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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.

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!

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.

The objective of this study is to investigate the impact of longitudinal forces on extreme-long heavy-haul trains, providing new insights and methods for their design and operation, thereby enhancing safety, operational efficiency and track system design.

A longitudinal dynamics simulation model of the super long heavy haul train was established and verified by the braking test data of 30,000 t heavy-haul combination train on the long and steep down grade of Daqing Line. The simulation model was used to analyze the influence of factors on the longitudinal force of super long heavy haul train.

Under normal conditions, the formation length of extreme-long heavy-haul combined train has a small effect on the maximum longitudinal coupler force under full service braking and emergency braking on the straight line. The slope difference of the long and steep down grade has a great impact on the maximum longitudinal coupler force of the extreme-long heavy-haul trains. Under the condition that the longitudinal force does not exceed the safety limit of 2,250 kN under full service braking at the speed of 60 km/h the maximum allowable slope difference of long and steep down grade for 40,000 t super long heavy-haul combined trains is 13‰, and that of 100,000 t is only 5‰.

The results will provide important theoretical basis and practical guidance for further improving the transportation efficiency and safety of extreme-long heavy-haul trains.

The purpose of this paper is to present a novel framework for strategic decision making using Big Data Analytics (BDA) methodology.

In this study, two different machine learning algorithms, Random Forest (RF) and Artificial Neural Networks (ANN) are employed to forecast export volumes using an extensive amount of open trade data. The forecasted values are included in the Boston Consulting Group (BCG) Matrix to conduct strategic market analysis.

The proposed methodology is validated using a hypothetical case study of a Chinese company exporting refrigerators and freezers. The results show that the proposed methodology makes accurate trade forecasts and helps to conduct strategic market analysis effectively. Also, the RF performs better than the ANN in terms of forecast accuracy.

This study presents only one case study to test the proposed methodology. In future studies, the validity of the proposed method can be further generalized in different product groups and countries.

In today’s highly competitive business environment, an effective strategic market analysis requires importers or exporters to make better predictions and strategic decisions. Using the proposed BDA based methodology, companies can effectively identify new business opportunities and adjust their strategic decisions accordingly.

This is the first study to present a holistic methodology for strategic market analysis using BDA. The proposed methodology accurately forecasts international trade volumes and facilitates the strategic decision-making process by providing future insights into global markets.

In recent years, equity premiums have been unusually large and efforts to forecast them have been largely unsuccessful. This paper presents evidence suggesting that artificial neural networks (ANNs) outperform traditional statistical methods and can forecast equity premiums reasonably well.

This study replicates out‐of‐sample estimates of regression using ANN with economic fundamentals as inputs. The theory states that recent large equity premium values cannot be explained (the equity premium puzzle).

The dividend yield variable was found to produce the best out‐of‐sample forecasts for equity premium.

Although the equity premium puzzle can be partly explained by fundamentals, they do not imply immediate policy prescriptions since all forecasting techniques including ANN are susceptible to joint assumptions of the techniques and the models used.

This result is useful in capital asset pricing model and in asset allocation decisions.

Unlike the findings from previous research that are unable to explain equity premium behavior, this paper suggests that equity premium can be reasonably forecasted.