bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exploring changes in residents' daily activity patterns through sequence visualization analysis
Authors: Peng Xiaoran , Hu Ruimin , Wang Xiaochen , Huang Nana 
Published: October 2024

AbstractThe analysis of people's daily activities has played a crucial role in various applications, such as urban geography, activity prediction, and homogeneous population detection. However, limited studies have explored changes in the residents’ activity patterns in a particular region across various periods. To explore the changes, a methodological framework of sequence visualization analysis based on machine learning that extracts the activity patterns across various periods using sequence analysis, visualizes the activity patterns by calculating the frequency of different activities at time points and categorizes them through graphical similarity, and then compares the activity patterns in terms of activity and demographic characteristics is proposed. Empirical testing on the New York Metropolitan data of the National Household Travel Survey (NHTS) is conducted for 2001, 2009, and 2017. The findings reveal significant intra‐similarities, inter‐differences, and distinct changes in activity patterns across three periods for different social populations in the New York Metropolitan. From the perspective of information analysis, this work is anticipated to enhance the understanding of travel needs for diverse social populations in a particular region, thereby facilitating targeted policy adjustments for the departments concerned.
Enhancing freight train delay prediction with simulation‐assisted machine learning
Authors: Minbashi Niloofar , Zhao Jiaxi , Dick C. Tyler , Bohlin Markus 
Published: October 2024

AbstractBoosting the rail freight modal share is an ambitious target in Europe and North America. Yards, where freight trains are arranged, can be crucial in realizing this target by reliable dispatching to the network. This paper predicts freight train departures by developing a simulation‐assisted machine learning model with two concepts: general (adding all predictors at once) and step‐wise (adding predictors as they become available in sub‐yard operations) for hump yards with the conventional layout to provide a generalized model for European and North American contexts. The developed model is a decision tree algorithm, validated via 10‐fold cross‐validation. The model's performance on three data sets—a real‐world European yard, a baseline simulation, and an ultimate randomness simulation for a comparable North American yard—shows a respective of 0.90, 0.87, and 0.70. Step‐wise inclusion of the predictors results differently for the real‐world and simulation data. The global feature importance highlights maximum planned length, departure weekday, the number of arriving trains, and minimum arrival deviation as key predictors for the real‐world data. For the simulation data, the most significant predictors are departure yard predictors, the number of arriving trains, and the maximum hump duration. Additionally, utilization rates—except for the receiving yard—enhance the predictions.
Fast stability enhancement of inverter‐based microgrids using NGO‐LSTM algorithm
Authors: Pang Kai , Tang Zhiyuan 
Published: October 2024

AbstractTo improve the stability of the inverter‐based microgrid (MG), this paper employs a novel data‐driven based method to coordinately adjust control parameters of inverters in a fast local manner. During the design process, an offline eigenvalue based optimization problem that is used to calculate the optimal control parameters under various operating conditions is first constructed. In order to reduce reliance on full system information, a feature selection algorithm is utilized to extract the most relevant local measurements that influence the adjustment of each control parameter. Then, regarding local measurements as input variables and optimal control parameters as output variables, based on northern goshawk optimization (NGO) and long short‐term memory (LSTM) network, a novel deep learning algorithm is proposed to train the local parameter adjustment model (LPAM) by learning the mapping relationship between them. During the application, to guarantee the stability of MG all the time, a security region based shielding mechanism is developed, where the improper control parameter adjustment will be replaced by a safe one. The case study indicates that the proposed algorithm has better mapping accuracy than traditional LSTM neural networks and also faster calculation speed than the traditional offline optimization‐based method. The effectiveness and advantages of the proposed method are demonstrated in a modified 9‐bus MG.
Powering the future: A survey of ambient RF‐based communication systems for next‐gen wireless networks
Authors: Singh Shweta , Kumar Manish , Kumar Rahul 
Published: October 2024

AbstractEmerging wireless communication networks, exemplified by the evolution from 5G to subsequent technologies, necessitate extensive connectivity among myriad devices to fuel the ongoing technological progress. However, the magnitude of this network demands an extensive power source, requiring an advanced and sustainable system to be practically deployable. This study introduces a cutting‐edge system utilising ambient RF signals for both wireless information transfer (WIT) and wireless power transfer. The proposed system addresses the energy deficiencies of billions of low‐powered wireless devices within the network. Wireless‐powered communication networks (WPCN) and simultaneous wireless energy and power transfer (SWIPT) technologies, operating on ambient RF signals, provide a solution for the energy requirements of these devices. Harvesting energy from ambient RF signals is pivotal for the signal transmissions of WPCN and SWIPT systems. The research focuses on enhancing the efficiency and feasibility of such systems, emphasising aspects like maximising energy efficiency (EE) and improving outage performance (OP). The paper underscores the ubiquitous connectivity resulting from node mobility and delves into the emerging models of WPCN and SWIPT, along with collaborative technologies integrated with these models. It explores resource allocation (RA), multiple‐input multiple‐output (MIMO) technology in the context of WPCN, and various aspects of relaying operations, including SWIPT‐MIMO and SWIPT receiver architecture. Conclusively, the comprehensive survey affirms that leveraging ambient RF signals for WIT and power transfer can significantly enhance EE, OP, RA, and overall network capabilities. This improvement positions the proposed system as a promising solution for meeting the connectivity demands of future wireless communication technologies.
Investigation of the failure mechanisms of photocurable resins under explosive shock loads
Authors: Chen Jiahui , Li Yilong , Huang Yong 
Published: October 2024

Resin components produced by photocuring technology and 3D printing are commonly utilized as seals across various media owing to their flexible molding, precise structure, and excellent watertightness. Under certain specific conditions, it is necessary to detach the photocurable resin seals from the main body using explosive cord blasting. However, the mechanical performance of photocurable resins under explosive shock loads remains underexplored. This study aimed to explore the mechanisms underlying the destruction of photocurable resins by explosive cords in different environments. We conducted explosive shock experiments on photocurable resin specimens both in air and underwater and employed explicit dynamics software to simulate the damage process of the resin material. The experiments showed that a 10 mm thick specimen in air reached a critical fracture state under the equivalent explosive shock of the explosive cord, leading to adjustments in the constitutive parameters of the photocurable resin model in the simulations. In addition, the JH-2 strength and failure model was applied in AUTODYN explicit dynamics software to characterize the mechanical properties of the resin materials. The fluid–structure interaction method was utilized for modeling and simulating the shock process on photocurable specimens in air and water, clarifying the role of groove shape in the fracture state of the specimens and the failure mechanisms of the photocurable resin materials under explosive shock.
Language teachers’ awareness of their accents at a Thai university
Authors: Ulla Mark Bedoya , Perales William F. , Paulino Freda Bacuso 
Published: October 2024

Abstract
The need to incorporate Global Englishes (GE) awareness and informed practices into English language teaching (ELT) has encouraged practitioners to revisit their language curricula and include Other Englishes in their language teaching practices, impacting how language is taught and learned. The present study explores how five non-native English language teachers at a university in Thailand are aware of and view their accents when speaking English and how such awareness is reflected in their language pedagogy for Global English Language Teaching (GELT). Findings from the in-depth semi-structured individual interviews reveal that participants are aware of their accents and have a favorable disposition toward their own accent, linking it to their heightened sense of linguistic ownership based on their nationality and race. In their pedagogical practices, such awareness enables the participants to innovate their language teaching, giving an important implication in integrating GE awareness and informed practices into ELT. The findings also demonstrate how important teachers’ accents are in encouraging and incorporating GE-informed practices into ELT.
Vascular smooth muscle cells can be circumferentially aligned inside a channel using tunable gelatin microribbons
Authors: Mastoor Yusuf , Karimi Mahsa , Sun Michael , Ahadi Fereshteh , Mathieu Pattie , Fan Mingyue , Han Lin , Han Li-Hsin , Clyne Alisa Morss 
Published: October 2024

Abstract
The gold standard to measure arterial health is vasodilation in response to nitric oxide. Vasodilation is generally measured via pressure myography of arteries isolated from animal models. However, animal arteries can be difficult to obtain and may have limited relevance to human physiology. It is, therefore, critical to engineer human cell-based arterial models capable of contraction. Vascular smooth muscle cells (SMCs) must be circumferentially aligned around the vessel lumen to contract the vessel, which is challenging to achieve in a soft blood vessel model. In this study, we used gelatin microribbons to circumferentially align SMCs inside a hydrogel channel. To accomplish this, we created tunable gelatin microribbons of varying stiffnesses and thicknesses and assessed how SMCs aligned along them. We then wrapped soft, thick microribbons around a needle and encapsulated them in a gelatin methacryloyl hydrogel, forming a microribbon-lined channel. Finally, we seeded SMCs inside the channel and showed that they adhered best to fibronectin and circumferentially aligned in response to the microribbons. Together, these data show that tunable gelatin microribbons can be used to circumferentially align SMCs inside a channel. This technique can be used to create a human artery-on-a-chip to assess vasodilation via pressure myography, as well as to align other cell types for 3D in vitro models.
Programmable mechanical properties of additively manufactured novel steel
Authors: Su Jinlong , Li Qian , Teng Jie , Ng Fern Lan , Shen Zheling , Goh Min Hao , Jiang Fulin , Sing Swee Leong , Yang Tao , Tan Chaolin 
Published: October 2024

Abstract
Tailoring thermal history during additive manufacturing (AM) offers a feasible approach to customise the microstructure and properties of materials without changing alloy compositions or post-heat treatment, which is generally overlooked as it is hard to achieve in commercial materials. Herein, a customised Fe–Ni–Ti–Al maraging steel with rapid precipitation kinetics offers the opportunity to leverage thermal history during AM for achieving large-range tunable strength-ductility combinations. The Fe–Ni–Ti–Al steel was processed by laser-directed energy deposition (LDED) with different deposition strategies to tailor the thermal history. As the phase transformation and in-situ formation of multi-scale secondary phases of the Fe–Ni–Ti–Al steel are sensitive to the thermal histories, the deposited steel achieved a large range of tuneable mechanical properties. Specifically, the interlayer paused deposited sample exhibits superior tensile strength (∼1.54 GPa) and moderate elongation (∼8.1%), which is attributed to the formation of unique hierarchical structures and the in-situ precipitation of high-density η-Ni3(Ti, Al) during LDED. In contrast, the substrate heating deposited sample has an excellent elongation of 19.3% together with a high tensile strength of 1.24 GPa. The achievable mechanical property range via tailoring thermal history in the LDED-built Fe–Ni–Ti–Al steel is significantly larger than most commercial materials. The findings highlight the material customisation along with AM’s unique thermal history to achieve versatile mechanical performances of deposited materials, which could inspire more property or function manipulations of materials by AM process control or innovation.
Robust quantum dots charge autotuning using neural network uncertainty
Authors: Yon Victor , Galaup Bastien , Rohrbacher Claude , Rivard Joffrey , Godfrin Clément , Li Ruoyu , Kubicek Stefan , De Greve Kristiaan , Gaudreau Louis , Dupont-Ferrier Eva , Beilliard Yann , Melko Roger G , Drouin Dominique 
Published: October 2024

Abstract
This study presents a machine learning-based procedure to automate the charge tuning of semiconductor spin qubits with minimal human intervention, addressing one of the significant challenges in scaling up quantum dot technologies. This method exploits artificial neural networks to identify noisy transition lines in stability diagrams, guiding a robust exploration strategy leveraging neural network uncertainty estimations. Tested across three distinct offline experimental datasets representing different single-quantum-dot technologies, this approach achieves a tuning success rate of over 99% in optimal cases, where more than 10% of the success is directly attributable to uncertainty exploitation. The challenging constraints of small training sets containing high diagram-to-diagram variability allowed us to evaluate the capabilities and limits of the proposed procedure.
Designing the next generation of polymers with machine learning and physics-based models
Authors: Chew Alex K , Afzal Mohammad Atif Faiz , Chandrasekaran Anand , Kamps Jan Henk , Ramakrishnan Vaidya 
Published: October 2024

Abstract
The development of next-generation polymers necessitates optimizing several key properties simultaneously, a task that is expensive and infeasible using traditional trial-and-error experimental approaches. A promising alternative is employing a combination of machine learning and physics-based tools to rapidly screen the polymer design space and provide suggestions of new polymers that meet the critical properties required for industrial applications. In this study, we introduce a comprehensive workflow that utilizes machine learning and molecular modeling approaches to design new polymers with the focus on improving five polymer properties: (1) glass transition temperature, (2) dielectric constant, (3) refractive index, (4) stress optic coefficient, and (5) linear coefficient of thermal expansion. Using a small dataset (

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200 unique polymers), we developed quantitative structure-property relationships (QSPRs) models to accurately predict the experimental polymer properties for both homo- and co-polymer systems. We tested several ML algorithms and identified the best models for predicting these polymer properties, achieving test set R
2 greater than 0.77 across all properties. We then explored new polymers by creating a library of over ∼10 000 homopolymers using R-group enumeration tools and applied the trained QSPR models to rapidly predict the five polymer properties. The predictions of QSPR models were used to create a multi-parameter optimization score, which helped downselect the large polymer space to ∼10 promising candidates. The properties of these selected polymer candidates were subsequently validated with classical molecular dynamics simulations and density functional theory, revealing a strong correlation with the QSPR model predictions. Finally, one of the top candidates was validated by experiments, which showed good agreement against QSPR and physics-based models. Our workflow underscores the power of combining data-driven and theoretical methods in the polymer design process given a small dataset size, offering a valuable resource for experimentalists looking to leverage computer-aided strategies in materials innovation.
Introduction
Authors: Hewitt Martin 
Published: October 2024

Abstract
This chapter explores the theoretical and historical context of the rest of the book. It discusses weaknesses of current theories of generations, offers a revised theoretical model, and proposes a new schema for the generational make-up of Victorian Britain. It argues that despite current trends in the history of Darwinism and evolutionary biology, it is still important to address the question of how contemporaries engaged directly with Darwin’s writings and the key arguments they advanced. It suggests that to do so will allow a rethinking of the conventional wisdom that there was a ‘Darwinian Revolution’ in the years after the publication of On the Origin of Species, which involved the relatively rapid acceptance of Darwin’s ideas.
The Publication of On the Origin of Species
Authors: Hewitt Martin 
Published: October 2024

Abstract
This chapter examines the immediate response in Britain to the publication of Charles Darwin’s On the Origin of Species in 1859. It shows that, for all its originality, the evolutionary argument of the Origin was not as much of a radical departure as is sometimes suggested. The immediate sensation caused by the Origin, a scandal of the season, is described. The largely hostile nature of the response is explored, shaped by the ages of the respondents, from a mostly forthright dismissal by those born before 1814, through to a much greater likelihood of approval from those born from 1830 onwards. Examining some of the well-known set pieces of the early debates over the Origin, notably the confrontation between T.H. Huxley and Samuel Wilberforce at the British Association in 1860, the chapter demonstrates that acceptance of Darwin’s ideas was much slower and more confined than is often suggested.
The Descent of Man and the High Victorians
Authors: Hewitt Martin 
Published: October 2024

Abstract
This chapter explores the shifting contours of the evolutionary debate in Britain after publication of Darwin’s The Descent of Man (1871). The formal inclusion of humanity into Darwin’s evolutionary schema removed some of the productive uncertainty which had moderated opposition to his views in the 1860s. After Descent, older opponents like E.B. Pusey intensified their attempts to counter Darwinian ideas. Mid Victorians hardened their opposition to ‘Darwinism’, despite being more accepting of some limited version of evolution. In the final decade of Darwin’s life, the debate shifted with the death or withdrawal from controversy of older opponents, and greater prominence in public debate and institutional positions of the high-Victorian generation, who had imbibed anti-evolutionary views in early adulthood, but demonstrated greater flexibility in accepting Darwin’s ideas than older generations. The new periodicals of the 1860s and 1870s offered a greatly augmented platform for their overwhelmingly pro-Darwinian views.
Darwinian Debates at the Fin de Siècle
Authors: Hewitt Martin 
Published: October 2024

Abstract
The emergence of the Edwardian generation, born c.1860–75, shifted the terms of debate over Darwinism and evolution in Britain, from the mid-1890s to the Origin’s half-centenary in 1909. These years, often conceived of as an ‘eclipse’ for Darwinism, were marked by a complex interaction between long-standing doubters, different generational groups of pro-Darwinians, and Edwardians increasingly less likely to see in Darwinian ideas the core of their own biological beliefs or methods. The Edwardians were the first generation who confronted Darwinism as the orthodoxy of their parents and elders, and for whom as a result assertion of intellectual independence was likely to involve Darwinian scepticism or even rejection. The turn to questions of heredity and the rediscovery of Mendelism shifted biological discussions in ways which marginalised Darwin. It is possible to see a parallel retreat from Darwinian preoccupations in areas of thought such as sociology, anthropology, and philosophy.
Study on operation strategy of pumped storage power station under different development models: a case study of China
Authors: Ma Luyu , Yu Xiaowei , Zhai Haiyan , Tan Zhongfu , Wu Xuehui 
Published: October 2024

Abstract
Pumped storage, a flexible resource with mature technology, a good economy, and large-scale development, is an important part of the new power system. According to the different stages of the development of the power market, this paper puts forward the corresponding development models of pumped storage power stations, which are successively the “two-part price system” model, the “partial capacity fixed compensation” model, and the “completely independent market participation” model. Then put forward the corresponding business strategies of different development models, establish the economic benefit calculation model, and conduct the simulation. The simulation results show that the internal rate of return corresponding to the above three development models is 6.5%, 7.44%, and 8.23%, respectively. With the continuous improvement of market participation, the economic benefits of pumped storage power stations are also gradually improved, which promotes the cost recovery of pumped storage power stations. In addition, under the three development models, the three factors of capacity electricity price, capacity ratio covered by approved electricity price, and energy conversion efficiency also impact the economic benefits of pumped storage power stations.
Assessing Age-Friendly Community Initiatives: Developing a Novel Survey Tool for Assessment and Evaluation
Authors: Webster Sarah , Robertson Madison , Keresztes Christian , Puxty John 
Published: October 2024

Abstract

Background and Objectives
Age-friendly community initiatives (AFCIs) have gained recognition as essential responses to the needs of aging populations. Despite their growing significance, there is a notable lack of effective measurement tools to assess the planning, implementation, and sustainability of AFCIs. The purpose of this study was to develop and validate a survey tool for evaluating AFCIs.

Research Design and Methods
A sequential exploratory mixed-method design was used in 2 phases. First, we identified key themes from interviews with AFCI leads to generate AFCI survey items and regional workshops. Then, we conducted a pilot of the survey and assessed its measurement properties.

Results
Thematic analysis of interviews with 68 key informants from 58 AFCIs revealed 4 main themes: AFCI priorities, enablers, challenges, and benefits. These themes, combined with feedback from AFCI stakeholders at the regional workshops and an AFCI conference, informed the development and refinement of a reliable and valid AFCI survey in 2019, supported by a high Cronbach’s alpha value (α = 0.881). Steps were identified to maintain and sustain the AFCI survey over time.

Discussion and Implications
The survey accommodates AFCIs’ diverse demographics, governance structures, and priorities with a standardized and flexible approach for effective measurement. This research contributes to the academic understanding of AFCIs and aids community leaders and policy-makers in planning, implementing, and evaluating AFCIs.