bluuebunny/crossref_metadata_2025_split huggingface dataset:
Modeling and analyzing patterns of residential manual window operation
Authors: Li M , Gao J G , Li T , Liu G D , Hu C C , Liu Y Q 
Published: July 2024

Abstract
Window operating behaviour can improve indoor air quality, human thermal comfort, and building energy efficiency. Studies on occupants’ window opening behaviour in hot summer and warm winter region of China are limited and influencing factors and prediction models are not clear. Another limitation is the large number of proposed machine learning-based window opening behaviour models. However, the applicability and stability of these models in different datasets has not been proven. In response to these questions, modelling and field measurements were conducted in Quanzhou, China. Two different types of window-opening behaviour were noticed in the tested households. The first type was the all-closed windows, which had an average daily window-opening rate of 0.03%. The second type was the low-intensity window opening. The average daily window-opening rate was 10.6% and 9.1%, respectively. Then, the analysis of point biserial correlation coefficients revealed different reasons for closing windows in low-intensity households. One household closed the windows due to high outdoor humidity and the other mainly due to high outdoor wind speed and outdoor temperature. Furthermore, the suitable hyperparameters were screened for the support vector machine (SVM) model by K-fold cross-validation and grid search. The prediction model achieved an accuracy of 98.5% on the test set. Finally, the SVM model was trained and tested to verify the robustness of the model using data from the published literature. The prediction accuracy was improved from 0.7% to 7.4% compared to the different models used in the published literature.
Multi-objective optimization of a dual fuel CI engine powered with syngas and pilot diesel using TLBO algorithm: A metaheuristic approach
Authors: Das Samar , Tamang S. K. 
Published: July 2024

Abstract
The thermochemical conversion of biomass into producer gas presents an attractive alternative fuel option for compression ignition (CI) engines, making biomass gasification a critical driver for achieving sustainable development goals. Considering the application of producer gas (PG) in CI engine, the most potential gases include H2 and CO as main fuel compounds and it is crucial to comprehensively understand the impact of these two gas components on the engine behaviour. Nowadays, artificial intelligence-powered models are frequently applied for simulating engines that run on a single type of fuel. However, their usage is not as common when it comes to modeling dual-fuel CI engines run on synthetic producer gas or syngas. The present study explores the feasibility of optimizing operational parameters, such as engine load and syngas composition, in improving the efficiency and lowering the levels of pollutants emitted by a 3.5 kW CI engine operated under dual fuel (DF) mode using syngas as primary fuel and diesel as pilot fuel. The performance and emission characteristics of syngas (H2:CO) is examined by studying its behaviour in four different combinations. The compositions of syngas are prepared based on the volumetric percentage of the H2 and CO and is inducted into the combustion chamber using a novel venturi-type air-gas mixer. In the present study, an intelligent metaheuristics-based optimization algorithm i.e., Teaching–Learning Based Optimization (TLBO) is developed and introduced, to develop a predictive model within constrained range of engine operating conditions. Further, the algorithm is used to estimate multiple engine performance characteristics simultaneously viz., brake thermal efficiency (BTE), unburned hydrocarbons (HC), and carbon monoxide (CO). The resultant findings identify the optimal engine load of 68.87% and the ideal syngas composition of 63.9% H2 and 49.5% CO as key parameters for maximizing engine efficiency while minimizing exhaust emission. At these optimized operating condition, 19.49% BTE is observed, while HC and CO emission was found to be 384.6 ppm and 445.33 ppm respectively. This shows the effective and efficiency of the proposed algorithm.
A comprehensive bibliometric review and analysis on the evolution of nanotube-based hydrogen storage materials via DFT simulations
Authors: Villagracia A R C 
Published: July 2024

Abstract
Hydrogen’s promise as a clean energy carrier is tempered by the challenges of efficient storage and safety concerns. While it offers an alternative to finite fossil fuel resources, current hydrogen storage methods, like cryo-compression and liquefaction, are often economically impractical. To tackle these issues, researchers are turning to nanotube materials (NTMs), crystalline substances with unique attributes ideal for hydrogen storage. Structural adaptability - NTMs can be precisely engineered for optimized hydrogen adsorption. These materials boast significant porosity, providing ample room for hydrogen molecules. NTMs offer a large surface area, enhancing their hydrogen adsorption capacity. NTMs employ weak van der Waals forces for hydrogen adsorption, enabling easy release via heat or pressure. Efforts are underway to enhance NTMs’ surface area and hydrogen uptake capabilities, along with a focus on mechanisms like the hydrogen spill-over for achieving high-density storage. NTMs go beyond storage; they can act as proton exchange membranes and fuel cell electrodes, making them versatile components in hydrogen-based energy systems. One strategy for improving NTM hydrogen storage involves introducing dopants or defects. Transition metals, due to their ability to attract and store hydrogen molecules in NTMs, are commonly explored. However, this addition may reduce the material’s gravimetric density, a critical practical consideration. In summary, research into NTMs and their potential for hydrogen storage via density functional theory is ongoing. This work explores strategies to enhance hydrogen storage, especially through transition metal doped NTMs. While these metals can improve hydrogen adsorption, the trade- offs in gravimetric density must be carefully weighed. Overall, this research contributes to the broader goal of harnessing hydrogen’s potential as a clean energy carrier, addressing the world’s growing energy needs.
Application of response surface method (RSM) to investigate the effects of process parameters on the microstructures and specific surface area of hierarchical 3D nanostructured NiCo2O4 electrocatalyst synthesized through hydrothermal method
Authors: Raduwan N.F. , Shaari N. , Masdar M.S. 
Published: July 2024

Abstract
The hierarchical 3D nanostructured NiCo2O4 was successfully synthesized through facile hydrothermal method for fuel cell electrocatalysis. It was observed that the specific surface area of NiCo2O4 was strongly influenced by the synthesizing parameters namely temperature (T) and reaction time (t) in hydrothermal process. A high specific surface area in electrocatalyst materials is crucial for maximizing the efficiency and effectiveness of electrochemical reactions by providing more active sites, improving reactivity, enhancing mass transport, and reducing the utilization of expensive catalyst materials. The response surface method (RSM) coupled with central composite design (CCD) was utilised to statistically specify the effects of the reaction time and temperature on the specific surface area of the synthesized NiCo2O4. The optimum synthesize parameters of T = 188.41 °C and t = 12.86 hours were performed to obtain the highest specific surface area of 166.98 m2 g-1 which demonstrated the RSM was an excellent tool to implement and tailor the specific surface area of NiCo2O4. Finally, a mathematical model was derived to predict the relationship between these parameters and the structural properties. This study proved that the optimum hydrothermal parameters improved the final electrocatalyst structures based on numerical analysis and validated by the observation from field emission scanning electron microscopy and calculation from Brunauer–Emmett–Teller (BET) measurement.
Effect of molecular crowders on ligand binding kinetics with G-quadruplex DNA probed by fluorescence correlation spectroscopy
Authors: Alam Parvez , Clovis Ndege Simisi , Chand Ajay Kumar , Khan Mohammad Firoz , Sen Sobhan 
Published: July 2024

Abstract
Guanine-rich single-stranded DNA folds into G-quadruplex DNA (GqDNA) structures, which play crucial roles in various biological processes. These structures are also promising targets for ligands, potentially inducing antitumor effects. While thermodynamic parameters of ligand/DNA interactions are well-studied, the kinetics of ligand interaction with GqDNA, particularly in cell-like crowded environments, remain less explored. In this study, we investigate the impact of molecular crowding agents (glucose, sucrose, and ficoll 70) at physiologically relevant concentrations (20% w/v) on the association and dissociation rates of the benzophenoxazine-core based ligand, cresyl violet (CV), with human telomeric antiparallel-GqDNA. We utilized fluorescence correlation spectroscopy (FCS) along with other techniques. Our findings reveal that crowding agents decrease the binding affinity of CV to GqDNA, with the most significant effect—a nearly three-fold decrease—observed with ficoll 70. FCS measurements indicate that this decrease is primarily due to a viscosity-induced slowdown of ligand association in the crowded environment. Interestingly, dissociation rates remain largely unaffected by smaller crowders, with only small effect observed in presence of ficoll 70 due to direct but weak interaction between the ligand and ficoll. These results along with previously reported data provide valuable insights into ligand/GqDNA interactions in cellular contexts, suggesting a conserved mechanism of saccharide crowder influence, regardless of variations in GqDNA structure and ligand binding mode. This underscores the importance of considering crowding effects in the design and development of GqDNA-targeted drugs for potential cancer treatment.
A low-cost protocol for reconditioning of deep-brain neural microelectrodes with material failure for electrophysiology recording
Authors: Rezayat Leila , Ghajar Mohammad Hossein , Naji Alireza , Noroozi Jalaledin , Dehaqani Mohammad-Reza A , Rezayat Ehsan 
Published: July 2024

Abstract
To date, a myriad of neural microelectrodes has been meticulously developed, but the focus of existing literature predominantly revolves around fabrication methodologies rather than delving into the reconditioning processes or strategies for salvaging electrodes exhibiting diminished performance due to material failure. This study aims to elucidate the underlying factors contributing to the degradation in performance of neural microelectrodes. Additionally, it introduces a comprehensive, cost-effective protocol for the reconditioning and repurposing of electrodes afflicted by material failure, tailored for a broad spectrum of electrode types. The efficacy of the proposed reconditioning protocol is substantiated through experimental validation on single-site tungsten microelectrodes. The results of neural signal recording unequivocally demonstrate the successful restoration of a substantial number of electrodes, underscoring the protocol’s effectiveness.
Counterfactuality, back-action, and information gain in multi-path interferometers
Authors: Hance Jonte R , Matsushita Tomonori , Hofmann Holger F 
Published: July 2024

Abstract
The presence of an absorber in one of the paths of an interferometer changes the output statistics of that interferometer in a fundamental manner. Since the individual quantum particles detected at any of the outputs of the interferometer have not been absorbed, any non-trivial effect of the absorber on the distribution of these particles over these paths is a counterfactual effect. Here, we quantify counterfactual effects by evaluating the information about the presence or absence of the absorber obtained from the output statistics, distinguishing between classical and quantum counterfactual effects. We identify the counterfactual gain which quantifies the advantage of quantum counterfactual protocols over classical counterfactual protocols, and show that this counterfactual gain can be separated into two terms: a semi-classical term related to the amplitude blocked by the absorber, and a Kirkwood-Dirac quasiprobability assigning a joint probability to the blocked path and the output port. A negative Kirkwood-Dirac term between a path and an output port indicates that inserting the absorber into that path will have a focussing effect, increasing the probability of particles arriving at that output port, resulting in a significant enhancement of the counterfactual gain. We show that the magnitude of quantum counterfactual effects cannot be explained by a simple removal of the absorbed particles, but originates instead from a well-defined back-action effect caused by the presence of the absorber in one path, on particles in other paths.
Characteristic features of strong correlation: lessons from a 3-fermion one-dimensional harmonic trap
Authors: Caliva Victor , Fuks Johanna I 
Published: July 2024

Abstract
Many quantum phenomena responsible for key applications in material science and quantum chemistry arise in the strongly correlated regime. This is at the same time, a costly regime for computer simulations. In the limit of strong correlation analytic solutions exist, but as we move away from this limit numerical simulation are needed, and accurate quantum solutions do not scale well with the number of interacting particles. In this work we propose to use few-particle harmonic traps in combination with twisted light as a quantum emulator to investigate the transition into a strongly-correlated regime. Using both analytic derivations and numerical simulations we generalize previous findings on 2 Coulomb interacting fermions trapped in a one-dimensional harmonic trap to the case of 3 fermions. The 4 signatures of strong correlation we have identified in the one-dimensional harmonic trap are: (i) the ground state density is highly localized around N equilibrium positions, where N is the number of particles, (ii) the symmetric and antisymmetric ground state wavefunctions become degenerate, (iii) the von Neumann entropy grows, (iv) the energy spectrum is fully characterized by N normal modes or less. Our findings describe the low-energy behavior of electrons in quantum wires and ions in Paul traps. Similar features have also been reported for cold atoms in optical lattices.
Designing efficient brain tumor classifier using hybrid EfficientNet-faster R-CNN deep learning model
Authors: Kharb Aashutosh , Chaudhary Prachi 
Published: July 2024

Abstract

Purpose. Brain tumors have historically posed a significant risk to individuals, potentially leading to fatality. However, in contemporary times, they have emerged as one of the most dangerous health issues affecting both children and adults. The effects of this phenomenon result in the unregulated proliferation of brain cells. Therefore, early detection and precise classification of brain tumor is very necessary and important for saving lives and avoid future complications. Within the field of biological image processing, the utilisation of deep learning algorithms offers a distinct and unparalleled experience. The Convolutional Neural Network (CNN) is a pivotal component in brain tumor categorization applications. However, a notable limitation of CNNs is their extensive execution time, primarily attributed to the substantial number of trainable parameters involved. Also, the automatic detection and classification of brain tumor is still a challenging task due to its variability in shape, size and location. Methods. This study proposes a hybrid model for brain tumor classification that combines faster R-CNN and EfficientNet. The objective is to minimise the time required for classification without compromising accuracy. The data collection from Figshare was employed during the model’s development. Results and Conclusion. The hybrid model proposed in this study demonstrates a notable accuracy of 98.96% during the training phase and 99.2% during the testing phase, surpassing the performance of both the EfficientNet model and the Faster R-CNN model when employed individually. The aforementioned findings were derived through a comparative analysis of the Hybrid model, the EfficientNet model, and the Faster R-CNN model.
Alzheimer’s disease detection and stage identification from magnetic resonance brain images using vision transformer
Authors: Alshayeji Mohammad H 
Published: July 2024

Abstract
Machine learning techniques applied in neuroimaging have prompted researchers to build models for early diagnosis of brain illnesses such as Alzheimer’s disease (AD). Although this task is difficult, advanced deep-learning (DL) approaches can be used. These DL models are effective, but difficult to interpret, time-consuming, and resource-intensive. Therefore, neuroscientists are interested in employing novel, less complex structures such as transformers that have superior pattern-extraction capabilities. In this study, an automated framework for accurate AD diagnosis and precise stage identification was developed by employing vision transformers (ViTs) with fewer computational resources. ViT, which captures the global context as opposed to convolutional neural networks (CNNs) with local receptive fields, is more efficient for brain image processing than CNN because the brain is a highly complex network with connected parts. The self-attention mechanism in the ViT helps to achieve this goal. Magnetic resonance brain images belonging to four stages were utilized to develop the proposed model, which achieved 99.83% detection accuracy, 99.69% sensitivity, 99.88% specificity, and 0.17% misclassification rate. Moreover, to prove the ability of the model to generalize, the mean distances of the transformer blocks and attention heat maps were visualized to understand what the model learned from the MRI input image.
The Chief Business of the True Judges in Hume’s Standard of Taste
Authors: Kim Byoungjae 
Published: July 2024

Abstract
In his essay ‘Of the Standard of Taste’, Hume identifies two standards of taste: the general rules of art and the joint verdict of true judges. From this the following questions arise: Why did Hume present two standards? And, how should we understand the relation between them? We must first get a firmer grasp on each of the standards that Hume presents. Hence, this paper has two main goals. The first is to understand the general rules of art in the light of Hume’s philosophical method, which is consonant with Newtonian methods of analysis and synthesis. The second is to provide a new interpretation of what Hume takes to be the joint verdict of true judges. In my interpretation, the joint verdict of true judges constitutes the standard of taste, not because their verdicts converge, but because their verdicts encompass blameless differences. With this new foundation for understanding the two standards, I will present my own reading of the relation between them.
Overcoming time-varying confounding in self-controlled case series with active comparators: application and recommendations
Authors: Schultze Anna , Brown Jeremy , Logie John , Cunnington Marianne , Requena Gema , Gillespie Iain A , Evans Stephen J W , Douglas Ian , Galwey Nicholas 
Published: July 2024

Abstract
Confounding by indication is a key challenge for pharmacoepidemiologists. Although self-controlled study designs address time-invariant confounding, indications sometimes vary over time. For example, infection might act as a time-varying confounder in a study of antibiotics and uveitis, because it is time-limited and a direct cause of both receipt of antibiotics and uveitis. Methods for incorporating active comparators in self-controlled studies to address such time-varying confounding by indication have only recently been developed. In this paper, we formalize these methods and provide a detailed description for how the active comparator rate ratio can be derived in a self-controlled case series: either by explicitly comparing the regression coefficients for a drug of interest and an active comparator under certain circumstances using a simple ratio approach or through the use of a nested regression model. The approaches are compared in 2 case studies, one examining the association between thiazolidinedione use and fractures and one examining the association between fluoroquinolone use and uveitis, using the United Kingdom’s Clinical Practice Research Datalink. Finally, we provide recommendations for the use of these methods, which we hope will support the design, execution, and interpretation of self-controlled case series using active comparators and thereby increase the robustness of pharmacoepidemiologic studies.
This article is part of a Special Collection on Pharmacoepidemiology.
Development of an experimental technique to determine the barrier performance of medical gloves when stretched
Authors: Soo Jhy-Charm , Portnoff Lee , Bickson Joseph , Fisher Edward M 
Published: July 2024

Abstract
Protective clothing standards, such as test methods published by ASTM International, play an integral role in ensuring the performance of personal protective equipment. The standard tests are not without limitations and are periodically reviewed and often updated. Some tests may not be reflective of in-use conditions. A new test cell was designed using sanitary fixtures to evaluate the effect of glove stretch on barrier performance using fluorescein solution as the challenge agent for enhanced visualization and fluorometer detection. Domed-shaped and flat screens were developed to permit and limit glove stretch within the test cell. The barrier performance of glove swatches was evaluated for both stretched and unstretched states. Latex, nitrile, and vinyl glove models of various thicknesses were evaluated. The tests were conducted following pressure and time parameters specified in ASTM F903, ASTM F1670, and ASTM F1671. Fluorescein solution movement, which may occur through penetration, was measured using a fluorometer. Glove stretch caused a reduction in glove thickness ranging from 16% to 40%. Overall, 21 sample failures were found (16.7%; n = 126) regardless of test condition. Nitrile gloves provided better barrier efficacy with the lowest failure rates (2.38%; 1 failure out of 42) compared to latex (19.4%; 7 failures out of 36) and vinyl gloves (27.1%; 13 failures out of 48). Differences in failure rates between stretched and unstretched gloves were insignificant; however, the latex material showed a 2.5 times increase in failures when stretched compared to unstretched. The new test apparatus was able to differentiate between the barrier performance of different glove materials. The use of a domed screen allowed the gloves to stretch, a condition that better represents the state of gloves when in use. Analysis of samples collected from the glove surface opposite to the exposure may provide a way to assess chemical permeation in addition to penetration.