bluuebunny/crossref_metadata_2025_split huggingface dataset:
Diets of white‐headed langurs (Trachypithecus leucocephalus) inhabiting limestone forests: The effects of habitat fragmentation and implication for conservation
Authors: Lai Ying , Chen Yanqiong , Zhang Kechu , Huang Zhonghao 
Published: July 2024

AbstractInformation about wildlife diets is crucial for comprehending how species adapt to varying environments in fragmented habitats and for developing effective conservation strategies. White‐headed langurs (Trachypithecus leucocephalus) are exclusively found in fragmented limestone forests in southwestern China. To investigate the effects of habitat fragmentation on langurs' diets, we collected published dietary data and relevant environmental factors spanning from 1996 to 2021 at two regions with different degrees of fragmentation (Banli > Bapen), from 10 studies (three of Banli and seven of Bapen). The results demonstrated that the diets of white‐headed langurs were significantly influenced by environmental factors, including habitat fragmentation, annual rainfall, and mean annual temperature. Food item diversity index was significantly and positively affected by the fragmentation index, the higher fragmentation the langurs suffered, the more diverse food items they consumed. Besides, fruit consumption was negatively influenced by annual rainfall and the consumption of other items was influenced by mean annual temperature. Notably, although there are no significant differences in the feeding proportions of food items or food item diversity indices were observed between the Banli and Bapen groups, the Banli groups extensively consumed ground‐supported kudzu (Pueraria montana var. lobata), a plant rarely recorded in the dietary preferences of the Bapen groups, implying that the large plants likely lacking in the fragmented limestone forests. Our findings provide evidence of the major impact of habitat fragmentation on the dietary composition of white‐headed langurs, highlighting the need of considering the possibility that the habitats of the white‐headed langurs have all undergone extreme fragmentation, inferring the conservation efforts should prioritize protecting native vegetation and reducing human disturbance.
Multireceptor Analysis for Evaluating the Antidiabetic Efficacy of Karanjin: A Computational Approach
Authors: Nag Sagnik , Stany B. , Mishra Shatakshi , Kumar Sunil , Mohanto Sourav , Ahmed Mohammed Gulzar , Mathew Bijo , Subramaniyan Vetriselvan 
Published: July 2024

ABSTRACTBackgroundDiabetes mellitus, notably type 2, is a rising global health challenge, prompting the need for effective management strategies. Common medications such as metformin, insulin, repaglinide and sitagliptin can induce side effects like gastrointestinal disturbances, hypoglycemia, weight gain and specific organ risks. Plant‐derived therapies like Karanjin from Pongamia pinnata present promising alternatives due to their historical use, holistic health benefits and potentially fewer adverse effects. This study employs in silico analysis to explore Karanjin's interactions with diabetes‐associated receptors, aiming to unveil its therapeutic potential while addressing the limitations and side effects associated with conventional medications.MethodologyThe research encompassed the selection of proteins from the Protein Data Bank (PDB), followed by structural refinement processes and optimization. Ligands such as Karanjin and standard drugs were retrieved from PubChem, followed by a comprehensive analysis of their ADMET profiling and pharmacokinetic properties. Protein–ligand interactions were evaluated through molecular docking using AutoDockTools 1.5.7, followed by the analysis of structural stability using coarse‐grained simulations with CABS Flex 2.0. Molecular dynamics simulations were performed using Desmond 7.2 and the OPLS4 force field to explore how Karanjin interacts with proteins over 100 nanoseconds, focusing on the dynamics and structural stability.ResultsKaranjin, a phytochemical from Pongamia pinnata, shows superior drug candidate potential compared to common medications, offering advantages in efficacy and reduced side effects. It adheres to drug‐likeness criteria and exhibits optimal ADMET properties, including moderate solubility, high gastrointestinal absorption and blood–brain barrier penetration. Molecular docking revealed Karanjin's highest binding energy against receptor 3L2M (Pig pancreatic alpha‐amylase) at −9.1 kcal/mol, indicating strong efficacy potential. Molecular dynamics simulations confirmed stable ligand–protein complexes with minor fluctuations in RMSD and RMSF, suggesting robust interactions with receptors 3L2M.ConclusionKaranjin demonstrates potential in pharmaceutical expansion for treating metabolic disorders such as diabetes, as supported by computational analysis. Prospects for Karanjin in pharmaceutical development include structural modifications for enhanced efficacy and safety. Nanoencapsulation may improve bioavailability and targeted delivery to pancreatic cells, while combination therapies could optimize treatment outcomes in diabetes management. Clinical trials and experimental studies are crucial to validate its potential as a novel therapeutic agent.
Chemoselective Synthesis of Cyanoformamides from Isocyanates and a Highly Reactive Nitrile Anion Reservoir
Authors: Miele Margherita , Castoldi Laura , Roller‐Prado Alexander , Pisano Luisa , Pace Vittorio 
Published: July 2024

AbstractThe direct addition of a nitrile anion to isocyanates is reported for a straightforward preparation of valuable cyanoformamides. Through the proper activation of an adequate silicon‐ate complex precursor (PhMe2SiCN) with a Lewis base (potassium tert‐amylate) under Barbier‐type conditions, the cyanide was instantaneously released, thus yielding the desired motif with full chemocontrol. No particular structural restriction was noticed for engaging in the transformation a wide series of commercially available isocyanates.
Two main highstands during the last interglacial: Insights from palaeoshorelines and marine terraced deposits along the Ionian coast of the Apulia region, southern Italy
Authors: De Santis Vincenzo , Montagna Paolo , Scicchitano Giovanni , Mastronuzzi Giuseppe , Pons‐Branchu Edwige , Scardino Giovanni , Ortiz José E. , Sánchez‐Palencia Yolanda , Torres Trinidad , Caldara Massimo 
Published: July 2024

AbstractField data, U‐Th dating and amino acid racemisation (AAR) and epimerisation (IAE) analyses were performed in a sector of the Ionian coast of Apulia. Palaeoshoreline 1 (PS1) was identified at +40 ± 5 m with the associated terraced deposit 1 (TD1). Palaeoshoreline 2 (PS2) was identified at +30 ± 2 m with the associated terraced deposit 2 (TD2), characterised by five sub‐units. The sub‐units that mark the transgression have an age of 127.851 ± 1.47 ky BP (by U‐Th dating) and 128.4 ± 26.2 ky BP (by AAR); one of sub‐units marking the highstand falls within aminozone E (MIS 5.5) based on IAE. Palaeoshoreline 3 (PS3) was identified at +19 ± 2 m with the associated terraced deposit 3 (TD3); they mark the occurrence of a second, lower elevation MIS 5.5 highstand. TD3 dates back to aminozones E–D (MIS 5.5‐MIS 5.3) based on IAE and to 122.49 ± 1.25 ky BP by U‐Th dating. We considered two scenarios where the first highstand of MIS 5.5 occurred at 125 and 123 ky BP, with a sea level at +7.5 ± 1.5 m; we then calculated the uplift rate of PS2 for both scenarios: it resulted to be 0.18 ± 0.03 mm/year. These uplift rates were used to calculate the position of PS3 at 122.49 ± 1.25 (rounded to ⁓122 ky BP) as well as the position of the layer dated to 127.851 ± 1.47 ky BP (rounded to ⁓128 ky BP). The position of PS3 at the time of its formation (i.e., the sea level at ⁓122 ky BP, corresponding to the second highstand of MIS 5.5) was calculated to be −2.96 ± 5.42 m for Scenario 1 and −3.32 ± 5.47 m for Scenario 2. The sea level obtained by the layer dated to ~128 ky BP was found to have a value of −11.79 ± 6.29 m. Finally, we reconstructed a relative sea level (RSL) trend suggesting two main highstands during MIS 5.5, with the earlier higher than the second.
Development of novel intrusion detection in Internet of Things using improved dart game optimizer‐derived optimal cascaded ensemble learning
Authors: Shali A. , Chinnasamy A. , Selvakumari P. 
Published: July 2024

AbstractBackground of the StudyInternet of things (IoT) industry has accelerated its development with the support of advanced information technology and economic expansion. A complete industrial foundation includes software, chips, electronic components, IoT services, integrated systems, machinery, and telecom operators, which the gradual improvement in the IoT industry system has formulated. As the exponential growth of IoT devices increases, the attack surface available to cybercriminals enables them to carry out potentially more damaging operations. As a result, the security sector has witnessed a rise in cyberattacks. Hackers use several methods to copy and modify the information in the IoT environment. Machine learning techniques are used by the intrusion detection (ID) model to determine and categorize attacks in IoT networks.ObjectivesThus, this study explores the ID system with the heuristic‐assisted deep learning approaches for effectively detect the attacks in the IoT. At first, the IoT data are garnered in benchmark resources. Then, the gathered data is preprocessed to perform data cleaning. Next, the data is transformed and fed to the feature extraction stage. The feature extraction is performed with the help of one‐dimensional convolutional neural network (1D‐CNN), where the features are extracted from the target‐based pooling layer. Then, these attained deep features are fed to the ID phase, where the cascaded ensemble learning (CEL) approach is adopted for detecting the intrusions. Here, the hyperparameter tuning is done with a new suggested improved darts game optimizer (IDGO) algorithm. Here, the main objective of the developed algorithm helps to maximize accuracy in ID.FindingsThroughout the experimental findings, the developed model provides 86% of accuracy. Thus, the finding of the developed model shows less detecting time and higher detection efficiency.
GIJA:Enhanced geyser‐inspired Jaya algorithm for task scheduling optimization in cloud computing
Authors: Abualigah Laith , Hussein Ahmad MohdAziz , Almomani Mohammad H. , Zitar Raed Abu , Daoud Mohammad Sh. , Migdady Hazem , Alzahrani Ahmed Ibrahim , Alwadain Ayed 
Published: July 2024

AbstractTask scheduling optimization plays a pivotal role in enhancing the efficiency and performance of cloud computing systems. In this article, we introduce GIJA (Geyser‐inspired Jaya Algorithm), a novel optimization approach tailored for task scheduling in cloud computing environments. GIJA integrates the principles of the Geyser‐inspired algorithm with the Jaya algorithm, augmented by a Levy Flight mechanism, to address the complexities of task scheduling optimization. The motivation for this research stems from the increasing demand for efficient resource utilization and task management in cloud computing, driven by the proliferation of Internet of Things (IoT) devices and the growing reliance on cloud‐based services. Traditional task scheduling algorithms often face challenges in handling dynamic workloads, heterogeneous resources, and varying performance objectives, necessitating innovative optimization techniques. GIJA leverages the eruptive dynamics of geysers, inspired by nature's efficiency in channeling resources, to guide task scheduling decisions. By combining this Geyser‐inspired approach with the simplicity and effectiveness of the Jaya algorithm, GIJA offers a robust optimization framework capable of adapting to diverse cloud computing environments. Additionally, the integration of the Levy Flight mechanism introduces stochasticity into the optimization process, enabling the exploration of solution spaces and accelerating convergence. To evaluate the efficacy of GIJA, extensive experiments are conducted using synthetic and real‐world datasets representative of cloud computing workloads. Comparative analyses against existing task scheduling algorithms, including AOA, RSA, DMOA, PDOA, LPO, SCO, GIA, and GIAA, demonstrate the superior performance of GIJA in terms of solution quality, convergence rate, diversity, and robustness. The findings of GIJA provide a promising solution quality for addressing the complexities of task scheduling in cloud environments (95%), with implications for enhancing system performance, scalability, and resource utilization.
Extraction and identification of bioactive compounds from areca nut (Areca catechu L.) and potential for future applications
Authors: Guo Ziyuan , Wang Zihan , Luo Yinghua , Ma Lingjun , Hu Xiaosong , Chen Fang , Li Daotong , Jia Min 
Published: July 2024

AbstractAreca (Areca catechu L.) nut is a tropical plantation fruit cultivated mainly in South and Southeast Asia. As a chewing hobby, it has become the most common psychoactive substance in the world, besides tobacco, alcohol, and caffeine. Areca catechu contains abundant nutrients and active components such as alkaloids, polyphenols, polysaccharides, proteins, and vitamins, which have been reported to have anti‐inflammatory, antioxidant, antibacterial, anti‐depressant, anti‐hypertensive, anti‐fatigue, and other biological properties. However, at present, the resource utilization rate of the whole‐plant areca nut is low, which not only causes resource waste but also damages the environment. Establishing effective, safe, and environmentally friendly techniques and methods is necessary for the comprehensive utilization of A. catechu resources. In this review, we summarized the traditional and advanced methods for the extraction and identification of main bioactive substances in A. catechu and compared the advantages and disadvantages of these methods. Furthermore, the possible trends and perspectives for future use of A. catechu are also discussed. Our objective is to extend the application of this bioactive ingredient to improve the added value, provide valuable information for developing new A. catechu products and derivatives, and improve the comprehensive utilization of areca nut resources.
Predicting the geospatial distribution of Chinese rice nutrient element in regional scale for the geographical origin—A case study on the traceability of Japonica rice
Authors: Sheng Meiling , Li Chunlin , Zhang Weixing , Nie Jing , Hu Hao , Lou Weidong , Deng Xunfei , Shao Shengzhi , Lyu Xiaonan , Ren Zhouqiao , Rogers Karyne M. , Wadood Syed Abdul , Zhang Yongzhi , Yuan Yuwei 
Published: July 2024

AbstractEffective geographical origin discrimination of Chinese rice requires a large database of samples to ensure sufficient data for origin verification at a regional scale. In this study, environmental similarity was used to establish a spatial database of rice nutrient element, and then the validity of the database was verified using the back propagation artificial neural networks modeling (BPNN). The spatial distribution model of 14 rice nutrient element (Al, Ba, Ca, Cu, Cr, Fe, K, Mg, Mn, Mo, Na, Ni, Rb, and Zn) on regional scale was built using an environmental similarity method for the first time. Elemental concentrations of 692 samples were used to build a simulated geographical origin prediction model for northeastern (N‐E), middle to lower Yangtze River plain (Y‐R), southwestern (S‐W), and southeastern (S‐E) in China. The results indicated that the performance of the environmental similarity model for these four growing regions was S‐W > N‐E > S‐E > Y‐R based on the lowest ranking root mean square error (RMSE) for each region. For example, the RMSEs of Zn in S‐W, N‐E, S‐E, and Y‐R regions were 2.0, 2.4, 2.7, and 3.7 mg/kg, respectively. A case study on the traceability of Japonica rice was shown that Japonica rice could be discriminated with higher origin accuracy using a simulated database (91.8%) than by the actual database (87.0%) using the BPNN model. This indicates that a simulated rice element database could improve the accuracy of geographical origin discrimination for Chinese rice and potentially be applied to other large national‐scale crop datasets.
Trade‐offs and Synergies between Economic and Environmental Cocoa Farm Management Decisions
Authors: Bandanaa Joseph , Asante Isaac. K. , Egyir Irene S. , Annang Ted Y. , Blockeel Johan , Heidenreich Anja , Kadzere Irene , Schader Christian 
Published: July 2024

AbstractOptimizing sustainability among smallholder farms poses challenges due to inherent trade‐offs. In the study of organic and conventional cocoa smallholder farming in Ghana, 398 farms are assessed using the Food and Agriculture Organsation of the United Nations (FAO) Sustainability Assessment of Food and Agriculture systems (SAFA) Guidelines and Sustainability Monitoring and Assessment Routine (SMART)‐Farm Tool. Organic farming exhibited synergies in environmental aspects (e.g., soil quality, energy efficiency) and between biodiversity conservation and risk management. Conventional farming showed potential vulnerabilities, including trade‐offs with long‐range investments (e.g., chemical inputs) and species diversity. Both systems demand tailored approaches for short‐term economic and environmental sustainability, aligning with community‐wide long‐term goals. To mitigate trade‐offs in conventional farming, smallholders should adopt practices like material reuse, recycling, and recovery within their operations.
Commitment capital: Bridging the gap between organizational commitment and human capital resources
Authors: Andersén Jim , Jansson Christian 
Published: July 2024

AbstractEven though, the human resource management literature has highlighted the importance of having employees that are committed to the organization, research on strategic human capital has yet to fully consider how commitment is related to human capital resources. In order to overcome the dominant individual‐level conceptualization of commitment and to detail how commitment affects human capital resources, we develop the unit‐level concept of commitment capital, which we divide into three levels: affiliative commitment capital, affinitive commitment capital, and absolute commitment capital. These conceptualizations are based on a 10‐year case study and incorporate commitment into a strategic human capital framework, thus bridging the current gap between organizational commitment and human capital resources.
Infant and child mortality in Afghanistan: A scoping review
Authors: Qamar Khulud , Essar Mohammad Yasir , Siddiqui Javeria Arif , Salman Ariba , Salman Yumna , Head Michael G. 
Published: July 2024

AbstractBackground and AimsSince 1990, global child and infant mortality rates have typically stabilized or decreased due to improved healthcare, vaccination rollouts, and international funding. However, Afghanistan continues to face the highest child and infant mortality rates globally, with 43 deaths per 1000 live births. This study aims to examine the factors contributing to this high mortality rate and propose interventions to address the issue.MethodsA comprehensive literature search was conducted using databases such as Google Scholar and PubMed, focusing on articles published in English within the last 10 years (2013–2023). The search terms included “Child mortality,” “Infant mortality,” “SIDS,” “COVID‐19,” and “Afghanistan.” Original studies, systematic reviews, case studies, and reports meeting the inclusion criteria were selected for analysis. Additional sources from organizations such as UNICEF, the World Bank Group, WHO, and EMRO were also reviewed.ResultsThe study findings reveal significant challenges contributing to Afghanistan's high infant and child mortality rates. These challenges include birth defects, preterm birth, malnutrition, sudden infant death syndrome (SIDS), traumatic injuries, fatal infections, infanticide, and abuse. The ongoing conflict, insecurity, and humanitarian crises further exacerbate the situation, leading to increased child casualties. Despite efforts by international agencies like UNICEF to provide vaccines and maternal education, the infant mortality rate remains high.ConclusionIn conclusion, Afghanistan's child and infant mortality rates are of significant concern, and it is imperative that action be taken to reduce the incidence of child and infant mortality rates.
Short‐term ozone exposure and cancer mortality in Brazil: A nationwide case‐crossover study
Authors: Yu Pei , Xu Rongbin , Huang Wenzhong , Yang Zhengyu , Coelho Micheline S. Z. S. , Saldiva Paulo H. N. , Wen Bo , Wu Yao , Ye Tingting , Zhang Yiwen , Sun Haitong Z. , Abramson Michael J. , Li Shanshan , Guo Yuming 
Published: July 2024

AbstractExposure to ambient ozone (O3) is linked to increased mortality risks from various diseases, but epidemiological investigations delving into its potential implications for cancer mortality are limited. We aimed to examine the association between short‐term O3 exposure and site‐specific cancer mortality and investigate vulnerable subgroups in Brazil. In total 3,459,826 cancer death records from 5570 Brazilian municipalities between 2000 and 2019, were included. Municipal average daily O3 concentration was calculated from a global estimation at 0.25°×0.25° spatial resolution. The time‐stratified case‐crossover design was applied to assess the O3‐cancer mortality association. Subgroup analyses by age, sex, season, time‐period, region, urban hierarchy, climate classification, quantiles of GDP per capita and illiteracy rates were performed. A linear and non‐threshold exposure‐response relationship was observed for short‐term exposure to O3 with cancer mortality, with a 1.00% (95% CI: 0.79%–1.20%) increase in all‐cancer mortality risks for each 10‐μg/m3 increment of three‐day average O3. Kidney cancer was most strongly with O3 exposure, followed by cancers of the prostate, stomach, breast, lymphoma, brain and lung. The associated cancer risks were relatively higher in the warm season and in southern Brazil, with a decreasing trend over time. When restricting O3 concentration to the national minimum value during 2000–2019, a total of 147,074 (116,690–177,451) cancer deaths could be avoided in Brazil, which included 17,836 (7014–28,653) lung cancer deaths. Notably, these associations persisted despite observed adaptation within the Brazilian population, highlighting the need for a focus on incorporating specific measures to mitigate O3 exposure into cancer care recommendations.