Search results

The purpose of this paper is to report on a study examining the perceptions of secondary principals, deputies and teachers, of deputy principal (DP) instructional leadership (IL), as well as deputies’ professional learning (PL) needs. Framed within an interpretivist approach, the specific objectives of this study were: to explore the extent to which DPs are perceived as leaders of learning, to examine the actual responsibilities of these DPs and to explore the PL that support DP roles.

The researchers used multiple perspective case studies which included semi-structured interviews and key school document analysis. A thematic content analysis facilitated qualitative descriptions and insights from the perspectives of the principals, DPs and teachers of four high-performing secondary schools in Sydney, Australia.

The data revealed that deputies performed a huge range of tasks; all the principals were distributing leadership to their deputies to build leadership capacity and supported their PL in a variety of ways. Across three of the case study schools, most deputies were frequently performing as instructional leaders, improving their school’s performance through distributing leadership, team building and goal setting. Deputy PL was largely dependent on principal mentoring and self-initiated but was often ad hoc. Findings add more validity to the importance of principals building the educational leadership of their deputies.

This study relied upon responses from four case study schools. Further insight into the key issues discussed may require a longitudinal data that describe perceptions from a substantial number of schools in Australia over time. However, studying only four schools allowed for an in-depth investigation.

The findings from this study have practical implications for system leaders with responsibilities of framing the deputies’ role as emergent educational leaders rather than as administrators and the need for coherent, integrated, consequential and systematic approaches to DP professional development. Further research is required on the effect of deputy IL on school performance.

There is a dearth of research-based evidence exploring the range of responsibilities of deputies and perceptions of staff about deputies’ IL role and their PL needs. This is the first published New South Wales, Australian DP study and adds to the growing evidence around perceptions of DPs as instructional leaders by providing an Australian perspective on the phenomenon. The paper raises important concerns about the complexity of the DP’s role on the one hand, and on the other hand, the PL that is perceived to be most appropriate for dealing with this complexity.

This study seeks to understand the connection of methodology by finding relevant papers and their full review using the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA).

Concrete-filled steel tubular (CFST) columns have gained popularity in construction in recent decades as they offer the benefit of constituent materials and cost-effectiveness. Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Gene Expression Programming (GEP) and Decision Trees (DTs) are some of the approaches that have been widely used in recent decades in structural engineering to construct predictive models, resulting in effective and accurate decision making. Despite the fact that there are numerous research studies on the various parameters that influence the axial compression capacity (ACC) of CFST columns, there is no systematic review of these Machine Learning methods.

The implications of a variety of structural characteristics on machine learning performance parameters are addressed and reviewed. The comparison analysis of current design codes and machine learning tools to predict the performance of CFST columns is summarized. The discussion results indicate that machine learning tools better understand complex datasets and intricate testing designs.

This study examines machine learning techniques for forecasting the axial bearing capacity of concrete-filled steel tubular (CFST) columns. This paper also highlights the drawbacks of utilizing existing techniques to build CFST columns, and the benefits of Machine Learning approaches over them. This article attempts to introduce beginners and experienced professionals to various research trajectories.

The purpose of this study is to investigate why users turn to the university library’s reference desk and whether librarians make use of the opportunity to conduct reference interviews to disclose any unexpressed information needs.

This paper presents the results from a qualitative exploration study where interactions between librarians and users were observed in authentic situations at the reference desk and analyzed using a modified version of Radford and Connaway’s (2013) categorization of inquiries.

Most inquiries were seemingly easy to answer and pertained to collections and procedures in the library. Lending out desk supplies accounted for a high proportion of the activity. Only a small number of requests were subject-oriented and reference interview techniques were only used in 5% of the recorded inquiries. This means that the users’ information needs were not probed in the vast majority of the interactions.

The study is exploratory and mirrors the activity that takes place in one specific library. The low number of reference interview techniques used may indicate a lack of interest in users’ information needs, which signifies a risk of the reference desk being reduced to an arena for instrumental and superficial interaction between librarians and users.

This study illustrates current developments in work at a physical library desk. Few recent studies address face-to-face interactions between librarians and users.

As the number of users on social network services (SNSs) continues to increase at a remarkable rate, privacy and security issues are consistently arising. Although users may not want to disclose their private attributes, these can be inferred from their public behavior on social media. In order to investigate the severity of the leakage of private information in this manner, the purpose of this paper is to present a method to infer undisclosed personal attributes of users based only on the data available on their public profiles on Facebook.

Facebook profile data consisting of 32 attributes were collected for 111,123 Korean users. Inferences were made for four private attributes (gender, age, marital status, and relationship status) based on five machine learning-based classification algorithms and three regression algorithms.

Experimental results showed that users’ gender can be inferred very accurately, whereas marital status and relationship status can be predicted more accurately with the authors’ algorithms than with a random model. Moreover, the average difference between the actual and predicted ages of users was only 0.5 years. The results show that some private attributes can be easily inferred from only a few pieces of user profile information, which can jeopardize personal information and may increase the risk to dignity.

In this paper, the authors’ only utilized each user’s own profile data, especially text information. Since users in SNSs are directly or indirectly connected, inference performance can be improved if the profile data of the friends of a given user are additionally considered. Moreover, utilizing non-text profile information, such as profile images, can help increase inference accuracy. The authors’ can also provide a more generalized inference performance if a larger data set of Facebook users is available.

A private attribute leakage alarm system based on the inference model would be helpful for users not desirous of the disclosure of their private attributes on SNSs. SNS service providers can measure and monitor the risk of privacy leakage in their system to protect their users and optimize the target marketing based on the inferred information if users agree to use it.

This paper investigates whether private attributes of SNS users can be inferred with a few pieces of publicly available information although users are not willing to disclose them. The experimental results showed that gender, age, marital status, and relationship status, can be inferred by machine-learning algorithms. Based on these results, an early warning system was designed to help both service providers and users to protect the users’ privacy.

The purpose of this paper is to compare nine different models to evaluate consumer credit risk, which are the following: Logistic Regression (LR), Naive Bayes (NB), Linear Discriminant Analysis (LDA), k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), Classification and Regression Tree (CART), Artificial Neural Network (ANN), Random Forest (RF) and Gradient Boosting Decision Tree (GBDT) in Peer-to-Peer (P2P) Lending.

The author uses data from P2P Lending Club (LC) to assess the efficiency of a variety of classification models across different economic scenarios and to compare the ranking results of credit risk models in P2P lending through three families of evaluation metrics.

The results from this research indicate that the risk classification models in the 2013–2019 economic period show greater measurement efficiency than for the difficult 2007–2012 period. Besides, the results of ranking models for predicting default risk show that GBDT is the best model for most of the metrics or metric families included in the study. The findings of this study also support the results of Tsai et al. (2014) and Teplý and Polena (2019) that LR, ANN and LDA models classify loan applications quite stably and accurately, while CART, k-NN and NB show the worst performance when predicting borrower default risk on P2P loan data.

The main contributions of the research to the empirical literature review include: comparing nine prediction models of consumer loan application risk through statistical and machine learning algorithms evaluated by the performance measures according to three separate families of metrics (threshold, ranking and probabilistic metrics) that are consistent with the existing data characteristics of the LC lending platform through two periods of reviewing the current economic situation and platform development.

UNEASY though it might be — and we just hope and trust it is not merely a truce — the settlement achieved in both British Leyland and British Steel is to be welcomed. Strikes are never pleasant and, in general, there are none who win and all lose. Worse, they all too often leave a feeling of resentment that is frequently fostered and exploited by those who have least either to gain or lose by continual conflict except their personal aggrandisement. It is so easy to wield a big stick when you yourself are safe from any rebounding blows from it!

This study aims to evaluate Artificial Intelligence (AI) research in the hospitality industry based on the service AI framework (mechanical-thinking-feeling) and highlight prospective avenues for future inquiry in this growing domain.

This paper conceptualizes timely concepts supported by research spanning multiple domains.

This research introduces a novel classification for the domain of AI hospitality research. This classification encompasses prediction and pattern recognition, computer vision, NLP, behavioral research, and synthetic data generation. Based on this classification, this study identifies and elaborates upon five emerging research topics, each linked to a corresponding set of research questions. These focal points encompass the realms of interpretable AI, controllable AI, AI ethics, collaborative AI, and synthetic data generation.

This viewpoint provides a foundational framework and a directional compass for future research in AI within the hospitality industry. It pushes the industry forward with a balanced approach to leveraging AI to augment human potential and enrich customer experiences. Both the classification and the research agenda would contribute to the body of knowledge that will guide the industry toward a future where technology and human service coalesce to create unparalleled value for all stakeholders.

Despite the increasing popularity of natural dyeing of textiles, the low substantivity between the fibers and the natural dyes is a problem. Several methods have been used to overcome this problem. In this study, wool fibers were pretreated with oxygen plasma under different conditions and dyed with the extract of grape leaves. The purpose of this study is to investigate the effects of plasma treatment parameters on the color strength of the dyed samples using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) and evaluate the ability of these methods for predicting the color strength.

Woolen yarns were modified under different conditions of oxygen plasma treatment. Oxygen flow rate, power and time were considered as the treatment variable factors. Plasma-treated samples were dyed under constant conditions with the extract of grape leaves as a natural dye. ANN and ANFIS were applied to model and analyze the effect of plasma treatment parameters on the color strength of the dyed samples.

The results showed that increasing all the plasma treatment process variables, including oxygen flow rate, power and time increased the color strength of the dyed samples. The results showed that the developed ANN and ANFIS could accurately predict the experimental data with correlation coefficients of 0.986 and 0.997, respectively. According to the obtained correlation coefficients, ANFIS had a higher accuracy in prediction of the results of this study compared with the ANN and RSM models (correlation coefficient = 0.902, from our previous study).

This study uses ANN and ANFIS for predicting color strength of naturally dyed textiles for the first time. The use of computational intelligence for the optimization and prediction of the effects plasma treatment for the improvement of natural dyeing of wool is another novelty of this study.

Recycled aggregate self-compacting concrete (RASCC) has the potential for sustainable resource utilization and has been widely applied. Predicting the compressive strength (CS) of RASCC is challenging due to its complex composite nature and nonlinear behavior.

This study comprehensively evaluated commonly used machine learning (ML) techniques, including artificial neural networks (ANN), random trees (RT), bagging and random forests (RF) for predicting the CS of RASCC. The results indicate that RF and ANN models typically have advantages with higher R2 values, lower root mean square error (RMSE), mean square error (MSE) and mean absolute error (MAE) values.

The combination of ML and Shapley additive explanation (SHAP) interpretable algorithms provides physical rationality, allowing engineers to adjust the proportion based on parameter analysis to predict and design RASCC. The sensitivity analysis of the ML model indicates that ANN’s interpretation ability is weaker than tree-based algorithms (RT, BG and RF). ML regression technology has high accuracy, good interpretability and great potential for predicting the CS of RASCC.

ML regression technology has high accuracy, good interpretability and great potential for predicting the CS of RASCC.