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Prefabricated products are continually entering the building construction market; yet, the decision to use prefabricated products in a construction project is based mostly on personal preferences and the evaluation of direct costs. Researchers and practitioners have debated appropriate measurement systems for evaluating the impacts of prefabricated products and for comparing them with conventional on-site construction practices. The more advanced, cost–benefit approach to evaluating prefabricated products often inspires controversy because it may generate inaccurate results when converting non-monetary effects into costs. As prefabrication may affect multiple organisations and product subsystems, the method used to decide on production methods should consider multiple direct and indirect impacts, including nonmonetary ones. Thus, this study aims to develop a multi-criteria method to evaluate both the monetary and non-monetary impacts of prefabrication solutions to facilitate decision-making on whether to use prefabricated products.

Drawing upon a literature review, this research suggests a multi-criteria method that combines the choosing-by-advantage approach with a cost–benefit analysis. The method was presented for validation in focus group discussions and tested in a case involving a prefabricated bathroom.

The analysis indicates that the method helps a project’s stakeholders communicate about the relative merits of prefabrication and conventional construction while facilitating the final decision of whether to use prefabrication.

This research contributes a method of evaluating the monetary and non-monetary impacts of prefabricated products. The research underlines the need to evaluate the diverse benefits and sacrifices that stakeholder face when considering production methods in construction.

The purpose of this paper is to examine and to categorize the tendencies of blockchain-based applications in the shipping industry and supply chain as well as the interrelations between them, including possible correlation of found categories with theoretical background and existing concepts. This study also explores whether blockchain can be adopted into existing maritime shipping and port document workflow management.

The current study builds a conceptual framework through a systematic project review carried along with scientific and grey literature, published in journals and conference proceedings during the past decade and giving information or proposals on an issue.

The results showed that reviewed projects can be compiled into three main conceptual areas: document workflow management, financial processes and device connectivity. However, having clear interlinkages, none of the reviewed projects consider all three areas at once. Concepts associated with maritime document workflow received broad support among the reviewed projects. In addition, reviewed projects unintentionally follow the similar goals that were laid down within port management scientific projects before the introduction of blockchain technology.

This study contributes to research by revealing a consistent framework for understanding the blockchain applications within maritime port environment, a less-studied part of blockchain implementation in the supply chain field. Moreover, this work is the first to find out conceptual intersections and correlations between existing projects, mapping current tendencies and potentially increasing knowledge about the field.

Automatic anatomical therapeutic chemical (ATC) classification is progressing at a rapid pace because of its potential in drug development. Predicting an unknown compound's therapeutic and chemical characteristics in terms of how it affects multiple organs and physiological systems makes automatic ATC classification a vital yet challenging multilabel problem. The aim of this paper is to experimentally derive an ensemble of different feature descriptors and classifiers for ATC classification that outperforms the state-of-the-art.

The proposed method is an ensemble generated by the fusion of neural networks (i.e. a tabular model and long short-term memory networks (LSTM)) and multilabel classifiers based on multiple linear regression (hMuLab). All classifiers are trained on three sets of descriptors. Features extracted from the trained LSTMs are also fed into hMuLab. Evaluations of ensembles are compared on a benchmark data set of 3883 ATC-coded pharmaceuticals taken from KEGG, a publicly available drug databank.

Experiments demonstrate the power of the authors’ best ensemble, EnsATC, which is shown to outperform the best methods reported in the literature, including the state-of-the-art developed by the fast.ai research group. The MATLAB source code of the authors’ system is freely available to the public at https://github.com/LorisNanni/Neural-networks-for-anatomical-therapeutic-chemical-ATC-classification.

This study demonstrates the power of extracting LSTM features and combining them with ATC descriptors in ensembles for ATC classification.

Many libraries and archives maintain collections of research documents, such as administrative records, with paper-based formats that limit the documents' access to in-person use. Digitization transforms paper-based collections into more accessible and analyzable formats. As collections are digitized, there is an opportunity to incorporate deep learning techniques, such as Document Image Analysis (DIA), into workflows to increase the usability of information extracted from archival documents. This paper describes the authors' approach using digital scanning, optical character recognition (OCR) and deep learning to create a digital archive of administrative records related to the mortgage guarantee program of the Servicemen's Readjustment Act of 1944, also known as the G.I. Bill.

The authors used a collection of 25,744 semi-structured paper-based records from the administration of G.I. Bill Mortgages from 1946 to 1954 to develop a digitization and processing workflow. These records include the name and city of the mortgagor, the amount of the mortgage, the location of the Reconstruction Finance Corporation agent, one or more identification numbers and the name and location of the bank handling the loan. The authors extracted structured information from these scanned historical records in order to create a tabular data file and link them to other authoritative individual-level data sources.

The authors compared the flexible character accuracy of five OCR methods. The authors then compared the character error rate (CER) of three text extraction approaches (regular expressions, DIA and named entity recognition (NER)). The authors were able to obtain the highest quality structured text output using DIA with the Layout Parser toolkit by post-processing with regular expressions. Through this project, the authors demonstrate how DIA can improve the digitization of administrative records to automatically produce a structured data resource for researchers and the public.

The authors' workflow is readily transferable to other archival digitization projects. Through the use of digital scanning, OCR and DIA processes, the authors created the first digital microdata file of administrative records related to the G.I. Bill mortgage guarantee program available to researchers and the general public. These records offer research insights into the lives of veterans who benefited from loans, the impacts on the communities built by the loans and the institutions that implemented them.

The purpose of this paper is to identify the compensation between research groups and companies that contribute the most for the innovative performance of Brazilian public higher educational institutions (PHEI), using as database the 2010’s tabular plan from CNPq’s Directory of Research Groups.

Descriptive and multivariate statistical techniques such as spearman correlation, cluster analysis, ANOVA and discriminant analysis were used.

Compensations that contribute the most for the updating of the PHEI are identified as transfer of financial resources from the partner to the group; providing grants for the group; transfer of material supplies to partner’s activities; temporary physical transfer of human resources from the group to the activities conducted by the partner; other forms of compensation that do not fit in the previous categories; and partnering with transfers of resources of any kind going in any direction.

As a limitation, it is pointed out the discontinuity of the tabular plan, which presents 2010 as the last available data.

The results can contribute to programs and policies to encourage innovation within universities.

It may be inferred that the stimulus to specific compensations may expand the quantitative idea of interaction points between the university and companies, linking qualitative aspects, which leads to an understanding that such interactions may, in fact, contribute directly to the activity of generating and spreading knowledge and innovation.

The literature review stated that financial inclusion (FI) influences economic growth through different channels. Hence, this paper aims to investigate the underlying process of FI in Egypt theoretically, and to derive some policy implications for promoting the process and achieving more improvement in different financial and economic aspects, that is basically through discussing the opinions of FI's main stockholders in Egypt.

The analysis used secondary data from the Global Findex and FAS Database, namely, automated teller machines, outstanding deposits and loans with commercial banks, debit and credit cards ownership. The research particularly used scientific methods as method of deduction, methods of graphical and tabular representation of data, comparative analysis and synthesis of partial knowledge. The paper is also based on a descriptive approach in addition to in-depth interviews with the main stakeholders of the financial inclusion process in Egypt.

The analyzed results of interviews revealed that new FI vision should have a deep understanding of the financial lives of the poor and low-income groups, including how they acquire, manage and use their money. However, the impact is becoming more prominent for the efficiency of the banking system and hence economic growth rather a regulatory and sound institutional framework enhances it. This finding supported the fact that Egypt can design an appropriate FI strategy, but the main challenge is how to implement it with the required speed and outreach capacity, especially in underprivileged communities.

The result of this study has interesting implications for Egypt's ability to attain effective FI initiatives that promote sound financial choices and behavior which in turn help to stimulate financial and economic growth.

The study contributes to the literature by assessing the FI level in Egypt, its implications and how it should be enhanced for better performance and results in the future. It addresses the deep fact of this process through inclusive surveys and interviews that help in determining the road ahead.

This paper aims to present a literature review of the most recent optimisation methods applied to Credit Scoring Models (CSMs).

The research methodology employed technical procedures based on bibliographic and exploratory analyses. A traditional investigation was carried out using the Scopus, ScienceDirect and Web of Science databases. The papers selection and classification took place in three steps considering only studies in English language and published in electronic journals (from 2008 to 2022). The investigation led up to the selection of 46 publications (10 presenting literature reviews and 36 proposing CSMs).

The findings showed that CSMs are usually formulated using Financial Analysis, Machine Learning, Statistical Techniques, Operational Research and Data Mining Algorithms. The main databases used by the researchers were banks and the University of California, Irvine. The analyses identified 48 methods used by CSMs, the main ones being: Logistic Regression (13%), Naive Bayes (10%) and Artificial Neural Networks (7%). The authors conclude that advances in credit score studies will require new hybrid approaches capable of integrating Big Data and Deep Learning algorithms into CSMs. These algorithms should have practical issues considered consider practical issues for improving the level of adaptation and performance demanded for the CSMs.

The results of this study might provide considerable practical implications for the application of CSMs. As it was aimed to demonstrate the application of optimisation methods, it is highly considerable that legal and ethical issues should be better adapted to CSMs. It is also suggested improvement of studies focused on micro and small companies for sales in instalment plans and commercial credit through the improvement or new CSMs.

The economic reality surrounding credit granting has made risk management a complex decision-making issue increasingly supported by CSMs. Therefore, this paper satisfies an important gap in the literature to present an analysis of recent advances in optimisation methods applied to CSMs. The main contribution of this paper consists of presenting the evolution of the state of the art and future trends in studies aimed at proposing better CSMs.

The physical internet (PI) is an emerging logistics and supply chain management (SCM) concept that draws on different technologies and areas of research, such as the Internet of Things (IoT) and key performance indicators, with the purpose of revolutionizing existing logistics and SCM practices. The growing literature on the PI and its noteworthy potential to be a disruptive innovation in the logistics industry call for a systematic literature review (SLR), which we conducted that defines the current state of the literature and outlines future research directions and approaches.

The SLR that was undertaken included journal publications, conference papers and proceedings, book excerpts, industry reports and white papers. We conducted descriptive, citation, thematic and methodological analyses to understand the evolution of PI literature.

Based on the literature review and analyses, we proposed a comprehensive framework that structures the PI domain and outlines future directions for logistics and SCM researchers.

Our research findings are limited by the relatively low number of journal publications, as the PI is a new field of inquiry that is composed primarily of conference papers and proceedings.

The proposed PI-based framework identifies seven PI themes, including the respective facilitators and barriers, which can inform researchers and practitioners on future potentially disruptive SC strategies.

Semantic retrieval and discovery of datasets published as open data remains a challenging task. The datasets inherently originate in the globally distributed web jungle, lacking the luxury of centralized database administration, database schemes, shared attributes, vocabulary, structure and semantics. The existing dataset catalogs provide basic search functionality relying on keyword search in brief, incomplete or misleading textual metadata attached to the datasets. The search results are thus often insufficient. However, there exist many ways of improving the dataset discovery by employing content-based retrieval, machine learning tools, third-party (external) knowledge bases, countless feature extraction methods and description models and so forth.

In this paper, the authors propose a modular framework for rapid experimentation with methods for similarity-based dataset discovery. The framework consists of an extensible catalog of components prepared to form custom pipelines for dataset representation and discovery.

The study proposes several proof-of-concept pipelines including experimental evaluation, which showcase the usage of the framework.

To the best of authors’ knowledge, there is no similar formal framework for experimentation with various similarity methods in the context of dataset discovery. The framework has the ambition to establish a platform for reproducible and comparable research in the area of dataset discovery. The prototype implementation of the framework is available on GitHub.