bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Effect of Washing and Air Scouring on The Performance of Immersed Membrane Microfiltration
Authors: Marsono Bowo Djoko , Ujiane Virgina Jane 
Published: December 2022

Abstract
The quality of raw water in Surabaya is decreasing, and conventional treatment units are no longer efficient in treating raw water to become drinking water. Microfiltration membranes applied in sedimentation units can produce better quality results on limited land that uses a small number of chemicals. The material will be deposited on the membrane’s surface throughout the filtration process. The deposition of these materials can cause fouling, leading to a decrease in membrane productivity. This study involved a detailed investigation into the membrane washing method and air scouring implication in the microfiltration process to understand the most efficient technique to increase membrane productivity. Washing was done manually with a gentle scrubbing method and backwashing with time variation for 20 minutes and 10 minutes. Air Scouring was done with air pressure variations of 0,3 bar and 0,6 bar. The study showed that water flux was affected by the washing and air scouring method. Permeate quality was also found to be affected by washing and air scouring variation. Permeate quality analyses in this study are turbidity, TSS, micro-plastics, E-Coli, and organic substances.
Determining the Feasibility of a Circular Economy for Plastic Waste from the Construction Sector in New Zealand
Authors: Berry T-A , Low J K , Wallis S L , Kestle L , Day A , Hernandez G 
Published: December 2022

Abstract
By 2012, the annual quantity of C&D waste produced by 40 countries had reached three billion tonnes, contributing 10-50% to total municipal solid waste around the world. Recent data from Australia and New Zealand estimated a combined contribution of approximately 28 million tonnes C&D waste to landfill for just 0.4% of the world’s population. If C&D waste was produced at an equal rate around the world, global production could be close to seven billion tonnes. In 2015, the global production of plastic waste from building and construction was 13 million tonnes. It is estimated that annually, C&D waste contributes ≥25,000 tonnes to the total amount of plastic landfilled in one major city (Auckland) alone. Waste audits from four sites demonstrated that this was predominantly polyethylene (PE) or polyvinyl chloride (PVC), and was derived from packaging, building componentry and building protection equally. The aims of this study were to implement a ‘foundations to completion’ plastic waste audit on a new secondary school in Auckland, New Zealand. This sheds light on the nature of the plastic waste, e.g., type, use and potential for recyclability or reuse. The aim was to also identify the challenges in the construction industry that hinder effective waste diversion from landfill, and to trial practical on-site solutions.
Development of A Framework for Urban Heat Adaptation in 15-Minute City
Authors: Chen Xueke , He Bao-Jie 
Published: December 2022

Abstract
Urban heat has challenged many cities with serious environmental, economic, social and health consequences. Urban heat also deteriorates the achievement of 15-Minute City (FMC) which is a basic community unit to apply planning and design practices to achieve safety, sustainability, resilience and inclusion required by the goal of Sustainable Cities and Communities (Goal 11) by the United Nations Sustainable Development Goals. Heat adaptation strategies are of importance to secure people from heat-induced immediate impacts. However, there are limited studies on how to integrate heat adaptation strategies into FMC for avoiding heat-induced impacts. Therefore, this paper aims to construct a framework that can support the integration of urban heat adaptation (UHA) into FMC planning and design. The framework is expected to combine FMC and UHA goals, identify urban heat impacts, recognise heat-induced impact influential factors, suggest heat adaptation strategies and optimise adaptation performance. Following this conception, an analysis of FMC and UHA goals, heat-induced impact and assessment indicators, regulatory factors, heat adaptation strategies, and sensitivity assessment models has been conducted. Finally, a comprehensive FMC-UHA framework for addressing urban heat challenges is generated. Overall, this framework provides a reference for creating cool 15-minute cities, as well as guidance on how to avoid heat impacts, health and safety inclusion, low-carbon travel, and outdoor productivity.
Developing a ‘Design with Reuse’ guide. Case study analysis from a design procedural perspective for the integration of reclaimed wood in façade architecture.
Authors: Kawa G. , Galle W. , De Temmerman N. 
Published: December 2022

Abstract
Today’s excessing construction industry leads to exhaustion of raw materials and large-scale waste production, resulting in environmental and societal problems. In response, there is a growing interest among academic researchers, designers, and policymakers to accelerate the shift toward a circular construction sector. To achieve this transition towards an economy with closed material loops, solutions for resources need to be developed and adopted. Material reuse is such a solution. Yet today reuse is still an uncommon practice.
This study aims to increase the viability of reuse possibilities of reclaimed materials in façade architecture and to attain insights into the design processes that enable and support reuse. Reuse projects are emerging, yet limited literature exists on design processes to reuse building materials. Through collaboration with designers of reuse projects, their design approach can be grasped and inspire others. In this paper, the focus is laid on two exemplary building projects, in Belgium and France, where reclaimed wood is used for the façade architecture. Interviews with designers allowed to map and analyse their design processes, revealing similar crucial steps for reuse in each design phase. As a result, a prototype of a ‘design with reuse’ guide is developed. Such a design instrument offers guidance for building designers to consider and include reclaimed materials, and hence, more circular design solutions, in their projects.
Embodied Carbon: A call to the building industry
Authors: Rondinel-Oviedo Daniel R. , Keena Naomi 
Published: December 2022

AbstractThe demand for building materials will increase drastically in the following decades due to urbanization and population growth. Buildings generate almost 40% of annual global carbon dioxide (CO2) emissions contributing to human-induced climate change. Of this fraction, 11% is associated with material production and building construction, and 28% is contributed to the operational activities of a building (heating, cooling, lighting). One primary measure of the climate impact of buildings is embodied carbon (EC). This paper aims to find the recent developments in relation to embodied carbon within the building industry. To achieve this goal, a literature review was conducted to study the EC concept concerning materials and construction. The article explores and presents the state-of-the-art of diverse research and development related to this concept, focusing primarily on (1) the carbon cycle and the building sector, (2) EC benchmarking, (3) bio-based materials, and (4) other low-carbon material alternatives such as the development of carbon capture technologies. The objective of this paper is to summarize current and emerging trends as well as research priorities and tools to inform designers and engineers and facilitate their decision-making during the design process. It also aims to facilitate in advancing the creation of regulations and policies toward natural-based material solutions and the development of low-carbon high-tech material technologies deemed central for a sustainable built environment.
Variability in modelled reservoir greenhouse gas emissions: comparison of select US hydropower reservoirs against global estimates
Authors: Hansen Carly , Pilla Rachel , Matson Paul , Skinner Bailey , Griffiths Natalie , Jager Henriette 
Published: December 2022

Abstract
Greenhouse gas (GHG) emissions from reservoirs have most often been evaluated on a global extent through areal scaling or linear-regression models. These models typically rely on a limited number of characteristics such as age, size, and average temperature to estimate per reservoir or areal flux. Such approaches may not be sufficient for describing conditions at all types of reservoirs. Emissions from hydropower reservoirs have received increasing attention as industry and policy makers seek to better understand the role of hydropower in sustainable energy solutions. In the United States (US), hydropower reservoirs span a wide range of climate regions and have diverse design and operational characteristics compared to those most heavily represented in model literature (i.e., large, tropical reservoirs). It is not clear whether estimates based on measurements and modeling of other subsets of reservoirs describe the diverse types of hydropower reservoirs in the US. We applied the Greenhouse Gas from Reservoirs (G‐res) emissions model to 28 hydropower reservoirs located in a variety of ecological, hydrological, and climate settings that represent the range of sizes and types of facilities within the US hydropower fleet. The dominant pathways for resulting GHG emissions estimates in the case-study reservoirs were diffusion of carbon dioxide, followed by methane ebullition. Among these case-study reservoirs, total post-impoundment areal flux of carbon ranges from 84 to 767 mgCm−2d−1, which is less variable than what has been reported through measurements at other US and global reservoirs. The net GHG reservoir footprint was less variable and towards the lower end of the range observed from modeling larger global reservoirs, with a range of 138 to 1,052 g CO2 eq m−2 y−1, while the global study reported a range of 115 to 145,472 g CO2 eq m−2 y−1. High variation in emissions normalized with respect to area and generation highlights the need to be cautious when using area or generation in predicting or communicating emissions footprints for reservoirs relative to those of other energy sources, especially given that many of the hydropower reservoirs in the US serve multiple purposes beyond power generation.
Integrated model for BIM and risk data in construction projects
Authors: Yasser Mamdoh , Rashid Ibrahim Abdel , Nagy Alsayed Mohamed , Elbehairy Hatem Shaker 
Published: December 2022

Abstract
The acceleration in the adoption of building information modelling (BIM) techniques in many countries around the world makes BIM technology a global research trend. A few studies have shown how risk information can be properly managed in a BIM environment. This research aims to improve the risk management process using BIM techniques and proposes a new methodology within a BIM system to manage risk information for construction projects. The risk register, which represents the final risk information, and the Revit program, which represents one of the BIM platforms, are integrated using the Dynamo program in this paper’s proposed new method for integrating risk information with the BIM system. Two types of information are automatically transferred from the risk register to the BIM model: first, data and numbers, such as ‘Risk score,’ ‘Risk status,’ etc; this allows integration of risk information with the rest of the project parameters in the BIM system to achieve BIM system integration. Second, colors, where the elements are coloured the same colour as the degree of risk in the risk register. The proposed methodology demonstrates how visualisation can be used to identify risks, as visualisation is one of the most important features of BIM, and how risk information can be linked to BIM, making risk management more effective and accurate than the traditional method.
Comprehensive evaluation of peptide de novo sequencing tools for monoclonal antibody assembly
Authors: Beslic Denis , Tscheuschner Georg , Renard Bernhard Y , Weller Michael G , Muth Thilo 
Published: December 2022

Abstract
Monoclonal antibodies are biotechnologically produced proteins with various applications in research, therapeutics and diagnostics. Their ability to recognize and bind to specific molecule structures makes them essential research tools and therapeutic agents. Sequence information of antibodies is helpful for understanding antibody–antigen interactions and ensuring their affinity and specificity. De novo protein sequencing based on mass spectrometry is a valuable method to obtain the amino acid sequence of peptides and proteins without a priori knowledge. In this study, we evaluated six recently developed de novo peptide sequencing algorithms (Novor, pNovo 3, DeepNovo, SMSNet, PointNovo and Casanovo), which were not specifically designed for antibody data. We validated their ability to identify and assemble antibody sequences on three multi-enzymatic data sets. The deep learning-based tools Casanovo and PointNovo showed an increased peptide recall across different enzymes and data sets compared with spectrum-graph-based approaches. We evaluated different error types of de novo peptide sequencing tools and their performance for different numbers of missing cleavage sites, noisy spectra and peptides of various lengths. We achieved a sequence coverage of 97.69–99.53% on the light chains of three different antibody data sets using the de Bruijn assembler ALPS and the predictions from Casanovo. However, low sequence coverage and accuracy on the heavy chains demonstrate that complete de novo protein sequencing remains a challenging issue in proteomics that requires improved de novo error correction, alternative digestion strategies and hybrid approaches such as homology search to achieve high accuracy on long protein sequences.
spaCI: deciphering spatial cellular communications through adaptive graph model
Authors: Tang Ziyang , Zhang Tonglin , Yang Baijian , Su Jing , Song Qianqian 
Published: December 2022

Abstract
Cell–cell communications are vital for biological signalling and play important roles in complex diseases. Recent advances in single-cell spatial transcriptomics (SCST) technologies allow examining the spatial cell communication landscapes and hold the promise for disentangling the complex ligand–receptor (L–R) interactions across cells. However, due to frequent dropout events and noisy signals in SCST data, it is challenging and lack of effective and tailored methods to accurately infer cellular communications. Herein, to decipher the cell-to-cell communications from SCST profiles, we propose a novel adaptive graph model with attention mechanisms named spaCI. spaCI incorporates both spatial locations and gene expression profiles of cells to identify the active L–R signalling axis across neighbouring cells. Through benchmarking with currently available methods, spaCI shows superior performance on both simulation data and real SCST datasets. Furthermore, spaCI is able to identify the upstream transcriptional factors mediating the active L–R interactions. For biological insights, we have applied spaCI to the seqFISH+ data of mouse cortex and the NanoString CosMx Spatial Molecular Imager (SMI) data of non-small cell lung cancer samples. spaCI reveals the hidden L–R interactions from the sparse seqFISH+ data, meanwhile identifies the inconspicuous L–R interactions including THBS1−ITGB1 between fibroblast and tumours in NanoString CosMx SMI data. spaCI further reveals that SMAD3 plays an important role in regulating the crosstalk between fibroblasts and tumours, which contributes to the prognosis of lung cancer patients. Collectively, spaCI addresses the challenges in interrogating SCST data for gaining insights into the underlying cellular communications, thus facilitates the discoveries of disease mechanisms, effective biomarkers and therapeutic targets.
NetSHy: network summarization via a hybrid approach leveraging topological properties
Authors: Vu Thao , Litkowski Elizabeth M , Liu Weixuan , Pratte Katherine A , Lange Leslie , Bowler Russell P , Banaei-Kashani Farnoush , Kechris Katerina J 
Published: December 2022

Abstract

Motivation
Biological networks can provide a system-level understanding of underlying processes. In many contexts, networks have a high degree of modularity, i.e. they consist of subsets of nodes, often known as subnetworks or modules, which are highly interconnected and may perform separate functions. In order to perform subsequent analyses to investigate the association between the identified module and a variable of interest, a module summarization, that best explains the module’s information and reduces dimensionality is often needed. Conventional approaches for obtaining network representation typically rely only on the profiles of the nodes within the network while disregarding the inherent network topological information.

Results
In this article, we propose NetSHy, a hybrid approach which is capable of reducing the dimension of a network while incorporating topological properties to aid the interpretation of the downstream analyses. In particular, NetSHy applies principal component analysis (PCA) on a combination of the node profiles and the well-known Laplacian matrix derived directly from the network similarity matrix to extract a summarization at a subject level. Simulation scenarios based on random and empirical networks at varying network sizes and sparsity levels show that NetSHy outperforms the conventional PCA approach applied directly on node profiles, in terms of recovering the true correlation with a phenotype of interest and maintaining a higher amount of explained variation in the data when networks are relatively sparse. The robustness of NetSHy is also demonstrated by a more consistent correlation with the observed phenotype as the sample size decreases. Lastly, a genome-wide association study is performed as an application of a downstream analysis, where NetSHy summarization scores on the biological networks identify more significant single nucleotide polymorphisms than the conventional network representation.

Availability and implementation
R code implementation of NetSHy is available at https://github.com/thaovu1/NetSHy

Supplementary information
Supplementary data are available at Bioinformatics online.
NanoSNP: a progressive and haplotype-aware SNP caller on low-coverage nanopore sequencing data
Authors: Huang Neng , Xu Minghua , Nie Fan , Ni Peng , Xiao Chuan-Le , Luo Feng , Wang Jianxin 
Published: December 2022

Abstract

Motivation
Oxford Nanopore sequencing has great potential and advantages in population-scale studies. Due to the cost of sequencing, the depth of whole-genome sequencing for per individual sample must be small. However, the existing single nucleotide polymorphism (SNP) callers are aimed at high-coverage Nanopore sequencing reads. Detecting the SNP variants on low-coverage Nanopore sequencing data is still a challenging problem.

Results
We developed a novel deep learning-based SNP calling method, NanoSNP, to identify the SNP sites (excluding short indels) based on low-coverage Nanopore sequencing reads. In this method, we design a multi-step, multi-scale and haplotype-aware SNP detection pipeline. First, the pileup model in NanoSNP utilizes the naive pileup feature to predict a subset of SNP sites with a Bi-long short-term memory (LSTM) network. These SNP sites are phased and used to divide the low-coverage Nanopore reads into different haplotypes. Finally, the long-range haplotype feature and short-range pileup feature are extracted from each haplotype. The haplotype model combines two features and predicts the genotype for the candidate site using a Bi-LSTM network. To evaluate the performance of NanoSNP, we compared NanoSNP with Clair, Clair3, Pepper-DeepVariant and NanoCaller on the low-coverage (∼16×) Nanopore sequencing reads. We also performed cross-genome testing on six human genomes HG002–HG007, respectively. Comprehensive experiments demonstrate that NanoSNP outperforms Clair, Pepper-DeepVariant and NanoCaller in identifying SNPs on low-coverage Nanopore sequencing data, including the difficult-to-map regions and major histocompatibility complex regions in the human genome. NanoSNP is comparable to Clair3 when the coverage exceeds 16×.

Availability and implementation
https://github.com/huangnengCSU/NanoSNP.git.

Supplementary information
Supplementary data are available at Bioinformatics online.
SIMBSIG: similarity search and clustering for biobank-scale data
Authors: Adamer Michael F , Roellin Eljas , Bourguignon Lucie , Borgwardt Karsten 
Published: December 2022

Abstract

Summary
In many modern bioinformatics applications, such as statistical genetics, or single-cell analysis, one frequently encounters datasets which are orders of magnitude too large for conventional in-memory analysis. To tackle this challenge, we introduce SIMBSIG (SIMmilarity Batched Search Integrated GPU), a highly scalable Python package which provides a scikit-learn-like interface for out-of-core, GPU-enabled similarity searches, principal component analysis and clustering. Due to the PyTorch backend, it is highly modular and particularly tailored to many data types with a particular focus on biobank data analysis.

Availability and implementation
SIMBSIG is freely available from PyPI and its source code and documentation can be found on GitHub (https://github.com/BorgwardtLab/simbsig) under a BSD-3 license.