bluuebunny/crossref_metadata_2025_split huggingface dataset:
Environmental Self-Identity and Pro-Environmental Behavior in Climate Change Issue: Mediation Effect of Belief in Global Warming and Guilty Feeling
Authors: Shadiqi Muhammad Abdan , Djuwita Ratna , Febriana Silvia Kristanti Tri , Septiannisa Lulu , Wildi Muhammad , Rahmawati Yuli 
Published: December 2022

Abstract
There are several arguments about the role of identity factors behind someone’s pro-environmental behavior (PEB) encountering climate change. The authors assumed other factors might explain the correlation between identity factors and PEB. This study aims to examine the mediating effect of belief in global warming and guilty feeling on the correlation between environmental self-identity and pro-environmental behavior related to climate change issues. We conducted a cross-sectional survey involving 202 university students selected using the purposive sampling method. Using multiple mediators of model 4 from PROCESS Macro Hayes, the result shows that environmental self-identity is the most decisive variable in predicting PEB. Belief in global warming is also a significant PEB predictor, while the guilty feeling is insignificant. The mediation model analysis showed that belief in global warming partly mediated the correlation between environmental self-identity and PEB, and the guilty feeling was not a significant mediator. This finding explains that self-identity as an environmentalist is directly related to PEB, or it could also be mediated through the belief in global warming. This study implies that encouraging students to be pro-environmentalist and developing the belief in current climate change is essential to increase students’ PEB.
The Effectiveness of Community Participation in Urban Water Supply: A Narrative Review
Authors: Purba Debora E. , Wahyu Agung Minto 
Published: December 2022

Abstract
Debates on the effectiveness of top-down versus bottom-up approaches in water management in urban areas are still ongoing. This study examines the effectiveness of community participation as a bottom-up approach. The narrative review is employed to describe 16 cases of community participation. Three themes emerge from the case studies: the success story and failure of community participation from outside of Indonesia, lessons learned and best practices of community participation from Indonesia, and self-supply as an alternative water provision. Then a discussion of the findings based on the themes found in the literature was carried out. The study found that community participation would be successful if the participatory elements were satisfied, including sufficient capacity and knowledge of the community involved to ensure meaningful participation, involving local government in the education of how to manage and conserve water supply, and involving women in project sustainability. Hence, a pure community participation approach, particularly in disadvantaged urban areas, cannot be achieved. Therefore, a holistic approach combining a top-down and a bottom-up approach is recommended. Self-supply water provision emerged as another method that has largely been ignored by the government and academia but serves as a common practice, particularly in developing countries.
New kind of IoT platform for Smart City by innovative procurement procedure
Authors: Vehviläinen M , Vainio T 
Published: December 2022

Abstract
The City of Tampere, like many other cities, has opened databases available to citizens and businesses. The information has been opened using existing information channels. Since the city typically works in silos, there is also open data made available in silos. The process by which information is first collected and then made openly available does not serve the needs of a digitalized society. Since the problem is common, measures to remedy it had been set as an objective under the EU Smart City theme, from which the City of Tampere received funding. The aim was to design and acquire an information system capable of both collecting and distributing information to both internal and external users. The specific objectives were to make information flow between the actions of the city and to facilitate the participation of the residents in the decision, rather than simply being the recipients of the information. Innovative procurement was applied to acquire a completely new type of information system. Hackathons were used to refine the characteristics of the acquisition with the participation of multi-disciplinary consortia. The project resulted in an IoT platform for the City of Tampere, which streamlines information processing and helps cooperation within the city’s departments. The model of public procurement because of the project is generally valid and useful in other cities as well.
Socially and culturally sustainable public participation in urban development: Map questionnaire as a bridge-building tool in Kontula shopping mall
Authors: Nummi P , Harsia E 
Published: December 2022

Abstract
Social sustainability is a prerequisite for legitimate public participation. Currently, multilingualism and multiculturalism place particular demands on participation methods and processes. This article presents a case study where entrepreneurs with an immigrant background were engaged in the development process of Kontula shopping mall in Helsinki, Finland. The results show that an existing digital participation tool, specifically a map questionnaire can be utilized in a novel way in a multicultural context to build a bridge between this marginalized group and urban development. New skills and tailored methods are required to support the participation of immigrant entrepreneurs.
The modern tropical architecture and social-environmental classification - An analysis of the Prefeito Mendes de Moraes Housing Complex
Authors: Calazães M B O , Di Trapano P , Bastos L E G , Pecly M L V 
Published: December 2022

Abstract
Modern architecture took different forms and shapes around the world, trying, even though its well defined concepts and values, to find out ideal measures and answers to a technological contemporary world. In the tropics this architecture is largely related to the construction of strong and modern governmental institutions. In this cenarium, social housing and adaptation to the climate were really popular topics, being carefully cared for by Brazilian modernists. The present study analyzes the residential block A from the social housing complex Conjunto Habitacional Prefeito Mendes de Moraes, based on the requirements of a social-environmental classification instrument created specifically for Brazilians construction sector, called Selo Casa Azul. The building to be analyzed is an icon of modern architecture, and part of a pioneering experience in the construction of a “neighborhood unit”. Designed by Afonso Reidy and Carmen Portinho in 1946 It contains 272 apartments grouped in one single 260 meters long block and a range of design solutions regarding thermal comfort by passive solutions. For the realization of the work iconographic material was collected and a site visit was taken. As a result, it was possible to observe the topicality of the building, despite the many years that separate the project from the creation of the seal, and the consequent changes in the construction technologies and benchmarks.
Combining BIM & GIS Information to Simulate Circularity of Building Stocks in a City—A Case Study of Taipei City
Authors: Chang Y T , Sierra E M , Hsieh S H 
Published: December 2022

Abstract
Building stocks in cities have consumed a large amount of energy and resources globally, resulting in serious greenhouse gas pollution and environmental problems. As a circular economy has become one of the effective solutions to current environmental problems, energy and material circularity indicators of building stocks in cities become important instruments for city planners in creating sustainable and resilient cities. However, such evaluation requires a high level of integration of both spatial and attribute information of both buildings and cities. A highly integrated system is required to produce reliable analysis results. This study aims to create an evaluation tool for city planners through utilizing information contained via Building Information Modeling and Geographic Information System. Through this tool, planners and decision makers can understand current and future circularity and environmental impacts of building stocks in cities and therefore can propose the most suitable planning and governance strategies and policies. Through visualization of simulation results on an information platform, the awareness of a circular city could also be raised. Taipei city and its city master plan are used as a case study for validation of the proposed tool.
Successful social entrepreneurship and its potential contribution to more sustainable cities and built environments
Authors: Antuña-Rozado C , García-Navarro J 
Published: December 2022

Abstract
Globally, social entrepreneurship has caught the attention of academics and researchers, and there is a growing body of knowledge on how it differs from traditional entrepreneurship and where its main challenges lie. In Finland, for example, the number of social enterprises has been growing consistently since 2010. The Finnish case is particularly intriguing because of the country’s wide social coverage, which on a first approach, might lead to the assumption that social enterprises are not “so necessary” in Finland. However, the matter is more complex than it initially appears. Moreover, the European commitment to make the transition to a just and green society, in compliance with the ambitious objectives of the European Green Deal, as well as to materialize the so-called economy of wellbeing, exerts pressure on public administrations that perhaps social enterprises can help alleviate.
Despite many open questions, successful social enterprises-that is, those whose mission, strategy, and impact measurement are strongly aligned-can be critical in addressing social or environmental issues that remain largely unaddressed by other actors, whether public or private, in any field, without compromising their growth potential and their ability to attract interested investors.
Considering, on the one hand, the wide range of opportunities offered by the built environment in terms of open challenges; and on the other hand, the fact that in the future there will be more pressure on companies to be more socially responsible, it may be worth looking at how social enterprises can help close these gaps.
To encourage the contribution of social enterprises to more sustainable cities and built environments, this document will discuss what successful social entrepreneurship looks like and show a couple of examples of social enterprises from the UK and Norway, one operating in the affordable housing sector and the other dedicated to sustainable urban development.
Building data models and data sharing. Purpose, approaches, and a case study on explainable demand response
Authors: Sakkas Nikos , Chaniotaki Christina , Sakkas Nikitas 
Published: December 2022

Abstract
There are several approaches to building data modelling and there is a well-established rationale for the various related standards emerging in the area. In this work we acknowledge the importance of these approaches but also discuss their limitations. To this extent we draw the line between open data and open sharing and discuss its relevance. We also introduce a case study of a demand response application integrated with a XAI (explainable artificial intelligence) demand forecasting and we use it to practically highlight how open data and open sharing features interplay and integrate. We also discuss how open building model design will need to develop, so as to account for vital, in some cases, explainability information.
ISAnalytics enables longitudinal and high-throughput clonal tracking studies in hematopoietic stem cell gene therapy applications
Authors: Pais Giulia , Spinozzi Giulio , Cesana Daniela , Benedicenti Fabrizio , Albertini Alessandra , Bernardo Maria Ester , Gentner Bernhard , Montini Eugenio , Calabria Andrea 
Published: December 2022

Abstract
Longitudinal clonal tracking studies based on high-throughput sequencing technologies supported safety and long-term efficacy and unraveled hematopoietic reconstitution in many gene therapy applications with unprecedented resolution. However, monitoring patients over a decade-long follow-up entails a constant increase of large data volume with the emergence of critical computational challenges, unfortunately not addressed by currently available tools. Here we present ISAnalytics, a new R package for comprehensive and high-throughput clonal tracking studies using vector integration sites as markers of cellular identity. Once identified the clones externally from ISAnalytics and imported in the package, a wide range of implemented functionalities are available to users for assessing the safety and long-term efficacy of the treatment, here described in a clinical trial use case for Hurler disease, and for supporting hematopoietic stem cell biology in vivo with longitudinal analysis of clones over time, proliferation and differentiation. ISAnalytics is conceived to be metadata-driven, enabling users to focus on biological questions and hypotheses rather than on computational aspects. ISAnalytics can be fully integrated within laboratory workflows and standard procedures. Moreover, ISAnalytics is designed with efficient and scalable data structures, benchmarked with previous methods, and grants reproducibility and full analytical control through interactive web-reports and a module with Shiny interface. The implemented functionalities are flexible for all viral vector-based clonal tracking applications as well as genetic barcoding or cancer immunotherapies.
GraphLncLoc: long non-coding RNA subcellular localization prediction using graph convolutional networks based on sequence to graph transformation
Authors: Li Min , Zhao Baoying , Yin Rui , Lu Chengqian , Guo Fei , Zeng Min 
Published: December 2022

AbstractThe subcellular localization of long non-coding RNAs (lncRNAs) is crucial for understanding lncRNA functions. Most of existing lncRNA subcellular localization prediction methods use k-mer frequency features to encode lncRNA sequences. However, k-mer frequency features lose sequence order information and fail to capture sequence patterns and motifs of different lengths. In this paper, we proposed GraphLncLoc, a graph convolutional network-based deep learning model, for predicting lncRNA subcellular localization. Unlike previous studies encoding lncRNA sequences by using k-mer frequency features, GraphLncLoc transforms lncRNA sequences into de Bruijn graphs, which transforms the sequence classification problem into a graph classification problem. To extract the high-level features from the de Bruijn graph, GraphLncLoc employs graph convolutional networks to learn latent representations. Then, the high-level feature vectors derived from de Bruijn graph are fed into a fully connected layer to perform the prediction task. Extensive experiments show that GraphLncLoc achieves better performance than traditional machine learning models and existing predictors. In addition, our analyses show that transforming sequences into graphs has more distinguishable features and is more robust than k-mer frequency features. The case study shows that GraphLncLoc can uncover important motifs for nucleus subcellular localization. GraphLncLoc web server is available at http://csuligroup.com:8000/GraphLncLoc/.
CODA: a combo-Seq data analysis workflow
Authors: Nazzari Marta , Hauser Duncan , van Herwijnen Marcel , Romitti Mírian , Carvalho Daniel J , Kip Anna M , Caiment Florian 
Published: December 2022

Abstract
The analysis of the combined mRNA and miRNA content of a biological sample can be of interest for answering several research questions, like biomarkers discovery, or mRNA–miRNA interactions. However, the process is costly and time-consuming, separate libraries need to be prepared and sequenced on different flowcells. Combo-Seq is a library prep kit that allows us to prepare combined mRNA–miRNA libraries starting from very low total RNA. To date, no dedicated bioinformatics method exists for the processing of Combo-Seq data. In this paper, we describe CODA (Combo-seq Data Analysis), a workflow specifically developed for the processing of Combo-Seq data that employs existing free-to-use tools. We compare CODA with exceRpt, the pipeline suggested by the kit manufacturer for this purpose. We also evaluate how Combo-Seq libraries analysed with CODA perform compared with conventional poly(A) and small RNA libraries prepared from the same samples. We show that using CODA more successfully trimmed reads are recovered compared with exceRpt, and the difference is more dramatic with short sequencing reads. We demonstrate how Combo-Seq identifies as many genes and fewer miRNAs compared to the standard libraries, and how miRNA validation favours conventional small RNA libraries over Combo-Seq. The CODA code is available at https://github.com/marta-nazzari/CODA.
Expert-Level Immunofixation Electrophoresis Image Recognition based on Explainable and Generalizable Deep Learning
Authors: Hu Honghua , Xu Wei , Jiang Ting , Cheng Yuheng , Tao Xiaoyan , Liu Wenna , Jian Meiling , Li Kang , Wang Guotai 
Published: December 2022

Abstract

Background
Immunofixation electrophoresis (IFE) is important for diagnosis of plasma cell disorders (PCDs). Manual analysis of IFE images is time-consuming and potentially subjective. An artificial intelligence (AI) system for automatic and accurate IFE image recognition is desirable.

Methods
In total, 12 703 expert-annotated IFE images (9182 from a new IFE imaging system and 3521 from an old one) were used to develop and test an AI system that was an ensemble of 3 deep neural networks. The model takes an IFE image as input and predicts the presence of 8 basic patterns (IgA-, IgA-, IgG-, IgG-, IgM-, IgM-, light chain and ) and their combinations. Score-based class activation maps (Score-CAMs) were used for visual explanation of the models prediction.

Results
The AI model achieved an average accuracy, sensitivity, and specificity of 99.82, 93.17, and 99.93, respectively, for detection of the 8 basic patterns, which outperformed 4 junior experts with 1 years experience and was comparable to a senior expert with 5 years experience. The Score-CAMs gave a reasonable visual explanation of the prediction by highlighting the target aligned regions in the bands and indicating potentially unreliable predictions. When trained with only the new system images, the models performance was still higher than junior experts on both the new and old IFE systems, with average accuracy of 99.91 and 99.81, respectively.

Conclusions
Our AI system achieved human-level performance in automatic recognition of IFE images, with high explainability and generalizability. It has the potential to improve the efficiency and reliability of diagnosis of PCDs.
Interactive Visualization of Large Point Clouds Using an Autotuning Multiresolution Out-Of-Core Strategy
Authors: Teijeiro Diego , Amor Margarita , Doallo Ramón , Deibe David 
Published: December 2022

Abstract
Due to the increasingly large amount of data acquired into point clouds, from LiDAR (Light Detection and Ranging) sensors and 2D/3D sensors, massive point clouds processing has become a topic with high interest for several fields. Current client-server applications usually use multiresolution out-of-core proposals; nevertheless, the construction of the data structures required is very time-consuming. Furthermore, these multiresolution approaches present problems regarding point density changes between different levels of detail and artifacts due to the rendering of elements entering and leaving the field of view. We present an autotuning multiresolution out-of-core strategy to avoid these problems. Other objectives are reducing loading times while maintaining low memory requirements, high visualization quality and achieving interactive visualization of massive point clouds. This strategy identifies certain parameters, called performance parameters, and defines a set of premises to obtain the goals mentioned above. The optimal parameter values depend on the number of points per cell in the multiresolution structure. We test our proposal in our web-based visualization software designed to work with the structures and storage format used and display massive point clouds achieving interactive visualization of point clouds with more than 27 billion points.
Perceptual Image Hashing Based on Canny Operator and Tensor for Copy-Move Forgery Detection
Authors: Liu Mengqi , Gao Hang , Xia Xiaofan , Gui Suying , Gao Tiegang 
Published: December 2022

Abstract
Copy-move is a common image forgery operation, which copies and moves a block of an image from one position to another place. Image hashing refers to extracting a unique number sequence from the image by using various image features. In practical application, image hashing is used to replace the image itself, which effectively reduces the cost of image storage and computational complexity. In this paper, we propose a novel image hash extraction scheme: constructing image hashing by combining local feature based on Canny operator and global feature based on tensor. In addition, instead of using the traditional correlation coefficient or Hamming distance, a novel method is proposed to calculate the hash distances. A large number of experiments have proved that our image hashing can achieve a better balance between robustness and discrimination with a shorter hash length. What’s more, we can directly locate the forgery areas from the hashing for copy-move forged images.