bluuebunny/crossref_metadata_2025 huggingface dataset:
Single cell dual-omic atlas of the human developing retina
Authors: Zuo Zhen , Cheng Xuesen , Ferdous Salma , Shao Jianming , Li Jin , Bao Yourong , Li Jean , Lu Jiaxiong , Jacobo Lopez Antonio , Wohlschlegel Juliette , Prieve Aric , Thomas Mervyn G. , Reh Thomas A. , Li Yumei , Moshiri Ala , Chen Rui
Published: August 2024

The development of the retina is under tight temporal and spatial control. To gain insights into the molecular basis of this process, we generate a single-nuclei dual-omic atlas of the human developing retina with approximately 220,000 nuclei from 14 human embryos and fetuses aged between 8 and 23-weeks post-conception with matched macular and peripheral tissues. This atlas captures all major cell classes in the retina, along with a large proportion of progenitors and cell-type-specific precursors. Cell trajectory analysis reveals a transition from continuous progression in early progenitors to a hierarchical development during the later stages of cell type specification. Both known and unrecorded candidate transcription factors, along with gene regulatory networks that drive the transitions of various cell fates, are identified. Comparisons between the macular and peripheral retinae indicate a largely consistent yet distinct developmental pattern. This atlas offers unparalleled resolution into the transcriptional and chromatin accessibility landscapes during development, providing an invaluable resource for deeper insights into retinal development and associated diseases.
Two-dimensional electrons at mirror and twistronic twin boundaries in van der Waals ferroelectrics
Authors: McHugh James G. , Li Xue , Soltero Isaac , Fal’ko Vladimir I.
Published: August 2024

Semiconducting transition metal dichalcogenides (MX2) occur in 2H and rhombohedral (3R) polytypes, respectively distinguished by anti-parallel and parallel orientation of consecutive monolayer lattices. In its bulk form, 3R-MX2 is ferroelectric, hosting an out-of-plane electric polarisation, the direction of which is dictated by stacking. Here, we predict that twin boundaries, separating adjacent polarisation domains with reversed built-in electric fields, are able to host two-dimensional electrons and holes with an areal density reaching  ~ 1013cm−2. Our modelling suggests that n-doped twin boundaries have a more promising binding energy than p-doped ones, whereas hole accumulation is stable at external surfaces of a twinned film. We also propose that assembling pairs of mono-twin films with a ‘magic’ twist angle θ* that provides commensurability between the moiré pattern at the interface and the accumulated carrier density, should promote a regime of strongly correlated states of electrons, such as Wigner crystals, and we specify the values of θ* for homo- and heterostructures of various TMDs.
Continuous tuning of persistent luminescence wavelength by intermediate-phase engineering in inorganic crystals
Authors: Zhang Xin , Suo Hao , Guo Yang , Chen Jiangkun , Wang Yu , Wei Xiaohe , Zheng Weilin , Li Shuohan , Wang Feng
Published: August 2024

Multicolor tuning of persistent luminescence has been extensively studied by deliberately integrating various luminescent units, known as activators or chromophores, into certain host compounds. However, it remains a formidable challenge to fine-tune the persistent luminescence spectra either in organic materials, such as small molecules, polymers, metal-organic complexes and carbon dots, or in doped inorganic crystals. Herein, we present a strategy to delicately control the persistent luminescence wavelength by engineering sub-bandgap donor-acceptor states in a series of single-phase Ca(Sr)ZnOS crystals. The persistent luminescence emission peak can be quasi-linearly tuned across a broad wavelength range (500–630 nm) as a function of Sr/Ca ratio, achieving a precision down to ~5 nm. Theoretical calculations reveal that the persistent luminescence wavelength fine-tuning stems from constantly lowered donor levels accompanying the modified band structure by Sr alloying. Besides, our experimental results show that these crystals exhibit a high initial luminance of 5.36 cd m−2 at 5 sec after charging and a maximum persistent luminescence duration of 6 h. The superior, color-tunable persistent luminescence enables a rapid, programable patterning technique for high-throughput optical encryption.
Clinical symptoms and neuroanatomical substrates of daytime sleepiness in Parkinson’s disease
Authors: Rosinvil Thaïna , Postuma Ronald B. , Rahayel Shady , Bellavance Amélie , Daneault Véronique , Montplaisir Jacques , Lina Jean-Marc , Carrier Julie , Gagnon Jean-François
Published: August 2024

Clinical and neuroanatomical correlates of daytime sleepiness in Parkinson’s disease (PD) remain inconsistent in the literature. Two studies were conducted here. The first evaluated the interrelation between non-motor and motor symptoms, using a principal component analysis, associated with daytime sleepiness in PD. The second identified the neuroanatomical substrates associated with daytime sleepiness in PD using magnetic resonance imaging (MRI). In the first study, 77 participants with PD completed an extensive clinical, cognitive testing and a polysomnographic recording. In the second study, 29 PD participants also underwent MRI acquisition of T1-weighted images. Vertex-based cortical and subcortical surface analysis, deformation-based morphometry, and voxel-based morphometry were performed to assess the association between daytime sleepiness severity and structural brain changes in participants. In both studies, the severity of daytime sleepiness and the presence of excessive daytime sleepiness (EDS; total score >10) were measured using the Epworth Sleepiness Scale. We found that individuals with EDS had a higher score on a component including higher dosage of dopamine receptor agonists, motor symptoms severity, shorter sleep latency, and greater sleep efficiency. Moreover, increased daytime sleepiness severity was associated with a larger surface area in the right insula, contracted surfaces in the right putamen and right lateral amygdala, and a larger surface in the right posterior amygdala. Hence, daytime sleepiness in PD was associated with dopaminergic receptor agonists dosage, motor impairment, and objective sleep measures. Moreover, neuroanatomical changes in cortical and subcortical regions related to vigilance, motor, and emotional states were associated with more severe daytime sleepiness.
Comparison of beta peak detection algorithms for data-driven deep brain stimulation programming strategies in Parkinson’s disease
Authors: Baker Sunderland K. , Radcliffe Erin M. , Kramer Daniel R. , Ojemann Steven , Case Michelle , Zarns Caleb , Holt-Becker Abbey , Raike Robert S. , Baumgartner Alexander J. , Kern Drew S. , Thompson John A.
Published: August 2024

Oscillatory activity within the beta frequency range (13–30 Hz) serves as a Parkinson’s disease biomarker for tailoring deep brain stimulation (DBS) treatments. Currently, identifying clinically relevant beta signals, specifically frequencies of peak amplitudes within the beta spectral band, is a subjective process. To inform potential strategies for objective clinical decision making, we assessed algorithms for identifying beta peaks and devised a standardized approach for both research and clinical applications. Employing a novel monopolar referencing strategy, we utilized a brain sensing device to measure beta peak power across distinct contacts along each DBS electrode implanted in the subthalamic nucleus. We then evaluated the accuracy of ten beta peak detection algorithms against a benchmark established by expert consensus. The most accurate algorithms, all sharing similar underlying algebraic dynamic peak amplitude thresholding approaches, matched the expert consensus in performance and reliably predicted the clinical stimulation parameters during follow-up visits. These findings highlight the potential of algorithmic solutions to overcome the subjective bias in beta peak identification, presenting viable options for standardizing this process. Such advancements could lead to significant improvements in the efficiency and accuracy of patient-specific DBS therapy parameterization.
DynaMight: estimating molecular motions with improved reconstruction from cryo-EM images
Authors: Schwab Johannes , Kimanius Dari , Burt Alister , Dendooven Tom , Scheres Sjors H. W.
Published: August 2024

How to deal with continuously flexing molecules is one of the biggest outstanding challenges in single-particle analysis of proteins from cryogenic-electron microscopy (cryo-EM) images. Here, we present DynaMight, a software tool that estimates a continuous space of conformations in a cryo-EM dataset by learning three-dimensional deformations of a Gaussian pseudo-atomic model of a consensus structure for every particle image. Inversion of the learned deformations is then used to obtain an improved reconstruction of the consensus structure. We illustrate the performance of DynaMight for several experimental cryo-EM datasets. We also show how error estimates on the deformations may be obtained by independently training two variational autoencoders on half sets of the cryo-EM data, and how regularization of the three-dimensional deformations through the use of atomic models may lead to important artifacts due to model bias. DynaMight is distributed as free, open-source software, as part of RELION-5.
Wearable high-density EMG sleeve for complex hand gesture classification and continuous joint angle estimation
Authors: Tacca Nicholas , Dunlap Collin , Donegan Sean P. , Hardin James O. , Meyers Eric , Darrow Michael J. , Colachis IV Samuel , Gillman Andrew , Friedenberg David A.
Published: August 2024

High-density electromyography (HD-EMG) can provide a natural interface to enhance human–computer interaction (HCI). This study aims to demonstrate the capability of a novel HD-EMG forearm sleeve equipped with up to 150 electrodes to capture high-resolution muscle activity, decode complex hand gestures, and estimate continuous hand position via joint angle predictions. Ten able-bodied participants performed 37 hand movements and grasps while EMG was recorded using the HD-EMG sleeve. Simultaneously, an 18-sensor motion capture glove calculated 23 joint angles from the hand and fingers across all movements for training regression models. For classifying across the 37 gestures, our decoding algorithm was able to differentiate between sequential movements with$$97.3 \pm 0.3\%$$97.3±0.3%accuracy calculated on a 100 ms bin-by-bin basis. In a separate mixed dataset consisting of 19 movements randomly interspersed, decoding performance achieved an average bin-wise accuracy of$$92.8 \pm 0.8\%$$92.8±0.8%. When evaluating decoders for use in real-time scenarios, we found that decoders can reliably decode both movements and movement transitions, achieving an average accuracy of$$93.3 \pm 0.9\%$$93.3±0.9%on the sequential set and$$88.5 \pm 0.9\%$$88.5±0.9%on the mixed set. Furthermore, we estimated continuous joint angles from the EMG sleeve data, achieving a$$R^2$$R2of$$0.884 \pm 0.003$$0.884±0.003in the sequential set and$$0.750 \pm 0.008$$0.750±0.008in the mixed set. Median absolute error (MAE) was kept below 10° across all joints, with a grand average MAE of$$1.8 \pm 0.04^\circ$$1.8±0.04∘and$$3.4 \pm 0.07^\circ$$3.4±0.07∘for the sequential and mixed datasets, respectively. We also assessed two algorithm modifications to address specific challenges for EMG-driven HCI applications. To minimize decoder latency, we used a method that accounts for reaction time by dynamically shifting cue labels in time. To reduce training requirements, we show that pretraining models with historical data provided an increase in decoding performance compared with models that were not pretrained when reducing the in-session training data to only one attempt of each movement. The HD-EMG sleeve, combined with sophisticated machine learning algorithms, can be a powerful tool for hand gesture recognition and joint angle estimation. This technology holds significant promise for applications in HCI, such as prosthetics, assistive technology, rehabilitation, and human–robot collaboration.
A fractional-order model for optimizing combination therapy in heterogeneous lung cancer: integrating immunotherapy and targeted therapy to minimize side effects
Authors: Amilo David , Izuchukwu Chinedu , Sadri Khadijeh , Yao Hao-Ren , Hincal Evren , Shehu Yekini
Published: August 2024

This research presents a novel approach to address the complexities of heterogeneous lung cancer dynamics through the development of a Fractional-Order Model. Focusing on the optimization of combination therapy, the model integrates immunotherapy and targeted therapy with the specific aim of minimizing side effects. Notably, our approach incorporates a clever fusion of Proportional-Integral-Derivative (PID) feedback controls alongside the optimization process. Unlike previous studies, our model incorporates essential equations accounting for the interaction between regular and mutated cancer cells, delineates the dynamics between immune cells and mutated cancer cells, enhances immune cell cytotoxic activity, and elucidates the influence of genetic mutations on the spread of cancer cells. This refined model offers a comprehensive understanding of lung cancer progression, providing a valuable tool for the development of personalized and effective treatment strategies. the findings underscore the potential of the optimized treatment strategy in achieving key therapeutic goals, including primary tumor control, metastasis limitation, immune response enhancement, and controlled genetic mutations. The dynamic and adaptive nature of the treatment approach, coupled with economic considerations and memory effects, positions the research at the forefront of advancing precision and personalized cancer therapeutics.
Agricultural intensification in Lake Naivasha Catchment in Kenya and associated nutrients and pesticides pollution
Authors: Onyango Joel , Kitaka Nzula , van Bruggen J. J. A. , Irvine Kenneth , Simaika John
Published: August 2024

Investments in agricultural intensification in sub-Saharan Africa aim to fulfill food and economic demands. However, the increased use of fertilizers and pesticides poses ecological risks to water bodies in agricultural catchments. This study focused on assessing the impact of agricultural intensification on nutrient and pesticide pollution in the L. Naivasha catchment in Kenya. The research revealed significant changes in the catchment’s agricultural landscape between 1989 and 2019, driven by intensified agricultural expansion. As a result, nutrient and pesticide emissions have worsened the lake’s trophic status, shifting it towards hypereutrophic conditions. The study found a weak relationship between total nitrogen (TN) and sum dichlorodiphenyltrichloroethane (∑DDT), indicating that an increase in TN slightly predicted a reduction in ∑DDT. Analysis also showed potential phosphorus (P) limitation in the lake. Additionally, the observed ratio between dichlorodiphenyldichloroethane and dichlorodiphenyldichloroethylene (DDD:DDE) and (DDE + DDD):DDT ratios suggest recent use of banned DDT in the catchment. The study concludes that the transformation of L. Naivasha landscape shows unsustainable agricultural expansion with reduced forest cover, increased croplands, and increased pesticide contamination. This reflects a common issue in sub-Saharan Africa, that sustainable catchment management must address, specifically for combined pollutants, to support water quality and achieve the SDGs in agriculture.
Synchronicity of the Gulf Stream path downstream of Cape Hatteras and the region of maximum wind stress curl
Authors: Gifford Ian , Gangopadhyay Avijit , Andres Magdalena , Oliver Hilde , Gawarkiewicz Glen , Silver Adrienne
Published: August 2024

The Gulf Stream, a major ocean current in the North Atlantic ocean is a key component in the global redistribution of heat and is important for marine ecosystems. Based on 27 years (1993–2019) of wind reanalysis and satellite altimetry measurements, we present observational evidence that the path of this freely meandering jet after its separation from the continental slope at Cape Hatteras, aligns with the region of maximum cyclonic vorticity of the wind stress field known as the positive vorticity pool. This synchronicity between the wind stress curl maximum region and the Gulf Stream path is observed at multiple time-scales ranging from months to decades, spanning a distance of 1500 km between 70 and 55W. The wind stress curl in the positive vorticity pool is estimated to drive persistent upward vertical velocities ranging from 5 to 17 cm day−1 over its ~ 400,000 km2 area; this upwelling may supply a steady source of deep nutrients to the Slope Sea region, and can explain as much as a quarter of estimated primary productivity there.
XPS depth profiling of nano-layers by a novel trial-and-error evaluation procedure
Authors: Racz Adel Sarolta , Menyhard Miklos
Published: August 2024

In spite of its superior chemical sensitivity, XPS depth profiling is rarely used because of the alteration introduced by the sputter removal process and the resulting inhomogeneous in-depth concentration distribution. Moreover, the application of XPS becomes increasingly challenging in the case of the analysis of thin layers, if the thickness is in the range of 2–3 inelastic mean free paths (IMFP) of the photoelectrons. In this paper we will show that even in these unfavorable cases the XPS depth profiling is applicable. Herein the XPS depth profiling of a model system tungsten-carbide-rich nano-layer of high hardness and corrosion resistance is presented. We will show that the problems arising because of the relatively high IMFP can be corrected by introducing a layer model for the calculation of the observed XPS intensities, while the alteration, e.g. ion mixing, compound formation and similar artefact, introduced by the sputter removal process can be handled by TRIDYN simulation. The method presented here overcomes the limitation of XPS depth profiling.
Low-light image enhancement using generative adversarial networks
Authors: Wang Litian , Zhao Liquan , Zhong Tie , Wu Chunming
Published: August 2024

In low-light environments, the amount of light captured by the camera sensor is reduced, resulting in lower image brightness. This makes it difficult to recognize or completely lose details in the image, which affects subsequent processing of low-light images. Low-light image enhancement methods can increase image brightness while better-restoring color and detail information. A generative adversarial network is proposed for low-quality image enhancement to improve the quality of low-light images. This network consists of a generative network and an adversarial network. In the generative network, a multi-scale feature extraction module, which consists of dilated convolutions, regular convolutions, max pooling, and average pooling, is designed. This module can extract low-light image features from multiple scales, thereby obtaining richer feature information. Secondly, an illumination attention module is designed to reduce the interference of redundant features. This module assigns greater weight to important illumination features, enabling the network to extract illumination features more effectively. Finally, an encoder-decoder generative network is designed. It uses the multi-scale feature extraction module, illumination attention module, and other conventional modules to enhance low-light images and improve quality. Regarding the adversarial network, a dual-discriminator structure is designed. This network has a global adversarial network and a local adversarial network. They determine if the input image is actual or generated from global and local features, enhancing the performance of the generator network. Additionally, an improved loss function is proposed by introducing color loss and perceptual loss into the conventional loss function. It can better measure the color loss between the generated image and a normally illuminated image, thus reducing color distortion during the enhancement process. The proposed method, along with other methods, is tested using both synthesized and real low-light images. Experimental results show that, compared to other methods, the images enhanced by the proposed method are closer to normally illuminated images for synthetic low-light images. For real low-light images, the images enhanced by the proposed method retain more details, are more apparent, and exhibit higher performance metrics. Overall, compared to other methods, the proposed method demonstrates better image enhancement capabilities for both synthetic and real low-light images.
Effects of anions on the electrospray ionization of proteins in strong acids
Authors: Han Zhongbao , Zheng Huizi , Wu Shuyao , Liu Liyan , Chen Lee Chuin
Published: August 2024

The effect of anions on the positive electrospray ionization (ESI) of proteins in different strong acids with varying pH values from 3 to 1 is studied using high-pressure ESI.
Electrochemical biosensors for clinical detection of bacterial pathogens: advances, applications, and challenges
Authors: Ding Shengyong , Chen Xiaodi , Yu Bin , Liu Zhiyuan
Published: August 2024

This review summarizes the advantages, challenges and potential applications of novel electrochemical biosensors for bacterial pathogen detection in clinical diagnostics.
Electrical monitoring of human-serum-albumin-templated molecularly imprinted polymer nanoparticles with high affinity based on molecular charges and their visualization
Authors: Man Youyuan , Nishitani Shoichi , Sawada Kazuaki , Sakata Toshiya
Published: August 2024

Human-serum-albumin (HSA)-templated molecularly imprinted polymer nanoparticles (nano-MIPs) were integrated with a solution-gated field-effect transistor-based biosensor.
One-, two- and three-dimensional interlocked polymers based on hybrid inorganic–organic rotaxanes
Authors: Lockyer Selena J. , Whitehead George F. S. , Timco Grigore A. , McInnes Eric J. L. , Winpenny Richard E. P.
Published: August 2024

We report three new polymers, based on mechanically interlocked inorganic–organic rotaxanes.
Ni spin coupling and NixN (x = 1, 4) (111) growth at low and room temperatures and different strains using the CrN (111) surface as initial substrate
Authors: Moreno H. J. C. , Cocoletzi Gregorio H. , Calleja A. Wilfrido
Published: August 2024

We have investigated the Ni magnetic alignment when it is deposited on the CrN (111) magnetic surface and the possible growth of NiN and Ni4N on this surface using first principles calculations.
Hg–Hg bonding and its influence on the stability of (HgS)n clusters
Authors: Jayasekharan Thankan
Published: August 2024

Pulsed laser ablation of HgS(s) shows the formation of (HgS)n=2–4+, HgSn=1–8±, and HgmSn± cluster ions. Theoretical analysis evidences that Hg–Hg interaction plays a vital role in the stability of (HgS)n and validates experimental observation.
In silico investigation on the separation of disulfide bonds by N-heterocyclic carbene
Authors: Sinha Swapan , Das Subhra , Ray Kritish Kumar , Maity Sibaprasad , Roymahapatra Gourisankar , Giri Santanab
Published: August 2024

Disulfide bond cleavage by nucleophiles TMP, TCEP, and NHC is explored. While TMP and TCEP reactions are endothermic via SN2, NHC-mediated cleavage is exothermic. NBO, PIO, and ETS-NOCV analyses reveal electron transfer dynamics during reaction.
Data-efficient fine-tuning of foundational models for first-principles quality sublimation enthalpies
Authors: Kaur Harveen , Della Pia Flaviano , Batatia Ilyes , Advincula Xavier R. , Shi Benjamin X. , Lan Jinggang , Csányi Gábor , Michaelides Angelos , Kapil Venkat
Published: August 2024

We present an accurate and data-efficient protocol for fine-tuning the MACE-MP-0 foundational model for a given system. Our model achieves kJ/mol in predicting sublimation enthalpies and below 1% error in the density of ice polymorphs.
Shatianyu (Citrus grandis L. Osbeck) whole fruit alleviated loperamide-induced constipation via enhancing gut microbiota-mediated intestinal serotonin secretion and mucosal barrier homeostasis
Authors: Deng Mei , Ye Jiamin , Zhang Ruifen , Zhang Shuai , Dong Lihong , Su Dongxiao , Zhang Mingwei , Huang Fei
Published: August 2024

The effects of Citrus grandis Shatianyu whole fruit powder enriched in flavonoids and dietary fiber on loperamide-induced constipation after a 4-week administration in the diet, together with possible microbiota-mediated mechanisms were explored.
A novel Ba0.5Sr0.5Co0.8Fe0.2O3−δ and CeO2 hybrid electrocatalyst for both the oxygen evolution and methanol oxidation reactions
Authors: Liu Fuyue , Wu Fei , Guo Ran , Dang Jiaxin , Qiu Haoqi , Zhang Qiuju , Yang Chao , Miao He , Yuan Jinliang
Published: August 2024

We synthesize a hybrid catalyst of BSCF@CeO2-10 with superior OER and MOR catalytic properties, which can be ascribed to the stable phase structure of BSCF, enhanced OH− adsorption ability, reduced RDS energy barrier, and increased Co/Fe valence.
Flow tweezing of anisotropic magnetic microrobots in a dynamic magnetic trap for active retention and localized flow sensing
Authors: Liu Yuan , Cao Quanliang , Xu Haifeng , Lin Gungun
Published: August 2024

We report the dynamic tweezing of an anisotropic magnetic microrobot in a rotating magnetic trap for active retention and localized flow sensing under confined fluidic conditions.
Retention of lithium atoms by A@C20 (A = Li, Na and K) metallofullerenes: possible anodes for batteries?
Authors: Zubieta López F. A. , Chigo-Anota E.
Published: August 2024

We have investigated the electronic properties of lithium, sodium, and potassium doped endohedrally on C20 fullerenes, through DFT calculations.
Electrochemically driven regioselective construction of 4-sulfenyl-isochromenones from o-alkynylbenzoates and diaryl disulfides
Authors: Dapkekar Anil Balajirao , Satyanarayana Gedu
Published: August 2024

Herein, we have developed an electrochemical technique that enables the regioselective construction of 4-sulfenyl-1H-isochromen-1-ones in an undivided cell under external acid, catalyst, oxidant, or metal-free conditions.
Biosynthesis of brevinic acid from lawsone
Authors: Hohmann Maximilian , Ohlrogge Jonas F. , Gulder Tobias A. M.
Published: August 2024

The violet pigment brevinic acid is identified in extracts of E. coli cultures and a biosynthetic route from natural dye lawsone through phosphorylation or acetylation is presented.
Approaches to enhance the antimicrobial activity of carbapenems within bacterial biofilms
Authors: Lamb Matthew A. , Wiedbrauk Sandra , Fairfull-Smith Kathryn E.
Published: August 2024

This review highlights current strategies to improve the anti-biofilm activity of clinically approved carbapenem-based antibiotics.
Mesoporous NiMoO4 nanorod electrode materials for flexible and asymmetric energy storage devices
Authors: He Qi , Liu Xingyu , Wu Xiang
Published: August 2024

We synthesize NiMoO4-2 electrode material with a specific capacitance of 1194 F g−1 of 1 A g−1. The as-fabricated devices retain 89.2% of initial capacitance after 10 000 cycles.
Waste bamboo-derived magnetically separable bamboo-activated carbon: from characterization to effective remediation of fluoride (F−) ions from water
Authors: Bagbi Yana , Yomgam Pokjum , Libang Enuk , Boruah Bijoylakshmi , Kaur Jaspreet , Jayanthi S. , Kumar Sanjeev , Dhania Narender K.
Published: August 2024

Magnetically separable magnetite-activated bamboo carbon (MABC), was obtained from waste bamboo biomass via pyrolysis of bamboo chunks and the co-precipitation method.
Harnessing potassium peroxymonosulfate activation of WO3/diatomite composites for efficient photocatalytic degradation of tetracycline
Authors: Nguyen The Luan , Tran Hong Huy , Huynh Tu Cam , Le Khoa Tien , Cao Thi Minh , Pham Viet Van
Published: August 2024

The increasing prevalence of pharmaceutical contaminants in aquatic ecosystems poses profound challenges for both environmental sustainability and public health.
Enhancing the accessibility of chemistry assessments for multilingual learners: understanding challenging features in assessment items
Authors: Lee Eshani N. , Orgill MaryKay
Published: August 2024

Multilingual learners face significant challenges when navigating the linguistic complexities of chemistry assessments. This study, employing the Equitable Framework for Classroom Assessment, identified these specific challenging features in general chemistry assessment items on the topics of limiting reactant and percent yield. Through in-depth, semi-structured interviews with multilingual students, we discovered critical barriers to comprehension: lack of metacognitive support, complex vocabulary and syntax, dense text layout, and extraneous information. These findings emphasize the need to better understand and alleviate these types of linguistic features in assessment items to more accurately measure chemistry knowledge, rather than linguistic proficiency. By addressing these challenges, instructors can design more accessible assessment items for a diverse group of students. The results also offer valuable insights and practical guidance for writing equitable assessment items.
Sodium compensation: a critical technology for transforming batteries from sodium-starved to sodium-rich systems
Authors: Zhu Bin , Zhang Wei , Jiang Zhenjing , Chen Jie , Li Zheng , Zheng Jingqiang , Wen Naifeng , Chen Ruwei , Yang Hang , Zong Wei , Dai Yuhang , Ye Chumei , Zhang Qi , Qiu Tianyun , Lai Yanqing , Li Jie , Zhang Zhian
Published: August 2024

This review highlights the significance, fundamental principle, and recent advances of sodium compensation technologies, and provides insights into future improvement.
A redox-active organic cage as a cathode material with improved electrochemical performance
Authors: Bera Saibal , Goujon Nicolas , Melle-Franco Manuel , Mecerreyes David , Mateo-Alonso Aurelio
Published: August 2024

Cathodes formulated with an organic cage show enhanced performance compared to those formulated with the cage's individual subunits.
Wearable strain sensors: design shapes, fabrication, encapsulation and performance evaluation methods
Authors: Zahri Nur Nazihah Abu Hassan , Nordin Anis Nurashikin , Azlan Norsinnira Zainul , Hassan Ibrahim Hafizu , Tung Lun Hao , Lim Lai Ming , Samsudin Zambri
Published: August 2024

Highly durable, stretchable, sensitive and biocompatible wearable strain sensors are crucial for healthcare, sports, and robotics applications.
Transcriptional bursting: from fundamentals to novel insights
Authors: Hebenstreit Daniel , Karmakar Pradip
Published: August 2024

Transcription occurs as irregular bursts in a very wide range of systems, including numerous different species and many genes within these. In this review, we examine the underlying theories, discuss how these relate to experimental measurements, and explore some of the discrepancies that have emerged among various studies. Finally, we consider more recent works that integrate novel concepts, such as the involvement of biomolecular condensates in enhancer-promoter interactions and their effects on the dynamics of transcriptional bursting.
Carbon market risk estimation using quantum conditional generative adversarial network and amplitude estimation
Authors: Zhou Xiyuan , Zhao Huan , Cao Yuji , Fei Xiang , Liang Gaoqi , Zhao Junhua
Published: August 2024

Accurately and efficiently estimating the carbon market risk is paramount for ensuring financial stability, promoting environmental sustainability, and facilitating informed decision‐making. Although classical risk estimation methods are extensively utilized, the implicit pre‐assumptions regarding distribution are predominantly contained and challenging to balance accuracy and computational efficiency. A quantum computing‐based carbon market risk estimation framework is proposed to address this problem with the quantum conditional generative adversarial network‐quantum amplitude estimation (QCGAN‐QAE) algorithm. Specifically, quantum conditional generative adversarial network (QCGAN) is employed to simulate the future distribution of the generated return rate, whereas quantum amplitude estimation (QAE) is employed to measure the distribution. Moreover, the quantum circuit of the QCGAN improved by reordering the data interaction layer and data simulation layer is coupled with the introduction of the quantum fully connected layer. The binary search method is incorporated into the QAE to bolster the computational efficiency. The simulation results based on the European Union Emissions Trading System reveals that the proposed framework markedly enhances the efficiency and precision of value‐at‐risk and conditional value‐at‐risk compared to original methods.
Fast simulation mapping: From standard to modified gravity cosmologies using the bias assignment method
Authors: García-Farieta Jorge Enrique , Balaguera-Antolínez Andrés , Kitaura Francisco-Shu
Published: August 2024

Context. We assess the effectiveness of a non-parametric bias model in generating mock halo catalogues for modified gravity (MG) cosmologies, relying on the distribution of dark matter from either MG or Λ cold dark matter (ΛCDM) simulations.

Aims. We aim to generate halo catalogues that effectively capture the distinct impact of MG, ensuring high accuracy in both two- and three-point statistics for a comprehensive analysis of large-scale structures. We investigated the inclusion of MG in non-local bias to directly map the tracers onto ΛCDM fields, which would significantly reduce computational costs.

Methods. We employed the bias assignment method (BAM) to model halo distribution statistics by leveraging seven high-resolution COLA simulations of MG cosmologies. Taking cosmic-web dependences into account when learning the bias relations, we designed two experiments to map the MG effects: one utilising the consistent MG density fields and the other employing the benchmark ΛCDM density field.

Results. BAM generates MG halo catalogues from both calibration experiments with excellent summary statistics, achieving a ~1% accuracy in the power spectrum across a wide range of k modes, with minimal differences well below 10% for modes subject to cosmic variance, particularly below k < 0.07 h Mpc−1. The reduced bispectrum remains consistent with the reference catalogues within 10% for the studied configuration. Our results demonstrate that a non-linear and non-local bias description can model the effects of MG starting from a ΛCDM field.
X-ray polarisation in AGN circumnuclear media
Authors: Vander Meulen Bert , Camps Peter , Savić Ðorđe , Baes Maarten , Matt Giorgio , Stalevski Marko
Published: August 2024

Context. Cold gas and dust reprocess the central X-ray emission of active galactic nuclei (AGN), producing characteristic spectro-polarimetric features in the X-ray band. The recent launch of IXPE allows for observations of this X-ray polarisation signal, which encodes unique information on the parsec-scale circumnuclear medium of obscured AGN. However, the models for interpreting these polarimetric data are under-explored and do not reach the same level of sophistication as the corresponding spectral models.

Aims. We aim at closing the gap between the spectral and spectro-polarimetric modelling of AGN circumnuclear media in the X-ray band by providing the tools for simulating X-ray polarisation in complex geometries of cold gas alongside X-ray spectra.

Methods. We lay out the framework for X-ray polarisation in 3D radiative transfer simulations and provide an implementation to the 3D radiative transfer code SKIRT, focussing on (de)polarisation due to scattering and fluorescent re-emission. As an application, we explored the spectro-polarimetric properties of a 2D toroidal reprocessor of cold gas, modelling the circumnuclear medium of AGN.

Results. For the 2D torus model, we find a complex behaviour of the polarisation angle with photon energy, which we interpret as a balance between the reprocessed photon flux originating from different sky regions, with a direct link to the torus geometry. We calculated a large grid of AGN torus models and demonstrated how spatially resolved X-ray polarisation maps could form a useful tool for interpreting the geometrical information that is encoded in IXPE observations. With this work, we release high-resolution AGN torus templates that simultaneously describe X-ray spectra and spectro-polarimetry for observational data fitting with XSPEC.

Conclusions. The SKIRT code can now model X-ray polarisation simultaneously with X-ray spectra and provide synthetic spectro-polarimetric observations for complex 3D circumnuclear media, with all features of the established SKIRT framework available.
Dynamical friction from self-interacting dark matter
Authors: Fischer Moritz S. , Sagunski Laura
Published: August 2024

Context. Merging compact objects such as binary black holes provide a promising probe for the physics of dark matter (DM). The gravitational waves emitted during inspiral potentially allow one to detect DM spikes around black holes. This is because the dynamical friction force experienced by the inspiralling black hole alters the orbital period and thus the gravitational wave signal.

Aims. The dynamical friction arising from DM can potentially differ from the collisionless case when DM is subject to self-interactions. This paper aims to understand how self-interactions impact dynamical friction.

Methods. To study the dynamical friction force, we use idealised N-body simulations, where we include self-interacting dark matter.

Results. We find that the dynamical friction force for inspiralling black holes would be typically enhanced by DM self-interactions compared to a collisionless medium (ignoring differences in the DM density). At lower velocities below the sound speed, we find that the dynamical friction force can be reduced by the presence of self-interactions.

Conclusions. DM self-interactions have a significant effect on the dynamical friction for black hole mergers. Assuming the Chandrasekhar formula may underpredict the deceleration due to dynamical friction.
Advancing spectroscopic understanding of HOCS+: Laboratory investigations and astronomical implications
Authors: Lattanzi Valerio , Sanz-Novo Miguel , Rivilla Víctor M. , Araki Mitsunori , Bunn Hayley A. , Martín-Pintado Jesús , Jiménez-Serra Izaskun , Caselli Paola
Published: August 2024

Sulphur-bearing species play crucial roles in interstellar chemistry, yet their precise characterisation remains challenging. Here, we present laboratory experiments aimed at extending the high-resolution spectroscopy of protonated carbonyl sulphide (HOCS+), a recently detected molecular ion in space. Using a frequency-modulated free-space absorption spectrometer, we detected rotational transitions of HOCS+ in an extended negative glow discharge with a mixture of H2 and OCS, extending the high-resolution rotational characterisation of the cation well into the millimetre wave region (200–370 GHz). Comparisons with prior measurements and quantum chemical calculations revealed an overall agreement in the spectroscopic parameters. With the new spectroscopic dataset in hand, we re-investigated the observations of HOCS+ towards G+0.693−0.027, which were initially based solely on Ka = 0 lines contaminated by HNC34S. This re-investigation enabled the detection of weak Ka ≠ 0 transitions, free from HNC34S contamination. Our high-resolution spectroscopic characterisation also provides valuable insights for future millimetre and submillimetre astronomical observations of these species in different interstellar environments. In particular, the new high-resolution catalogue will facilitate the search for this cation in cold dark clouds, where very narrow line widths are typically observed.
Data Security in Cloud Environment Using Cryptography Technique for End-to-End Encryption
Authors: Badhan Akash , Vasudev Hitesh , Kapila Dhiraj , Himanshu
Published: August 2024

In the modern era, data security becomes the major issue as million or trillion of data in units is being generated to cloud with every moment of time. Here the main role of cloud is to provide virtual storage for storing information or we can say it works as transmission medium for wireless data communication. So, it is very important to choose our cloud platform wisely as data is never secured while it is over cloud or in transmission medium. So, study is helping to understand the impact of data security over cloud platform, and we are reviewing the existing techniques that have been implemented by other researchers to provide encrypted communication when cloud is intermediate between sender and receiver.
Novel CNN Integration with Pre-Trained model for Enhanced Plant Disease Detection
Authors: Saluja Rishabh , Shukla Mahima , Kaur Gursimran , Rana Pooja
Published: August 2024

Agriculture is one of the major aspects of national development, yet plant diseases present a significant threat to agricultural production. In order to reduce associated losses and mitigate this threat, plant disease identification in early stages is very essential. Deep learning emerged as a significant advancement in the effective detection of various plant diseases as it can visualize and categorize the symptoms from the leaves of the plants This study examines the state-of-the-art deep learning methods for leafbased plant disease detection, with a particular emphasis on Convolutional Neural Networks (CNNs), Transfer Learning, and Ensemble Learning. In addition, a novel ensemble architecture is proposed, which combines a customized CNN architecture and a pretrained model called GoogLeNet. This proposed architecture can detect ten of the most prevalent plant diseases from the PlantVillage dataset, including tomato, apple, bell pepper, and potato. The suggested ensemble architecture achieves an astonishing 99.07% accuracy, demonstrating its potential to enhance plant disease diagnostics and promote sustainable agriculture
A Comprehensive Review of Android Malware Detection Techniques
Authors: Singh Divyanshu , Kaur Harpreet , Sajid Shayan , Sagar Gaja
Published: August 2024

The Android malware is at peak with overwhelming ubiquity of the Android Operating Systems. Malware creators have been using and devising different novel strategies to build Android apps that are malicious that are capable of creating severe damages to the device and thus extremely weakens the capability of conventional malware locators that are inept in identifying these obscure noxious applications. The highlights gotten from inactive and energetic examination of Android apps could be utilized for identifying obscure malware by utilizing ML procedures. This paper provides the analysis of different malware displays in showcase and their effect along with their location frameworks, inactive and energetic apparatuses utilized for the reason. We were able to discover investigate work in all the Android malware discovery strategies which utilize machine learning which too highlights the reality that machine learning calculations are utilized habitually in this range for recognizing Android malware in the wild.
Design And Fabrication of IoT Based Car Parking
Authors: Singh Jasvinder , Chandra Gupta Gurram Mourya , Rayyan Shaik Darban , Madhusudhan Gollapudi , Pratik Niraj , Singh Ajitesh
Published: August 2024

The escalating urbanization and rapid traffic growth have accentuated the need for smart and efficient parking solutions. This paper explores the implementation of IoT technology to maximize the optimization of multi-storied parking facilities. The system that was proposed aims to enhance space utilization reduce congestion and improve the parking experience. It all begins by studying current challenges related to traditional parking structures including space utilization, limited accessibility, and huge waiting time. The system offers real-time data analysis by integrating IoT devices like sensors, data acquisition and communicating modules. It facilitates dynamic range space allocation for efficient utilization of allocated slots. For effectiveness, a prototype is developed and tested, where the results are described as a significant reduction rate in congestion and space utilization which further enhances user satisfaction. Using a lift and a DC motor for the movement of vehicles from one floor to another, the Installation of IoT technology enables the operators to manage and monitor the facility remotely, contributing to cost-effectiveness and operational efficiency.
Role of Artificial Intelligence in Revolutionizing the Automotive Industry: A Review
Authors: Verma Jai Krishan , Kanday Rajeev , Gupt Sachin
Published: August 2024

AI is now a days adopted in various applications such as industries and automotive companies. AI initiatives, collaborations, and partnerships in advancing providing case studies of AI implementation in automotive manufacturing. Addressing regulatory and ethical considerations is crucial in the adoption of AI-driven automotive innovation. Legal barriers and compliance challenges, good consideration of AI in self-independent vehicles are discussed in this paper and strategies for ensuring safety and accountability. Looking towards the future, the paper anticipates trends in AI-driven automotive innovation, discusses technological challenges, research directions, socio-economic impacts, and policy recommendations. It provides insights into key findings, implications for the future of the automotive industry, and final thoughts and recommendations for stakeholders. Overall, comprehensive guide to understanding the role of artificial intelligence in driving towards autonomy & reshaping the vehicle manufacturing industry. Furthermore, give provision of precise knowledge for policy makers, experimenters, industry professionals and researchers who are enthusiastic towards this knowledge.
Design and Development of Clap Based Switch Regulator in Relation to Smart Home Applications
Authors: Verma Jai Krishan , Kanday Rajeev , Gupta Sachin
Published: August 2024

The clap-based regulator proves an important role or invention in today’s day to day life for industries and home automation technology, contribute the wireless or automated approach to control the home, school and industries automations and appliances. By using sound recognition technique, the system provides signal to the ac and dc appliances with the help of Arduino which control the relay circuit. This invention provides a lot of benefits for environment as well. Moreover, through this innovation the project helps in energy preservation and encourage urban growth and progress. In this Project we associated an Artificial intelligence-based sound module which stimulates the lights in the home based on the threshold value of Clap and then off the lights based on consecutive clap sound.
Evaluation of mechanical and thermal properties of Multi-Walled Carbon Nano-tube/Graphene hybrid nano-composite
Authors: Tuli Aashish , Singh Amrinder Pal
Published: August 2024

Multi-walled carbon nano-tube (MWCNT)-graphene (GR) hybrid nano-composite were developed using the solution blending technique, to characterize mechanical and thermal properties using universal testing machine (UTM) and thermogravimetric analyses (TGA). Improvement in tensile strength, Young’s modulus and elongation at break was one significant synergistic effect that MWCNT & GR had on Ethylene propylene diene monomer (EPDM)-Butyl rubber (BR), after their incorporation. Compatibility between the nano-fillers and the elastomers led to improvement of thermal stability as analyzed from TGA results. Significant improvement in residual mass percentage of the hybrid nanocomposites was observed, as compared to pristine EPDM-BR samples. All this indicates that EPDM-BR hybrid nano-composites will find use in many advanced mechanical applications like strain sensors, motion-controlled gaming, home rehabilitation, soft robotics, vibration control etc.