bluuebunny/crossref_metadata_2025_split huggingface dataset:
A critical review and analysis of decision-support processes and tools for project delivery method selection
Authors: Demetracopoulou Vassiliki , O'Brien William J. , Khwaja Nabeel , Feghaly Jeffrey , El Asmar Mounir 
Published: September 2022

PurposeOver the last three decades, construction projects have increasingly been delivered through alternative delivery methods. As a result, many owners have a range of delivery methods to choose from and aim to use the right one for each of their projects. Researchers have developed several tools and decision-support processes to facilitate this selection procedure. The purpose of this study is to review and discuss differences and common themes across selection tools developed by academic researchers and project owners.Design/methodology/approachThe study reviews prominent selection processes and tools used for infrastructure projects by conducting an in-depth literature review and using the content analysis method to elicit findings on the methodologies and criteria presented in the literature.FindingsThis study presents three principal findings. First, findings show three common themes emerge within the selection criteria—characteristics, goals and risks. Second, while academic studies most commonly suggest employing multi-attribute analysis, this study reveals that, in practice, selection tools most frequently employ a staged or gated evaluation based on the type of criteria and their importance to the decision. Finally, this review further highlights the importance of institutional context in decision-making.Originality/valueThis work contributes to the body of knowledge by providing guidance to practitioners and opening new directions for researchers around the way selection criteria are categorized in the relevant literature and the institutional context considerations when structuring or evaluating a selection process or tool.
How do firms use virtual brand communities to improve innovation performance? Based on consumer participation and organizational learning perspectives
Authors: Zheng Jian , Liu Renjing , Zhang Ru , Xu Hao 
Published: September 2022

PurposeIn the context of virtual brand communities (VBCs), based on self-determination theory and organizational binary learning (OBL) theory, this study investigates the direct and indirect effects of various dimensions of consumer participation (CP) on firm innovation performance (FIP) and the mediating role of OBL. By introducing the perspective of knowledge absorptive capacity (KAC), this study examines the moderating effect of KAC on the relationship between OBL and TIP.Design/methodology/approachIn this study, 751 samples are collected from Chinese firms, and stratified adjusted regression analysis is used to conduct empirical tests.FindingsInformation provision or co-creation directly affects FIP; the latter is more significant than the former, while social interaction does not directly affect FIP. Exploitative or exploratory learning plays a mediating role in the relationship between information provision or co-creation and FIP. In contrast, exploratory learning plays a mediating role in the relationship between social interaction and FIP. KAC has a positive moderating effect on the relationship between exploitative learning or exploratory learning and FIP. When the firm has a low KAC, the improvement of FIP through exploratory learning is slight.Originality/valueIn VBCs, this study tries to divide the dimensions of CP. It also clarifies the mediating effect of OBL on CP and FIP. In addition, it explores the moderating effect of KAC on OBL and FIP. The conclusions of this study provide theoretical support and practical inspiration for firms to use VBCs to improve FIP.
Research on the emission reduction effects of carbon trading mechanism on power industry: plant-level evidence from China
Authors: Han Yonghui , Tan Shuting , Zhu Chaowei , Liu Yang 
Published: September 2022

Purpose
Carbon trading mechanism has been adopted to foster the green transformation of the economy on a global scale, but its effectiveness for the power industry remains controversial. Given that energy-related greenhouse gas emissions account for most of all anthropogenic emissions, this paper aims to evaluate the effectiveness of this trading mechanism at the plant level to support relevant decision-making and mechanism design.

Design/methodology/approach
This paper constructs a novel spatiotemporal data set by matching satellite-based high-resolution (1 × 1 km) CO2 and PM2.5 emission data with accurate geolocation of power plants. It then applies a difference-in-differences model to analyse the impact of carbon trading mechanism on emission reduction for the power industry in China from 2007 to 2016.

Findings
Results suggest that the carbon trading mechanism induces 2.7% of CO2 emission reduction and 6.7% of PM2.5 emission reduction in power plants in pilot areas on average. However, the reduction effect is significant only in coal-fired power plants but not in gas-fired power plants. Besides, the reduction effect is significant for power plants operated with different technologies and is more pronounced for those with outdated production technology, indicating the strong potential for green development of backward power plants. The reduction effect is also more intense for power plants without affiliation relationships than those affiliated with particular manufacturers.

Originality/value
This paper identifies the causal relationship between the carbon trading mechanism and emission reduction in the power industry by providing an innovative methodology for identifying plant-level emissions based on high-resolution satellite data, which has been practically absent in previous studies. It serves as a reference for stakeholders involved in detailed policy formulation and execution, including policymakers, power plant managers and green investors.
LMH-RPL: a load balancing and mobility aware secure hybrid routing protocol for low power lossy network
Authors: Cyriac Robin , Durai M.A. Saleem 
Published: September 2022

Purpose
Routing protocol for low-power lossy network (RPL) being the de facto routing protocol used by low power lossy networks needs to provide adequate routing service to mobile nodes (MNs) in the network. As RPL is designed to work under constraint power requirements, its route updating frequency is not sufficient for MNs in the network. The purpose of this study is to ensure that MNs enjoy seamless connection throughout the network with minimal handover delay.

Design/methodology/approach
This study proposes a load balancing mobility aware secure hybrid – RPL in which static node (SN) identifies route using metrics like expected transmission count, and path delay and parent selection are further refined by working on remaining energy for identifying the primary route and queue availability for secondary route maintenance. MNs identify route with the help of smart timers and by using received signal strength indicator sampling of parent and neighbor nodes. In this work, MNs are also secured against rank attack in RPL.

Findings
This model produces favorable result in terms of packet delivery ratio, delay, energy consumption and number of living nodes in the network when compared with different RPL protocols with mobility support. The proposed model reduces packet retransmission in the network by a large margin by providing load balancing to SNs and seamless connection to MNs.

Originality/value
In this work, a novel algorithm was developed to provide seamless handover for MNs in network. Suitable technique was developed to provide load balancing to SNs in network by maintaining appropriate secondary route.
Drivers of smoked marine fish prices and challenges along the value chain
Authors: Osei Awura-Abena Amoah , Etuah Seth , Aidoo Robert , Fialor Simon Cudjoe , Adams Faizal 
Published: September 2022

PurposeThe objectives of this paper are to analyze the drivers of smoked marine fish prices and examine the challenges along the value chain to inform policy.Design/methodology/approachThe study relies on cross-sectional data from 158 fish smokers, 100 wholesalers, and 120 retailers in Ghana. The challenges faced by the actors along the chain are identified and ranked using Kendall's coefficient of concordance. The three-stage least square estimation approach is employed to control for endogeneity resulting from the simultaneous equations for prices of different fish types and their substitutes.FindingsThe results reveal that marketing experience, the extent of competition, price of substitutes, payment terms, and variable cost items, such as transportation cost and storage charges, are key determinants of smoked fish prices along the value chain. Lack of access to credit is the main constraint to the marketing of smoked marine fish, irrespective of the actor or the fish type.Originality/valueMany studies have established the linkages between fish consumption, prices, and food security in the literature. However, there is a dearth of information on the responsiveness of fish prices to changes in the market and seller-specific factors to drive policy to stabilize prices along the value chain.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-09-2021-0566
Agricultural credit and productivity of crops in India: field evidence from small and marginal farmers across social groups
Authors: Yadav Inder Sekhar , Rao M. Sanatan 
Published: September 2022

PurposeThis work examines the impact of institutional agricultural credit on crop productivity of some major crops such as paddy, cotton, wheat and pulses for small and marginal farmers across various social groups.Design/methodology/approachThe cross-sectional field data on socio economic variables was collected from three Indian states from about 400 small and marginal farmers across various social groups using multi-stage stratified random and purposive sampling through a structured questionnaire by interviewing. The method of propensity score matching (PSM) was employed to calculate average treatment effect (ATE) and average treatment effect on the treated (ATET) by categorising sample farmers as treatment group and control group where crop productivity was considered as outcome variable and access to institutional credit was considered as treatment variable.FindingsThe PSM estimates reveal that ATE and ATET for all the selected crops are found to be significantly higher for the treated group vis-à-vis non-treated group suggesting that institutional agricultural credit has a statistically and significant positive impact on the crop productivity.Research limitations/implicationsSimilar study can be extended for more crops and across regions in India for a universal coverage.Originality/valueThe agricultural credit policy of India has been to increase the access and availability of institutional farm credit. This has led to in general increase in the flow of formal farm credit to agricultural sector. However, the impact of institutional credit and crop productivity especially for small and marginal farmers across social groups is not well recognized in India using field data. Accordingly, this field data study contributes to the existing research by providing fresh evidence from field across social groups for both kharif and rabi crops using recent survey data from small and marginal farmers which has important policy implications.
The role of corporate governance towards insider trading profitability
Authors: Alawi Suha Mahmoud 
Published: September 2022

PurposeAn effective corporate governance system helps to smoothly run business operations and manage financial matters. To ensure that management behavior is ethical, and their decisions are in the best interest of shareholders, corporate governance plays a vital role. This study aims to examine the impact of corporate governance on the insider trading profitability of listed banks in Pakistan, Bangladesh and India.Design/methodology/approachThe authors take data from the financial statements of 70 listed banks and stock exchanges of the respective countries. The period of the data for our study is from 2010 to 2020. The authors use board independence, the board size, institutional ownership and managerial ownership as measures of corporate governance characteristics. While inside trading profitability is measured with abnormal returns. The authors apply the fixed effect panel regression for hypothesis testing and the two-step dynamic panel system-generalized method of moments (GMM) regression technique for checking the robustness of the findings.FindingsThe authors found that corporate governance has a significant impact on insider trading profitability in Pakistan, Bangladesh and India. Board independence and institutional ownership are negatively related while board size and managerial ownership are positively associated with insider trading profitability.Originality/valueTo the best of our knowledge, this study is the first one to explore the role of corporate governance in limiting insider trading on South Asian banks. It recommends that corporations should follow the code of corporate governance for the protection of shareholders' and other investors' profits.
E-learning: substitutability of learner–learner, and learner–facilitator interactions to enhance learner satisfaction in higher education
Authors: Nyathi Musa , Sibanda Emeka 
Published: September 2022

PurposeThe purpose of this study is to evaluate the effect of learner–learner and learner–facilitator interactions on learner satisfaction and their substitutability.Design/methodology/approachA quantitative survey research focusing on 130 students was used to collect data. Stratified sampling was preferred for the study, with a Likert type research instrument being administered online.FindingsLearner–learner and learner–facilitator interactions mediate the effect of e-learning on learner satisfaction. These sets of interactions act as independent mediators, each playing a pivotal role in enhancing learner satisfaction. The interactions are however not substitutable.Practical implicationsThe findings of the study will provide insights to academic administrators, to ensure that the two sets of interactions contribute to an effective and conducive e-learning environment and ultimately learner satisfaction. Deliberate efforts should be made to design increased e-learning interactivity into the cyber learning environment for effective learning.Originality/valueThe findings result in a ranking of learner–learner and learner–facilitator interactions for increased learner satisfaction. The interactions cannot be substituted without affecting the level of learner satisfaction. The interactions play a complementarity role, and collectively, enrich the quality of e-learning. A collective deployment is recommended.
Prevalence of skin diseases and prognosis of atopic dermatitis in primary school children in populated areas of Japan from 2010 to 2019: The Asa Study in Hiroshima, Japan
Authors: Tanaka Akio , Niimi Naomasa , Takahashi Masakazu , Takahashi Hiroyuki , Nagata Keiji , Nishiyama Naruhisa , Noda Hideki , Hara Takeshi , Maeda Motoo , Mizuiri Yasuhiro , Morikawa Hirofumi , Yanase Tetsuji , Yano Takahiko , Yamura Munehisa , Okano Shinji 
Published: September 2022

AbstractSkin diseases are common in children. However, the prevalence of childhood skin diseases in Japan has not been reported extensively. In this study, dermatologists conducted face‐to‐face examinations of primary school children over a 10‐year period to determine the prevalence of each skin disease. Atopic dermatitis (AD, 12.3%) was the most common disease among first graders, followed by eczema other than AD (9.7%), molluscum contagiosum (1.9%), and verruca vulgaris (1.1%). Among sixth graders, acne vulgaris was most common (9.6%), followed by AD (8.9%), eczema other than AD (6.7%), and verruca vulgaris (3.1%). The prevalence of AD remained stable among first graders after 2010, at approximately 10%–15%, but it increased slightly among sixth graders. Of the 87 children who had AD in first grade, 51 (58.6%) were in remission in sixth grade. On the other hand, AD was diagnosed in 30 (4.5%) sixth graders who had been among the 665 children who did not have it in first grade. The more severe the symptoms were in first grade, the more likely a child was to still have AD in sixth grade, and the more severe the later symptoms tended to be. This study investigated recent trends in skin diseases in Japanese primary school children and found that the prevalence of some skin diseases differed between first and sixth graders. It was also found that children with moderate AD in first grade were more likely to be moderate in sixth grade than those with mild AD.
External validation of the Paddington International Virtual Electronic ChromoendoScopy ScOre as a good endoscopic score to define mucosal healing and predict long‐term clinical outcomes in ulcerative colitis
Authors: Ruan Ge Chong , Jin Xin , Zhou Wei Xun , You Yan , Iacucci Marietta , Ghosh Subrata , Gui Xian Yong , Li Ji , Qian Jia Ming 
Published: September 2022

ObjectivesTo define endoscopic and histological remission in ulcerative colitis (UC) accurately, several scoring systems have been established. A novel virtual electronic chromoendoscopy score, the Paddington International Virtual ChromoendoScopy ScOre (PICaSSO), was developed, validated, and reproduced to assess inflammation grade and predict patient prognosis. We externally verified and validated the clinical value of PICaSSO in UC patients.MethodsThis prospective study enrolled 63 UC patients. The Mayo endoscopic score (MES), UC Endoscopic Index of Severity (UCEIS), and PICaSSO were adopted for endoscopic evaluation. All biopsy samples were scored using the Robarts histological index (RHI), Nancy histological index (NHI), and “Extent, Chronicity, Activity, Plus additional findings” (ECAP) score. Patients with an endoscopic MES of 0–1 at baseline were followed up during a median time of 23.5 months.ResultsPICaSSO was strongly correlated with other endoscopic and histological scoring systems. PICaSSO ≤3 had advantages in assessing histological remission (HR), with the highest accuracy of 88.9% for ECAP‐HR. Relapse‐free survival rates were significantly different between patients with MES 0 and MES 1 and patients with PICaSSO ≤3 and >3 (P = 0.010 and 0.018, respectively).ConclusionsPICaSSO was externally validated with strong correlations with other endoscopic and histological scoring systems in UC, and PICaSSO‐ER might potentially predict a better long‐term clinical outcome in UC patients.
A statistical framework for modelling migration corridors
Authors: Nuñez Tristan A. , Hurley Mark A. , Graves Tabitha A. , Ortega Anna C. , Sawyer Hall , Fattebert Julien , Merkle Jerod A. , Kauffman Matthew J. 
Published: September 2022

Abstract

Management of animal populations requires spatially explicit knowledge of movement corridors, such as those used during seasonal migrations. Global Positioning System (GPS) tracking data allow for mapping of corridors from directly observed movements, but such tracking data are absent for many populations.

We developed a novel statistical corridor modelling approach that predicts movement corridors from cost‐distance models fit directly to migration tracking data. Unlike existing predictive approaches, this does not require the ad hoc transformation of habitat suitability surfaces into resistance surfaces. We tested the ability of the approach to recover parameters used to generate simulated movements. We then used GPS data from three migrating mule deer Odocoileus hemionus herds in Idaho and Wyoming to model corridors as a function of elevation, slope, aspect, percent shrub, date of peak green‐up, snow‐off date and human footprint. We assessed the predictive ability of the fitted models using validation tracks from the same herd as well as from the other herds.

The approach reproduced parameters used to generate the simulated movements, predicted the corridors used by migratory populations, and described the direction, magnitude and confidence levels of the effects of environmental variables on corridors. Within‐herd validation indicated that fitted corridor models are more accurate at predicting migration corridors than null models, and cross‐herd validation indicated that fitted models for some herds accurately predicted the observed migrations of other herds.

In addition to the practical benefit of mapping corridors for management, our statistical corridor modelling framework sets the stage for evaluating fundamental questions about the fitness trade‐offs, navigation, learning, fidelity and movement constraints that influence migratory and other corridor‐generating behaviour. Models of predictive corridors can inform management and planning for the conservation of migrations across taxa, including the potential restoration of corridors. Our corridor modelling approach is also readily applied to non‐migratory animal movements.
Tree species classification from complex laser scanning data in Mediterranean forests using deep learning
Authors: Allen Matthew J. , Grieve Stuart W. D. , Owen Harry J. F. , Lines Emily R. 
Published: September 2022

Abstract

Recent advances in terrestrial laser scanning (TLS) technology have enabled the automatic capture of three‐dimensional vegetation structure at high resolution, but the scalability of using these data for large‐scale forest monitoring is limited by reliance on intensive manual data processing, including the use of stem maps generated in the field to determine tree species. New methods from data science have the capacity to automate this identification process, reducing the hurdles towards automated inventories with TLS. In particular, contemporary developments in point cloud processing methods, alongside large increases in the computing power of consumer‐level graphics processing units, provide new opportunities.

Here, we apply a deep learning‐based approach, based on joint classification from multiple viewpoints for each stem, to automatically classify tree species directly from laser scanning data obtained in structurally complex Mediterranean forests. We also explore the use of data augmentation techniques to maximise performance for a fixed number of manually labelled stems. Our method does not require expensive pre‐processing such as leaf‐wood separation or quantitative reconstructions.

Using modern network architectures and data augmentation techniques, and without extensive pre‐processing, we are able to achieve high overall and per‐species accuracy that is comparable or higher than in existing work while using data from a water‐limited ecosystem complicated by structural convergence and multi‐stem trees.

Our findings demonstrate the power of deep learning to remove a major TLS data processing obstacle—individual species identification—and to minimise the bottleneck created by manual data labelling requirements in the use of TLS for standard forest monitoring.