bluuebunny/crossref_metadata_2025_split huggingface dataset:
WITHDRAWN: Experimental investigation on Interrelation between hydraulic conductivity and Compressive strength of soft soil using metakaolin as stabilizer
Authors: Jangde Himanshu , Khan Farhan 
Published: July 2023

Abstract

The building industry requires method which improves the quality of the soil, if they want the foundation to be more durable and rigid. This is especially true for residential construction. Modifying clay soils with metakaolin (MK) has proven to be an effective method for stabilizing soil. This research examined the physiochemical properties of clay soil samples obtained from a construction site. After that, the specimens were treated with different concentrations of metakaolin (0%, 5%, 10%, and 15% by weight) and placed through a series of tests in the laboratory to determine their level of strength and hydraulic conductivity. The observations showed that the hydraulic conductivity significantly decreased after the incorporation of metakaolin into the clay soil. Compared to unaltered soil, the hydraulic conductivity of the soil decreased by about 70% for 15% metakaolin concentration. The establishment of metakaolin-hydrated calcium silicate gel (C-S-H) in the soil matrix results in an increased pore-filling impact and smaller pore dimensions, both of which contribute to a reduction in hydraulic conductivity. The Un-confined compressive strength (UCS) of the altered soil samples dramatically improved after MK was incorporated into the mix. The UCS of the samples increased by 75% after adding the 15% MK substance. The increase in UCS can be attributed to the pozzolanic interaction that takes place between MK and soil. This interaction results in the production of new saturated products, which contribute to an increase in the material's strength. The effect of the curing time on the altered soil characteristics was another aspect that was analyzed in this investigation. The authors found that soil faces continuous changes with extended curing time in strength and permeability due to the addition of MK, which is supported by the test results showing that permeability decreases and strength increases with increasing time of curing of treat-ed soil samples with MK. Which suggests that the process of modifying the soil was ongoing because the strength and hydraulic conductivity of the modified soil increased and decreased simultaneously with extended curing periods. In conclusion, MK has the ability to improve the resiliency of clay soil while simultaneously lowering its permeability. According to the results of this study, altering the MK of clay soil in order to improve its mechanical and hydrodynamic properties is a strategy that can be implemented success-fully and is both realistic and useful. The findings of the research can be utilized to enhance the planning and construction of geological structures such as embankments, dams, and retaining walls that are built on clay soil. This finding has significant repercussions for the construction industry because it paves the way for an alternative that is both workable and inexpensive to the earth-stabilization methods that are currently in use.
MSDMCCG: Design of an efficient Multimodal Satellite Data Processing Model for Component-level analysis of Contextual Geographic entities
Authors: Bhoi Rosy , Patel Ashok Kumar 
Published: July 2023

Abstract
This paper presents an efficient model for analysing contextual geographic entities in satellite data at a component level. The model utilizes various feature extraction techniques, including Fourier, Entropy, Wavelet, and Gabor, along with vegetation indices such as NDVI, SAVI, and EVI, to capture the multimodal characteristics of the satellite data. Classification operations are performed using a binary cascaded convolutional neural network (CNN), while incremental learning is facilitated through the Q-learning process. The proposed model offers a novel approach by combining different feature extraction methods, allowing for a comprehensive representation of satellite data. It employs the BCCNN, which exhibits high accuracy in identifying specific geographical features like land, forests, structures, and rivers. Additionally, by utilizing Q-learning for incremental learning, the model's accuracy can improve over time as new data becomes available. Evaluation of the model using an augmented cluster of datasets and samples demonstrated its ability to accurately identify contextual geographic entities with 99.5% accuracy, 98.5% precision, and 98.3% recall. This model holds promise for various applications, including environmental monitoring, disaster management, and urban planning.
The influence of intraseasonal oscillations on rainfall over Central Africa: Case of the 25 - 70 days variability
Authors: Tchinda Claudin Wamba , Sandjon Alain Tchakoutio , Tchotchou Angennes Lucie Djiotang , Siwe Audrick Nzeudeu , Vondou Derbetini Appolinaire , Nzekou Armand 
Published: July 2023

Abstract
The influence of intra-seasonal oscillations (ISO) on rainfall in Central Africa (CA) is assessed using the National Oceanic and Atmospheric Administration Climate Prediction Center daily gridded rainfall data. ISO indices are defined using the time series analysis of the first two principal components resulting from the empirical orthogonal function, applied to daily filtered outgoing longwave radiation. Based on these indices, a total of 71 strong Intraseasonal Events (SIEs) and 66 weak Intraseasonal Events (WIEs) were selected using threshold method. The results show that SIEs are associated with enhanced rainfall conditions over almost all the study area, while WIEs provide a meridional dipole-like rainfall pattern, consisting of increasing precipitation in the western part and decreasing in the eastern part of CA. The relationship with Madden-Julian Oscillation (MJO) was also examined. The positive rainfall anomalies associated with MJO phases progress eastward and are modulated by the 850 and 200 hPa horizontal wind. The circulation, linked to geopotential height anomalies at lower layers, tends to strengthen (reduce) the convective activity over the region. Uncentered pattern correlation was further used to assess the link between ISO and MJO phases. We found a correlation of 0.5 in precipitation anomalies between phases 1 and 2 of the MJO and the SIEs; -0.4 and -0.6 between phases 5 and 6 and the SIEs respectively. This result suggests a strong relationship between ISO events and MJO phases.
Continuous-time stochastic analysis of rumor spreading with multiple operations
Authors: Castella François , Sericola Bruno , Anceaume Emmanuelle , Mocquard Yves 
Published: July 2023

Abstract
In this paper, we analyze a new asynchronous rumor spreading protocol to deliver a rumor to all the nodes of a large-scale distributed network. This protocol relies on successive pull operations involving k different nodes, with k ≥ 2, and called k-pull operations. Specifically during a k-pull operation, an uninformed node a contacts k − 1 other nodes at random in the network, and if at least one of them knows the rumor, then node a learns it. We perform a detailed study in continuous-time of the total time Θk,n needed for all the n nodes to learn the rumor. These results extend those obtained in a previous paper which dealt with the discrete-time case. We obtain the mean value, the variance and the distribution of Θk,n together with their asymptotic behavior when the number of nodes n tends to infinity.
QAmplifyNet: Pushing the Boundaries of SupplyChain Backorder Prediction Using InterpretableHybrid Quantum–Classical Neural Network
Authors: Jahin Md Abrar , Shovon Md Sakib Hossain , Islam Md. Saiful , Shin Jungpil , Mridha M. F. , Okuyama Yuichi 
Published: July 2023

Abstract
Supply chain management relies on accurate backorder prediction for optimizing inventory control, reducing costs, andenhancing customer satisfaction. Traditional machine-learning models struggle with large-scale datasets and complexrelationships. This research introduces a novel methodological framework for supply chain backorder prediction, addressingthe challenge of collecting large real-world datasets. Our proposed model demonstrates remarkable accuracy in predictingbackorders on short and imbalanced datasets. We capture intricate patterns and dependencies by leveraging quantum-inspiredtechniques within the quantum-classical neural network QAmplifyNet. Experimental evaluations on a benchmark datasetestablish QAmplifyNet’s superiority over eight classical models, three classically stacked quantum ensembles, five quantumneural networks, and a deep reinforcement learning model. Its ability to handle short, imbalanced datasets makes it ideal forsupply chain management. We evaluate seven preprocessing techniques, selecting the best one based on Logistic Regression’sperformance on each preprocessed dataset. The model’s interpretability is enhanced using Explainable Artificial Intelligencetechniques. Practical implications include improved inventory control, reduced backorders, and enhanced operational efficiency. QAmplifyNet seamlessly integrates into real-world supply chain management systems, empowering proactive decision-makingand efficient resource allocation. Future work involves exploring additional quantum-inspired techniques, expanding thedataset, and investigating other supply chain applications. This research unlocks the potential of quantum computing insupply chain optimization and paves the way for further exploration of quantum-inspired machine learning models in supplychain management. Our framework and QAmplifyNet model offer a breakthrough approach to supply chain backorderprediction, offering superior performance and opening new avenues for leveraging quantum-inspired techniques in supply chain management.
A Systematic Review of Knowledge, Attitude, Practice And The Associated Factors Of Medication Error Among Registered Nurses.
Authors: Visvalingam Priscila Anesha , Hamid Assistant Prof Dr Shidqiyyah Abdul , Basha Assistant Prof Dr Muzaitul Akma Mustapha Kamal 
Published: July 2023

Abstract
Medication errors happen all over the world and can cause people real harm. To solve this problem, it is important to know what healthcare workers know, how they feel about medication errors, and what they do about them. The goal of this systematic review of the literature is to find and summarize the current data about the KAP of healthcare workers toward medication errors and related factors. Electronic sources like PubMed, Scopus, and Web of Science were thoroughly searched for applicable studies released between January 2013 and December 2023. There were a total of 31 studies looked at in this study. The results showed that healthcare workers have a middling amount of knowledge about medication errors, but this varies from country to country and specialty to specialty. Most people did not like the idea of medication errors, and the biggest reason they didn't tell them was fear of being sued. Doctors and nurses not following safety rules and not communicating to each other well enough often cause medication errors. Too much work, stress, and not enough training also is affected by medication safety habits. The results of this study show that to reduce the number of medication errors there needs to be more education, better communication and teamwork among healthcare workers, and the creation of processes that put medication safety first.
A Novel Predictive Trend Analytics of Rainfall Data in the North-Eastern Part of Visakhapatnam District, India
Authors: Kunchala Kalyani , G. Muni Reddy M. , Kapuganti Chitti Babu 
Published: July 2023

Abstract
The predictive trend analytic plays a crucial role in estimating the rainfall pattern that impacts on climate change, especially in water resource management. The cyclones and irregular monsoon patterns are two important resulting in unpredictable rainfall which affects the yield and cropping system in agriculture sector and shows major impact on other diversified sectors leading to negative impact on natural drainage system and urban habitation. The present study area is highly prone to cyclones and rainfall irregularities that show a greater impact in the Water management in the Northern part of Visakhapatnam. Present work focuses on the predictive trend analysis of the rainfall data for 38 years from 1982 to 2020 in the study area. By considering monthly, annually, seasonally, annual average rainfall data and a non-parametric analysis the total annual rainfall and seasonal rainfall is estimated using Mann Kendall and Sen’s slope trend analysis algorithms. The predictive calculation results shows that trend analysis performance of Mann Kendall and Sen’s slope values of positive and negative trends. Based on the results, it is predicted that the rainfall pattern in study area may be mostly irregular in nature with significant variability for the next few years also.
Reconciling equatorward migration of Southern Ocean fronts with minor ice volume change during Miocene cooling
Authors: Hou Suning , Stap Lennert , Paul Ryan , Nelissen Mei , Hoem Frida , Ziegler Martin , Sluijs Appy , Sangiorgi Francesca , Bijl Peter 
Published: July 2023

Abstract
A Miocene phase of gradual climate cooling and CO2 decline was recently shown not to be associated with major ice volume expansion, challenging a fundamental paradigm in the functioning of the Antarctic cryosphere. Here, we explore Miocene ice-ocean-climate interactions by presenting a multi-proxy reconstruction of subtropical front (STF) migration, bottom water temperature (BWT) and global ice volume change, using dinoflagellate cyst biogeography, benthic foraminiferal clumped isotopes, and sea surface temperature (SST) reconstructions from offshore Tasmania. We demonstrate a mid-late Miocene (16–9 Ma) equatorward migration (from ~53°S to ~42°S) and strengthening of the STF, concurrent with SST decline. We expand evidence for strong BWT decline and apparent absence of ice volume change into the late Miocene with new clumped isotope data. To reconcile these counterintuitive findings, we argue based on new, idealized ice sheet model simulations that the Miocene Antarctic ice sheet progressively lowered in height while expanding seawards during the mid-Miocene, to maintain a stable volume. This can only be achieved with rigorous intervention in model precipitation regimes and ice-ocean interactions and requires rethinking the interactions between ice-ocean and climate during Neogene cooling.
Why consumers have impulsive purchase behavior in live streaming: the role of the streamer
Authors: LI Xiaolin , Huang Dunhu , Dong Guofeng , Wang Bing 
Published: July 2023

Abstract
To explore the influencing factors of consumers' impulsive purchase behavior in live streaming, based on the Stimulus-Organism-Response framework, we construct the model of how streamers influence consumers' impulsive purchase behavior by consulting literature. Collected data by means of a questionnaire, and made an empirical study by using the structural equation model to explore the mechanism of streamer affecting consumers' impulsive purchase behavior. The results show that streamer characteristics (personal charisma, professionalism) and streamer performance (interactivity, entertainment) affect consumers' impulsive purchase behavior by affecting consumers' trust and flow experience. The empirical results have important theoretical and practical significance.
Bipolar neutrosophic WINGS for green technology innovation
Authors: Tian Yuan , Li Tianjian , Zhang Kecheng 
Published: July 2023

Abstract
Green technology innovation is a crucial assurance of achieving sustainable economic and environmental development, so improving the capability of green technology innovation is an urgent problem. In order to provide a more objective and accurate tool for identifying the most important impact factor of green technology innovation, this study innovatively proposes a new method by combining the bipolar neutrosophic sets with Weighted Influence Nonlinear Gauge System (WINGS) method. Furthermore, this paper intends to provide recommendations in improving green technology innovation capability. We invite five experts to evaluate fifteen factors influencing green technology innovation using the bipolar neutrosophic linguistic variables. Then, the proposed bipolar neutrosophic set WINGS (Bipolar NS-WINGS) method is applied to measure the influence of each impact factor of green technology innovation. Finally, we divide all the factors into cause group and effect group. Moreover, the network relation map is constructed to visualize the interrelationships between all impact factors. The Bipolar NS-WINGS suggests that Science and Technology Innovation Environment (Ω7) is the most important factor of green technology innovation. The result also indicates that R&D Investment (Ω8) is the most influential factor in which it has impacted many other factors. It is obvious that the integrated method not only enriches the research in the field of decision theory, which has not combined the bipolar-NS and WINGS method for analyzing relationships of factors, but also contributes to the improvement of green technology innovation capabilities.
Cancer Survivorship: Understanding the patients’ journey and perspectives on post-treatment needs
Authors: Hussey Conor , Hanbridge Moira , Dowling Maura , Gupta Ananya 
Published: July 2023

Abstract
Background: Cancer treatments have many adverse effects on patient’s health leading to poor cardiorespiratory capacity, muscular- degeneration, fatigue, loss of strength and physical function, altered body-composition, compromised immune-function, peripheral neuropathy, and reduced quality of life (QOL). Exercise programs can significantly increase functional capacity when tailored to individual needs, thus improving patients' health. Exercise interventions in cancer rehabilitation, when supported by appropriate nutrition can be effective in attaining a healthy weight and body-composition. The successful rehabilitation program should also include psycho-social education aimed to reduce anxiety and improve motivation. Several studies have outlined the need to develop rehabilitation-services to minimise the impact of cancer and to reduce future burden on the patient and on health services. A comprehensive service that can provide long-term support for cancer-survivors has not been established in Ireland.
Study aims: The current study aimed to identify the post-treatment needs of cancer patients including information on the barriers and expectations facing them, their caregivers and their families through consultation in focus group interviews. The information would serve as a guide for the development and evaluation of an individualised, adaptive, exercise program for physical rehabilitation of cancer survivors.
Methods: Cancer survivors living in the Republic of Ireland were recruited to study and invited to attend a focus group interview. The focus group discussions were designed to obtain information on the collective views of cancer survivors on relevant topics and the meanings that lie behind those views.
Results: 37 participants took part in 9 focus groups. Focus group transcripts were then subjected to a thematic framework analysis using NVivo. Our analysis uncovered two themes. The first theme ‘cast adrift with no direction’ was grouped into three sub-themes: everything revolves around treatment; panic and fear; and what exercise should I be doing? The second theme ‘everybody is different’ was clustered into two sub-themes: side effects get in the way; and personalized exercise program
Conclusion: The main findings of our study as illustrated by the thematic analysis shows that the patients have unmet post-treatment needs that can be broadly classified into (i) Information and support needs, (ii) Psycho-social needs, (iii) Physical needs and need for a “personalised” exercise programme. The study highlighted the lack of information and support that needed by patients in living with and beyond cancer. The study also highlighted effectiveness of a personalised exercise programme in the mitigation of long term symptoms and in improving QOL. There is an urgent need for design and delivery of such a programme through community based cancer centres.
Designing active chitosan-based biofilms loaded with the mixture of herbal essential oils and reinforced with nanofibers: Their effects on the shelf life and sensory properties of red meat
Authors: Jahed Eisa , Khaledabad Mohammad Alizadeh , Aziz Khalid Esmahil , Almasi Hadi , Hassanzadeh Hamed 
Published: July 2023

Abstract
Background: Designing the antimicrobial food packaging is needed for food protection and quality maintenance. In this research, active packaging based on chitosan biopolymer with antimicrobial properties has been prepared to investigate its effects on shelf life and sensory properties of red meat. Origanum vulgare subsp. gracile essential oil (GEO) and Carum copticum essential oil (CEO) compounds (as an antioxidant/antimicrobial substances) from the packaging material into red meat.
Results: According to the obtained results, it was found that the highest percentage of organic compounds identified in Carum copticum EO were related to carvacrol (43.94%), gamma-terpinene (26.86%), m-cymene (24.37%) and alpha-pinene (41.2%). The results showed that active films containing different ratios of EOs had high antimicrobial activity against E. coli and B. cereus bacteria, which were reduced by adding CNF and LCNF due to the role of controlling release of EO compounds.
Conclusion: Microbial evaluation of red meat pieces also showed that the use of edible film coating containing EO significantly reduced the growth rate of microorganisms compared to the uncoated sample. The evaluation of sensory properties of meat samples also showed that the nanocomposite film containing EO increased the shelf life of red meat for more than 20 days at refrigerator temperature due to its antimicrobial properties and favorable aroma.
Detection of Sybil &amp; DDoS Attacks in VANET using Intelligent Technique
Authors: Bhanja Urmila , Majhi Ankit , Sahoo Smrutirekha , Parida Debapriya 
Published: July 2023

Abstract
Vehicular Adhoc network (VANET) are more prone to various types of attacks. Sybil attack is the most dangerous attack in vehicular adhoc network as it creates multiple fake identities and creates traffic congestion.Fake identities are used to enter the network illegally.On the other hand, distributed denial of service (DDoS) attack intentionally blocks the users from accessing any online services. It temporarily disrupts or interrupts the service of the hosting server. These types of attacks in VANET cause severe damage to vehicles, passengers travelling in the vehicles by inducing traffic congestion, and may also cause minor or fatal accidents.Hence, it is highly essential to early detect such attacks in VANET to protect the vehicles and human kind. In this work, a novel model is proposed using fuzzy logic controllers (FLCs) to detect both the Sybil and the DDoS attacks in VANET. Furthermore, performance of attack detection is also analysed and compared with the existing techniques. The proposed model yields better accuracy, sensitivity, and recall value compared to the existing techniques. Margin of error for the attack detection is also estimated for 95% of the confidence interval.