bluuebunny/crossref_metadata_2025_split huggingface dataset:
Application of nanotechnology and proteomic tools in crop development towards sustainable agriculture
Authors: Daniel Augustine Innalegwu , Hüsselmann Lizex , Shittu Oluwatosin Kudirat , Gokul Arun , Keyster Marshall , Klein Ashwil 
Published: February 2024

AbstractThe increase in global population which translates to increased demand for food called for urgent attention from key players and policy makers in agricultural sector. Also, the effects of climate change and its consequent biotic and abiotic stresses in plants has greatly affect the sustainability of agriculture and production of food. These challenges require novel and sustainable approaches to improve the quality and yield of crops. The vast application of nanoparticles in different fields of study is attributed to their distinct chemical and physical characteristics. However, in agriculture, their application is limited because of their safety concern. Currently, research is tailored to study the response of plants to various nanoparticle treatments; however, these studies are inconclusive due to limited knowledge on the mechanisms of plant–nanoparticle interactions. Recently, studies on nanobiotechnology have taken a new dimension from preliminary bioassay experiments to more complex, research-oriented studies using various omics tools. Changes in protein expression caused by plant–nanoparticle interaction at any developmental stages, or tissue types may be investigated using suitable proteomics techniques. This review discussed the various applications of nanotechnology and proteomic tools in plant growth and development. Plant–nanoparticle interactions and the application of nanoparticles and proteomic tools in genetic engineering of plants to attain agricultural sustainability and food safety are also discussed. Informative and thorough understanding of plant–nanoparticle interaction will serve as a blueprint that will enable plant scientist and genetic engineers to develop plant biomarkers and explore their potential application for crop improvement.
The engine mission simulation system: coupled simulation of aircraft and engine in the mission
Authors: Remiger Jonas , Stößel Marcel , Kožulović Dragan , Niehuis Reinhard , Krummenauer Michael 
Published: February 2024

AbstractWhen designing future aircraft, the entire system including airframe, propulsion system, energy management and mission control must be considered in a coupled approach at an early design stage. To model and study this complex interaction, the Engine Mission Simulation System (EMSS) environment was created in cooperation with the Bundeswehr Technical Center for Aircraft and Aircraft Equipment. EMSS is intended to serve as a digital, flying test bed at the Institute of Jet Propulsion for the rapid and low-cost evaluation of new concepts for propulsion and power supply systems for new and legacy aircraft designs. This tool chain allows a detailed engine simulation under consideration of its flight condition while using an external propulsion and power control system. First, the software architecture of EMSS is discussed, which involves a propulsion simulation in NPSS® coupled with the X-Plane 11 flight simulator via MATLAB®/Simulink®. The aircraft itself is modeled as a six degrees of freedom simulation using the blade element theory, allowing both complex flight maneuvers and unusual aircraft to be easily simulated. The EMSS achieves real-time capability and enables user-friendly pre-design as well as quick trend detection through the ability to perform focused parametric studies. To evaluate the accuracy of the system’s results, the engine model used is validated against experimental data. Subsequently, the aerodynamic characteristics of the aircraft model are verified with a CFD simulation. Finally, a parametric study is used to demonstrate the mission as well as propulsion evaluation capabilities of the Engine Mission Simulation System.
A Finite Element Modeling Approach for Analyzing the Cyclic Behavior of RC Frames
Authors: Shamsi Tamana , Sümer Yusuf 
Published: February 2024

AbstractNumerous approaches and inputs for nonlinear finite element analysis used for modeling reinforced concrete systems under cyclic loads have been outlined. Practitioners usually take into account a number of significant factors when modeling, for example deciding upon the constitutive relations, the connection among concrete and steel reinforcement, and the mesh sensitivities, in order to accurately simulate the nonlinear response of reinforced concrete under cyclic loads. Therefore, it is essential to thoroughly assess popular modeling approaches and develop a reliable modeling approach with reliability, stability, and consistency. A modeling technique and practical guidelines for nonlinear finite element analysis of reinforced concrete frames subjected to cyclic loading are described in this study, taking into account the influence of parameters such as dilation angle, stiffness recovery, friction coefficient, and mesh sizes. The hysteretic force–displacement behavior and load-carrying capacity of reinforced concrete frames subjected to cyclic loading have been compared using numerical and experimental curves. Discrete fracture paths are specified in the model’s geometry to achieve an accurate depiction of the stress distribution and an adequate fit of the hysteretic behavior. The proposed modeling approach provides a precise representation of the stress distribution and hysteretic behavior while keeping computational costs reasonable.
Intersecting Inequities in COVID-19 Vaccination: A Discourse Analysis of Information Use and Decision-Making Among Ethnically Diverse Parents in Canada
Authors: Marfo Emmanuel A. , Manca Terra , Cha Eunah , Aylsworth Laura , Driedger S. Michelle , Meyer Samantha B. , Pelletier Catherine , Dubé Ève , MacDonald Shannon E. 
Published: February 2024

Abstract
Background
Little is known about how intersecting social privilege and disadvantage contribute to inequities in COVID-19 information use and vaccine access. This study explored how social inequities intersect to shape access to and use of COVID-19 information and vaccines among parents in Canada.

Methods
We conducted semi-structured interviews on COVID-19 vaccination information use with ethnically diverse parents of children ages 11 to 18 years from April to August 2022. We purposefully invited parents from respondents to a national online survey to ensure representation across diverse intersecting social identities. Five researchers coded transcripts in NVivo using a discourse analysis approach informed by intersectionality. Our analysis focused on use of vaccine information and intersecting privileges and oppressions, including identifying with equity-denied group(s).

Results
Interview participants (N = 48) identified as ethnically diverse non-Indigenous (n = 40) and Indigenous (n = 8) Peoples from seven Canadian provinces. Racialized minority or Indigenous participants reflected on historical and contemporary events of racism from government and medical institutions as barriers to trust and access to COVID-19 information, vaccines, and the Canadian healthcare system. Participants with privileged social locations showed greater comfort in resisting public health measures. Despite the urgency to receive COVID-19 vaccines, information gaps and transportation barriers delayed vaccination among some participants living with chronic medical conditions.

Conclusion
Historicization of colonialism and ongoing events of racism are a major barrier to trusting public health information. Fostering partnerships with trusted leaders and/or healthcare workers from racialized communities may help rebuild trust. Healthcare systems need to continuously implement strategies to restore trust with Indigenous and racialized populations.
Data-Driven Approaches for Vibroacoustic Localization of Leaks in Water Distribution Networks
Authors: Liu Rongsheng , Tariq Salman , Tijani Ibrahim A. , Fares Ali , Bakhtawar Beenish , Fan Harris , Zhang Rui , Zayed Tarek 
Published: February 2024

AbstractThis study aims to propose Micro-electromechanical System (MEMS) accelerometers for leak localization in the water distribution network and assess the performance of machine learning models in accurately estimating leak locations. Intensive field experimentation was conducted to collect data for model development. Machine learning algorithms were employed to develop leak localization models, specifically artificial neural network (ANN) and support vector machine (SVM). Seventeen time-domain and frequency-domain features were extracted, and feature selection was performed using the backward elimination method. The results indicate that the ANN and SVM models are suitable classifiers for localizing leak distance. Both models achieved leak location predictions with over 80% accuracy, and the mean absolute errors were measured at 0.858 and 0.95 for the ANN and SVM models, respectively. The validation results demonstrated that the models maintained accuracies close to 80% when the distance between sensors and the leak was less than 15 m. However, the performance of the model deteriorates when leaks occur at distances greater than 15 m. This study demonstrates the applicability of MEMS accelerometers for leak localization in water distribution networks. The findings highlight the promising potential of employing MEMS accelerometers-based ANN and SVM models for accurate leak localization in urban networks, even under real-world, uncontrolled conditions. However, the current model exhibits limited performance in long-distance leak localization, requiring further research to address and resolve this issue.
On the Conditions for Total Orderings in Lexicographic Methods to Rank Fuzzy Numbers
Authors: Salas-Molina Francisco , Reig-Mullor Javier , Pla-Santamaria David , Garcia-Bernabeu Ana 
Published: February 2024

Abstract
Lexicographic methods to rank fuzzy numbers present the advantages of simplicity, consistency with human intuition, and power of discrimination. In this paper, we tackle the problem of finding the conditions for these methods to produce a rank in specific steps. Our main results are twofold. First, we prove that a necessary and sufficient condition for a ranking function to be a total order is that this function is either injective, surjective, or bijective. Second, we provide further insight into the required steps for a lexicographic order to rank same-type and different-type fuzzy numbers. A counterexample refutes a conjecture in the literature about the maximum number of steps needed to rank different-type fuzzy numbers.
Highly Porous Yet Transparent Mechanically Flexible Aerogels Realizing Solar-Thermal Regulatory Cooling
Authors: Lian Meng , Ding Wei , Liu Song , Wang Yufeng , Zhu Tianyi , Miao Yue-E. , Zhang Chao , Liu Tianxi 
Published: February 2024

AbstractThe demand for highly porous yet transparent aerogels with mechanical flexibility and solar-thermal dual-regulation for energy-saving windows is significant but challenging. Herein, a delaminated aerogel film (DAF) is fabricated through filtration-induced delaminated gelation and ambient drying. The delaminated gelation process involves the assembly of fluorinated cellulose nanofiber (FCNF) at the solid–liquid interface between the filter and the filtrate during filtration, resulting in the formation of lamellar FCNF hydrogels with strong intra-plane and weak interlayer hydrogen bonding. By exchanging the solvents from water to hexane, the hydrogen bonding in the FCNF hydrogel is further enhanced, enabling the formation of the DAF with intra-layer mesopores upon ambient drying. The resulting aerogel film is lightweight and ultra-flexible, which possesses desirable properties of high visible-light transmittance (91.0%), low thermal conductivity (33 mW m−1 K−1), and high atmospheric-window emissivity (90.1%). Furthermore, the DAF exhibits reduced surface energy and exceptional hydrophobicity due to the presence of fluorine-containing groups, enhancing its durability and UV resistance. Consequently, the DAF has demonstrated its potential as solar-thermal regulatory cooling window materials capable of simultaneously providing indoor lighting, thermal insulation, and daytime radiative cooling under direct sunlight. Significantly, the enclosed space protected by the DAF exhibits a temperature reduction of 2.6 °C compared to that shielded by conventional architectural glass.
Time varying effects in survival analysis: a novel data-driven method for drift identification and variable selection
Authors: Babutsidze Zakaria , Guerzoni Marco , Riso Luigi 
Published: February 2024

AbstractIn this paper we address the problem of survival models when high-dimensional panel data are available. We discuss two related issues: The first one concerns the issue of variable selection and the second one deals with the stability over time of such a selection, since presence of time dimension in survival data requires explicit treatment of evolving socio-economic context. We show how graphical models can serve two purposes. First they serve as the input for a first algorithm to to assess the temporal stability of the data: Secondly, allow the deployment of a second algorithm which partially automates the process of variable selection, while retaining the option to incorporate domain expertise in the process of empirical model-building. To put our proposed methodology to the test, we utilize a dataset comprising Italian firms funded in 2009 and we study the survival of these entities over the period of 10 years. In addition to revealing significant volatility in the set of variables explaining firm exit over the years, our novel methodology enables us to offer a more nuanced perspective than the conventional one regarding the critical roles played by traditional variables such as industrial sector, geographical location, and innovativeness in firm survival.
Where’s the E in OE? A critical analysis of Irish outdoor education
Authors: Pierce John , Beames Simon 
Published: February 2024

AbstractVery little empirical research has been conducted on Irish outdoor education practice(s). This inquiry aims to critically explore outdoor education practices in public Outdoor Education and Training Centres in the Republic of Ireland. First, an overview of the development of Irish outdoor education from the mid-twentieth century onwards is provided to locate the study within its unique cultural and historical background. Data were generated through document analysis, participant observation, and informal conversation with practitioners and management through multiple field visits. Creative non-fiction was used as a method to organise and present the data in a coherent and anonymised manner. Thematic analysis revealed three principal findings: a lack of engagement with theory, programming by logistics, and ideological dissonance. How these findings affect practice are discussed in terms of a place-based focus to practice as well as a need for more direction for practice to be informed by research and theory.
Preparation, characterization and cooling performance of ZnO based Nanofluids
Authors: Hussain Muzaffar , Ansari M. A. , Mir Feroz A. 
Published: February 2024

AbstractNanoparticles (NPs) of Zinc Oxide (ZnO) were synthesized by the sol–gel method. These NPs were characterized for structural vibrational, morphological, optical, and electrical properties by X-ray diffraction (XRD), Fourier Transform infrared (FT-IR) spectroscopy, Scanning Electron Microscopy (SEM), UV–Visible optical absorption spectroscopy, and dielectric spectroscopy. Structural analysis confirms that all the NPs have a single-phase rutile tetragonal structure. Also from the Scherer formula, the average particle size for this compound was found to be around 10.25 nm. From SEM images, the observed NPs are spherical in shape, with an average size ranging from 10 to 100 nm. FT-IR data indicates a Zn–O vibrational bond and confirms the ZnO structure. Further, according to the optical data analysis, these NPs show an optical band gap of around 3.27 eV and follow an indirect transition. The dielectric constant (εr) and loss constant (ε) show good dispersion with the applied frequency. Also using these NPs, a nanofluid in transformer oil (mineral oil) was prepared. Further εr & εi shows good variation with the contents of NPs in the mineral oil. This nanofluid was tested for the cooling of transformers and shows some encouraging results. Based on these results, this material seems like a good candidate for transformer insulation and cooling applications.
Impact of CSR disclosure on profitability and firm performance of Malaysian halal food companies
Authors: Ahmad Zaki , Hidthiir Mohammad Helmi Bin , Rahman Md Mahfujur 
Published: February 2024

AbstractThe purpose of this study is to examine the impact of corporate social responsibility (CSR) disclosure on the profitability and firm performance of Halal food companies in Malaysia. Applying a quintitative method, the study utilised a dynamic GMM-based statistical technique for analysing the data from 75 public-listed Halal food companies in Malaysia from 2012 to 2021.The findings highlight a substantial relationship between CSR disclosure and profitability (ROA) as well as firm performance (TBQ) of Halal food companies in Malaysia. The result showed economic, environmental, and social CSR disclosures have a significant effects on ROA and TBQ in dynamic models. The outcome of the study implies that actively engaging in economic, environmental, and social CSR practices provides with a competitive advantage, contributes to long-term viability, and attracts socially conscious investors. It also emphasises the strategic importance of comprehensive CSR strategies for encouraging positive stakeholder relationships, managing diverse aspects of sustainability, and enhancing overall business success, including employee engagement and motivation.
Managing the race to the moon: Global policy and governance in Artificial Intelligence regulation—A contemporary overview and an analysis of socioeconomic consequences
Authors: Walter Yoshija 
Published: February 2024

AbstractThis paper delves into the complexities of global AI regulation and governance, emphasizing the socio-economic repercussions of rapid AI development. It scrutinizes the challenges in creating effective governance structures amidst the AI race, considering diverse global perspectives and policies. The discourse moves beyond specific corporate examples, addressing broader implications and sector-wide impacts of AI on employment, truth discernment, and democratic stability. The analysis focuses on contrasting regulatory approaches across key regions—the United States, European Union, Asia, Africa, and the Americas and thus highlighting the variations and commonalities in strategies and implementations. This comparative study reveals the intricacies and hurdles in formulating a cohesive global policy for AI regulation. Central to the paper is the examination of the dynamic between rapid AI innovation and the slower pace of regulatory and ethical standard-setting. It critically evaluates the advantages and drawbacks of shifting regulatory responsibilities between government bodies and the private sector. In response to these challenges, the discussion proposes an innovative and integrated regulatory model. The model advocates for a collaborative network that blends governmental authority with industry expertise, aiming to establish adaptive, responsive regulations (called “dynamic laws”) that can evolve with technological advancements. The novel approach aims to bridge the gap between rapid AI advancements in the industry and the essential democratic processes of law-making.
Nonlinear Stochastic Operators and Associated Inhomogeneous Entangled Quantum Markov Chains
Authors: Souissi Abdessatar , Mukhamedov Farrukh 
Published: February 2024

Abstract
In the present paper, we introduce a class of F-stochastic operators on a finite-dimensional simplex, each of which is regular, ascertaining that the species distribution in the succeeding generation corresponds to the species distribution in the previous one in the long run. It is proposed a new scheme to define non-homogeneous Markov chains contingent on the F-stochastic operators and given initial data. By means of the uniform ergodicity of the non-homogeneous Markov chain, we define a non-homogeneous (quantum) entangled Markov chain. Furthermore, it is established that the non-homogeneous entangled Markov chain enables

$$\psi$$

ψ

-mixing property.
Treatment for social media addiction with hypnotic-oriented counseling
Authors: Sugara Gian Sugiana , Fadhilah Rikza 
Published: February 2024

Abstract
Background
Research has identified social media addiction as a new and often unrecognized clinical disorder that impacts users' ability to control social media use which can lead to relational, work, and social problems. Adolescent who experiences addiction to social media will be very dependent and willing to spend a long time accessing social media so that they experience addiction to social media. This paper reports the findings of a study that used an A-B-A single-case research design to investigate the effect of hypnotherapy to reduce social media addiction in adolescents. Participants consisted of two high school students (N = 2) who had social media addiction behavior in the severe category. Hypnotic-oriented counseling interventions in reducing social media addiction was provided in 5 sessions. Treatment focused on enhancing motivation, emotional release, conflict resolution and empowerment using direct suggestion, parts therapy, circle of excellence technique and anchor. Participants completed the Social Media Addiction Scale for Adolescent. Data was collected before, during, and after the hypnotic-oriented counseling intervention.

Result
Students who took part in hypnosis-oriented counseling sessions experienced a significant reduction in social media addiction symptoms during and after the intervention. They report becoming increasingly able to manage social media use healthily.

Conclusion
Hypnotic-oriented counseling intervention was effective in reducing social media addiction symptoms in students.
A prior knowledge guided deep learning method for building extraction from high-resolution remote sensing images
Authors: Hao Ming , Chen Shilin , Lin Huijing , Zhang Hua , Zheng Nanshan 
Published: February 2024

AbstractThere are problems such as poor interpretability and insufficient generalization ability when extracting buildings from high-resolution remote sensing images based on deep learning. This paper proposes a building extraction model called BPKG-SegFormer (Building Prior Knowledge Guided SegFormer) that combines prior knowledge of buildings with data-driven methods. This model constructs a building feature attention module and utilizes the multi-task loss function to optimize the extraction of buildings. Experimental results show that on the WHU building dataset, the proposed model outperforms UNet, Deeplabv3 + , and SegFormer models with OA, P, R, and MIoU of 96.63%, 95.94%, 94.76%, and 90.6%, respectively. The BPKG-SegFormer model extracts buildings with more regular shapes and flatter edges, reducing internal voids and increasing the number of correctly detected buildings.