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This study aims to determine the effect of replacing a portion of the cement in the concrete mixture with silica fume (SF) on the corrosion resistance of reinforcing bars, the compressive strength of concrete and the tensile strength of hook bars in both corroded and non-corroded external joints of structures. The external beam-column connection was studied because of its critical role in maintaining structural continuity in all three directions and providing resistance to rotation.

In external concrete joints, the bars at the end of the beams are often bent at 90° to form hooks that embed in columns. Owing to the importance of embedding distance and the need to understand its susceptibility to corrosion damage from chloride attack, a series of experiments were conducted on 12 specimens that accurately simulate real-site conditions in terms of dimensions, reinforcement and hook bars. SF was replaced with 10% and 15% of the weight of cement in the concrete mixture. To simulate corrosion, the specimens were subjected to accelerated corrosion in the laboratory by applying a low continuous current of 0.35 mA for 58 days.

The results revealed the effect of SF in improving the compressive strength of concrete, the pullout resistance of the hook bars and the corrosion resistance. In addition, it showed an apparent effect of the corrosion of reinforcing bars in reducing the bonding strength of hook bars with concrete and the effect of SF in improving this strength.

It was noted that the improvement of the results, achieved by replacing 10% of the weight of cement with SF, was significantly close to the results obtained by replacing 15% of the SF. It is recommended that an SF ratio of 10% be adopted to achieve the greatest economic savings.

Compliance risk management in the banking sector is crucial because of its multifaceted nature and its potential repercussions on reputation and financial stability. This study aims to present a systematic approach to mapping compliance risks, leveraging the risk self-control assessment (RSCA) method. Integrating quantitative and qualitative assessment techniques and drawing on human expertise and industry best practices.

The authors followed a methodology that involves conducting semistructured interviews with banking compliance professionals and analyzing professional frameworks to gather pertinent information. This comprehensive approach allows to obtain a holistic view of the risks involved and to refine the risk analysis process.

A step-by-step guide to map compliance risk within banks is proposed. The authors offer a rigorous process to enhance compliance risk management practices within the banking sector, providing actionable insights to effectively navigate regulatory complexities and safeguard financial stability.

The insights from this paper will guide compliance officers and risk managers to better assess and monitor compliance risk within their organizations.

To the best of the authors’ knowledge, this may be the first thorough paper that tackles the topic of mapping compliance risk in banks, from identifying areas of risk to corrective measures after drawing up the risk map, using the RSCA approach.

Fine-tuning pre-trained language models (PLMs), e.g. SciBERT, generally require large numbers of annotated data to achieve state-of-the-art performance on a range of NLP tasks in the scientific domain. However, obtaining fine-tuning data for scientific NLP tasks is still challenging and expensive. In this paper, the authors propose the mix prompt tuning (MPT), which is a semi-supervised method aiming to alleviate the dependence on annotated data and improve the performance of multi-granularity academic function recognition tasks.

Specifically, the proposed method provides multi-perspective representations by combining manually designed prompt templates with automatically learned continuous prompt templates to help the given academic function recognition task take full advantage of knowledge in PLMs. Based on these prompt templates and the fine-tuned PLM, a large number of pseudo labels are assigned to the unlabelled examples. Finally, the authors further fine-tune the PLM using the pseudo training set. The authors evaluate the method on three academic function recognition tasks of different granularity including the citation function, the abstract sentence function and the keyword function, with data sets from the computer science domain and the biomedical domain.

Extensive experiments demonstrate the effectiveness of the method and statistically significant improvements against strong baselines. In particular, it achieves an average increase of 5% in Macro-F1 score compared with fine-tuning, and 6% in Macro-F1 score compared with other semi-supervised methods under low-resource settings.

In addition, MPT is a general method that can be easily applied to other low-resource scientific classification tasks.

This paper proposes a solution for recognizing human faces under mask-wearing. The lower part of human face is occluded and could not be used in the learning process of face recognition. So, the proposed solution is developed to recognize human faces on any available facial components which could be varied depending on wearing or not wearing a mask.

The proposed solution is developed based on the FaceNet framework, aiming to modify the existing facial recognition model to improve the performance of both scenarios of mask-wearing and without mask-wearing. Then, simulated masked-face images are computed on top of the original face images, to be used in the learning process of face recognition. In addition, feature heatmaps are also drawn out to visualize majority of parts of facial images that are significant in recognizing faces under mask-wearing.

The proposed method is validated using several scenarios of experiments. The result shows an outstanding accuracy of 99.2% on a scenario of mask-wearing faces. The feature heatmaps also show that non-occluded components including eyes and nose become more significant for recognizing human faces, when compared with the lower part of human faces which could be occluded under masks.

The convolutional neural network based solution is tuned up for recognizing human faces under a scenario of mask-wearing. The simulated masks on original face images are augmented for training the face recognition model. The heatmaps are then computed to prove that features generated from the top half of face images are correctly chosen for the face recognition.

One of the most contested and anticipated research issues is the acceptability of using recycled aggregates instead of fresh aggregates. This study aims to look at the possibility of replacing fresh aggregates with 15%, 30%, 60% and 100% recycled aggregates.

The research is divided into two stages. The compressive, split tensile, flexural and bond strength of the various mixes were examined in the first phase using untreated recycled concrete aggregates (RCA). The second phase entails chemically treating RCA with a 10% 0.1 M sodium metasilicate solution to evaluate differences in strength, indicating the success of the treatment performed. Microstructural experiments such as scanning electron microscopy and X-ray diffraction were also conducted to evaluate the formation of interfacial transition zone (ITZ) in treated and untreated RCA specimens.

The observed findings reveal a decrease in concrete strength with increasing RCA concentration; however, when treated RCA was used, the strengths increased significantly when compared to untreated samples. The findings also include curves indicating the correlation between compressive strength and other mechanical strength parameters for an optimum mix of concrete prepared with 30% RCA replacement.

The study through its novel approach, demonstrates the effect of pretreatment of RCA in the absence of any standardized chemical treatment methodology and presents significant potential in minimizing reliance on fresh aggregates used in concrete, lowering building costs and promoting the use of waste materials in construction.

The purpose of this paper is to investigate the effect of the replacement of natural coarse aggregate (NCA) with different percentages of recycled coarse aggregate (RCA) on properties of low calcium fly ash (FA)-based geopolymer concrete (GPC) cured at oven temperature. Further, this paper aims to study the effect of partial replacement of FA by ground granulated blast slag (GGBS) in GPC made with both NCA and RCA cured under ambient temperature curing.

M25 grade of ordinary Portland cement (OPC) concrete was designed according to IS: 10262-2019 with 100% NCA as control concrete. Since no standard guidelines are available in the literature for GPC, the same mix proportion was adopted for the GPC by replacing the OPC with 100% FA and W/C ratio by alkalinity/binder ratio. All FA-based GPC mixes were prepared with 12 M of sodium hydroxide (NaOH) and an alkalinity ratio, i.e. sodium hydroxide to sodium silicate (NaOH:Na₂SiO₃) of 1:1.5, subjected to 90^°C temperature for 48 h of curing. The NCA were replaced with 50% and 100% RCA in both OPC and GPC mixes. Further, FA was partially replaced with 15% GGBS in GPC made with the above percentages of NCA and RCA, and they were given ambient temperature curing with the same molarity of NaOH and alkalinity ratio.

The workability, compressive strength, split tensile strength, flexural strength, water absorption, density, volume of voids and rebound hammer value of all the mixes were studied. Further, the relationship between compressive strength and other mechanical properties of GPC mixes were established and compared with the well-established relationships available for conventional concrete. From the experimental results, it is found that the compressive strength of GPC under ambient curing condition at 28 days with 100% NCA, 50% RCA and 100% RCA were, respectively, 14.8%, 12.85% and 17.76% higher than those of OPC concrete. Further, it is found that 85% FA and 15% GGBS-based GPC with RCA under ambient curing shown superior performance than OPC concrete and FA-based GPC cured under oven curing.

The scope of the present paper is limited to replace the FA by 15% GGBS. Further, only 50% and 100% RCA are used in place of natural aggregate. However, in future study, the replacement of FA by different amounts of GGBS (20%, 25%, 30% and 35%) may be tried to decide the optimum utilisation of GGBS so that the applications of GPC can be widely used in cast in situ applications, i.e. under ambient curing condition. Further, in the present study, the natural aggregate is replaced with only 50% and 100% RCA in GPC. However, further investigations may be carried out by considering different percentages between 50 and 100 with the optimum compositions of FA and GGBS to enhance the use of RCA in GPC applications. The present study is further limited to only the mechanical properties and a few other properties of GPC. For wider use of GPC under ambient curing conditions, the structural performance of GPC needs to be understood. Therefore, the structural performance of GPC subjected to different loadings under ambient curing with RCA to be investigated in future study.

The replacement percentage of natural aggregate by RCA may be further enhanced to 50% in GPC under ambient curing condition without compromising on the mechanical properties of concrete. This may be a good alternative for OPC and natural aggregate to reduce pollution and leads sustainability in the construction.

Concrete-filled double skin steel tubes (CFDST) columns are taken more attention due to their ability to withstand high structural loads in structures such as high-rise buildings, bridges' piers, offshore and marine structures. This paper is intended to improve the CFDST column's capacity without the need to increase the column's size to maintain its lightweight by filling it with self-compacted concrete (SCC) containing nanoclay (NC).

First, experimental investigation is conducted to select the optimal NC percentage that improves the mechanical properties. Different mixing method, mixture ingredients, cement content, and NC percentage are considered. Then, slender and short CFDST columns are tested for axial capacity to investigate the effect of adding the optimum NC percentage on column's capacity and failure mode.

The test results show that adding 3% NC by cement weight using dry mixing method to SCC is the optimum ratio. It is concluded that adding 3% NC by cement weight increased the CFDST column's capacity, especially the specimens with higher slenderness ratio. Moreover, it is concluded that more specimens should be tested under various geometric and reinforcement details.

Recently, CFDST tube columns solve many structural and architectural problems that engineers have encountered in traditional systems. Therefore, more studies are required to design high-performance columns capable of carrying complex loads with high efficiency since the traditional design could not achieve the required performance. Since concrete contributes to a large portion in the axial capacity of the CFDST columns, it is proposed to improve the CFDST column's capacity without the need to increase the column's size to maintain its lightweight by filling it with (SCC containing NC. Previous research has affirmed the effectiveness of employing nanoclay in the concrete's workability, durability, microstructures, and mechanical properties.

This study applies the modified brand avoidance model to examine factors that influence sustainable fashion avoidance behaviour among millennial shoppers in South Africa.

A positivistic approach and a web-based online survey were employed to collect cross-sectional data from 423 millennial fashion shoppers. Standard multiple regression analysis was used to test proposed hypotheses.

Unmet expectations, materialism and symbolic incongruence emerged as major predictors of millennials' intention to avoid sustainable fashion. Sustainable fashion avoidance intention was found to have a positive effect on sustainable fashion avoidance behaviour.

This study relied on self-reported data collected from millennial shoppers. Future studies may improve the generalizability of this study's results by conducting a comparative study with other cohorts such as baby boomers and Generation X who espouse different shopping values. Future studies may benefit from the use of longitudinal data in order to understand how millennial shoppers relate to sustainable fashion as it evolves.

The results of this study suggest the importance of developing value propositions that align sustainable fashion with cultural, personality and symbolic cues valued by millennial shoppers. Consumer education on the benefits of sustainable fashion is recommended as a long-term behavioural change strategy.

The purchase behaviour of sustainable fashion should be encouraged as it enhances environmental sustainability including safeguarding the livelihoods of future generations.

This study contributes to literature on sustainable fashion avoidance behaviour. This is one of the pioneering studies to empirically examine the influence of unmet expectations, symbolic incongruence and ideological incompatibility in the context of an emerging market, such as South Africa.

The paper explores the implementation and process of a Participatory Action Research (PAR) project in Jacksonville, Florida. Using PAR principles, we sought to disrupt traditional dynamics in research, co-generating knowledge with the community. We aimed to elevate disenfranchised voices and foster effective solutions for neighborhood revitalization and resilience, which are designed and led by the community. In this paper we explore researcher positionality, examining how engagement with the community context profoundly influences researchers' perspectives and roles, thereby shaping the study trajectory.

We use reflexive and thematic approaches to analyze researcher positionalities and dynamics with the community. Researchers' reflections were scrutinized with a focus on their backgrounds, adaptability to community pace of work, lessons learned and evolving perspectives on community-centered research. We present a comprehensive discussion of emerging themes and emphasize the transformative nature of PAR and the significance of the researcher pursuing a relationship of belonging within the community.

The study uncovered key themes clarifying the transformations experienced by researchers engaged in PAR. We underwent a significant shift from a predetermined, structured PAR model to a more organic, community-driven model. This shift is illustrated under key themes in the paper: being vulnerable, valuing relationships, transforming the sense of self and solidarity, focusing on the process and recognizing and valuing community-derived knowledge. We write about the metamorphosis of researchers' roles from passive observers to active and empathic participants and about the implications for our methodological approach. As a result of this adaptation, the research process was enhanced and a symbiotic learning environment was created between the researchers and the community.

This paper distinguishes itself through its community-centric approach, diverging from traditional research paradigms. It explores how PAR-based community engagement can have a positive impact on those who are involved in PAR themselves, contributing to a better understanding of reciprocal dynamics in community-based research. We hope that our process of reflection can help other disaster researchers to ask better questions of themselves when engaging with communities and be open to following the lead and the pace of the community.

The purpose of this study is to examine dilemmas in a district–university partnership that used network improvement communities (NIC) as a levers for systems change after COVID-19.

We draw on observations and in-depth, semi-structured interviews to understand the benefits and limitations of using network improvement communities embedded within a longstanding district–university partnership to address problems of practice.

Five dilemmas that emerged: (1) the need to continue the pilot roll-out of the NIC while still communicating a vision of the long-term, large-scale vision of NIC work, (2) center collaborative inquiry while still moving at a fast enough pace for participants to see progress, (3) include those most involved in the work while still having efficient decision making processes, (4) respect the knowledge and agency of participants while still supporting rigorous and impactful change and (5) honor the work the district is already engaged in while still extending beyond existing initiatives.

We conclude that the work of leading network improvement communities as part of a district–university partnership is complex. We add to the emerging research on NIC hub leadership by highlighting the dilemmas that must be managed in this work.