bluuebunny/crossref_metadata_2025_split huggingface dataset:
Stable MXene Dough with Ultrahigh Solid Fraction and Excellent Redispersibility toward Efficient Solution Processing and Industrialization
Authors: Deng Shungui , Guo Tiezhu , Nüesch Frank , Heier Jakob , Zhang Chuanfang (John) 
Published: June 2023

AbstractTwo‐dimensional (2D) transition metal carbides, and/or nitrides, so‐called MXenes, have triggered intensive research interests in applications ranging from electrochemical energy storage to electronics devices. Producing these functional devices by printing necessitates to match the rheological properties of MXene dispersions to the requirements of various solution processing techniques. In particular, for additive manufacturing such as extrusion‐printing, MXene inks with high solid fraction are typically required, which is commonly achieved by tediously removing excessive free water (top‐down route). Here, the study reports on a bottom‐up route to reach a highly concentrated binary MXene‐water blend, so‐called MXene dough, by controlling the water admixture to freeze‐dried MXene flakes by exposure to water mist. The existence of a critical threshold of MXene solid content (≈60%), beyond which no dough is formed, or formed with compromised ductility is revealed. Such metallic MXene dough possesses high electrical conductivity, excellent oxidation stability, and can withstand a couple of months without apparent decay, providing that the MXene dough is properly stored at low‐temperature with suppressed dehydration environment. Solution processing of the MXene dough into a micro‐supercapacitor with gravimetric capacitance of 161.7 F g−1 is demonstrated. The impressive chemical and physical stability/redispersibility of MXene dough indicate its great promise in future commercialization.
Gaps in clinical research in frontotemporal dementia: A call for diversity and disparities–focused research
Authors: Franzen Sanne , Nuytemans Karen , Bourdage Renelle , Caramelli Paulo , Ellajosyula Ratnavalli , Finger Elizabeth , Illán‐Gala Ignacio , Loi Samantha M. , Morhardt Darby , Pijnenburg Yolande , Rascovsky Katya , Williams Monique M. , Yokoyama Jennifer S. , Alladi Suvarna , Ayhan Yavuz , Broce Iris , Castro‐Suarez Sheila , Coleman Kristy , de Souza Leonardo Cruz , Dacks Penny A. , de Boer Sterre C. M. , de Leon Jessica , Dodge Shana , Grasso Stephanie , Gupta Veer , Gupta Vivek , Ghoshal Nupur , Kamath Vidyulata , Kumfor Fiona , Matias‐Guiu Jordi A. , Narme Pauline , Nielsen T. Rune , Okhuevbie Daniel , Piña‐Escudero Stefanie D. , Garcia Ramiro Ruiz , Scarioni Marta , Slachevsky Andrea , Suarez‐Gonzalez Aida , Tee Boon Lead , Tsoy Elena , Ulugut Hülya , Babulal Ganesh M. , Onyike Chiadi U. ,   for the ISTAART FTD PIA and ISTAART Diversity and Disparities PIA
Published: June 2023

AbstractFrontotemporal dementia (FTD) is one of the leading causes of dementia before age 65 and often manifests as abnormal behavior (in behavioral variant FTD) or language impairment (in primary progressive aphasia). FTD's exact clinical presentation varies by culture, language, education, social norms, and other socioeconomic factors; current research and clinical practice, however, is mainly based on studies conducted in North America and Western Europe. Changes in diagnostic criteria and procedures as well as new or adapted cognitive tests are likely needed to take into consideration global diversity. This perspective paper by two professional interest areas of the Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment examines how increasing global diversity impacts the clinical presentation, screening, assessment, and diagnosis of FTD and its treatment and care. It subsequently provides recommendations to address immediate needs to advance global FTD research and clinical practice.
Generating keyphrases for readers: A controllable keyphrase generation framework
Authors: Jiang Yi , Meng Rui , Huang Yong , Lu Wei , Liu Jiawei 
Published: June 2023

AbstractWith the wide application of keyphrases in many Information Retrieval (IR) and Natural Language Processing (NLP) tasks, automatic keyphrase prediction has been emerging. However, these statistically important phrases are contributing increasingly less to the related tasks because the end‐to‐end learning mechanism enables models to learn the important semantic information of the text directly. Similarly, keyphrases are of little help for readers to quickly grasp the paper's main idea because the relationship between the keyphrase and the paper is not explicit to readers. Therefore, we propose to generate keyphrases with specific functions for readers to bridge the semantic gap between them and the information producers, and verify the effectiveness of the keyphrase function for assisting users’ comprehension with a user experiment. A controllable keyphrase generation framework (the CKPG) that uses the keyphrase function as a control code to generate categorized keyphrases is proposed and implemented based on Transformer, BART, and T5, respectively. For the Computer Science domain, the Macro‐avgs of , , and on the Paper with Code dataset are up to 0.680, 0.535, and 0.558, respectively. Our experimental results indicate the effectiveness of the CKPG models.
A future‐oriented approach to the selection of artificial intelligence technologies for knowledge platforms
Authors: Skulimowski Andrzej M. J. , Köhler Thomas 
Published: June 2023

AbstractThis article presents approaches used to solve the problem of selecting AI technologies and tools to obtain the creativity fostering functionalities of an innovative knowledge platform. The aforementioned selection problem has been lagging behind other software‐specific aspects of online knowledge platform and learning platform development so far. We linked technological recommendations from group decision support exercises to the platform design aims and constraints using an expert Delphi survey and multicriteria analysis methods. The links between expected advantages of using selected AI building tools, AI‐related system functionalities, and their ongoing relevance until 2030 were assessed and used to optimize the learning scenarios and in planning the future development of the platform. The selected technologies allowed the platform management to implement the desired functionalities, thus harnessing the potential of open innovation platforms more effectively and delivering a model for the development of a relevant class of advanced open‐access knowledge provision systems. Additionally, our approach is an essential part of digital sustainability and AI‐alignment strategy for the aforementioned class of systems. The knowledge platform, which serves as a case study for our methodology has been developed within an EU Horizon 2020 research project.
Human‐superviseddata science framework for city governments: A design science approach
Authors: Hagen Loni , Patel Mihir , Luna‐Reyes Luis 
Published: June 2023

AbstractThe importance of involving humans in the data science process has been widely discussed in the literature. However, studies lack details onhowto involve humans in the process. Using a design science approach, this paper proposes and evaluates ahuman‐supervised data science frameworkin the context of local governments. Our findings suggest that the involvement of a stakeholder group, public managers in this case, in the process of data science project enhanced quality of data science outcomes. Public managers' detailed knowledge on both the data and context was beneficial for improving future data science infrastructure. In addition, the study suggests that local governments can harness the value of data‐driven approaches to policy and decision making through focalized investments in improving data and data science infrastructure, which includes culture and processes necessary to incorporate data science and analytics into the decision‐making process.
The “Collections as ML Data” checklist for machine learning and cultural heritage
Authors: Lee Benjamin Charles Germain 
Published: June 2023

AbstractWithin cultural heritage, there has been a growing and concerted effort to consider a critical sociotechnical lens when applying machine learning techniques to digital collections. Though the cultural heritage community has collectively developed an emerging body of work detailing responsible operations for machine learning in galleries, museums, archives, and libraries at the organizational level, there remains a paucity of guidelines created for researchers embarking on machine learning projects with digital collections. The manifold stakes and sensitivities involved in applying machine learning to cultural heritage underscore the importance of developing such guidelines. This article contributes to this need by formulating a detailed checklist with guiding questions and practices that can be employed while developing a machine learning project that utilizes cultural heritage data. I call the resulting checklist the “Collections as ML Data” checklist, which, when completed, can be published with the deliverables of the project. By surveying existing projects, including my own project, Newspaper Navigator, I justify the “Collections as ML Data” checklist and demonstrate how the formulated guiding questions can be employed by researchers.
In which fields are citations indicators of research quality?
Authors: Thelwall Mike , Kousha Kayvan , Stuart Emma , Makita Meiko , Abdoli Mahshid , Wilson Paul , Levitt Jonathan 
Published: June 2023

AbstractCitation counts are widely used as indicators of research quality to support or replace human peer review and for lists of top cited papers, researchers, and institutions. Nevertheless, the relationship between citations and research quality is poorly evidenced. We report the first large‐scale science‐wide academic evaluation of the relationship between research quality and citations (field normalized citation counts), correlating them for 87,739 journal articles in 34 field‐based UK Units of Assessment (UoA). The two correlate positively in all academic fields, from very weak (0.1) to strong (0.5), reflecting broadly linear relationships in all fields. We give the first evidence that the correlations are positive even across the arts and humanities. The patterns are similar for the field classification schemes of Scopus and Dimensions.ai, although varying for some individual subjects and therefore more uncertain for these. We also show for the first time that no field has a citation threshold beyond which all articles are excellent quality, so lists of top cited articles are not pure collections of excellence, and neither is any top citation percentile indicator. Thus, while appropriately field normalized citations associate positively with research quality in all fields, they never perfectly reflect it, even at high values.
Coupon or my privacy: How consumers choose to disclose their personal information and accept mobile location‐based advertising (LBA) through privacy calculus
Authors: Ryu Sann 
Published: June 2023

AbstractWith their capacity to track geo‐specific location information rapidly expanding, the practice of location‐based advertising (LBA) is becoming increasingly prevalent. While LBA can provide relevant and financial benefits, concerns about data collection and privacy are also on the rise. In the following two experiments, based on the privacy calculus theory and parallel mediation analyses, an explanatory framework is presented to lay out how consumers choose to disclose their personal information and accept LBA depending on location congruent levels of a mobile ad message by assessing the benefits as well as costs of disclosing personal information. In Study 1 (N = 186), it was found that both perceived benefits and costs fully mediated the experimental effects of location congruence (congruent vs. incongruent) on intentions to disclose personal information as well as accept LBA practice. In Study 2 (N = 230), a 2 (location congruence: congruent vs. incongruent) × 2 (sales promotion: yes vs. no) factorial experiment was conducted. The results from moderated mediation analysis showed that privacy concerns due to location tracking disappeared when sales promotion was offered. These results indicate that consumers' information disclosure and LBA acceptance can be understood as a trade‐off between incentives and privacy issues in which privacy concerns can be alleviated when financial benefits are offered.
Emergency management systems using mobile cloud computing: A survey
Authors: Nanda Sarmistha , Panigrahi Chhabi Rani , Pati Bibudhendu 
Published: June 2023

SummaryAmong today's rapidly evolving technologies, artificial intelligence plays a significant role in making decisions by a system without any human intervention. An Emergency Management System (EMS) is a decision support system where emergencies such as tsunami, landslide, fire, cyclone, and electrical short circuits can be prevented with prior detection and can be addressed immediately in an efficient way after the emergency occurred. Automation of EMS can avoid or manage multiple emergencies, which alternatively can save lives, economy, and environment. Quality demand response with fast data transfer, error minimized computation, and effective resource utilization is very much essential while developing the EMSs. This will provide a bridge between the technology and emergency responders. Resource‐limited smart devices can be made rich in computational behavior by outsourcing their requirements such as storage, virtual servers, and web services using Mobile Cloud Computing (MCC). In this work, we have done a detailed survey on both MCC applications and EMS applications proposed in the literature, and we have also identified the design challenges handled in both MCC and EMS applications. We have presented the design challenges and possible solutions for development of EMS using MCC. We propose an architecture for building an automated EMS using MCC. Finally, we conclude the paper with specific future directions.
Clinical determinants of involuntary psychiatric hospitalization: A clinical profile approach
Authors: Georgaca Eugenie , Machaira Sofia , Stamovlasis Dimitrios , Peppou Lily Evangelia , Papachristou Christina , Arvaniti Aikaterini , Samakouri Maria , Stylianidis Stelios , Bozikas Vasileios Panteleimon , Diakogiannis Ioannis , Fokas Konstantinos , Garyfallos Georgios , Gkolia Ioanna , Karpouza Vassiliki , Nimatoudis Ioannis , Patsinakidis Georgios , Sevris Dimitrios , Vlachaki Aikaterini 
Published: June 2023

AbstractObjectivesThe study examines the clinical determinants of involuntary psychiatric hospitalization. Specifically, it investigates whether distinct clinical profiles of hospitalized patients can be discerned, what other characteristics they are linked with, and which profiles predict involuntary admission.MethodsIn this cross‐sectional multicentre population study, data were collected for 1067 consecutive admissions in all public psychiatric clinics of Thessaloniki, Greece, during 12 months. Through Latent Class Analysis distinct patient clinical profiles were established based on Health of the Nation Outcome Scales ratings. The profiles were then correlated with sociodemographic, other clinical, and treatment‐related factors as covariates and admission status as a distal outcome.ResultsThree profiles emerged. The “Disorganized Psychotic Symptoms” profile, combining positive psychotic symptomatology and disorganization, included mainly men, with previous involuntary hospitalizations and poor contact with mental health services and adherence to medication, indicating a deteriorating condition and chronic course. Τhe “Active Psychotic Symptoms” profile included younger persons with positive psychotic symptomatology in the context of normal functioning. The “Depressive Symptoms” profile, characterized by depressed mood coupled with nonaccidental self‐injury, included mainly older women in regular contact with mental health professionals and treatment. The first two profiles were associated with involuntary admission and the third with voluntary admission.ConclusionsIdentifying patient profiles allows the examination of the combined effect of clinical, sociodemographic, and treatment‐related characteristics as risk factors for involuntary hospitalization, moving beyond the variable‐centered approach mainly adopted to date. The identification of two profiles associated with involuntary admission necessitates the development of interventions tailored to chronic patients and younger persons suffering from psychosis respectively.
Oxymatrine Loaded Cross‐Linked PVA Nanofibrous Scaffold: Design and Characterization and Anticancer Properties
Authors: Ahmed Salahuddin , Keniry Megan , Anaya‐Barbosa Narcedalia , Padilla Victoria , Javed Md Noushad , Gilkerson Robert , Narula Acharan S. , Ibrahim Eman , Lozano Karen 
Published: June 2023

AbstractThis study focuses on the fabrication, characterization and anticancer properties of biocompatible and biodegradable composite nanofibers consisting of poly(vinyl alcohol) (PVA), oxymatrine (OM), and citric acid (CA) using a facile and high‐yield centrifugal spinning process known as Forcespinning. The effects of varying concentrations of OM and CA on fiber diameter and molecular cross‐linking are investigated. The morphological and thermo‐physical properties, as well as water absorption of the developed nanofiber‐based mats are characterized using microscopical analysis, energy dispersive X‐ray spectroscopy, Fourier transform infrared spectroscopy, differential scanning calorimetry, and thermogravimetric analysis. In vitro anticancer studies are conducted with HCT116 colorectal cancer cells. Results show a high yield of long fibers embedded with beads. Fiber average diameters range between 462 and 528 nm depending on OM concentration. The thermal analysis results show that the fibers are stable at room temperature. The anticancer study reveals that PVA nanofiber membrane with high concentrations of OM can suppress the proliferation of HCT116 colorectal cancer cells. The study provides a comprehensive investigation of OM embedded into nanosized PVA fibers and the prospective application of these membranes as a drug delivery system.
Biomaterial‐based gene therapy
Authors: Yu Yi , Gao Yijun , He Liming , Fang Bairong , Ge Wenhui , Yang Pu , Ju Yikun , Xie Xiaoyan , Lei Lanjie 
Published: June 2023

AbstractGene therapy, a medical approach that involves the correction or replacement of defective and abnormal genes, plays an essential role in the treatment of complex and refractory diseases, such as hereditary diseases, cancer, and rheumatic immune diseases. Nucleic acids alone do not easily enter the target cells due to their easy degradation in vivo and the structure of the target cell membranes. The introduction of genes into biological cells is often dependent on gene delivery vectors, such as adenoviral vectors, which are commonly used in gene therapy. However, traditional viral vectors have strong immunogenicity while also presenting a potential infection risk. Recently, biomaterials have attracted attention for use as efficient gene delivery vehicles, because they can avoid the drawbacks associated with viral vectors. Biomaterials can improve the biological stability of nucleic acids and the efficiency of intracellular gene delivery. This review is focused on biomaterial‐based delivery systems in gene therapy and disease treatment. Herein, we review the recent developments and modalities of gene therapy. Additionally, we discuss nucleic acid delivery strategies, with a focus on biomaterial‐based gene delivery systems. Furthermore, the current applications of biomaterial‐based gene therapy are summarized.
Topology Planning in Swarm Production System: Framework and Optimization
Authors: Avhad Akshay , Schou Casper , Madsen Ole 
Published: June 2023

AbstractA Swarm Production System (SPS) aims to be an agile and resilient Reconfigurable manufacturing system (RMS) paradigm that incorporates mobile workstations and transport robots on the factory production floor. This paper primarily focuses on SPS’s initial but recurring planning stage termed topology planning, which dynamically changes throughout the production runtime with spatially adaptive workstations and transporters handled exclusively by a Topology Manager (TM). TM is essential to multi-variant production with the optimal positioning of the workstations and provides a topology that optimizes the traffic flow for the product carrier robots. TM is a bridge to enable SPS to integrate with general planning and scheduling systems like ERP and MES and is comprised of a Topology Planner (TP) that evaluates the ideal configuration of on factory floor for a batch of product mix and a Reconfiguration Decision System (RDS) that decides on applying the estimated new topology during the batch changeover. The paper proposes a framework for the TM to identify its essential functionalities, responsibilities and working principle in a swarm production system. The paper also describes a grid-based heuristic approach applicable to two-dimensional spatial problems to reduce the complexity of the NP-hard problem. The paper focuses on a framework to estimate a reconfigurable shop floor layout with a Force-directed Graph-theory algorithm. A stochastic statistical model evaluates the performance of the optimal topology for throughput and makespan.
Using Meta-Learning to Reduce the Effort of Training New Workpiece Geometries for Entanglement Detection in Bin-Picking Applications
Authors: Moosmann Marius , Bleifuß Julian , Rosport Johannes , Spenrath Felix , Kraus Werner , Bormann Richard , Huber Marco F. 
Published: June 2023

AbstractIn this paper, we introduce a scaling method for the training of neural networks used for entanglement detection in Bin-Picking. In the Bin-Picking process of complex-shaped and chaotically stored objects, entangled workpieces are a common source of problems. It has been shown that deep neural networks, which are trained using supervised learning, can be used to detect entangled workpieces. However, this strategy requires time-consuming data generation and an additional training process when adapting to previously unseen geometries. To solve this problem, we analyze and compare several Meta-Learning techniques like Reptile, MAML and TAMS for their feasibility as a scaling method for the entanglement detection. These methods search for a strongly generalized model for entanglement detection by learning from the training process of various workpieces with different geometries. Using this generalized model for entanglement detection as initialization helps to increase the learning success with only few training epochs and reduces the required amount of data and therefore the setup effort significantly.