bluuebunny/crossref_metadata_2025_split huggingface dataset:
Rethinking dynamic capabilities in light of sustainability: A bibliometric analysis
Authors: Correggi Cecilia , Di Toma Paolo , Ghinoi Stefano 
Published: August 2024

AbstractThe literature on sustainable dynamic capabilities is becoming increasingly relevant in business research. In this study, we adopt a bibliometric approach to analyze the development of the literature, provide an overview of its theoretical and empirical evolution, and identify future research paths. To reach this goal, we collected 602 scientific documents from Scopus published between January 2002 and May 2023. The analysis is based on a two‐step process. First, we created and analyzed thematic maps for two different time periods (2002–2016 and 2017–2023) to assess the changes in the themes investigated. Second, we used bibliographic coupling to measure the influence and similarity between 147 scientific documents that were selected from leading scientific journals. Results show the adequacy of the dynamic capabilities framework for understanding the integration of sustainability within business strategies while opening up new perspectives (i.e., stakeholder engagement) and themes. Overall, we observe an increasing interest in reconceptualizing dynamic capabilities while considering their sustainability dimension, with reference to the identification of new micro‐foundations and the level of analysis (from an individual or organizational level to an inter‐organizational level). This study also proposes avenues for future research, including an exploration of new contexts, such as new industries, through a cross‐sectoral analysis approach or the underexplored context of family firms. We also highlight the need to advance our understanding of sustainable dynamic capabilities by focusing on the social dimension, which remains understudied.
On the Correlation between Group III‐A Elements Doping and Structure Performance of Cu/ZnO/ZrO2 Catalysts System for CO2 Hydrogenation to Methanol
Authors: Shrivastaw Vivek Kumar , Kaishyop Jyotishman , Khan Tuhin Suvra , Khurana Deepak , Singh Gaje , Paul Subham , Chowdhury Biswajit , Bordoloi Ankur 
Published: August 2024

AbstractA set of Cu/ZnO/ZrO2 catalysts doped with Group‐IIIA elements (M=B, Al, Ga & In) were synthesized via a facile single‐step evaporation‐induced self‐assembly (EISA) method to tune up the catalyst basicity and modulate the structure to improve the methanol yield in CO2 hydrogenation reaction. To understand the catalyst‘s textural properties and catalytic activity, prepared catalysts were exposed to several in‐situ/ex‐situ characterization techniques like Physisorption & Chemisorption studies, XRD, XPS, TEM, and in‐situ DRIFT. The addition of group IIIA elements has a significant impact on the CO2 conversion and Methanol selectivity via tailoring the important textural properties such as metallic surface area of Cu, reducibility of catalysts, particle size, controlled oxygen vacancy, and basicity of catalyst surface. CZZ doped with Al appeared to be the best catalyst, in this study. The modified Cu‐ZnO interface via density functional theory (DFT) calculations also indicated that the CO2 adsorption energy is found to be highest for CZZAl, which is concomitant with CO2‐TPD analysis results. The lowest to highest CO2 adsorption energy order over the catalyst set follows CZZIn<CZZB<CZZGa<CZZO<CZZAl. Moreover, the surface model of Cu/ZnO after doping each element was also studied to elucidate the elemental promotional effect on CO2 adsorption.
Electrochemical CO2 Reduction Reaction: Comprehensive Strategic Approaches to Catalyst Design for Selective Liquid Products Formation
Authors: Sikdar Nivedita 
Published: August 2024

AbstractThe escalating concern regarding the release of CO2 into the atmosphere poses a significant threat to the contemporary efforts in mitigating climate change. Amidst a multitude of strategies for curtailing CO2 emissions, the electrochemical CO2 reduction presents a promising avenue for transforming CO2 molecules into a diverse array of valuable gaseous and liquid products, such as CO, CH3OH, CH4, HCO2H, C2H4, C2H5OH, CH3CO2H, 1‐C3H7OH and others. The mechanistic investigations of gaseous products (e. g. CO, CH4, C2H4, C2H6 and others) broadly covered in the literature. There is a noticeable gap in the literature when it comes to a comprehensive summary exclusively dedicated to coherent roadmap for the designing principles for a selective catalyst all possible liquid products (such as CH3OH, C2H5OH, 1‐C3H7OH, 2‐C3H7OH, 1‐C4H9OH, as well as other C3‐C4 products like methylglyoxal and 2,3‐furandiol, in addition to HCO2H, AcOH, oxalic acid and others), selectively converted by CO2 reduction. This entails a meticulous analysis to justify these approaches and a thorough exploration of the correlation between materials and their electrocatalytic properties. Furthermore, these insightful discussions illuminate the future prospects for practical applications, a facet not exhaustively examined in prior reviews.
Minimum spouting velocity of fine particles in fountain confined conical spouted beds using machine learning and least square fitting approaches
Authors: Moradkhani Mohammad Amin , Miroliaei Ali Reza , Ghasemi Nasim , Hosseini Seyyed Hossein , Tellabide Mikel , Olazar Martin 
Published: August 2024

AbstractThe present study concerns the development of new models to estimate the minimum spouting velocity (Ums) in various configurations of fountain‐confined conical spouted beds (FC‐CSBs) with fine particles. Existing literature correlations were found to be inaccurate for FC‐CSBs. Therefore, smart modelling techniques were employed to design more accurate predictive tools. The radial basis function (RBF) approach provided the best predictions for systems without draft tubes as well as those with open‐sided draft tubes. Additionally, the Gaussian process regression (GPR) approach yielded the best predictions for systems with nonporous draft tubes. The mean absolute percentage error (MAPE) values for the testing phase were 5.80%, 5.67%, and 5.59%, respectively. These models consider how bed shape and particle properties affect Ums. The sensitivity analysis was conducted to determine the factors with more importance in controlling Ums. Finally, simpler correlations were derived for Ums prediction in different FC‐CSB configurations, with accuracy around 12% error.
Selective adsorption of aromatic sulfonic acid from wastewater using a surface imprinted polymer: H‐acid as a representative contaminant
Authors: Yao Jiaqi , Sun Yue , Liu Yan , Gu Yingpeng , Zheng Weisheng 
Published: August 2024

AbstractAromatic sulfonic acids (ASAs) play a pivotal role as essential intermediates in numerous industrial manufacturing, while a large amount wastewater with various ASAs and high concentration of inorganic salts is subsequently generated. The effective separation and removal of ASAs from wastewater is challenging due to their complex chemical composition and the limited selectivity of common adsorbents. Herein, a novel surface imprinted polymer (H‐SIP) with high selectivity and excellent salt resistance was designed with PEI/Cl‐PS‐DVB as the carrier and 1‐amino‐8‐naphthol‐3,6‐disulfonic acid (H‐acid) as the target pollutant. Compared to non‐imprinted polymer (NIP), H‐SIP exhibited superior salt resistance in the presence of Na2SO4 concentration ranging from 20 to 80 mg/L. The relative selectivity coefficients determined in the binary‐solutes experiments proved that H‐SIP demonstrated favourable selectivity towards H‐acid in binary systems of H‐acid/T‐acid or H‐acid/2‐NSA. Moreover, H‐SIP could effectively treat the simulated complex wastewater within 24 bed volume (BV) in the column adsorption, and the desorption rate exceeded 90% when eluted by NaOH solution and distilled water, respectively. Therefore, these results confirmed that surface imprinting technique was a promising method for effectively and selectively removal of ASA wastewater in the application.
Root phenotyping and plant breeding of crops for enhanced ecosystem services
Authors: Griffin Alexandra J. , Jungers Jacob M. , Bajgain Prabin 
Published: August 2024

AbstractDiversifying and perennializing cropping systems can increase productivity while supporting ecosystem services such as soil protection, nutrient retention, and greenhouse gas mitigation. New crops can help achieve these goals, and advanced computational tools allow plant breeders to rapidly domesticate new crops and select for many traits that support both ecosystem services and profitable production. Intermediate wheatgrass [Thinopyrum intermedium (Host.) Barkworth. & D.R. Dewey; IWG] is a cool‐season perennial grass undergoing domestication to function as a perennial grain crop. Key aboveground domestication traits have been improved to support economically viable yields using genomic selection. However, few studies have quantified belowground traits despite their potential role in conferring ecosystem services. We present a platform for using minirhizotron cameras and machine learning software to analyze rhizotron images for inclusion in genomic selection models. The strength and direction of pairwise correlations between traits were variable with correlation coefficients (r) ranging from −0.27 to 0.99. Grain yield was positively, although weakly, correlated with total root length, area, and volume (r = 0.21, 0.21, and 0.19, respectively). Estimates of narrow sense heritabilities ranged from 0.41 to 0.76 for all traits and 0.46 to 0.66 for root traits. Root trait predictions using a genomic prediction model, measured by correlating model‐predicted values and field‐observed values, ranged from 0.08 to 0.23. Aboveground traits were better predicted (0.17 < r < 0.33). Simply selecting for aboveground traits could result in populations with desirable root traits, but our results demonstrate the potential for genomic selection to aid in advancing populations with specific root traits important for ecosystem services.
Valence Band‐Tunable NiFe Electrocatalyst Triggered by the Dynamic Mo Exudation and Re‐Deposition for Superior High Current Density Oxygen Evolution Reaction
Authors: Wang Zhichao , Qiu Tiandong , Jian Rui , Zhang Yufeng , Feng Junjie , Gong Lianxin , Yin Siyi , Li Luming , Zhu Yachao , Chen Si , Deng Jie 
Published: August 2024

AbstractDeveloping energy‐ and time‐efficient strategies to derive high‐performance non‐precious electrocatalysts for anodic oxygen evolution reaction (OER), especially stably working at industrial‐demanding current density, is still a big challenge. In this work, a concise molten salt erosion scenario was devised to rapidly modulate the smooth surface of the commercial NiMo foam substrate into the rough, electronically coupled, and hierarchically porous Ni/Fe/Mo(oxy)hydroxide catalyst layer assembled by the nanosphere array. This self‐supported catalyst is super‐hydrophilic for the alkaline electrolyte and distinguished by a balanced Mo leaching/surface‐readsorption process to tune the metal d band center and electronic perturbation. The altered electronic environment with the favored OER intermediate adsorption behavior attains the outstanding OER activity in terms of a very small overpotential of 230.21 mV at 10 mA cm−2 and an ultra‐long stability for 1179.45 h to sustain the initial commercial‐level current density of ca. 1000 mA cm−2. This superb performance transcends most of the edge‐cutting transition metal peers reported recently and can satisfy the standards of industrial applications. This industrial‐compatible synthesis technology holds profound implications for hydrogen production via water splitting and other electrochemical applications.
Oocyte‐specific deletion of eukaryotic translation initiation factor 5 causes apoptosis of mouse oocytes within the early‐growing follicles by mitochondrial fission defect‐reactive oxygen species‐DNA damage
Authors: Wang Weiyong , Liu Huiyu , Liu Shuang , Hao Tiantian , Wei Ying , Wei Hongwei , Zhou Wenjun , Zhang Xiaodan , Hao Xiaoqiong , Zhang Meijia 
Published: August 2024

AbstractBackgroundMutations in several translation initiation factors are closely associated with premature ovarian insufficiency (POI), but the underlying pathogenesis remains largely unknown.Methods and resultsWe generated eukaryotic translation initiation factor 5 (Eif5) conditional knockout mice aiming to investigate the function of eIF5 during oocyte growth and follicle development. Here, we demonstrated that Eif5 deletion in mouse primordial and growing oocytes both resulted in the apoptosis of oocytes within the early‐growing follicles. Further studies revealed that Eif5 deletion in oocytes downregulated the levels of mitochondrial fission‐related proteins (p‐DRP1, FIS1, MFF and MTFR) and upregulated the levels of the integrated stress response‐related proteins (AARS1, SHMT2 and SLC7A1) and genes (Atf4, Ddit3 and Fgf21). Consistent with this, Eif5 deletion in oocytes resulted in mitochondrial dysfunction characterized by elongated form, aggregated distribution beneath the oocyte membrane, decreased adenosine triphosphate content and mtDNA copy numbers, and excessive accumulation of reactive oxygen species (ROS) and mitochondrial superoxide. Meanwhile, Eif5 deletion in oocytes led to a significant increase in the levels of DNA damage response proteins (γH2AX, p‐CHK2 and p‐p53) and proapoptotic proteins (PUMA and BAX), as well as a significant decrease in the levels of anti‐apoptotic protein BCL‐xL.ConclusionThese findings indicate that Eif5 deletion in mouse oocytes results in the apoptosis of oocytes within the early‐growing follicles via mitochondrial fission defects, excessive ROS accumulation and DNA damage. This study provides new insights into pathogenesis, genetic diagnosis and potential therapeutic targets for POI.Key points
Eif5 deletion in oocytes leads to arrest in oocyte growth and follicle development.
Eif5 deletion in oocytes impairs the translation of mitochondrial fission‐related proteins, followed by mitochondrial dysfunction.
Depletion of Eif5 causes oocyte apoptosis via ROS accumulation and DNA damage response pathway.
DIWGAN‐WBSN: A novel health monitoring approach for wireless body sensor networks
Authors: Jayasutha D. , Hemamalini V. , Sangeetha S. , Yeruva Ajay Reddy 
Published: August 2024

SummaryWireless body sensor network (WBSN) is essential for monitoring patients' health problems and offers a low‐cost option for various healthcare applications. In this manuscript, a Novel Health Monitoring Approach for WBSNs (DIWGAN‐WBSN) is proposed, which uses Dual Interactive Wasserstein Generative Adversarial Network (DIWGAN) optimized with War Strategy Optimization Algorithm (WSOA). After sensing the aforementioned attribute information, it is the responsibility of WBSN nodes to transfer the sensed data to the sink node. The Volcano Eruption Algorithm (VEA) is applied to select the optimum cluster heads in WBSN. The results from VEA are fed to the target node; it consists of DIWGAN to classify the health records and to portray the patient's health status. Generally, DIWGAN does not adopt any optimization methods for measuring the ideal parameters and guaranteeing accurate health monitoring and risk assessment. So the proposed WSOA is considered to enhance the DIWGAN. The proposed method is activated in MATLAB; its efficacy is estimated under performance metrics, like precision, specificity, accuracy, and energy utilization. The proposed approach attains 23.9%, 21.34%, and 51.09% higher accuracy; 21.45%, 13.94%, and 20.6% higher precision; 31.32%, 29.61%, and 11.03% higher specificity; and 20.9%, 19.87%, and 24.6% lower energy utilization for HD classification using the Cleveland database than the existing methods like back propagation neural network‐based risk detection in WBSN for health monitoring, random forest algorithm–based health monitoring in WBSN, and ensemble deep learning and feature fusion for health monitoring using WBSN methods, respectively.
Deep learning‐based channel estimation for OFDM‐IM systems over Rayleigh fading channels
Authors: Adiguzel Omer , Develi Ibrahim 
Published: August 2024

SummaryDeep learning (DL)‐based channel estimation for orthogonal frequency division multiplexing with index modulation (OFDM‐IM) under Rayleigh fading channel conditions is presented in this paper. A deep neural network (DNN) is utilized to estimate the channel response in simulations. The proposed DNN is trained using the channel coefficient derived through the least squares (LS) method. Then channel estimation is conducted using the trained DNN. Within the DNN, the long short‐term memory (LSTM) layer is included as the hidden layer. Different scenarios are handled in simulations and the proposed DNN is compared with traditional channel estimation methods. The simulations demonstrate that the DL‐based channel estimation significantly surpasses the LS and minimum mean‐square error (MMSE) techniques.
Pitfalls in the Cytological Diagnosis of Nodal Hodgkin Lymphoma
Authors: Handa Uma , Mohamedali Rasheeda , Punia Rajpal Singh , Singh Simrandeep , Bhagat Ranjeev , Aggarwal Phiza , Kaur Manveen 
Published: August 2024

ABSTRACTBackgroundHodgkin lymphoma (HL) is a hematopoietic neoplasm characterized by malignant Reed–Sternberg (RS) cells in an inflammatory background. Although the cytological features of HL are well elucidated in literature, yet many postulated factors cause its misdiagnosis. This study aims to assess the diagnostic reliability of fine needle aspiration cytology (FNAC) in HL and evaluate the factors contributing to a false‐negative and false‐positive diagnosis, taking histopathology as the gold standard.MethodsThis was a retrospective study in which 47 cases of HL diagnosed on histopathology were compared with their prior cytological diagnosis.ResultsThe patient's age ranged from 3 to 80 years (median: 36 years) with a M:F ratio of 2.9:1. Lymph node aspirations were performed from multiple anatomical sites, out of which the cervical was the most common (57.8%). FNAC was inconclusive in two cases due to unsatisfactory smears. The false‐negative diagnosis of reactive lymphadenitis was given in four cases, and false‐positive in four cases, which included three cases of non‐HL, and one case of malignant small round blue cell tumor. The overall diagnostic accuracy of FNAC in the diagnosis of HL was 82.2%.ConclusionsThe cytological diagnosis of HL can be challenging when classic RS cells are absent. Contributing factors for a false‐negative diagnosis include obscuring reactive inflammatory cells, fibrosis of the involved lymph nodes, partial involvement of the lymph node, and misinterpretation. A thorough clinical examination with evaluation of FNAC smears from multiple areas, and ancillary tests help improve the diagnostic accuracy of cytological diagnosis.
Distribution pattern and driving factors of mite communities in karst cave ecosystems
Authors: Fei Yifan , Shi Zheng , Zhou Yuanyuan , Wei Qiang , Liu Ying , Shen Yan , Chen Hu 
Published: August 2024

AbstractMites are among the most abundant invertebrates in subsurface ecosystems, and their community assemblages and distributions are often significantly influenced by the diversity of habitat resources. The cave ecosystem encompasses drastic changes in nonbiological factors, such as changes in lighting conditions from bright to extraordinarily dark and habitat gradients of surface plant resources from abundant to scarce or even disappearing, providing an ideal unique environment for evaluating the assembly mechanism of soil animal communities. Nevertheless, there still needs to be a sufficient understanding of the biodiversity patterns and drivers of mite communities across environmental gradients in karst caves. We conducted a comprehensive survey on the composition and diversity of soil mites in three photometric zones (dark, twilight, and light) of a typical karst cave and its adjoining extractive environments (forest scrub and farmland). Our research aimed to investigate the ecological relationships of mite communities between above‐ and below‐ground habitats and the effects of abiotic factors on mite communities. We collected 49 families, 86 genera, and 1284 mites. In the external cave environment, we captured 1052 mites from 72 genera and 45 families; in the internal cave environment, we captured 232 mites from 46 genera and 29 families. The abundance, richness of genera, and diversity parameters of the mite community decreased from the cave entrance to the cave interior with decreasing light intensity. Oribatid mites dominated the mite community. Protoribates and Scheloribates were the dominant genera, along with Tectocepheus and 11 other genera, which primarily distinguished the mite communities among different habitats. Forty endemic taxa were found in the external cave environment, compared to 14 endemic taxa in the internal cave environment. The mite community showed a strong preference for the cave ecosystem habitat. Temperature, humidity, and soil nitrogen content significantly influenced the distribution pattern of mite communities (VIP > 0.8, p < 0.05).
Comparison of phylogenetic trees defined on different but mutually overlapping sets of taxa: A review
Authors: Li Wanlin , Koshkarov Aleksandr , Tahiri Nadia 
Published: August 2024

AbstractPhylogenetic trees represent the evolutionary relationships and ancestry of various species or groups of organisms. Comparing these trees by measuring the distance between them is essential for applications such as tree clustering and the Tree of Life project. Many distance metrics for phylogenetic trees focus on trees defined on the same set of taxa. However, some problems require calculating distances between trees with different but overlapping sets of taxa. This study reviews state‐of‐the‐art distance measures for such trees, covering six major approaches, including the constraint‐based Robinson–Foulds (RF) distance RF(−), the completion‐based RF(+), the generalized RF (GRF), the dissimilarity measure, the vectorial tree distance, and the geodesic distance in the extended Billera‐Holmes‐Vogtmann tree space. Among these, three RF‐based methods, RF(−), RF(+), and GRF, were examined in detail on generated clusters of phylogenetic trees defined on different but mutually overlapping sets of taxa. Additionally, we reviewed nine related techniques, including leaf imputation methods, the tree edit distance, and visual comparison. A comparison of the related distance measures, highlighting their principal advantages and shortcomings, is provided. This review offers valuable insights into their applicability and performance, guiding the appropriate use of these metrics based on tree type (rooted or unrooted) and information type (topological or branch lengths).
Small Cationic Boron and Boron‐Nitrogen Rings, Synthesized from Diboranes with Charge‐Delocalizing Guanidinate Substituents
Authors: Vogler Daniel , Horn Julian , Kaifer Elisabeth , Himmel Hans‐Jörg 
Published: August 2024

AbstractWe report the straightforward synthesis and characterization of small polycationic boron and boron‐nitrogen rings, stabilized by charge‐delocalizing guanidinate substituents. Reaction between a ditriflato‐diborane with two electron‐donating bridging bicyclic guanidinate substituents and the corresponding dihydrido‐diborane leads to a dicationic B4 ring with a 4c,4e‐bond in the planar rhomboid B4 skeleton. Reaction of the same ditriflato‐diborane with the corresponding diazido‐diborane gives a dicationic compound with a B4N2 core. On the basis of experimental data in combination with quantum‐chemical calculations, pathways leading to these dicationic boron and boron‐nitrogen rings are suggested, and further preliminary studies demonstrate the scope of this strategy for the synthesis of polycationic boron‐nitrogen rings.