bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sugar‐Sweetened Beverages Taxation Plan in Indonesia: Call for Political Commitment
Authors: Siregar Risyad Abiyyu , Qorina Fona , Kalaij Ayers Gilberth Ivano , Afidjati Hera , Latifani Dhani , Utomo Muhammad Faisal Putro , Sophia Louisa Patricia , Mahmud Azizah Salsabila 
Published: July 2024

ABSTRACTIndonesia is grappling with a rise in obesity and diabetes, partially driven by a high consumption of sugar‐sweetened beverages. The prevalence of obesity among adults more than doubled from 2007 to 2018, and diabetes rates have also increased. In response, the Indonesian government has proposed an excise tax on sugar‐sweetened beverages to reduce consumption. However, the implementation of this policy has repeatedly been delayed, with the latest postponement to 2024. Sugar‐sweetened beverages contribute significantly to chronic diseases, increasing the healthcare burden and reducing economic productivity. Taxation, a widely used public health strategy in the form of fiscal measures, can decrease consumption by raising prices, raising public awareness, encouraging product reformulation, and generating revenue. Despite its potential benefits, the proposed tax in Indonesia faces substantial political and commercial challenges. The president‐elect Prabowo Subanto has not explicitly supported the tax in his political manifesto, raising concerns about further delays due to industry lobbying. Successful implementation of the tax requires robust political will, public and civil society pressure, and effective cross‐sector cooperation. The government must ensure clear policy goals, equal application to domestic and foreign products, and supportive measures such as providing free drinking water alternatives. Transparent stakeholder consultations can build broad‐based support, whereas effective monitoring and evaluation frameworks are essential for compliance. To gain public support, allocating tax revenues to public health and social programs is crucial. Although initial resistance is expected, strong enforcement mechanisms can ensure adherence. With determined political commitment and public advocacy, the sugar‐sweetened beverage tax can significantly reduce obesity and diabetes rates, improving public health outcomes and economic productivity in Indonesia. This commentary provides an overview of the proposed tax, explores its challenges, and offers recommendations for successful implementation.
Influence of mono‐ and bi‐vacancy doped alkaline‐earth metals on the optoelectronic properties of monolayer defective state MoTe2: A first‐principles study
Authors: Dai Ying , Liu Guili , He Jianlin , Yang Zhonghua , Zhang Guoying 
Published: July 2024

AbstractThe effects of single‐vacancy and double‐vacancy doping with alkaline‐earth metals on the stability, electronic structure, charge transfer, and optical properties of molybdenum ditelluride in the monolayer defective state (MoTe2) have been investigated using first‐principles methods. It is found that the structure of the molybdenum ditelluride system is more stable after double Te vacancies, with direct band‐gap to indirect band‐gap transitions occurring in single Te vacancies and Mo vacancies, and semiconductor to quasi‐metal transitions occurring in double Mo vacancies. The binding energy in the defect state system of molybdenum ditelluride after vacancy defects is always negative, and the structure remains stable. In this paper, the most stable double Te vacancy state was selected for substitutional doping with alkaline‐earth metals, and the Be atoms were doped with the largest amount of morphology, and Ca atoms were doped with the most pronounced decrease in band gap value. The direct band‐gap semiconductor is maintained after doping with Be atoms. Doping with Mg, Sr and Ba changes the band‐gap from direct to indirect. The double Te defect state MoTe2 doped with Ca atoms has a tendency to transition to a quasi‐metal. Regarding photoelectric properties, the system is blue‐shifted on both the absorption and reflection peaks.
Quantum‐Noise‐Driven Generative Diffusion Models
Authors: Parigi Marco , Martina Stefano , Caruso Filippo 
Published: July 2024

AbstractGenerative models realized with Machine Learning (ML) techniques are powerful tools to infer complex and unknown data distributions from a finite number of training samples in order to produce new synthetic data. Diffusion Models (DMs) are an emerging framework that have recently overcome Generative Adversarial Networks (GANs) in creating high‐quality images. Here, is proposed and discussed the quantum generalization of DMs, i.e., three Quantum‐Noise‐Driven Generative Diffusion Models (QNDGDMs) that could be experimentally tested on real quantum systems. The idea is to harness unique quantum features, in particular the non‐trivial interplay among coherence, entanglement, and noise that the currently available noisy quantum processors do unavoidably suffer from, in order to overcome the main computational burdens of classical diffusion models during inference. Hence, the suggestion is to exploit quantum noise not as an issue to be detected and solved but instead as a beneficial key ingredient to generate complex probability distributions from which a quantum processor might sample more efficiently than a classical one. Three examples of the numerical simulations are also included for the proposed approaches. The results are expected to pave the way for new quantum‐inspired or quantum‐based generative diffusion algorithms addressing tasks as data generation with widespread real‐world applications.
Data‐based nonlinear learning control for aircraft trajectory tracking via Gaussian process regression
Authors: Wei Chuyu , Meng Deyuan , Zhang Jingyao , Cai Kaiquan 
Published: July 2024

AbstractIn this article, a new data‐based iterative learning control (ILC) algorithm is proposed via Gaussian process regression (GPR) to accomplish the trajectory tracking objective of aircraft subject to completely unknown dynamics and strong nonlinearities. The nonlinear system input–output relationship of the unknown aircraft is formulated through GPR by leveraging historical data, based on which an optimal ILC framework is established. The monotonic convergence analysis of the GPR‐based ILC is explored such that high‐precision tracking tasks can be accomplished without prior model knowledge. Simulation tests are further conducted on a commercial aircraft performing a continuous climb operation to illustrate the effectiveness of the GPR‐based ILC approach.
A CIELAB fusion‐based generative adversarial network for reliable sand–dust removal in open‐pit mines
Authors: Li Xudong , Liu Chong , Sun Yangyang , Li Wujie , Li Jingmin 
Published: July 2024

AbstractIntelligent electric shovels are being developed for intelligent mining in open‐pit mines. Complex environment detection and target recognition based on image recognition technology are prerequisites for achieving intelligent electric shovel operation. However, there is a large amount of sand–dust in open‐pit mines, which can lead to low visibility and color shift in the environment during data collection, resulting in low‐quality images. The images collected for environmental perception in sand–dust environment can seriously affect the target detection and scene segmentation capabilities of intelligent electric shovels. Therefore, developing an effective image processing algorithm to solve these problems and improve the perception ability of intelligent electric shovels has become crucial. At present, methods based on deep learning have achieved good results in image dehazing, and have a certain correlation in image sand–dust removal. However, deep learning heavily relies on data sets, but existing data sets are concentrated in haze environments, with significant gaps in the data set of sand–dust images, especially in open‐pit mining scenes. Another bottleneck is the limited performance associated with traditional methods when removing sand–dust from images, such as image distortion and blurring. To address the aforementioned issues, a method for generating sand–dust image data based on atmospheric physical models and CIELAB color space features is proposed. The impact mechanism of sand–dust on images was analyzed through atmospheric physical models, and the formation of sand–dust images was divided into two parts: blurring and color deviation. We studied the blurring and color deviation effect generation theories based on atmospheric physical models and CIELAB color space, and designed a two‐stage sand–dust image generation method. We also constructed an open‐pit mine sand–dust data set in a real mining environment. Last but not least, this article takes generative adversarial network (GAN) as the research foundation and focuses on the formation mechanism of sand–dust image effects. The CIELAB color features are fused with the discriminator of GAN as basic priors and additional constraints to improve the discrimination effect. By combining the three feature components of CIELAB color space and comparing the algorithm performance, a feature fusion scheme is determined. The results show that the proposed method can generate clear and realistic images well, which helps to improve the performance of target detection and scene segmentation tasks in heavy sand–dust open‐pit mines.
Spectral sensor‐based device for real‐time detection and severity estimation of groundnut bud necrosis virus in tomato
Authors: Kumari Karishma , Parray Roaf , Basavaraj Y. B. , Godara Samarth , Mani Indra , Kumar Rajeev , Khura Tapan , Sarkar Susheel , Ranjan Rajeev , Mirzakhaninafchi Hasan 
Published: July 2024

AbstractA machine learning‐based approach was utilized to develop a device for groundnut bud necrosis virus (GBNV) disease severity detection and estimation in tomato plants (Solanum lycopersicum L.). The study involved inoculating tomato plants with GBNV, monitoring changes in morphological and spectral characteristics, evaluating machine learning algorithms (decision tree [DT] classifier) for analysis and classification of disease severity, and developing and validating a device for disease detection and severity estimation. Spectral data analysis revealed distinct patterns in reflectance, with notable peaks observed in the 680 and 760 nm bands, while reflectance remained low and constant beyond 900 nm. Machine learning techniques, specifically a DT model, were employed to classify disease severity based on spectral data with high accuracy (95.01% training accuracy and 93.65% testing accuracy). The model identified the near‐infrared band as highly correlated (correlation coefficient of 0.82) with disease severity. Furthermore, a compact handheld device integrating a spectral sensor, organic light‐emitting diode display, and Raspberry Pi 3B was developed for real‐time disease severity estimation. The device demonstrated robust performance, accurately predicting disease severity at different growth stages, even in the absence of visible symptoms. Additionally, disease severity percentages obtained via reverse transcription polymerase chain reaction were used to validate the accuracy of the device's estimations. Its responsive nature, with estimated response times ranging from milliseconds to seconds, facilitates timely interventions in agricultural settings. Overall, this interdisciplinary approach, combining spectral analysis, machine learning, and device development, presents a promising solution for efficient disease monitoring and management in agriculture, contributing to enhanced crop health and food security.
The morphological and functional traits of water bodies enhance the explanatory power of directed spatial processes in predicting the variation of macrophyte communities under the conditions of river flow regulation
Authors: Lisovets Olena , Ruchiy Volodymyr , Kunakh Olga , Zhukov Olexander 
Published: July 2024

AbstractThe study evaluated the influence of aquatic environmental features, morphological and functional traits of water bodies and directional spatial processes on the macrophyte community of floodplain ecosystems formed as a result of water flow regulation by the construction of dams. The fluvial processes were found to be still the main factor in the formation of geomorphological and ecological characteristics of floodplain ecosystems, and also determine the spatial patterns of variability of aquatic macrophyte communities. The influence of water properties on macrophytes is significantly spatially structured, as it is modified by the morphological and functional characteristics of the water bodies. The exchange of water in the floodplain system is influenced by the functional types of water bodies, which explains their significant correlation with spatial variables. The development of macrophyte communities on a broad scale is driven by the characteristics of the aquatic environment, the morphology of the water bodies and the functional types of the water bodies. On a fine scale, the patterns are described by a pure spatial component. The impact of fine scale patterns on the variability of macrophyte communities increases with increasing community disturbance, both natural and anthropogenic.
A Review: Surface Engineering of Lipid‐Based Drug Delivery Systems
Authors: Patel Dhaval , Solanki Jyoti , Kher Mafatlal M. , Azagury Aharon 
Published: July 2024

AbstractThis review explores the evolution of lipid‐based nanoparticles (LBNPs) for drug delivery (DD). Herein, LBNPs are classified into liposomes and cell membrane‐based nanoparticles (CMNPs), each with unique advantages and challenges. Conventional LBNPs possess drawbacks such as poor targeting, quick clearance, and limited biocompatibility. One of the possible alternatives to overcome these challenges is surface modification of nanoparticles (NPs) with materials such as polyethylene glycol (PEG), aptamers, antibody fragments, peptides, CD44, hyaluronic acid, folic acid, palmitic acid, and lactoferrin. Thus, the main focus of this review will be on the different surface modifications that enable LBNPs to have beneficial properties for DD, such as enhancing mass transport properties, immune evasion, improved stability, and targeting. Moreover, various CMNPs are explored used for DD derived from cells such as red blood cells (RBCs), platelets, leukocytes, cancer cells, and stem cells, highlighting their unique natural properties (e.g., biocompatibility and ability to evade the immune system). This discussion extends to the biomimicking of hybrid NPs accomplished through the surface coating of synthetic (mainly polymeric) NPs with different cell membranes. This review aims to provide a comprehensive resource for researchers on recent advances in the field of surface modification of LBNPs and CMNPs. Overall, this review provides valuable insights into the dynamic field of lipid‐based DD systems.
Delivery of mRNA Using Biomimetic Vectors: Progress and Challenges
Authors: Yin Menghao , Sun Hanruo , Li Yanan , Zhang Jingge , Wang Jinjin , Liang Yan , Zhang Kaixiang 
Published: July 2024

AbstractMessenger RNA (mRNA) is an emerging class of therapeutic agents for treating a wide range of diseases. However, due to the instability and low cell transfection rate of naked mRNA, the expression of delivered mRNA in target cells or tissues in vivo requires delivery strategies. Biomimetic vectors hold advantages such as high biocompatibility, tissue specific targeting ability and efficient delivery mechanisms, potentially overcoming challenges faced by other delivery vectors. In this review, biomimetic vector‐based mRNA delivery systems are summarized and discuss the possible challenges and prospects of such delivery systems, which may contribute to the progress and application of mRNA‐based therapy in the biomedical field.
Mesoscale Acid–Base Complexes Display Size‐Associated Photophysical Property and Photochemical Activity
Authors: Wang Shuodong , Chen Yao , Liu Haohao , He Jinzhi , Bian Qilong , Guo Jing , Zhang Yang , Tu Yujie , Chen Bo , Zeng Zebing , Xie Sheng , Tang Ben Zhong 
Published: July 2024

AbstractThe properties of single molecules and molecular aggregates can differ dramatically, leading to a long‐standing interest in mesoscale aggregation processes. Herein, a series of acid‐base molecular complexes is developed by using a tetraphenylethylene‐backboned fluorophore, and investigated the photophysical properties and photochemical activities at different aggregation length scales. This fluorophore, with two basic diethylamine groups and two acidic tetrazole groups, exhibits sparse solubility due to multivalent interactions that cause infinite aggregation. The addition of a third acid leads to the formation of fluorophore/acid complexes with good dispersibility and colloidal stability. This assembly process can be controlled by the use of different acids and their stoichiometry, resulting in aggregates ranging in size from a few to hundreds of nanometers. A crystalline structure is obtained to illustrate the complex properties of the acid‐base network. Unlike the single molecule, these complexes show a trend of size‐related properties for photoluminescence efficiency and photochemical activity. As the amount of acid added increases, the size of the complexes decreases, the aggregation effect of the complexes on fluorescence emission increases, and the rates of the oxidative photocyclization and photodecomposition slow down. This work may help to understand size‐controlled molecular materials at the mesoscale for functional design.
Highly Conductive and Stable Composite Polymer Electrolyte with Boron Nitride Nanotubes for All‐Solid‐State Lithium Metal Batteries
Authors: He Qian , Liu Xiongxiong , Xiao Guang , He Xuhua , Gong Wenbin , Tang Lingfei , Chen Qi , Zhang Qichong , Yao Yagang 
Published: July 2024

AbstractAll‐solid‐state lithium metal batteries (ASSLMBs) have emerged as the most promising next‐generation energy storage devices. However, the unsatisfactory ionic conductivity of solid electrolytes at room temperature has impeded the advancement of solid‐state batteries. In this work, a multifunctional composite solid electrolyte (CSE) is developed by incorporating boron nitride nanotubes (BNNTs) into polyvinylidene fluoride‐hexafluoropropylene (PVDF‐HFP). BNNTs, with a high aspect ratio, trigger the dissociation of Li salts, thus generating a greater population of mobile Li+, and establishing long‐distance Li+ transport pathways. PVDF‐HFP/BNNT exhibits a high ionic conductivity of 8.0 × 10−4 S cm−1 at room temperature and a Li+ transference number of 0.60. Moreover, a Li//Li symmetric cell based on PVDF‐HFP/BNNT demonstrates robust cyclic performance for 3400 h at a current density of 0.2 mA cm−2. The ASSLMB formed from the assembly of PVDF‐HFP/BNNT with LiFePO4 and Li exhibits a capacity retention of 93.2% after 850 cycles at 0.5C and 25 °C. The high‐voltage all‐solid‐state LiCoO2/Li cell based on PVDF‐HFP/BNNT also exhibits excellent cyclic performance, maintaining a capacity retention of 96.4% after 400 cycles at 1C and 25 °C. Furthermore, the introduction of BNNTs is shown to enhance the thermal conductivity and flame retardancy of the CSE.
Construction of an Ohmic Contact Cathode by Two Metal Sulfides for efficient Capture and Catalysis of Polysulfide
Authors: Chen Jing‐Zhou , Hou Yun‐Lei , Zhang Bo‐Han , Chen Pei‐Pei , Lei Jia‐Ting , Li Zi‐Ang , Zhao Dong‐Lin 
Published: July 2024

AbstractThe slow reaction kinetics and severe shuttle effect of lithium polysulfide make Li‐S battery electrochemical performance difficult to meet the demands of large electronic devices such as electric vehicles. Based on this, an electrocatalyst constructed by metal phase material (MoS2) and semiconductor phase material (SnS2) with ohmic contact is designed for inhibiting the dissolution of lithium polysulfide with improving the reaction kinetics. According to the density‐functional theory calculations, it is found that the heterostructured samples with ohmic contacts can effectively reduce the reaction‐free energy of lithium polysulfide to accelerate the sulfur redox reaction, in addition to the excellent electron conduction to reduce the overall activation energy. The metallic sulfide can add more sulfophilic sites to promote the capture of polysulfide. Thanks to the ohmic contact design, the carbon nanotube‐MoS2‐SnS2 achieved a specific capacity of 1437.2 mAh g−1 at 0.1 C current density and 805.5 mAh g−1 after 500 cycles at 1 C current density and is also tested as a pouch cell, which proves to be valuable for practical applications. This work provides a new idea for designing an advanced and efficient polysulfide catalyst based on ohmic contact.
Web crippling design of cold‐formed ultra‐high strength steel lipped channels under ITF loading: A numerical parametric investigation
Authors: Anbarasu Mani , Dar Mohammad Adil , Ganesh Gopal Mohan , Kathiresan Muthiah 
Published: July 2024

AbstractIn recent years, there has been a compelling need to adopt cold‐formed ultra‐high‐strength steel (CFUSS) in the construction industry owing to its numerous advantages, such as a higher strength‐to‐weight ratio, flexibility in achieving desired shapes, and adaptability over longer spans. Among the various applications, CFUSS lipped channel sections are commonly used as purlins and joists in steel structural systems. However, these sections are susceptible to different failure modes, particularly web crippling, which presents significant challenges. Currently, the current design rules lack specific guidelines for estimating the web crippling capacity of CFUSS sections. To address this crucial gap, the present study focuses on a comprehensive numerical investigation of the web crippling response of CFUSS lipped channel sections under interior‐two‐flange (ITF) loading conditions. Finite element (FE) models were developed using the ABAQUS package, verified against published test data, and subsequently used in an extensive parametric study. The ultimate web crippling capacity obtained from the parametric study was used to evaluate the accuracy of the current design equations in various design standards. The findings revealed that the existing design equations inadequately predicted the ultimate web crippling capacity of CFUSS lipped channel sections subjected to the ITF loading condition. Consequently, a modified design equation is proposed, utilizing the same approach as the current design standards, and a new direct strength method (DSM) approach is developed and verified through reliability analysis. The proposed modified design equations offer promising solutions to ensure safer and more reliable design practices for CFUSS structures in the construction industry.
Resolving the evolutionary history of Ctenium and Kampochloa using phylogenomics, and a new circumscription of Cteniinae (Cynodonteae, Poaceae)
Authors: Oulo Millicent Akinyi , Wanga Vincent Okelo , Mbandi Elijah Mkala , Vorontsova Maria S. , Hu Guang‐Wan , Wang Qing‐Feng 
Published: July 2024

AbstractCtenium has about 20 species distributed in Africa, Arabia, and the Neotropics, with no species occurring on more than one continent. Its relationship with the morphologically similar Kampochloa is still unclear and has not been investigated. In this study, we performed phylogenomic analyses using 71 protein‐coding genes of 24 newly sequenced plastomes, of 13 species of Ctenium and 1 species of Kampochloa to understand their biogeography and the phylogenetic and evolutionary relationships between these two genera and within Ctenium. Our results support two major clades of Ctenium: the Neotropics and Old‐World clades. Kampochloa is shown to be a close relative of the genus Ctenium, and their morphological similarities support this. Divergence time estimation analyses show that Kampochloa and Ctenium diverged around 7.99 Ma, and might have originated in the late Miocene during the rapid expansion of C4 grasslands. With this evidence from morphology and phylogenomic data, we propose the inclusion of the previously unplaced Kampochloa into the subtribe Cteniinae.