bluuebunny/crossref_metadata_2025_split huggingface dataset:
Adaptive neural network‐based security asynchronous control for uncertain Markov jump power systems with dead zone under DoS attack
Authors: Dong Shanling , Liu Enjun , Wang Bo , Liu Meiqin , Chen Guanrong 
Published: May 2024

AbstractThe article deals with the security control stabilization problem of uncertain Markov jump power systems with input dead zone under stochastic denial‐of‐service (DoS) attack. DoS attack is modeled as a discrete‐time Markov process. Dual hidden Markov models are respectively used to detect the modes of the original power systems and the one under DoS attack. Based on the detected modes and neural networks (NNs), adaptive NN‐based security asynchronous control strategies are proposed, where both state feedback and output feedback are studied simultaneously. With the developed control laws, all trajectories of the closed‐loop systems are bounded stable in the stochastic setting. Simulation results demonstrate the correctness and usefulness of the proposed techniques.
Fluvial biogeomorphological feedbacks from plant traits to the landscape: Lessons from selected French rivers in line with A.M. Gurnell's influential contribution
Authors: Corenblit Dov , Steiger Johannes 
Published: May 2024

AbstractResearch in fluvial biogeomorphology largely aims to promote our understanding of the interactions between riparian vegetation and fluvial morphodynamics within riverine ecosystems. Starting at the end of the last century, Angela M. Gurnell has made a major contribution to fluvial geomorphology by considering water flow and sediment transport in combinaton with riparian and later also aquatic vegetation and thus significantly promoting the fluvial biogeomorphological approach from its early days until today. The objective of the present paper is to present a set of studies and results obtained over the last 20 years mainly by the authors and many collaborators, including Angela M. Gurnell, on a panel of French rivers: the Tech, Garonne, Isère and Allier. In particular, feedback mechanisms between fluvial morphodynamics and riparian vegetation dynamics were investigated directly in the field and by using high‐resolution remote sensor systems at the scale of individual plants, populations, communities and landscapes as well as during semi‐controlled ex‐situ experiments at the scale of individual plants. Collectively, the authors' research conducted over the past 20 years has helped to elucidate some key aspects of the feedback dynamics between the lowest and highest levels of the riparian ecosystem organization. This article reviews and discusses those key aspects. The gradually obtained results have contributed to a better understanding and quantification of feedback between river morphodynamics and vegetation at nested spatiotemporal scales, from plant species traits to the riverine landscape. Furthermore, the biogeomorphological approach advocated for more than 20 years now has clearly facilitated the extension of the discipline of geomorphology to ecology in general and evolutionary ecology in particular and thus contributed to a more integrative vision of earth surface processes.
Forecasting sustainable development goals scores by 2030 using machine learning models
Authors: Chenary Kimia , Pirian Kalat Omid , Sharifi Ayyoob 
Published: May 2024

AbstractThe Sustainable Development Goals (SDGs) set by the United Nations are a worldwide appeal to eliminate poverty, preserve the environment, address climate change, and guarantee that everyone experiences peace and prosperity by 2030. These 17 goals cover various global issues concerning health, education, inequality, environmental decline, and climate change. Several investigations have been carried out to track advancements toward these goals. However, there is limited research on forecasting SDG scores. This research aims to forecast SDG scores for global regions by 2030 using ARIMAX and LR (Linear Regression) smoothed by HW (Holt‐Winters') multiplicative technique. To enhance model performance, we used predictors identified from the SDGs that are more likely to be influenced by Artificial Intelligence (AI) in the future. The forecast results for 2030 show that “OECD countries” (80) (with a 2.8% change) and “Eastern Europe and Central Asia” (74) (with a 2.37% change) are expected to achieve the highest SDG scores. “Latin America and the Caribbean” (73) (with a 4.17% change), “East and South Asia” (69) (with a 2.64% change), “Middle East and North Africa” (68) (with a 2.32% change), and “Sub‐Saharan Africa” (56) (with a 7.2% change) will display lower levels of SDG achievement, respectively.
Does industrial sustainable development policy act as a booster for urban economic growth? Evidence from China's eco‐industrial parks
Authors: Wu Jianxian , Nie Xin , Wang Han 
Published: May 2024

AbstractEco‐industrial parks (EIPs), which reduce pollution and transform the industrial sector to be more environmentally benign, represent the industrial sustainable development policy. However, little research has concentrated on its economic effects. We explore the impacts of 93 national demonstration EIPs on urban economic development using time‐varying difference‐in‐difference, based on panel data from 330 Chinese cities from 1995 to 2018. We show that EIPs have contributed to an economic growth range between 7.47% and 7.61% in pilot cities, with robustness considering price deflators and alternative satellite data on night lights. Structure, technological, and scale effects help explain the mechanisms. Further estimates show that EIPs have had mixed success in addressing the resource curse, where they only alleviate the curse for growing resource‐based cities. We argue that the industrial sustainable development policy not only conveys significant economic benefits but also holds the potential to address the resource curse.
The Suppressive Effect of Novel Hydrazones‐Tethered Imidazoles in HCT‐116 and HT‐29 Colorectal Cancer Cells: Synthesis, Biological Activity and Molecular Modeling Studies
Authors: Kekeçmuhammed Hüseyin , Tapera Michael , Şahin Kader , Öztürk Sever Beyza , Anber Anber M. , Bora Rıfat Emin , Avşar Timuçin , Kılıç Türker , Günger Ezgi , Sarıpınar Emin , Coker‐Gurkan Ajda , Durdağı Serdar 
Published: May 2024

AbstractA series of hybrid compounds containing both the imidazole ring and the hydrazone moiety have been synthesized. Synthesized compounds were characterized by various spectral techniques, including FT‐IR,1H‐NMR,13C‐NMR, and HRMS. The compounds were evaluated for their antiproliferative activities on colorectal cancer cells HCT‐116 and HT‐29 in a time‐dependent manner. Among them, some compounds exhibited remarkable anti‐cancer activity with a less cytotoxic effect on non‐cancerous cell lines, especiallyHRK‐2with IC50value of 1.35±0.18 μM in HCT‐116 cells andHRK‐5with IC50value of 2.67±0.61 μM in HT‐29 cells. Investigations of colon cancer cell death were performed, and the most active compounds were found to trigger cell deathvianuclear localization and induce S phase arrest of the colon cancer cell. Moreover, molecular modeling studies for the synthesized compounds was performed to predict their binding affinities toward the active site of BCL‐2. The findings of the molecular modeling investigations were highly consistent with those of the cytotoxicity results.
Impacts of Aramid Fiber (PPTA), Glass Wool (GW), Aluminum (Al), and Silicon Carbide (SiC) Particles on Mechanical Behavior of Epoxy Hybrid Composites and their Morphological Investigation
Authors: Ahmed Rajib , Hasan Mahbub , Sheikh Rezaul Karim , Nayeem Faruqui A. 
Published: May 2024

AbstractParticle‐reinforced polymer composites have figured prominently in technological appliances owing to their high mechanical modulus and strength. Due to the obvious demand for lighter but more resilient composites to substitute heavy metal parts, several investigations have been made into particle‐based composites. The current research investigates the impact of reinforcing aramid fiber (PPTA), glass wool (GW), aluminum (Al), and silicon carbide (SiC) powder into an epoxy matrix on aspects of mechanical and morphological characteristics. Hand lay‐up technology was performed to manufacture composites at five different degrees of reinforcement loading (5, 10, 20, 30, and 40 wt. %). SEM and optical microscopes were used for microstructural investigation in order to evaluate the cohesion and dispersion of the reinforcing particles across the matrix phase of the hybrid composites. The elongation at break, flexural strength, flexural modulus, and hardness of epoxy‐PPTA‐GW−Al‐SiC hybrid composites improved, whereas the tensile strength and impact strength decreased from 5 wt. % to 40 wt. % loading. Among fabricated hybrid composite compositions, the morphological and mechanical characteristics obtained at 40 wt. % PPTA, GW, Al, and SiC powder loading are the best among all others.
Triphenylamine‐Supported Benzoquinone Polymer as High Performance Cathode for Lithium‐Ion Batteries
Authors: Zhou Anna , Li Zhe , Li Dan , Cheng Chunni , Chen Xiang , Wang Qunfang , Kasera Alice A. , Li Jianhui , Hou Qiong , Zeng Ronghua 
Published: May 2024

AbstractBenzoquinone (BQ) electrode is regarded as next generation energy storage material for lithium ion batteries (LIBs) because of its advantages of high theoretical specific capacity, abundant source, environmental friendliness. However, the two key challenges, the dissolution of BQ in organic electrolyte and the low discharge plateaus, impede practical application of BQ as cathode. Here, triphenylamine (TPA) with high working voltage and BQ with high theoretical capacity are used to synthesize triphenylamine‐benzoquinone monomer (TPA‐BQ) and its polymer (poly triphenylamine‐benzoquinone (PTPA‐BQ)) as cathodes for LIBs. The charge/discharge results reveal that the discharge plateaus of TPA‐BQ and PTPA‐BQ increase from ~2.5 V to ~3.5 V, and PTPA‐BQ exhibits high discharge capacity and remarkable cyclic stability compared with TPA‐BQ. The electrochemical mechanism shows that the redox peak potential of ~2.3 V/~2.2 V are assigned to the insertion/extraction of lithium ion in C=O groups of BQ, while at high potential of ~3.6 V/~3.5 V corresponds to the de‐dope/dope of the PF6− anion in TPA unit. The results demonstrate TPA was introduced BQ into small molecule to form PTPA‐BQ polymer that can improve discharge plateaus and charge/discharge performance of BQ small molecule, which provide guides for solving the dissolution and discharge platforms of the other organic electrode materials.
Fabrication and Characterization of Electrospun Diosmetin‐Loaded Membranes for Enhanced Solubility
Authors: Gia‐Thien Ho Thanh , Huynh Thi‐Kim‐Chi , Le Thi‐Kim‐Thoa , Nguyen Long H. K. , Ton Anh‐Khoa , Phan Ngoc‐Kim‐Ngan , Nguyen Hoang‐Phuc , Nguyen Thi‐Cam‐Thu , Nguyen Thi‐Hong‐An , Ngo Kim‐Khanh‐Huy , Le Quoc‐Tuan , Le Van‐Dung , Nguyen Minh‐Ty , Mai Thanh‐Chi , Hoang Thi‐Kim‐Dung 
Published: May 2024

AbstractDiosmetin (DT) exhibits various biological activities, including anticancer, antibacterial, antioxidant, estrogenic, and, most notably, anti‐inflammatory properties. Although it has a great deal of promise for advancement in therapeutic usage, DT has yet to be the subject of many applicable investigations in the realm of pharmacology or medicine in oral or dermal form due to the drawback of being insoluble in water. In this study, the fabrication of carboxymethyl cellulose/polyvinyl alcohol (CMC/PVA) nanofibers loaded with diosmetin (DT) using an electrospinning process was conducted to increase the solubility of DT. After electrospinning, PVA, CMC, and DT are a combination under fixed conditions, providing nano DT fibres having a smooth, homogeneous, non‐granular shape and uniform size of P11C2DT fibre (151 nm) with 82.8 % drug loading efficiency. The nanofibrous membrane P11C2DT was characterized via several physicochemical analyses, including scanning electron microscopy (SEM), Fourier‐transform infrared (FT‐IR), X‐ray diffraction (XRD), thermogravimetric analysis (TGA), and differential scanning calorimetry (DSC). Specifically, the nanofibrous membrane carrier dissolved approximately 85 % of the medication in the release medium after 5 h, starkly contrasting to the powdered form of DT, which did not dissolve in the test environment.
Synthesis of Imidazo[1,2‐b]pyridazine Scaffold Based Amide Derivatives as Potential Inhibitors for Bacterial Activity
Authors: Yesubabu Neelam , Bhargavi Dodda , Prashanth Jyothi , Prabhakara Rao Koya 
Published: May 2024

AbstractA series of fifteen novel amide derivatives based on imidazo[1,2‐b]pyridazine scaffold were synthesized by taking imidazo[1,2‐b]pyridazine‐3‐carboxylic acid intermediates with respective substituted amines to yield good to excellent. The molecular structures of the newly synthesized compounds were elucidated using spectroscopic techniques (1H NMR,13C NMR, and LCMS) and supported by single‐crystal X‐ray diffraction (SXRD) for the two compounds,9and18. These compounds were evaluated in vitro for antimicrobial activity against two Gram‐positive bacteria (S. aureusandB. subtilis) and two Gram‐negative bacteria (E. coliandP. aeruginosa). Interestingly, compounds10and16, displayed comparable antibacterial activity to the positive control (Tetracycline: 6.25 μg/mL) against Gram‐negative bacteria, particularlyPseudomonas aeruginosa, with a minimum inhibitory concentration (MIC) of 6.25 μg/mL. Compound17exhibited moderate antibacterial activity with an MIC of 10 μg/mL against the Gram‐positivebacterium Bacillussubtilis, whereas the positive control demonstrated an MIC of 6.25 μg/mL. These antibacterial activities were found to have a good correlation withInsilcocomputational molecular docking experiments using the receptor UDP‐N‐acetylglucosamine‐enolpyruvate reductase,Mtb MurB.
Nanotechnology in the Fabrication of Advanced Paints and Coatings: Dispersion and Stabilization Mechanisms for Enhanced Performance
Authors: Azani Mohammad‐Reza , Hassanpour Azin 
Published: May 2024

AbstractThis review comprehensively analyzes the challenge of dispersing and stabilizing of various common nanomaterials (NMs), including TiO₂, SiO₂, Ag, ZnO, graphene, carbon nanotubes (CNTs) and some others, in paints and coatings. It delves into the critical factors influencing dispersion, such as nanoparticle size, shape, concentration, and their distribution within the coating matrix.The review encompasses all stages of dispersion methods, including wetting, separation techniques, and stabilization. Regarding stabilization, both chemical and physical stabilization methods are discussed, along with their mechanisms, involving surface modification or functionalization of NMs, particularly tailored for aqueous and solvent‐based coatings and common resins (Acrylic, Urethane, Epoxy, Alkyd, etc.). Furthermore, common blending methods to fabricate uniform paints, coatings, and nanocomposites (NCPs) are examined. By providing a comprehensive understanding of the mechanisms governing NM dispersion and stabilization, this review facilitates the selection of appropriate surface modifications based on the specific resins or solvents, thereby enabling the fabrication of high‐performance paints and coatings. In summary, this review elucidates the essence of addressing the challenges associated with NM dispersion and stabilization, outlines methodologies employed, discusses findings regarding their effects on coating properties, and recommends tailored approaches for fabricating high‐performance paints and coatings.
Rapid Hydrolysis Precipitation of BiOBr Nanosheets for Boosting Photocatalytic N2 Fixation
Authors: Bao Liang , Yuan Beijia , Yuan Yongjun 
Published: May 2024

AbstractIn this paper, BiOBr nanosheets were synthesized through a novel hydrolysis precipitation method, which significantly reduced energy consumption and time cost compared to traditional hydrothermal synthesis methods. The subjected hydrothermal and high‐temperature calcination process promote the generation of surface oxygen vacancies, which inhibited the recombination of photogenerated electron hole pairs and improve the photocatalytic performance of BiOBr. Additionally, by adjusting the addition amount of KOH in the synthesis process, BiOBr can grow towards more complex crystal structure, which also provides a new path for the fabrication of 2D materials.
Metal and Ligand‐Free Approach Towards the Efficient One‐Pot Synthesis of Dipyridopyrimidinimine Derivatives
Authors: Kommuri Venkata Chandrasekhar , Vijayan Ajesh 
Published: May 2024

AbstractWe report a facile, expeditious, user‐friendly, and convenient metal‐free synthesis employing base catalysis in a one‐pot procedure to construct 11H‐dipyrido[1,2‐a : 3′,2′‐d]pyrimidin‐11‐imine derivatives. This protocol involves a domino process leading to the formation of double C−N bonds utilising KOtBu as the base and DMAc as the superior solvent at 25 °C for 2 h. The versatility of this methodology was demonstrated by its successful application to substrates with both electron‐withdrawing and electron‐donating functional groups, yielding novel functionalized stable 11H‐dipyrido[1,2‐a : 3′,2′‐d]pyrimidin‐11‐imine derivatives in good to excellent yields. Additionally, we have discussed a plausible reaction pathway for the synthesis.
Antibacterial and Wound Healing Efficacy of Silver Nanoparticles Synthesized from Root Bark ofBerberis lyciumRoyle
Authors: Mughal Tafail Akbar , Ali Shaukat , Mumtaz Shumaila , Summer Muhammad , Khatoon Shazia , Khalil Saeed 
Published: May 2024

AbstractInfectious diseases are becoming a worldwide threat due to antibiotic resistance. Therefore, it is necessary to prepare innovative antibiotics against bactericidal activities. The development of nanomedicine has been greatly aided by nanotechnology. A focal point of exploration involves the production of noble metal nanoparticles, owing to their diverse applications. The study's subjects are the synthesis, characterization, and assessment of silver nanoparticles (AgNPs) derived fromBerberis lyceumRoyle root bark (BLR) extract. The study revealed the potent antibacterial properties of BLR‐AgNPs, with heightened efficacy againstS. pyogenes(16.7±0.3 mm) and comparatively lower activity againstE. coli(1.1±0.1 mm). Minimum inhibitory concentration assessment indicated maximum activity at 10 μg/mL, whereas 2.5 μg/mL demonstrated the least inhibitory effect. Furthermore, the wound healing potential of BLR‐AgNPs was explored, demonstrating superior activity (0.19±0.02 cm) equated to BLR‐extract (0.46±0.02 cm) and untreated wounds (0.77±0.02 cm). These findings underscore the considerable antibacterial and wound‐healing capabilities of BLR‐AgNPs.
One‐Step Synthesis of Thiol‐Functional Mesoporous Silica Nanospheres and Selective Removal of Cationic Dyes
Authors: Wang Ruyi , Gu Shichun , Zhai Chongyuan , Deng Liping , Li Ruchun , Wang Xue , He Yapeng 
Published: May 2024

AbstractThe surface characteristics of nanoparticles have a huge impact on adsorption effects. In this paper, thiol‐functional mesoporous silica nanospheres (MSNs−SH) are successfully prepared through simple one‐step hydrolysis and co‐condensation of tetraethyl orthosilicate and (3‐mercaptopropyl) triethoxysilane with hexadecyltrimethylammonium bromide and sulfobetaine 12 as dual‐template. The obtained MSNs−SH have high surface area (1260 m2 g−1), accessible mesopores (2.2 nm), great pore volume (1.7 cm3 g−1), and uniform adjustable diameter (90 nm). Furthermore, the diameter and pore‐size of MSNs−SH can be controlled via adjusting the ethanol content in the synthesis system. MSNs−SH exhibit a fast adsorption kinetics, marked adsorption capacity of cationic rhodamine B (RB 534.2 mg g−1), and remarkable selective adsorption for cationic dyes. Theoretical analysis reveals the adsorption behavior of RB on MSNs−SH follows the Langmuir isotherm models and pseudo‐second‐order kinetic. Additionally, the thermodynamic results indicate that the adsorption process is a spontaneous process driven by temperature. The results demonstrate that MSNs−SH are greatly potential for effectively removing cationic dyes from wastewater, with the advantages of simple preparation, high adsorption performance and perfect selectivity.
Effect of Lanthanum and Iron Doping on Nickel‐Based Alumina and Ceria Supported Catalysts for Steam Reforming of Methane
Authors: Baudh Anjali , Sharma Rahul , Sharma Sweta , Kumar Upadhyay Rajesh 
Published: May 2024

AbstractMembrane reformers emerge as an efficient technology for ‘on‐site’ ultra‐pure hydrogen production. The membrane reformer can be integrated with PEM fuel cell (PEMFC) to provide power. However, as the PEMFC works at lower temperature hence, such integration requires a low temperature catalyst. Further, for successful integration of membrane reformer with PEMFC, a low CO selective catalyst is vital. In current work, Ni‐based alumina and ceria supported catalysts are used. The effect of La and Fe promoters on the performance of Ni‐based alumina and ceria supported catalysts are investigated. The catalysts are synthesized using wet impregnation method. A complete characterization of catalysts is performed using BET, XRD, FESEM, and EDX. Fe promoted catalysts shows low CO and high CO2 selectivity which is desirable for membrane reformer. However, the catalytic activity of Fe promoted Ni‐based alumina and ceria supported catalysts were low. La promoted Ni‐based catalysts show high catalytic activity on both alumina and ceria supported catalysts. La promoted catalysts also gives low start up time, high catalytic activity and low CO selectivity at low temperature. Ni−La/Al2O3 catalyst provides 50 % conversion and 12 % CO selectivity at 500 °C. This makes it suitable for ‘on‐site’ generation of ultra‐pure hydrogen through membrane reformer.
Making the most of AI and machine learning in organizations and strategy research: Supervised machine learning, causal inference, and matching models
Authors: Rathje Jason , Katila Riitta , Reineke Philipp 
Published: May 2024

AbstractResearch SummaryWe spotlight the use of machine learning in two‐stage matching models to deal with sample selection bias. Recent advances in machine learning have unlocked new empirical possibilities for inductive theorizing. In contrast, the opportunities to use machine learning in regression studies involving large‐scale data with many covariates and a causal claim are still less well understood. Our core contribution is to guide researchers in the use of machine learning approaches to choosing matching variables for enhanced causal inference in propensity score matching models. We use an analysis of real‐world technology invention data of public–private relationships to demonstrate the method and find that machine learning can provide an alternative approach to ad hoc matching. However, as with any method, it is also important to understand its limitations.Managerial SummaryThis article explores the use of machine learning to enhance decision‐making, particularly in addressing sample selection bias in large‐scale datasets. The rapid development of AI and machine learning offers new, powerful tools especially for digital ecosystems where complex data and causal relationships are complex to analyze. We offer managers and stakeholders insight into the effective integration of machine learning for selecting critical variables in propensity score matching models. Through a detailed examination of real‐world data on technology inventions within public–private relationships, we demonstrate the effectiveness of machine learning as a robust alternative to traditional matching methods.