bluuebunny/crossref_metadata_2025_split huggingface dataset:
Groundwater exploration using self potential and resistivity geoelectrical method as drought disaster mitigation at bukit intan sub-district, Pangkalpinang
Authors: Kusmita T , Hisyam E S , Puriza Y , Syam A I , Ambarwati J 
Published: December 2024

Abstract
Pangkalpinang City in Bangka Belitung Islands province faces a seasonal water crisis due to its location outside the island’s groundwater basin. This study focuses on assessing groundwater potential in the Bukit Intan sub-district to support drought mitigation efforts. Using a combination of self-potential (SP) and geoelectrical resistivity methods, groundwater flow and aquifer depth were analyzed. Field data were collected across multiple tracks, with SP measurements revealing areas of potential groundwater movement, while resistivity profiles indicated aquifer presence at depths of 3-30 meters. Results suggest that groundwater potential is concentrated in certain recharge zones, highlighting areas suitable for targeted extraction and the establishment of boreholes. These aquifers exhibit seasonal fluctuations in groundwater levels, whereas the confined zones are located at greater depths and are more isolated, likely due to the presence of resistive layers that restrict flow. This assessment provides crucial insights for improving groundwater management in drought-affected regions of Pangkalpinang City.
Synthesis and characterization of spent coffee grounds derived activated carbon-supported ZnO for photocatalyst
Authors: Fabiani V A , Kurniawan W B , Ropalia  , Candra  , Anggrainy A P 
Published: December 2024

Abstract
Spent coffee grounds derived activated carbon-supported ZnO as photocatalyst has been synthesized. This study analyzes the characteristics of adding activated carbon from spent coffee grounds to ZnO as a candidate photocatalyst material. The stages in this research are preparing activated carbon from spent coffee grounds, synthesis of ZnO/activated carbon, and characterization of ZnO/activated carbon. Based on the activated carbon quality test results, 13% ash content and 6.6% ash content were obtained, which met the standards. ZnO/activated carbon characteristics showed that all samples of activated carbon addition of 0.4 g, 0.9 g, and 1.4 g showed the dominant crystalline phase of ZnO. Adding 1.4 g activated carbon to ZnO gives suitable characteristics as a photocatalyst, resulting in a particle size of 45.22 nm and a band gap energy of 3.20 eV.
A methodological framework integrating land use land cover and ecosystem services for optimal urban land management
Authors: Nazer N , Kurukkanari C , Puthuvayi B 
Published: December 2024

Abstract
Urban planning is a crucial element in the sustainable development of cities, with increasing recognition of the critical role of ecosystem services. Ecosystem services are essential for urban well-being, and their effectiveness is closely linked to the spatial organization of land use. This study investigated the intricate relationship between land use and land cover (LULC) and ecosystem services (ES). To achieve optimal land use management, we propose a methodology that utilizes coupling coordination degree (CCD) and system dynamics (SD) modeling. This study acknowledges the impact of LULC on ecosystem services and suggests that controlling LULC can be an effective tool in managing and enhancing these services. The degree of coupling coordination serves as a valuable metric for examining the intricate nature of the interactions between LULC and ecosystem services. By quantifying the degree of coordination between these two systems, we get insights into the modeling of the dynamics of the LULC and ES systems. The control factors influencing these interactions can be identified using factors, interactions, risks, and ecological detectors in the geographic detector analysis process. System dynamic modeling enhances our ability to capture and understand the complex relationships within an urban ecosystem. By creating a dynamic model, we can better control and predict the factors that influence LULC and ecosystem services. This study contributes to the development of a methodological framework for urban planners, policymakers, and researchers to optimize land-use management and promote sustainable and resilient cities. The proposed methodology establishes a foundation for future research and applications in urban planning and ecosystem management.
Preparation and excitation power-dependent photoluminescence of tetrapod-quantum wells
Authors: Thi Luyen Nguyen , Thi Thuy Lieu Nguyen , Thi Minh Hien Nguyen , Huy Le Duc , Huy Tran Quang , Nghia Nguyen Xuan 
Published: December 2024

Abstract
Tetrapod-quantum well (TW) with CdSe core and quantum wells in gradient alloyed CdSeS arms was synthesized using the one-pot colloidal method. The morphology, crystal structure, UV–vis absorption, photoluminescence (PL), and excitation power-dependent PL properties of TWs prepared within a 30 min reaction time were investigated. Their PL spectrum exhibited two emission peaks centered at 2.187 eV and 2.207 eV due to radiative transitions within the core and quantum wells, respectively. Analysis of the excitation power-dependent PL spectra showed that the core and quantum wells’ radiative recombination channels are independent up to the excitation power density of 2.29 × 102 mW cm−2. At higher power densities, efficient carrier transfer from the quantum wells into the cores occurred. This can be explained by the state filling of quantum wells and the decreasing band gap energy of gradient alloyed CdSeS arms towards the CdSe core. This study demonstrates a simple, green, and cost-effective approach for synthesizing one-color and two-color tetrapods based on CdSe and CdSeS materials, as well as the recovery process in nanostructures with two emission channels under various excitation conditions. The results could help to design and prepare white-light-emitting tetrapod-double quantum wells for efficient light-emitting applications.
Bidirectional interaction directional variance attention model based on increased-transformer for thyroid nodule classification
Authors: Liu Ming , Yao Jianing , Yang Jianli , Wan Zhenzhen , Lin Xiong 
Published: December 2024

Abstract
Malignant thyroid nodules are closely linked to cancer, making the precise classification of thyroid nodules into benign and malignant categories highly significant. However, the subtle differences in contour between benign and malignant thyroid nodules, combined with the texture features obscured by the inherent noise in ultrasound images, often result in low classification accuracy in most models. To address this, we propose a Bidirectional Interaction Directional Variance Attention Model based on Increased-Transformer, named IFormer-DVNet. This paper proposes the Increased-Transformer, which enables global feature modeling of feature maps extracted by the Convolutional Feature Extraction Module (CFEM). This design maximally alleviates noise interference in ultrasound images. The Bidirectional Interaction Directional Variance Attention module (BIDVA) dynamically calculates attention weights using the variance of input tensors along both vertical and horizontal directions. This allows the model to focus more effectively on regions with rich information in the image. The vertical and horizontal features are interactively combined to enhance the model’s representational capability. During the model training process, we designed a Multi-Dimensional Loss function (MD Loss) to stretch the boundary distance between different classes and reduce the distance between samples of the same class. Additionally, the MD Loss function helps mitigate issues related to class imbalance in the dataset. We evaluated our network model using the public TNCD dataset and a private dataset. The results show that our network achieved an accuracy of 76.55% on the TNCD dataset and 93.02% on the private dataset. Compared to other state-of-the-art classification networks, our model outperformed them across all evaluation metrics.
Challenging excited states from adaptive quantum eigensolvers: subspace expansions vs. state-averaged strategies
Authors: Grimsley Harper R , Evangelista Francesco A 
Published: December 2024

Abstract
The prediction of electronic structure for strongly correlated molecules represents a promising application for near-term quantum computers. Significant attention has been paid to ground state wavefunctions, but excited states of molecules are relatively unexplored. In this work, we consider the adaptive, problem-tailored (ADAPT)-variational quantum eigensolver (VQE) algorithm, a single-reference approach for obtaining ground states, and its state-averaged generalization for computing multiple states at once. We demonstrate for both rectangular and linear H4, as well as for BeH2, that this approach, which we call multistate-objective, Ritz-eigenspectral (MORE)-ADAPT-VQE, can make better use of small excitation manifolds than an analogous method based on a single-reference ADAPT-VQE calculation, q-sc-EOM. In particular, MORE-ADAPT-VQE is able to accurately describe both avoided crossings and crossings between states of different symmetries. In addition to more accurate excited state energies, MORE-ADAPT-VQE can recover accurate transition dipole moments in situations where traditional ADAPT-VQE and q-sc-EOM struggle. These improvements suggest a promising direction toward the use of quantum computers for difficult excited state problems.
Analytical and Computational Investigation of VibroThermoAcoustic Behaviour of Cracks in Aerostructures
Authors: Rawat Varun Manoj , Swain Aditya 
Published: December 2024

Abstract
This research investigates the Vibro-thermoacoustic dynamics exhibited by Aerostructures, particularly those harbouring surface cracks. Employing a novel approach, a Thermo-Acoustic field coupled Transient Heat Transfer model is developed to model the interplay among vibrations, heat dissipation, and acoustic behaviour at crack tip interfaces. Local surface flaws proximal to stress concentration sites are investigated via the Newmark Beta Method and Finite Element Analysis, which are applied to displacement and thermal mapping at crack tips through temporal and spatial discretization. Recognizing the implications of flaws in structural integrity and potential lifespan reduction, the study incorporates Analytical, Numerical and Computational analysis of surface cracks into the broader context of Thermoacoustic phenomena. &#xD;The paper explores the application of Elastoplastic Fracture Mechanics (EPFM) to model the impact of plastic deformation on crack growth, local acoustic field and the overarching structural integrity of these components. &#xD;In this study, we also investigate the limitations of the original Ramberg-Osgood equation, particularly in its application to modelling nonlinearities in plastic strain at crack tip interfaces. To address these shortcomings, we propose a novel modified Ramberg-Osgood formulation that incorporates Padé Approximants. This modification enables effective modelling of nonlinear stress-strain variations of high-performance materials while improving computational performance and accuracy. &#xD;Real-world aerostructures experience varying loads, and incorporating these principles in our coupled model allows for accurate modelling of Thermoacoustic Field variations and Instability under dynamic conditions. The research offers valuable perspectives for engineering applications, providing a computational roadmap for thermoacoustic modelling in Aerostructures with cracks.
Overcoming occlusions in complex environments to achieve robust perception of human emotions
Authors: Ge Changqiang 
Published: December 2024

Abstract
To effectively address the emotional well-being of elderly individuals living alone using of home care robotic systems, it is essential to possess the ability to precisely identify facial expressions within complex domestic settings. Facial expression recognition (FER) in complex environments faces significant challenges due to factors such as facial occlusions. To address this challenge, this paper proposes a method called the Dual-Branch Attention and Multi-Scale Feature Fusion Network (DAMFF-Net). First, we perform feature extraction on facial images and input the resulting feature maps into an improved dual-branch attention fusion module (DBAF) to capture long-range dependencies between different facial regions. Simultaneously, using the residual multi-scale module that we designed, we obtain fine-grained multi-scale features to ensure that both preceding and subsequent feature subsets contain rich scale information. Next, we globally fuse the feature maps from the feature extraction stage with those from the residual multi-scale module to enhance facial expression recognition accuracy in cases where certain facial feature regions are occluded. Finally, we employ decision-level fusion in order to deal with the categorization findings. Experiments were conducted on the RAF-DB, CK+ and AffectNet-7 datasets, and comparative results indicated that the proposed method achieved a respective enhancement of 5.79%, 6.68% and 5.86% in facial expression recognition accuracy.
Home intrusion: smart security and live video surveillance system
Authors: M S Punith , I Mamatha , Tripathi Shikha 
Published: December 2024

Abstract
Security concerns are rampant in our world today. No location is immune to crime, whether it be public spaces, commercial areas, offices, or private property. While law enforcement agencies work to address crimes after they have taken place, there is no guarantee that they can prevent them from occurring. Communities and businesses often turn to security personnel and surveillance cameras to keep watch, but this only provides passive protection. Criminals may act while security is absent and cameras only record events, they do not prevent them. Although, there are few systems with face recognition and alerting mechanisms are existing, they are too expensive and not affordable to a common man. In response to this challenge, this work proposes a most economic, hassle free proactive home security system which can be used as a plug and play device for the already installed surveillance system employing CCTV cameras. This system features facial recognition technology, enabling it to identify authorized individuals by storing their images in a database, thus swiftly distinguishing between intruders and residents. In cases of unauthorized intrusions, the system promptly activates alerts and alarms. Additionally, it incorporates supplementary security measures utilizing sensors and actuators for heightened protection. Moreover, the system utilizes an IP camera, permitting remote monitoring via live streaming. An array of machine learning algorithms is assessed to ascertain the most effective method for facial recognition, integrated into a monitoring dashboard using RTSP streaming.
Refinable modeling for unbinned SMEFT analyses
Authors: Schöfbeck Robert 
Published: December 2024

Abstract
We present methods to estimate systematic uncertainties in unbinned large hadron collider (LHC) data analyses, focusing on constraining Wilson coefficients in the standard model effective field theory (SMEFT). Our approach also applies to broader parametric models of non-resonant phenomena beyond the standard model. By using machine-learned surrogates of the likelihood ratio, we extend well-established procedures from binned Poisson counting experiments to the unbinned case. This framework handles various theoretical, modeling, and experimental uncertainties, laying the foundation for future unbinned analyses at the LHC. We also introduce a tree-boosting algorithm that learns precise parametrizations of systematic effects, providing a robust, flexible alternative to neural networks for modeling systematics. We demonstrate this approach with an SMEFT analysis of highly energetic top quark pair production in proton–proton collisions.
Green synthesis of gold nanoparticles: pros and cons of natural compounds
Authors: Medeot Caterina , Alsadig Ahmed , D’Amico Francesco , Costantini Roberto , Lentini Campallegio Claudio , Cossaro Albano , Vondracek Hendrik , Birarda Giovanni , Posocco Paola , Parisse Pietro , Casalis Loredana , Bonin Serena 
Published: December 2024

Abstract
Gold nanoparticles (AuNPs) have attracted scientific interest for their unique properties and diverse applications, across biomedical fields, including sensing, diagnostics, and therapeutic interventions. However, standard wet chemistry synthetic methods for particles synthesis bare limitations, particularly in separating AuNPs from the reaction mixture. Therefore, there is a growing demand for environmentally friendly synthesis routes that minimize waste and utilize non-toxic solvents. Despite advances in this field, many current studies lack standardized protocols, and the underlying mechanisms of the reducing reaction remain unclear. Through comprehensive spectroscopic and morphological analyses, we investigated the redox reactions involved in AuNP formation with various natural extracts, aiming to establish an optimized protocol for producing small, spherical, and monodisperse AuNPs. Our findings demonstrate that cocoa powder extract effectively yields reproducible, spherical AuNPs with a diameter of 11 nm. Comparative analysis with single cocoa powder molecular components identified catechins, a class of flavonoids, as the primary reducing agent in green AuNP synthesis. Catechins also form a protective layer around the AuNPs. However, the simultaneous, unavoidable presence of this layer implemented with fatty acids and proteins, although fundamental for colloidal stability, limits further functionalization of the AuNPs, highlighting a trade-off between stability and functional versatility.
Microstructural, morphological, and optical properties of Fe2O3 nanoparticles obtained from Fe-MIM MOF
Authors: Olatunde Olalekan C , Ravele Murendeni P , Yusuf Tunde L , Onwudiwe Damian C 
Published: December 2024

Abstract
This study explores the microstructural, morphological, and optical properties of Fe2O3 nanoparticles synthesized from the pyrolysis of ZIF-8 like Fe-2-methyl imidazole (MIM) metal–organic frameworks (MOFs). Using X-ray diffraction (XRD) profile analysis methods, including the modified Scherrer, Williamson–Hall (W-H), size strain plot, and Halder-Wagner methods, the impact of annealing temperature on the microstructural parameters and crystal defects of the obtained Fe2O3 nanoparticles was investigated. The nanoparticles exhibited high crystallinity and a rhombohedral α-Fe2O3 phase. Morphological analysis through transmission electron microscopy (TEM) revealed distinct structural features, while UV–vis spectroscopy was employed to examine their optical properties. The results indicated that higher annealing temperatures enhance crystallinity, reduce defect density, and improve atomic mobility. This comprehensive analysis provides valuable insights into the synthesis-structure–property relationships of Fe2O3 nanoparticles, highlighting their potential applications in applications such as gas sensing and photocatalysis.
Application of International Telecommunication Union Recommendation L.1480 on measuring the greenhouse gas emission effects to a use case for photovoltaic power generation equipment
Authors: Bélorgey François , Fournier Jérôme , Labidurie Omnes Nathalie 
Published: December 2024

Abstract
This article generalizes ITU-T Recommendation L.1480 ‘Enabling the Net Zero transition: Assessing how the use of information and communication technology solutions impact greenhouse gas emissions of other sectors’ (www.itu.int/rec/T-REC-L.1480-202212-I) by applying it to an action outside the Information and Communication Technology (ICT) field covered by this ITU-T Recommendation L.1480, namely the use of a photovoltaic solar power plant in Poland, including the transition to scale. The study quantifies this use by accounting all greenhouse gases (GHG) emissions consequences (incl. installation, operation and maintenance) over the duration of the action (i.e. the supply and operation of the photovoltaic panels, inverter and associated services), through the construction of a consequence tree and the effective observation of usage behaviors; it thus avoids pushing potentially negative effects outside the scope of the study, like rebound effects (ex.: increase of 10% in electricity consumption after photovoltaic panels commissioning) or the consequences of the use of financial gains (or losses).
Three main innovations are shown:

•
a step-by-step implementation of Recommendation L.1480 and its supplement L Suppl. 54 to a non-ICT sector,

•
the effectiveness of the use of solar panels to reduce GHG emissions in Poland, through actual measurement of usage data, in particular data linked to rebound effects,

•
the possibility of generalizing this methodological framework to assess the changes in GHG emissions induced by any action already undertaken (
ex post
) or under consideration (
ex ante
).

By assessing all GHG emissions consequences and defining the steps for carrying out this assessment, L.1480 methodology covers all effects on a global scale and reflects real changes in GHG emissions. It could thus be applied to assessing the GHG emissions consequences of actions and decisions of various kinds: public policies (like carbon storage), corporate investment or household behavior. Moreover, adding other categories of environmental impact (biodiversity, scarcity of natural resources (metals, water), waste and pollution, radiative effect, etc) would improve the exhaustiveness of environmental effects measurement.
THOR: a TMB heterogeneity-adaptive optimization model predicts immunotherapy response using clonal genomic features in group-structured data
Authors: Wang Yixuan , Guan Yanfang , Lai Xin , Liu Yuqian , Chang Zhili , Wang Xiaonan , Wang Quan , Liu Jingjing , Zhao Jian , Yang Shuanying , Wang Jiayin , Song Xiaofeng 
Published: December 2024

Abstract
With the increasing number of indications for immune checkpoint inhibitors in early and advanced cancers, the prospect of a tumor-agnostic biomarker to prioritize patients is compelling. Tumor mutation burden (TMB) is a widely endorsed biomarker that quantifies nonsynonymous mutations within tumor DNA, essential for neoantigen production, which, in turn, correlates with the immune response and guides decision-making. However, the general clinical application of TMB—relying on simple mutational counts targeted at a single endpoint—does not adequately capture the complex clonal structure of tumors nor the multifaceted nature of prognostic indicators. This recognition has spurred the exploration of sophisticated high-dimensional regression techniques. Unfortunately, the limited cohort sizes in immunotherapy trials have hindered the full potential of these advanced methods. Our approach considers patient subgroups as related yet distinct entities, enabling precise tailoring and refinement to address subgroup-specific dynamics. Given the deficiencies and the constraints, we introduce a TMB heterogeneity-optimized regression (THOR). This innovative model enhances the predictive capabilities of TMB by integrating tumor clonality and a diverse spectrum of clinical endpoints, further augmented by fusion techniques across subgroups to facilitate robust data sharing and interpretation. Our simulations validate THOR’s superiority in parameter estimation for statistical inference. Clinically, we assess the utility of THOR in a structured cohort of 238 cancer patients undergoing immunotherapy, supplemented by 2212 patients across 19 subgroups from public datasets. The forecast of the responses and comparison of survival hazards demonstrate that THOR significantly enhances patient stratification and prognostic predictions by incorporating complex immunogenetic biology and subgroup-specific dynamics.