bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Dickson polynomial based group key agreement authentication scheme for ensuring conditional privacy preservation and traceability in VANETs
Authors: Rajkumar Y. , Santhosh Kumar S. V. N. 
Published: February 2025

Abstract
VANETs exchange data in highly dynamic open wireless access environments which are prone to security and privacy attacks. In order to safeguard the transmitted data, group key agreement authentication (GKAA) technique is used. Utilization of group key allows entities to corroborate a group key for secure VANET communication in an unsecure wireless communication channels. The traditional GKAA consumes a considerable amount of resources, verification delay is very high. Since the group key is computed and administered solely by TTA, it leads to central tendency. Additionally the communication delay soars high. To alleviate the problems of computational cost, communication cost, security, conditional privacy, central tendency, a Dickson polynomial based conditional privacy preservation authentication based on group key authentication (GKA) for VANETs has been proposed. The proposed work involves the use of Dickson polynomial to improve the security strength of TTA while authentication vehicles. Since it is based on chaotic mapping algorithm, wherein the chaotic map provides a one-way hash function; Dickson polynomial is used to corroborate a publicly distributed group key; it alleviates the complex modular or scalar multiplication performed using Elliptic curves. The group key gets computed in a distributed fashion by using the Chinese Remainder Theorem (CRT) and gets updated dynamically without the aid of TTA. Conditional Privacy has been ensured by the tracing back the pseudonyms in case of any illicit behavior exhibited by the vehicles. The proposed scheme is lightweight and lowers the communication, computation cost involved during authentication and verification. Performance analysis has been carried out by using BAN logic and ROR model thereby ensuring the security and efficiency. Thus the proposed authentication technique outperforms the traditional certificate-less and group key authentication schemes in terms of improvement in computation cost of 39%, communication cost of 672 bits for a single message with a less verification delay.
AquaYOLO: Advanced YOLO-based fish detection for optimized aquaculture pond monitoring
Authors: Vijayalakshmi M. , Sasithradevi A. 
Published: February 2025

Abstract
Aquaculture plays an important role in ensuring global food security, supporting economic growth, and protecting natural resources. However, traditional methods of monitoring aquatic environments are time-consuming and labor-intensive. To address this, there is growing interest in using computer vision for more efficient aqua monitoring. Fish detection is a key challenging step in these vision-based systems, as it faces challenges such as changing light conditions, varying water clarity, different types of vegetation, and dynamic backgrounds. To overcome these challenges, we introduce a new model called AquaYOLO, an optimized model specifically designed for aquaculture applications. The backbone of AquaYOLO employs CSP layers and enhanced convolutional operations to extract hierarchical features. The head enhances feature representation through upsampling, concatenation, and multi-scale fusion. The detection head uses a precise 40 × 40 scale for box regression and dropping the final C2f layer to ensure accurate localization. To test the AquaYOLO model, we utilize DePondFi dataset (Detection of Pond Fish) collected from aquaponds in South India. DePondFi dataset contains around 50k bounding box annotations across 8150 images. Proposed AquaYOLO model performs well, achieving a precision, recall and mAP@50 of 0.889, 0.848, and 0.909 respectively. Our model ensures efficient and affordable fish detection for small-scale aquaculture.
Analyzing patient perspectives with large language models: a cross-sectional study of sentiment and thematic classification on exception from informed consent
Authors: Kornblith Aaron E. , Singh Chandan , Innes Johanna C. , Chang Todd P. , Adelgais Kathleen M. , Holsti Maija , Kim Joy , McClain Bradford , Nishijima Daniel K. , Rodgers Steffanie , Shah Manish I. , Simon Harold K. , VanBuren John M. , Ward Caleb E. , Counts Catherine R. 
Published: February 2025

Abstract
Large language models (LLMs) can improve text analysis efficiency in healthcare. This study explores the application of LLMs to analyze patient perspectives within the exception from informed consent (EFIC) process, which waives consent in emergency research. Our objective is to assess whether LLMs can analyze patient perspectives in EFIC interviews with performance comparable to human reviewers. We analyzed 102 EFIC community interviews from 9 sites, each with 46 questions, as part of the Pediatric Dose Optimization for Seizures in Emergency Medical Services study. We evaluated 5 LLMs, including GPT-4, to assess sentiment polarity on a 5-point scale and classify responses into predefined thematic classes. Three human reviewers conducted parallel analyses, with agreement measured by Cohen’s Kappa and classification accuracy. Polarity scores between LLM and human reviewers showed substantial agreement (Cohen’s kappa: 0.69, 95% CI 0.61–0.76), with major discrepancies in only 4.7% of responses. LLM achieved high thematic classification accuracy (0.868, 95% CI 0.853–0.881), comparable to inter-rater agreement among human reviewers (0.867, 95% CI 0.836–0.901). LLMs enabled large-scale visual analysis, comparing response statistics across sites, questions, and classes. LLMs efficiently analyzed patient perspectives in EFIC interviews, showing substantial sentiment assessment and thematic classification performance. However, occasional underperformance suggests LLMs should complement, not replace, human judgment. Future work should evaluate LLM integration in EFIC to enhance efficiency, reduce subjectivity, and support accurate patient perspective analysis.
Modeling the non-integer dynamics of a vector-borne infection with nonlocal and nonsingular kernel
Authors: Ullah Nekmat , Shah Zahir , Jan Rashid , Vrinceanu Narcisa , Farhan Muhammad , Antonescu Elisabeta 
Published: February 2025

Abstract
Vector-borne infections impose a significant burden on global health systems and economies due to their widespread impact and the substantial resources required for prevention, control, and treatment efforts. In this work, we formulate a mathematical model for the transmission dynamics of a vector-borne infection with the effect of vaccination through the Atangana-Baleanu derivative. The solutions of the model are positive and bounded for positive initial values of the state variable. We presented the basic concept and theory of fractional calculus for the analysis of the model. We determine the threshold parameter, denoted by
$$\mathcal {R}_0$$
, using the next-generation matrix method. The local asymptotic stability of the system at the disease-free equilibrium is analyzed. To establish the existence of solutions for the proposed model, we employ fixed-point theory. A numerical scheme is developed to visualize the system’s dynamical behavior under varying input parameters. Numerical simulations are conducted to illustrate how these parameters influence the dynamics of the system. The results highlight key factors affecting the transmission and control of vector-borne diseases, offering insights into strategies for prevention and mitigation.
Gender-differences in imaging phenotypes of osteoarthritis in the osteoarthritis initiative
Authors: Kreutzinger Virginie , Ziegeler Katharina , Joseph Gabby B. , Lynch John A. , Lane Nancy E. , McCulloch Charles E. , Nevitt Michael , Link Thomas M. 
Published: February 2025

Abstract
In osteoarthritis (OA) research it is increasingly recognized that stratification according to disease phenotypes is essential for optimizing treatment regimens. Gender-specific differences in clinical OA manifestations have been identified, and this analysis aimed to assess whether these differences extend to imaging phenotypes.
From the Osteoarthritis Initiative database 2523 participants (1409 women and 1114 men) with completed 3T MRI and whole-organ magnetic resonance imaging scores (WORMS) of the right knee at baseline were included. Imaging phenotypes were assigned based on modified Rapid OsteoArthritis MRI Eligibility Score: the inflammatory, meniscus-cartilage, and bone phenotype. Logistic regression was performed to investigate the effect of gender on phenotype, independent of BMI, race, Kellgren & Lawrence (KL) grade and level of physical activity. Female gender was independently associated with lower odds for the meniscus-cartilage (OR 0.61, 95%CI 0.47–0.80, p < 0.001) but not for the inflammatory (OR 1.04, 95%CI 0.89–1.24, p = 0.697) or the subchondral bone phenotype (OR 1.13, 95%CI 0.95–1.36, p = 0.166). This difference highlights an opportunity for future refinements to better accommodate gender/sex differences in disease trajectories while investigating different treatment regimes in knee OA.
Learning to operate an imagined speech Brain-Computer Interface involves the spatial and frequency tuning of neural activity
Authors: Bhadra Kinkini , Giraud Anne-Lise , Marchesotti Silvia 
Published: February 2025

Abstract
Brain-Computer Interfaces (BCI) will revolutionize the way people with severe impairment of speech production can communicate. While current efforts focus on training classifiers on vast amounts of neurophysiological signals to decode imagined speech, much less attention has been given to users’ ability to adapt their neural activity to improve BCI-control. To address whether BCI-control improves with training and characterize the underlying neural dynamics, we trained 15 healthy participants to operate a binary BCI system based on electroencephalography (EEG) signals through syllable imagery for five consecutive days. Despite considerable interindividual variability in performance and learning, a significant improvement in BCI-control was globally observed. Using a control experiment, we show that a continuous feedback about the decoded activity is necessary for learning to occur. Performance improvement was associated with a broad EEG power increase in frontal theta activity and focal enhancement in temporal low-gamma activity, showing that learning to operate an imagined-speech BCI involves dynamic changes in neural features at different spectral scales. These findings demonstrate that combining machine and human learning is a successful strategy to enhance BCI controllability.
Conceptualization of a Universally Usable Dynamic Assessment Screener for Developmental Language Disorder
Authors: Hedges Christy 
Published: February 2025

Purpose:
This article reviews current best practice recommendations in the areas of assessment for developmental language disorder in multilingual populations, screening recommendations, and dynamic assessment in an effort to inform the creation of a universally usable screening tool for language development.

Method:
Best practice recommendations were considered in each of the identified areas. The convergence of these areas was then conceptualized and used to analyze a current screening tool. Through the review of information and the application to a current screening, recommendations for the development of a new screening tool are provided.

Conclusions:
The current screening tool was found to be lacking in almost all areas. The new recommendations will be useful to clinicians as they consider how best to screen language in a large group of children with diverse language backgrounds.
Electric vehicle charging demand forecasting at charging stations under climate influence for electricity dispatching
Authors: Chen Peilu , Qin Jianzhong , Dong Jinxi , Ling Long , Lin Xiaoming , Ding Huixian 
Published: February 2025

AbstractAs the prevalence of electric vehicles (EVs) continues to surge, the precise forecasting of charging demands at individual charging stations becomes imperative for effective power distribution management. However, the charging demand of EVs is often related to various factors and exhibits strong randomness. This paper aims to explore the impact of climatic factors on the charging demand of electric vehicles at charging stations, and to study a prediction model based on the attention mechanism of LSTM for predicting the load of charging stations, providing important guidance for the scheduling of electric vehicles. By analysing the load data of a single charging station under different climatic conditions, the paper finds that climatic factors such as the highest temperature of the day, the lowest temperature, and the type of weather significantly affect the charging demand of electric vehicles. Utilizing the aforementioned characteristics, this paper studies a climate feature‐guided charging demand prediction model for a single charging station, which adopts the LSTM architecture and introduces the attention mechanism, incorporating the above‐mentioned important climatic features, and is ultimately able to accurately predict the future charging demand of the charging station. The experimental results show that, compared to other time series forecasting models, this model has significantly improved performance on the dataset tests, with its accuracy ratio (AR) indicator and qualified rate indicator both exceeding 0.85 and 0.95, respectively. This study not only offers a new perspective and method for predicting the demand for electric vehicle charging but also provides support for the development of electric vehicle scheduling.
9.12.4 Tellurophenes (Update 2024)
Authors: Harris P. A. 
Published: February 2025

AbstractMethods for the synthesis of tellurophenes are reviewed in this chapter, covering the period from 2011 to 2022. This is a supplement to both the original Science of Synthesis review (Section 9.12) that covered the literature up to 2000, and the subsequent update covering from 2001 to 2010 (Section 9.12.3).
Factors driving farmers’ willingness to use organic liquid fertilizer (eco-green) in agricultural production in East Gojjam, north-western Ethiopia
Authors: Tariku Getasew Daru , Kebede Sinkie Alemu 
Published: February 2025

Abstract

Background:
Recently, the government of Ethiopia has started promoting the use of eco-green organic liquid fertilizer in response to high price hikes and shortage of supply of chemical fertilizer. However, the factors driving farmers to apply eco-green organic fertilizer to crop production are poorly studied. Hence, this study aims to identify factors influencing farmers to use eco-green fertilizers.
Methodology:
This study used a multi-stage sampling technique to select the study area and sample respondents. The primary data was collected from randomly selected organic liquid fertilizer users and non-user households through structured and semi-structured questionnaires, focus group discussions, and key informant interviews. Secondary data was collected from documents, publications, and websites. Binary Logit regression was used to analyze the factors deriving farmers’ willingness to use eco-green organic fertilizer.
Result:
According to the model result, annual income, membership to cooperatives, training access, information access, cost of chemical fertilizer, and landholding size positively influenced farmers to use eco-green fertilizer. While access to chemical fertilizers and low levels of awareness about eco-green were negatively influencing farmers’ willingness to use eco-green fertilizers in the study area.
Conclusion
: This result implies that to benefit farmers from organic fertilization, interventions that provided better technical training, better access to information media, and farmers’ group membership were crucial factors influencing farmers to apply eco-green fertilizer. The outcome of this study is expected to benefit farmers, extension agents, eco-green producer companies, input suppliers, and other agricultural growth stakeholders.
Deep learning to improve plant biosecurity and agriculture sustainability: A systematic literature review
Authors: Parul Sharma , Abhilasha Sharma 
Published: February 2025

Abstract
Plant stress significantly threatens agricultural productivity and quality, necessitating advanced solutions for effective plant biosecurity. Manual inspection is time-consuming and requires expert knowledge, making automated monitoring crucial for crop protection and food security. While deep learning has shown significant potential in plant stress monitoring and improving plant biosecurity, the existing literature remains unsystematic and highlights numerous challenges that hinder its real-time application. Therefore, a systematic literature review is essential to consolidate current knowledge, offer a comprehensive overview of deep learning-based plant biotic stress monitoring, and identify research gaps, trends, and challenges. In this view, this article employs the PRISMA framework to analyze 745 articles from six recognized electronic databases published between 2016 and April 2024, addressing eight research questions. Findings from this study indicate that convolutional neural networks dominate the field, appearing in 85.23% of articles, with limited consideration for hybrid models. Further, the reliance on supervised learning and high-quality annotated datasets highlights significant challenges related to data availability, diversity, and model generalization. Besides, the limited focus on efficiency metrics for model evaluation further compounds these issues, hindering accurate assessment of model performance in real-world applications. Additionally, only 8.19% of the articles offered directly applicable solutions to farmers, highlighting the necessity for practical and scalable applications. Moreover, while 63.42% of studies originated from the USA and China, research output from Africa, Central Asia, South America, and Southeast Asia remains low. These findings highlight the need for equitable research and localized solutions to address challenges in underrepresented regions. Future research should explore unsupervised and semi-supervised learning to reduce reliance on annotated datasets, integrate IoT, edge computing, and cloud-computing for real-time deployment, and incorporate efficiency metrics to ensure robust real-world performance. This review offers a comprehensive overview of the latest advancements and outlines potential future directions for computer science, agriculture, and ecology.
Analysis of the convective amplification process of a three-wave parametric instability arises from low frequency plasma fluctuation
Authors: Chen Kunyu , Gao Zhe 
Published: February 2025

Abstract
A formal derivation of the convective amplification of a three-wave parametric instability (PI) in an inhomogeneous plasma is revisited. The confusion of the mixed use of the initial values of PI is clarified and the consistency among the initial values is discussed. Through the exact solutions of the coupling equations of a three-wave PI, it is suggested that the initial amplitude of low frequency plasma fluctuations might be the appropriate initial value for all the PI scenarios, especially in magnetic confinement fusion plasma. The co-effect of plasma inhomogeneity and finite pump profile is accordingly evaluated through the exact solutions of the coupling equations, where the effects of a finite pump profile are found to be totally different for forward scatters and backward scatters.
An improved STT-MRAM energy consumption simulation model
Authors: Liu Yaogeng , Qiang Tian , Jiang Yanfeng 
Published: February 2025

Abstract
Spin-transfer torque magnetic random access memory (STT-MRAM) shows promising prospects for the next generation embedded non-volatile memory (NVM). In the paper, an improved model for calculation of the power consumption of STT-MRAM is proposed and verified. In this proposed model, the unique asymmetry properties of magnetic tunnel junction (MTJ) device during STT-MRAM read and write operations are taken into account. To more comprehensively calculate the energy consumption, the impact of temperature variations on the MTJ is considered. A comparison with NVSim on the power calculation is presented. The results indicate that the proposed model can provide energy consumption simulation results with higher accuracy. The results of the read/write delay and the leakage power can be simultaneously provided. An analysis of the results based on the proposed model is conducted. The proposed model shows potential applications for architectural design of power grid in STT-MRAM with large scale storage capacity.&#xD;
Simultaneous excitation of two atoms by a single photon using shortcut to adiabaticity
Authors: Zhou Kuan-Yu , Yan Ke-Xiong , Chen Ye-Hong , Xia Yan 
Published: February 2025

Abstract
In this paper, we propose a protocol to accelerate a weak nonlinear quantum process: a single photon simultaneously excites two atoms using shortcut to adiabaticity. We consider a physical model composed of a transmission-line resonator and two flux qubits, where the frequency of the resonator is approximately equal to the sum of the frequencies of the two qubits. Using unitary transformation and a series of approximate methods, the system can be reduced to an equivalent two-level model. Since this transition is a weak high-order process, it is relatively sensitive to errors and decoherence. To overcome the flaws, we use the shortcut to adiabaticity method based on Lewis–Riesenfeld invariant for inverse engineering of the effective Hamiltonian, so that the transition rate can be accelerated. The robustness of the system against noise and decoherence can be improved. Therefore, the protocol we proposed can be applicable to quantum information processing and quantum optics in the microwave regime.
Design and performance of trapezoidal honeycomb absorbing structure based on FDM
Authors: Wang Run , Han Yafeng , Lu Jiping , Gong Chenglong , Wang Haoren , Xia Yuhan , Xiang Zezhi 
Published: February 2025

Abstract
The increasing prevalence of electronic devices and the rapid evolution of communication technologies underscore the critical need for effective management of electromagnetic interference and assurance of electromagnetic compatibility. This study presents an innovative trapezoidal honeycomb absorber structure, optimized to enhance the absorption of electromagnetic waves across a broad spectrum. By leveraging Fused Deposition Modeling (FDM) 3D printing technology, we have fabricated a short-cut carbon fiber composite material that exhibits superior electromagnetic and mechanical properties. Our research specifically focused on optimizing the honeycomb sidewall inclination angle to maximize absorption performance. We discovered that a 15° angle configuration yielded the broadest effective absorption bandwidth and the highest absorption efficiency. The structure demonstrated a significant -10 dB absorption bandwidth of 9.95 GHz within the 2-18 GHz range, underscoring its potential for managing a wide spectrum of electromagnetic waves. Furthermore, we explored the absorber’s performance under various angles of incidence, confirming its robustness across a broad range. These findings significantly advance the development of electromagnetic wave absorption materials, positioning the trapezoidal honeycomb structure as a key technology for future electromagnetic wave shielding applications.