bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cell viability prediction and optimization in extrusion-based bioprinting via neural network-based Bayesian optimization models
Authors: Mohammadrezaei Dorsa , Podina Lena , Silva Johanna De , Kohandel Mohammad 
Published: December 2023

Abstract
The fields of regenerative medicine and cancer modeling have witnessed tremendous growth in the application of 3D bioprinting. Maintaining high cell viability throughout the bioprinting process is crucial for the success of this technology, as it directly affects the accuracy of the 3D bioprinted models, the validity of experimental results, and the discovery of new therapeutic approaches. Therefore, optimizing bioprinting conditions, which include numerous variables influencing cell viability during and after the procedure, is of utmost importance to achieve desirable results. So far, these optimizations have been accomplished primarily through trial and error and repeating multiple time-consuming and costly experiments. To address this challenge, we initiated the process by creating a dataset of these parameters for gelatin and alginate-based bioinks and the corresponding cell viability by integrating data obtained in our laboratory and those derived from the literature. Then, we developed machine learning models to predict cell viability based on different bioprinting variables. The trained neural network yielded regression

R
2

value of 0.71 and classification accuracy of 0.86. Compared to models that have been developed so far, the performance of our models is superior and shows great prediction results. The study further introduces a novel optimization strategy that employs the Bayesian optimization model in combination with the developed regression neural network to determine the optimal combination of the selected bioprinting parameters to maximize cell viability and eliminate trial-and-error experiments. Finally, we experimentally validated the optimization model’s performance.
A vascularized 3D model of the human pancreatic islet for ex vivo study of immune cell-islet interaction
Authors: Bender R Hugh F , O’Donnell Benjamen T , Shergill Bhupinder , Pham Brittany Q , Tahmouresie Sima , Sanchez Celeste N , Juat Damie J , Hatch Michaela M S , Shirure Venktesh S , Wortham Matthew , Nguyen-Ngoc Kim-Vy , Jun Yesl , Gaetani Roberto , Christman Karen L , Teyton Luc , George Steven C , Sander Maike , Hughes Christopher C W 
Published: December 2023

Abstract
Insulin is an essential regulator of blood glucose homeostasis that is produced exclusively by β cells within the pancreatic islets of healthy individuals. In those affected by diabetes, immune inflammation, damage, and destruction of islet β cells leads to insulin deficiency and hyperglycemia. Current efforts to understand the mechanisms underlying β cell damage in diabetes rely on in vitro-cultured cadaveric islets. However, isolation of these islets involves removal of crucial matrix and vasculature that supports islets in the intact pancreas. Unsurprisingly, these islets demonstrate reduced functionality over time in standard culture conditions, thereby limiting their value for understanding native islet biology. Leveraging a novel, vascularized micro-organ (VMO) approach, we have recapitulated elements of the native pancreas by incorporating isolated human islets within a three-dimensional matrix nourished by living, perfusable blood vessels. Importantly, these islets show long-term viability and maintain robust glucose-stimulated insulin responses. Furthermore, vessel-mediated delivery of immune cells to these tissues provides a model to assess islet-immune cell interactions and subsequent islet killing—key steps in type 1 diabetes pathogenesis. Together, these results establish the islet-VMO as a novel, ex vivo platform for studying human islet biology in both health and disease.
Exploration of Cd1−xZnxSe as a window layer for CIGS based solar cell with PEDOT: PSS as back surface field layer
Authors: Islam Md Samiul , Rashid Mohammad Junaebur , Akhtaruzzaman Md , Takashi Suemasu , Kazmi Jamal , Karim Mohammad R , Alnaser Ibrahim A , Sobayel K 
Published: December 2023

Abstract
This research investigates the potential of Cd1−xZnxSe thin film for photovoltaic applications. The electrical behavior of CIGS based solar cell is examined with the novel Cd1−xZnxSe as buffer layer material by Solar Cell Capacitance Simulator (SCAPS). The tunability of Cd1−xZnxSe facilities to reduce the defects between absorber and buffer layer by determining the ideal conduction band offset. It is revealed that cross-over occurs between the p-type absorber and the metal back contact if the metal work function is below 4.6 eV. In this research, a thin PEDOT: PSS back surface (BSF) layer was integrated which enhances the device efficiency from 22.5 percent to 28.32% while retaining the metal work function at 5.1 eV. The trade-off between the use of metal having higher work function and inclusion of heavily doped BSF layer is one of the important findings of this research. These findings pave the way for Cd1−xZnxSe to be commercially used as a buffer layer material for CIGS solar cell.
Comparative performance of machine learning models for the classification of human gait
Authors: Thakur Divya , Lalwani Praveen 
Published: December 2023

Abstract
The efficacy of human activity recognition (HAR) models mostly relies on the characteristics derived from domain expertise. The input of the classification algorithm consists of many characteristics that are utilized to accurately and effectively classify human physical activities. In contemporary research, machine learning techniques have been increasingly employed to automatically extract characteristics from unprocessed sensory input to develop models for Human Activity Recognition (HAR) and classify various activities. The primary objective of this research is to compare and contrast several machine learning models and determine a reliable and precise classification model for classifying activities. This study does a comparison analysis in order to assess the efficacy of 10 distinct machine learning models using frequently used datasets in the field of HAR. In this work, three benchmark public human walking datasets are being used. The research is conducted based on eight evaluating parameters. Based on the study conducted, it was seen that the machine learning classification models Random Forest, Extra Tree, and Light Gradient Boosting Machine had superior performance in all the eight evaluating parameters compared to specific datasets. Consequently, it can be inferred that machine learning significantly enhances performance within the area of Human Activity Recognition (HAR). This study can be utilized to provide suitable model selection for HAR-based datasets. Furthermore, this research can be utilized to facilitate the identification of various walking patterns for bipedal robotic systems.
Renewable energy planning in Africa: robustness of mean and extreme multi-model climate change patterns in solar PV and wind energy potentials
Authors: Larsen Morten Andreas Dahl , Bournhonesque Jean , Thiery Wim , Halsnæs Kirsten , Hattermann Fred F , Hoff Holger , Salack Seyni , Adenle Ademola , Liersch Stefan 
Published: December 2023

Abstract
Increasing renewable sources in the energy mix is essential to mitigate climate change, not least in countries where the energy demand is likely to rise over the coming decades to reduce or even skip durations of time where fossils dominate. For Africa, solar photovoltaic (PV) and inland wind energy, combined with hydropower, provide significant and untapped potentials, whereas trends and robustness measures need further investigation. This study aims to gain insight into distributed trends in solar PV and wind energy potentials over Africa. This study employs relevant metrics, including relative change, model agreement, robustness, bias, and absolute levels for every available model combination and two climate scenarios, with energy planning purposes in mind. The study finds that regional climate models were the primary control of spatio-temporal patterns over their driving global climate model. Solar PV potentials show more coherence between models, a lower bias and general high potentials in most African regions than wind potentials. Favourable locations for inland wind energy include mainly the regions of greater Sahara and the Horn region. For wind and solar potentials combined, scattered locations within Sahara stand out as the most favourable across scenarios and periods. The analysis of minimum energy potentials shows stable conditions despite low potentials in certain regions. The results demonstrate a potential for solar and wind power in most of the African regions and highlight why solar and wind power or synergies of energy mix should be considered for local energy planning and storage solutions.
Building-Integrated Concentrating Photovoltaics based on a low-toxicity photopolymer
Authors: Lloret Tomás , Morales-Vidal Marta , Nieto-Rodríguez Belén , Carlos García-Vázquez José , Beléndez Augusto , Pascual Inmaculada 
Published: December 2023

Abstract
Low-toxicity solar concentrator systems represent an important challenge for outstanding photovoltaic (PV) applications. Particularly, multiplexed holographic lenses (MHL) as Holographic Solar Concentrators (HSC) provide insight into promising possibilities for Building-Integrated Concentrating PVs. This technology does not affect crucial ecosystems, and can convert buildings from energy consumers into energy suppliers. They can be used in windows, roofs, or walls, and a high diffraction efficiency and wide acceptance angle are desired. In this work, we presented several designs of MHL of low spatial frequency 525 lines mm−1, based on a low-toxicity photopolymer and supported on a window glass. The average diffraction efficiency of these HSC was evaluated at 633 nm, whereas the acceptance angle was evaluated by measuring the short-circuit current under solar illumination at different incident angles. Versatile and high-efficiency holographic elements have been used to concentrate sunlight from different relative positions during the day, avoiding the need for expensive tracking systems. To the best of our knowledge, this is the best trade-off between high diffraction efficiency (85

%

) and wide acceptance angle (104∘) in a low-toxicity holographic solar concentrator.
3D printed electrolyte-supported solid oxide cells based on Ytterbium-doped scandia-stabilized zirconia
Authors: Márquez Santiago , Anelli Simone , Nuñez Marc , Lira Maritta , Maria Asensio Antonio , Torrell Marc , Tarancón Albert 
Published: December 2023

Abstract
Solid oxide cells (SOC) are an efficient and cost-effective energy conversion technology able to operate reversibly in fuel cell and electrolysis mode. Electrolyte-supported SOC have been recently fabricated employing 3D printing to generate unique geometries with never-explored capabilities. However, the use of the state-of-the-art electrolyte based on yttria-stabilized zirconia limits the current performance of such printed devices due to a limited oxide-ion conductivity. In the last years, alternative electrolytes such as scandia-stabilized zirconia (ScSZ) became more popular to increase the performance of electrolyte-supported cells. In this work, stereolithography 3D printing of Ytterbium-doped ScSZ was developed to fabricate SOC with planar and corrugated architectures. Symmetrical and full cells with about 250 μm- thick electrolytes were fabricated and electrochemically characterized using impedance spectroscopy and galvanostatic studies. Maximum power density of 500 mW cm−2 in fuel cell mode and an injected current of 1 A cm−2 at 1.3 V in electrolysis mode, both measured at 900 °C, were obtained demonstrating the feasibility of 3D printing for the fabrication of high-performance electrolyte-supported SOC. This, together with excellent stability proved for more than 350 h of operation, opens a new scenario for using complex-shaped SOC in real applications.
Exponential fast terminal sliding mode based finite-time control of manipulator systems
Authors: Zheng Zaihong , Li Ping 
Published: December 2023

Abstract
A finite-time controller is designed based on an exponential fast terminal sliding mode for multi-joint manipulators, which solves the problem of slow convergence in trajectory tracking. By adding an exponential function on the basis of fast terminal sliding mode, the convergence speed is greatly improved. In addition, a new fast variable power reaching law is designed to reduce the chattering problem. The approaching speed is adjusted by changing the size of the power term adaptively in different stages of the approximation, so that the states of the system enter the sliding mode surface faster and more stablely. The controller realizes the finite-time trajectory tracking of the manipulator, shorens the convergence time and weakens the chattering frequency. Finally, the above views are verified by simulation and comparison experiments.
New multilayer film photonic crystal grating multilayer anti-reflection layer for visible-near-infrared solar cells
Authors: Chenghao Zhan , Jun Cai , Yapeng Jia , Wenchao Shi 
Published: December 2023

Abstract
In this paper, a new anti-reflection layer(ARL) is designed by the finite element method, which is made of parabolic air slots with periodic distribution etched on a multilayer film structure with optimized thickness. The effective transmissivity in the 400–1450 nm band is formulated in conjunction with the AM1.5 solar spectrum, and the effects of the air slot depth (H), depth-to-width ratio, and the size of a period (T) on the transmissivity of the ARL are investigated separately. By optimizing the structural parameters, a new type of ARL with H = 310 nm and T = 105 nm acting in the 400–1450 nm waveband was obtained. The transmissivity of this stacked structure ARL is improved and the stability of the transmission effect in the full waveband is enhanced compared to the conventional membrane structure and the emerging micro-nano-optical structure. The effective transmissivity of this multilayer film photonic crystal grating ARL is calculated to be 98.43% in the operating full waveband (400–1450nm) of the Si-0.86 eV PbS double-junction solar cell. The transmissivity is higher than 93.83% of multilayer film ARL and 97.13% of TiO2 grating ARL.
Numerical and experimental investigations on AA6063 extrudates: effect of number of portholes on extrusion load and weld strength
Authors: Ammu V N S U Viswanath , Padole Pramod , Agnihotri Anupam , Dumpala Ravikumar 
Published: December 2023

Abstract
In aluminium extrusion process, metal flow is greatly controlled by the die geometry and a variety of intricate profiles can be achieved by designing dies of different configurations. In porthole die extrusion, number of portholes used for a profile is often determined by the subjective knowledge of designers. Nevertheless, extrusion load and weld strength of profile produced are not only dependent on extrusion ratio, temperature of deformation but also on the number of portholes along with other die geometry variables. In the present investigation, systematic studies were carried out on three different dies with four ports, three and two ports around die center to extrude profiles of outer diameter 29.4 mm and thickness 2 mm. Numerical simulations were carried out for estimation of pressure, temperature, strain, and strain rate parameters in the weld chamber for all the dies, followed by press trials and profiles produced were subjected to wedge expansion test. The results from numerical simulations indicated that pressure generated inside weld chamber increases with number of portholes. The surface plots indicated highest pressure of about 280 MPa for four port hole and about 276 MPa for three ports die and pressure of about 160 MPa was estimated for two port hole die at the entry of weld chamber , which is 65 mm from profile end. Similarly, temperature at profile exit was 551 °C for four portholes die and 546 °C for three portholes die and 537 °C for two portholes die. Furthermore, press trials indicated peak extrusion loads of 5241 kN, 4406 kN and 3961 kN respectively for four, three and two portholes die. Thus, the peak extrusion load decreased by about 16% for three port and about 25% for two ports die in comparison with four port die. Also, the extruded profiles in wedge expansion test indicated mean compression load of 22.70 ± 0.14 kN in four porthole die, mean compression load 22.29 ± 0.26 kN for three port die and mean compression load of 17.841 ± 0.27 kN.
‘United we stand, divided we fall’: intertwining as evidence of joint actions in pea plants
Authors: Bonato Bianca , Wang Qiuran , Guerra Silvia , Simonetti Valentina , Bulgheroni Maria , Quaggiotti Silvia , Ruperti Benedetto , Castiello Umberto 
Published: December 2023

Abstract
In life, it is common for almost every kind of organism to interact with one another. In the human realm, such interactions are at the basis of joint actions, when two or more agents syntonize their actions to achieve a common goal. Shared intentionality is the theoretical construct referring to the suite of abilities that enable such coordinated and collaborative interactions. While shared intentionality has become an important concept in research on social cognition, there is controversy surrounding its evolutionary origins. An aspect still unexplored but promising to bring new insights into this open debate is the study of aneural organisms. To fill this gap, here we investigate whether climbing plants can act jointly to achieve a common goal, i.e. reaching the light. We examined Pisum Sativum plants growing intertwined when there is a need to climb but a potential support is not present in the environment. Three-dimensional kinematic analysis of their movement revealed a coordinated and complementary behaviour. They tend to coordinate their movement in time and space to achieve a joint climbing. By deliberately extending the context in which a joint action takes place, we pay tribute to the complex nature of this social phenomenon. The next challenge for the field of joint action is to generate a perspective that links coordination mechanisms to an evolutionary framework across taxa.
Social ties, trust and the geography of discontent
Authors: McKay Lawrence , Jennings Will , Stoker Gerry 
Published: December 2023

Abstract
Alongside economic factors, regional variation in trust reflects the structure of social ties available to different regions. We support this claim by linking cross-national survey data from 22 countries in the European Social Survey with data on Facebook friendships. Regions with more local (within-region) connections exhibit lower political trust, but higher levels of (particular) social trust. Those regions with more social ties to national capitals and urban centres exhibit higher levels of political trust but lower (particular) social trust. Social ties to power centres support the creation of political trust and their absence limits it.
Clinical Dataset Classification Using Feature Ranking And Satin Bower Bird Optimized SVMs
Authors: K S Navin , Nehemiah H Khanna , Jane Nancy Y , Arputharaj Kannan 
Published: December 2023

Abstract
A clinical decision support system is a computer-based system that is designed to assist healthcare providers with clinical decision-making by analyzing electronic health records and other healthcare information systems to provide real-time support to clinicians at the point of care. A novel classification framework for clinical datasets in which the relevant features are selected by ranking them based on Fisher’s Score and a wrapper-based Satin Bower Bird Optimization algorithm with the combination of accuracy, G-mean and F-Score measured by support vector machine (SVM) as the fitness function is proposed. The classification is performed using an SVM classifier in which the hyperparameters of the SVM classifier are optimized using the Satin Bower Bird Optimization that improves the classification performance. In the context of statistical analysis, the research undergoes a non-parametric Friedman Test. This study selects relevant attributes from three clinical datasets from the Machine Learning Repository maintained by the University of California Irvine and achieved an accuracy of 86% for the Breast Cancer Wisconsin (Diagnostic) dataset, 89% for the Diabetic Retinopathy Debrecen dataset, and 91% for the EEG Eye State dataset respectively. When compared with other machine learning classifiers the proposed approach performed well with feature selection compared with other machine learning classifiers.
Strongly Menger Connectedness of a Class of Recursive Networks
Authors: Wang Yihong , Cheng Baolei , Wang Yan , Yu Jia , Fan Jianxi 
Published: December 2023

Abstract
According to Menger’s theorem, connectivity and edge connectivity are closely related to node-disjoint paths and edge-disjoint paths, respectively. Node- and edge-disjoint paths can keep the effective transmission of information and confidentiality. Therefore, node-disjoint paths and edge-disjoint paths are two important parameters to measure the reliability of a network. For a faulty node (resp. edge) set $S\subset V$ (resp. $S\subset E$), a connected graph $G=(V,E)$ is $S$-strongly Menger-node-connected (resp. Menger-edge-connected) if any two distinct nodes $x$ and $y$ in $G-S$ are connected by $\min \{\deg _{G-S}(x),\deg _{G-S}(y)\}$ internally node-disjoint (resp. edge-disjoint) paths in $G-S$, where $\deg _{G-S}(x)$ and $\deg _{G-S}(y)$ are the degrees of $x$ and $y$ in $G-S$, respectively. And most of the previous studies are based on networks that are triangle-free. In this paper, we consider the strongly Menger (edge) connectedness of a class of $r$-dimensional recursive networks RNCG $G_{r}$ with triangles. Moreover, we show that $G_{r}$ is $(rl-l-1)$-strongly Menger-node-connected. And then we show that $G_{r}$ is $[k+(r-1)l-2]$-strongly Menger-edge-connected of order 1 and $[2k+2(r-1)l-6]$-strongly Menger-edge-connected of order 2. Since the class of $r$-dimensional recursive networks RNCG $G_{r}$ includes not only data center networks DCell and generalized DCell but also interconnection network dragonfly, etc., all the results are appropriate for these networks.
Lycanthropus adulescens: the classical element in MTV’s Teen Wolf (2011–17)
Authors: Martínez Jiménez Javier 
Published: December 2023

Abstract
The adaptation of elements of Greek and Roman myth and folklore is a recurrent aspect of late-twentieth- and early-twenty-first-century fiction on page and screen. These elements are usually presented alongside other mythologies and serve to add depth and veracity to the world-building. This is no different in Teen Wolf, an MTV show that focused on Scott McCall’s journey through lycanthropy. However, in most modern fiction the classical element is one of many sources used in in-story world-building. This paper will present the many examples of the use of classical elements in Teen Wolf, including: the way the werewolves are presented as Lycaon’s descendants and with elements common to Roman versipelles, the way the classical past (historic, folkloric, mythic) is included as part of the intradiegetic lore, and how the show itself is built following ancient epic patterns with classical paradigms as points of reference.