bluuebunny/crossref_metadata_2025_split huggingface dataset:
Thermal-crosslinked acellular dermal matrix combined with adipose-derived stem cells to regenerate vascularized adipose tissue
Authors: Huang Xing , Chen Siyuan , Lu Lin , Jin Rui , Chang Mengling , Yuan Zhaoqi , Luo Xusong , Zhu Zhu , Liu Guangpeng 
Published: January 2025

Abstract
The reconstruction of large-sized soft tissue defects remains a substantial clinical challenge, with adipose tissue engineering emerging as a promising solution. The acellular dermal matrix (ADM), known for its intricate spatial arrangement and active cytokine involvement, is widely employed as a scaffold in soft tissue engineering. Since ADM shares high similarity with decellularized adipose matrix, it holds potential as a substitute for adipose tissue. This study explores the adipogenic ability of a spongy material derived from ADM via vacuum-thermal crosslinking (T-ADM), characterized by high porosity, adjustable thickness, and suitable mechanical strength. Adipose-derived stem cells (ADSCs) are considered ideal seed cells in adipose tissue engineering. Nevertheless, whether pre-adipogenic induction is necessary before their incorporation remains debatable. In this context, ADSCs, both with and without pre-adipogenic induction, were seeded into T-ADM to regenerate vascularized adipose tissue. A comparative analysis of the two constructs was performed to evaluate angiogenesis and adipogenesis in vitro, and tissue regeneration efficacy in vivo. Additionally, RNA-seq analysis was utilized to investigate the potential mechanisms. The results showed that T-ADM exhibited good performance in terms of volume retention and maintenance of adipocyte phenotype, confirming its suitability as a scaffold for adipose tissue engineering. In-vitro outcomes demonstrated that pre-adipogenic induction enhanced the adipogenic level of ADSCs, but reduced their ability to promote vascularization. Furthermore, constructs utilizing pre-induced ADSCs showed an insignificant superiority in in-vivo fat formation, and neovascularization compared with those with non-induced ADSCs, which may be attributed to similar macrophage regulation, and balanced modulation of the proliferator-activated receptor-γ and hypoxia-inducible factor 1 α pathways. Consequently, the direct use of ADSCs is advocated to streamline the engineering process and reduce associated costs. The combined strategy of T-ADM with ADSCs proves to be feasible, convenient and effective, offering substantial potential for addressing large-sized tissue deficits and facilitating clinical applications.
Recent trends in embedded 3D bioprinting of vascularized tissue constructs
Authors: Cho Won-Woo , Park Wonbin , Cho Dong-Woo 
Published: January 2025

Abstract
3D bioprinting technology offers significant advantages in the fabrication of tissue and organ structures by allowing precise layer-by-layer patterning of cells and various biomaterials. However, conventional bioinks exhibit poor mechanical properties, which limit their use in the fabrication of large-scale vascularized tissue constructs. To address these limitations, recent studies have focused on the development of rapidly crosslinkable bioinks through chemical modification. These enable rapid crosslinking within minutes, offering substantial advantages for engineering large-scale tissue constructs. Nevertheless, challenges remain in the production of constructs that fully incorporate the complex vascular networks inherent to native tissues. Recently, embedded bioprinting technique, which involves the direct writing of bioink into a support bath that provides physical support, has gained significant attention for enabling the freeform fabrication of 3D structures. This method has been extensively studied and offers the advantage of fabricating structures ranging from tissue constructs with simple vascular channels to complex structures containing multiscale vascular networks. This review presents an overview of the various materials utilized in embedded bioprinting and elucidates the rheological properties of these materials. Furthermore, it examines the current research trends in the biofabrication of vascularized tissue constructs using embedded bioprinting techniques, along with their associated limitations. The review concludes by proposing areas for future improvement, specifically addressing material and biofabrication approaches as well as bioprinting systems.
The role of α-RuCl3 flat bands in G peak splitting in graphene/α-RuCl3 heterostructures
Authors: Cai Xiaofan , Han Yaqing , Jiang Jiawei , Du Renjun , Bao Song , Guo Zijing , Huang Jiabei , Lian Fuzhuo , Xiao Jingkuan , Jiang Siqi , Ma Guodong , Wang Ping , Zhang Di , Cui Xiang , Watanabe Kenji , Taniguchi Takashi , Wen Jinsheng , Xi Xiaoxiang , Wu Xinglong , Gao Libo , Stolyarov Vasily , Mayorov Alexander S , Yang Hongxin , Wang Lei , Yu Geliang 
Published: January 2025

Abstract

α-RuCl3 is known for its potential to realize a Kitaev quantum spin liquid by tuning the competing interactions, such as doping effects. Here, we investigated the doping effect of α-RuCl3/graphene heterostructures and observed a G peak splitting related to different spins, as revealed by Raman spectroscopy. The deliberate disruption of the heterostructure led to a transition from a double G peak to a single peak, underscoring the significance of interface quality. This finding was corroborated by density functional theory calculations. Additionally, we distinguished the contributions of doping and strain by employing Raman mapping, drawing comparisons to CrCl3/graphene heterostructures. These findings present a high-resolution technique for assessing the uniformity of similar heterostructure surfaces and investigating the behavior of distinct spins in Raman scattering influenced by magnetic proximity effects.
Evaluation of performance and statistical analysis of rigid pavement incorporating recycled aggregate concrete through the BBD methodology
Authors: S Karuppasamy , Malarvizhi Ganapathi 
Published: January 2025

Abstract
The incorporation of recycled concrete aggregate (RCA) in the construction of rigid pavements has attracted considerable interest due to its environmental and economic benefits. A statistical method known as Response Surface Methodology (RSM) serves as an effective tool for optimizing concrete mix designs by adjusting independent variables to achieve desired characteristics. However, there is a lack of extensive research that combines modified mix designs with statistical modeling to predict the mechanical properties of concrete. Furthermore, many existing studies fail to consider the combined impacts of various factors, including cement content, water-to-cement ratio, and fine-to-coarse aggregate ratio, on the performance of concrete mixtures. This study aims to develop and optimize concrete mixtures that incorporate RCA for rigid pavements using the Box-Behnken Design method. The main goals were to forecast water absorption, compressive strength, flexural strength, split tensile strength, and slump of fiber-reinforced concrete, as well as to identify optimal mix designs that fulfill specific strength requirements. A total of 30 mixtures were tested, varying in four factors: cement content (300, 350, and 400 kg m−3), water/cementitious ratios (0.3, 0.4, and 0.5), fine/coarse aggregate ratios (0.3, 0.4, and 0.5), and silica fume content (0%, 5%, and 10% by weight of cement). RSM was employed to create predictive equations for the mechanical properties of the concrete mixtures, revealing that cement content and silica fume ratios had a significant impact on these properties, followed by the fine-to-coarse aggregate and water-to-cementitious ratios. The correlation coefficients (R2) for all predictive models exceeded 0.95, indicating a strong relationship between the independent variables and the mechanical properties. The optimal mix identified for achieving a compressive strength greater than 30 MPa and a flexural strength exceeding 4.1 MPa consisted of 365 kg m−3 of cement.
Sustainable optimization of concrete strength properties using artificial neural networks: a focus on mechanical performance
Authors: Manan Aneel , Pu Zhang , Majdi Ali , Alattyih Wael , Elagan S K , Ahmad Jawad 
Published: January 2025

Abstract
In this study, a comprehensive dataset containing 358 data points was collected from the literature, focusing on the compressive strength, split tensile strength, and modulus of elasticity of concrete made with recycled concrete aggregate (RCA). An Artificial Neural Network was used machine to predict mechanical properties of RCA concrete. Furthermore, K-fold cross validation was utilized to validate the model’s reliability, and sensitivity analysis was performed to identify the most influential input parameters among the independent variables. The model demonstrated strong performance during training, achieving R2 values of 0.93 for compressive strength, 0.92 for split tensile strength, and 0.99 for modulus of elasticity with corresponding RMSE of 2.55, 3.85, and 0.37, respectively. The MAE and MAPE values during training were 0.68 and 0.03 for compressive strength, 0.71 and 0.03 for split tensile strength, and 0.08 and 0.01 for modulus of elasticity, respectively. Testing results revealed R2 values of 0.75 for compressive strength, 0.78 for split tensile strength, and 0.67 for modulus of elasticity, with RMSE values of 8.57, 5.03, and 3.83, respectively. Moreover, the sensitivity analysis indicated that the cement percentage and water-to-cement ratio were the main input parameters which significantly influence RCA concrete strength.
Li+ concentration and morphological changes at the anode and cathode interphases inside solid-state lithium metal batteries
Authors: Huang Chun , Wilson Matthew D , Cline Ben , Sivarajah Abeiram , Stolp Wiebe , Boone Matthieu N , Connolley Thomas , Leung Chu Lun Alex 
Published: January 2025

Abstract
Irregular Li heterostructure growth at the interphase between the solid electrolyte and anode reduces solid-state Li metal battery (SSLMB) performance, but the fundamental cause is still elusive. Measuring and imaging Li+ ion diffusion in operando inside an SSLMB using a commercially standard cell configuration are extremely challenging because the ultra-light Li element exhibits a minute signal-to-noise ratio using most x-ray-related characterization methods, and the weak x-ray signals of Li+ are buried by strong signals of other heavy transition metal elements in the cathode and battery enclosure. Here, we pioneer novel operando correlative imaging of coupling x-ray Compton scattering with computed tomography (XCS-CT), which is able to quantify the interplay between spatially resolved Li+ ion diffusion kinetics and Li0 metal structure growth at the interphases of both the anode and cathode sides inside a full-cell SSLMB using a solid polymer electrolyte (SPE) and commercially standard cell configuration during (dis)charging. We show a 61% increase in the efficiency of extracting Li+ ions from the cathode LiNi0.6Mn0.2Co0.2O2 to the anode during charging at 0.1 C compared with at 1 C due to restricted Li+ ion diffusion at the higher rate inside SSLMB. However, this led to the formation of a more irregular interfacial morphology, consisting not only of Li0 dendrites, but also sub-surface pore formation at the anode/SPE interphase. We find that surprisingly, the irregular Li0 structure initiation and growth are accelerated during the first Li stripping step, not the Li plating step, and the root cause is the onset imbalance of Li+ ion diffusion and redox reactions between the anode and cathode. These insights highlight the benefits of asymmetric charging and discharging rates as a promising solution to improving SSLMB performance with SPEs. The operando correlative XCS-CT imaging technique has the potential to study the relationship between active ion concentrations and buried morphological changes for a variety of battery chemistries.
Self-powered biomedical devices: biology, materials, and their interfaces
Authors: Zhuang Yuan , Zhang Quan , Wan Zhanxun , Geng Hao , Xue Zhongying , Cao Huiliang 
Published: January 2025

Abstract
Integrating biomedical electronic devices holds profound promise for advancements in healthcare and enhancing individuals’ quality of life. However, the persistent challenges associated with the traditional batteries’ limited lifespan and bulkiness hinder these devices’ long-term functionality and consistent power supply. Here, we delve into the biology and material interfaces in self-powered medical devices by summarizing the intrinsic electric demands in humans, analyzing material and biological mechanisms for electricity generation and storage, and discussing the pathways toward self-chargeable powering. As a result, the current challenges in material designs and biological integrations emerged to shape the future directions in advancing self-powered medical devices. This paper calls on the community to integrate biology and material science to develop self-powering medical devices and improve their clinical prospects.
An improved golden jackal optimization-hybrid kernel extreme learning machine approach for fault diagnosis of chillers
Authors: Wang Hong , Sheng Yingjie , Guo Yang , Chu Pan , Tian Zengrui , Yuan Boyang 
Published: January 2025

Abstract
In the context of the dual carbon target, reducing energy consumption and carbon emissions in the building sector is crucial. Chillers, being the primary source of energy consumption in building equipment, can significantly increase energy use if they fail. To enhance the detection rate of early chiller failures, this paper proposes an improved chiller fault diagnosis model based on the golden jackal optimization algorithm (IGJO) and the hybrid kernel extremum learning machine (HKELM). To address the slow convergence speed and local optimization issues of the original golden jackal algorithm (GJO), we introduce four strategies: improved sine chaotic mapping, the simplex method, a fusion of the golden sinusoidal formula, and Cauchy’s variant. These strategies form the IGJO algorithm, which improves convergence speed and global optimization ability. Next, the IGJO algorithm is used to optimize the hyperparameters of the HKELM for fast search and efficient chiller fault detection. Finally, we simulate the performance of the proposed model using ASHRAE RP-1043. The results show that the IGJO-HKELM model achieves a chiller fault diagnosis accuracy of 99.83%, demonstrating a clear advantage over other algorithms.
A deep reinforcement learning based metro train operation control optimization considering energy conservation and passenger comfort
Authors: Tan Qinyu , He Deqiang , Sun Zheng , Yao Zuofang , Zhou Jixu , Chen Tianxi 
Published: January 2025

Abstract
As an efficient mode of public transportation, urban rail transit promotes sustainable development by reducing energy consumption and improving service quality. Numerous studies have been conducted on this subject. However, many existing methods struggle to adapt to environmental changes due to the complexity of actual line conditions. Therefore, based on deep deterministic policy gradient (DDPG) and genetic algorithm (GA), a new optimization algorithm GA-DDPG is proposed in this paper. This algorithm utilizes DDPG for continuous action control of trains and employs GA to optimize the noise generation hyperparameter of DDPG, thereby enhancing the learning and adaptive capabilities of the algorithm. Firstly, a reference system is established based on expert knowledge to ensure safe train operation and guide the training of the algorithm model. Then, under the intervention of reference system, the GA-DDPG is used to optimize and adjust the train operation control strategy to achieve a balance in punctuality, energy efficiency, and comfort. Finally, simulation experiment shows that GA-DDPG achieves at least 5.2% energy savings and 51.2% passenger comfort compared to DDPG. More experiments further demonstrate that GA-DDPG possesses better adaptability and robustness than DDPG when the environment changes.
Synchronization and coordination control based on enhanced deviation coupling for multi-motor rigid connection systems
Authors: Jiang Chenlong , Fan Bo , Hong Qinghao , Dong Jiahao , Sun Lifan 
Published: January 2025

Abstract
The operational performances of the conventional controlled multi-motor drive systems are affected by the parameters of the motor, sudden load changes, and external disturbances. These factors may cause issues such as motor speed loss and torque imbalance, with severe cases potentially resulting in motor shaft fractures. In this paper, an enhanced deviation coupling-based synchronization and coordination control method is proposed for multi-motor rigid connection systems. The monomer and gear structure of the induction motor are modeled, respectively, and the transfer functions are given to analyze the factors that affect speed unsynchrony and torque uncoordination for the multi-motor systems. The inherent shortcomings of simple deviation coupling control are clarified, which makes it difficult to achieve the speed synchronization and torque balance of the multi-motor systems. An enhanced deviation coupling control method that simultaneously compensates for speed and torque is designed, and its stability is analyzed. Compared with the deviation coupling control method, the experimental results show that the proposed control method effectively reduces the speed synchronization errors and suppresses the torque output fluctuation. The control system has better performance of real-time and robustness.
Discovering emergent connections in quantum physics research via dynamic word embeddings
Authors: Frohnert Felix , Gu Xuemei , Krenn Mario , van Nieuwenburg Evert 
Published: January 2025

Abstract
As the field of quantum physics evolves, researchers naturally form subgroups focusing on specialized problems. While this encourages in-depth exploration, it can limit the exchange of ideas across structurally similar problems in different subfields. To encourage cross-talk among these different specialized areas, data-driven approaches using machine learning have recently shown promise to uncover meaningful connections between research concepts, promoting cross-disciplinary innovation. Current state-of-the-art approaches represent concepts using knowledge graphs and frame the task as a link prediction problem, where connections between concepts are explicitly modeled. In this work, we introduce a novel approach based on dynamic word embeddings for concept combination prediction. Unlike knowledge graphs, our method captures implicit relationships between concepts, can be learned in a fully unsupervised manner, and encodes a broader spectrum of information. We demonstrate that this representation enables accurate predictions about the co-occurrence of concepts within research abstracts over time. To validate the effectiveness of our approach, we provide a comprehensive benchmark against existing methods and offer insights into the interpretability of these embeddings, particularly in the context of quantum physics research. Our findings suggest that this representation offers a more flexible and informative way of modeling conceptual relationships in scientific literature.
Expert perspectives on living with water (meebewegen) as climate adaptation in the Netherlands
Authors: Kraan Caroline M , Haasnoot Marjolijn , Mach Katharine J 
Published: January 2025

Abstract
In many places around the world, flood defenses—structures such as dams, levees, and dikes—have allowed societies to grow and thrive near bodies of water. However, adapting to intensifying climate change may require an alternative or additional flood management strategy for living with water, motivated by both long-term flood risk reduction and other societal goals. Based on 43 interviews with climate adaptation experts in the Netherlands, we evaluate perspectives on living with water—or meebewegen in Dutch—including the degree to which living-with-water measures are expected to be implemented, their likely role as part of the Netherlands’ future flood risk management, and enablers and barriers. We find widespread agreement that many living-with-water measures will be implemented at large to very large scale, but find the most disagreement about the subset of living-with-water measures focused on retreat. We develop a typology that organizes the diverse set of perspectives on the future role of living with water into four quadrants, based on whether experts expect flood risk management to depend more on engineering flood defenses or living-with-water strategies, and on whether living-with-water strategies are viewed as socio-politically favorable or unfavorable. We identify spatial constraints, political leadership, living-with-water narratives, and misaligned incentives as factors shaping the barriers to and opportunities for living with water. Our research shows that, despite wildly differing perspectives about flood adaptation through living-with-water measures, points of agreement exist about limits of current adaptation strategies and some need for living with water strategies. These could be harnessed to enhance living with water where this new paradigm of water management emerges.
Limit point and limit circle trichotomy for Sturm-Liouville problems with complex potentials
Authors: Leben Florian , Leguizamón Edison , Trunk Carsten , Winklmeier Monika 
Published: January 2025

The limit point and limit circle classification of real Sturm-Liouville problems by H. Weyl more than 100 years ago was extended by A.R. Sims around 60 years ago to the case when the coefficients are complex. Here, the main result is a collection of various criteria which allow us to decide to which class of Sims’ scheme a given Sturm-Liouville problem with complex coefficients belongs. This is subsequently applied to a second order differential equation defined on a ray in

C

\mathbb C

which is motivated by the recent intensive research connected with

P

T

\mathcal P \mathcal T

-symmetric Hamiltonians.
Accompanying Economic Transformation
Authors: Gentilini Ugo 
Published: January 2025

Abstract
Over history, cash transfers emerge as an accompanying ingredient to sustain economic evolution and mitigate its negative effects. The chapter presents three different pathways for such a role. First, the analysis begins by discussing cash transfers within capitalist modes of economic organization. This includes interpreting cash transfers as maintaining labor reserves, that is, how cash helped facilitate workers’ mobility towards industrial areas, support them in those locations during economic downturns, and enhance their readiness to work when labor market conditions improved. Second, cash transfers have also played an important role in shifts in economic models. The chapter provides a brief discussion of social protection practices in feudal systems, as well as locating cash transfers with socialist and communist experiences. Finally, it examines historical examples of economic inclusion. These entail cases whereby states leveraged systems of provisions and aligned them with “productivist” principles of industrial policy. In large part, the initiatives couldn’t adapt to the sweeping forces of urbanization, economic change, and conflict, but their approach and practical insights spread in different countries.