan Ashish , Choudhary Jayanti 
Published: October 2023

Abstract
The integration of hybrid renewable energy systems (HRES) in grid-connected load systems has become popular due to its ability to enhance reliability and reduce losses. However, this integration can lead to power quality (PQ) issues caused by non-linear, critical, and unbalanced load conditions. To address these PQ issues and compensate for load demand in HRES systems, a new method called Self-Improved Battle Royal Optimizer (SI-BRO) has been proposed. This method involves the use of a Unified Power Quality Conditioner (UPQC) device, which is designed to improve the system’s PQ performance. To enhance the performance of the UPQC device, an optimized fuzzy logic controller is introduced in the series and shunt active power filter, and its parameters are optimized using the SI-BRO model. The proposed method is implemented in MATLAB/Simulink, and its effectiveness is evaluated through different scenarios in IEEE 14 Bus system. These scenarios include the introduction of a nonlinear load to create PQ issues, and the comparison of Sag, Swell, and fluctuation of proposed technique i.e. SI-BRO optimizer with BRO,HHO ,EHO optimizer and calculation of THD in proposed technique.In all cases of Sag,Swell, Fluctuation , the proposed techniques gives better results in comparison to BRO,HHO & EHO.
TransIntegrator: capture nearly full protein-coding transcript variants via integrating Illumina and PacBio transcriptomes
Authors: Lin Zhe , Qin Yangmei , Chen Hao , Shi Dan , Zhong Mindong , An Te , Chen Linshan , Wang Yiquan , Lin Fan , Li Guang , Ji Zhi-Liang 
Published: October 2023

Abstract
Genes have the ability to produce transcript variants that perform specific cellular functions. However, accurately detecting all transcript variants remains a long-standing challenge, especially when working with poorly annotated genomes or without a known genome. To address this issue, we have developed a new computational method, TransIntegrator, which enables transcriptome-wide detection of novel transcript variants. For this, we determined 10 Illumina sequencing transcriptomes and a PacBio full-length transcriptome for consecutive embryo development stages of amphioxus, a species of great evolutionary importance. Based on the transcriptomes, we employed TransIntegrator to create a comprehensive transcript variant library, namely iTranscriptome. The resulting iTrancriptome contained 91 915 distinct transcript variants, with an average of 2.4 variants per gene. This substantially improved current amphioxus genome annotation by expanding the number of genes from 21 954 to 38 777. Further analysis manifested that the gene expansion was largely ascribed to integration of multiple Illumina datasets instead of involving the PacBio data. Moreover, we demonstrated an example application of TransIntegrator, via generating iTrancriptome, in aiding accurate transcriptome assembly, which significantly outperformed other hybrid methods such as IDP-denovo and Trinity. For user convenience, we have deposited the source codes of TransIntegrator on GitHub as well as a conda package in Anaconda. In summary, this study proposes an affordable but efficient method for reliable transcriptomic research in most species.
Fuse feeds as one: cross-modal framework for general identification of AMPs
Authors: Zhang Wentao , Xu Yanchao , Wang Aowen , Chen Gang , Zhao Junbo 
Published: October 2023

Abstract
Antimicrobial peptides (AMPs) are promising candidates for the development of new antibiotics due to their broad-spectrum activity against a range of pathogens. However, identifying AMPs through a huge bunch of candidates is challenging due to their complex structures and diverse sequences. In this study, we propose SenseXAMP, a cross-modal framework that leverages semantic embeddings of and protein descriptors (PDs) of input sequences to improve the identification performance of AMPs. SenseXAMP includes a multi-input alignment module and cross-representation fusion module to explore the hidden information between the two input features and better leverage the fusion feature. To better address the AMPs identification task, we accumulate the latest annotated AMPs data to form more generous benchmark datasets. Additionally, we expand the existing AMPs identification task settings by adding an AMPs regression task to meet more specific requirements like antimicrobial activity prediction. The experimental results indicated that SenseXAMP outperformed existing state-of-the-art models on multiple AMP-related datasets including commonly used AMPs classification datasets and our proposed benchmark datasets. Furthermore, we conducted a series of experiments to demonstrate the complementary nature of traditional PDs and protein pre-training models in AMPs tasks. Our experiments reveal that SenseXAMP can effectively combine the advantages of PDs to improve the performance of protein pre-training models in AMPs tasks.