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This research aims to investigate the relationship between environmental, social and governance (ESG) factors and the performance of real estate companies before and after the COVID-19 pandemic. By conducting a comprehensive case study analysis, we will explore how real estate companies' adoption of ESG practices has influenced their financial performance, market value and resilience during these uncertain times. The findings of this study will contribute to the existing body of knowledge on the relationship between ESG factors and company performance, specifically within the real estate sector. Moreover, the research outcomes will offer practical implications for real estate companies, investors, policymakers and other stakeholders, aiding them in making informed decisions regarding ESG integration and its potential benefits in uncertain times. Overall, this research aims to shed light on whether ESG factors truly enhance the performance of real estate companies, considering the unique challenges posed by the COVID-19 pandemic and sanctions. By examining the case study before and during uncertain times including COVID-19 pandemic and sanctions, we provide valuable insights into the role of ESG practices in shaping the future of the real estate industry.

The study focuses on the selection process and main model used to investigate the relationship between ESG factors and firm performance. The data is divided into four groups based on ESG quartiles to analyze differences between firms with high and low ESG scores. The Difference-in-Differences (DID) model is employed to assess anomalous returns and stock volatility across different ESG quartiles before and after the COVID-19 pandemic. Panel data models are utilized to study the association between ESG and firm performance, with random effects and fixed effects estimators considered. The study builds a model to analyze the impact of ESG on financial performance indicators, incorporating various factors and control variables. Additionally, the Average Treatment Effect on the Treated (ATET) analysis and DID model are explored to evaluate the causal impact of ESG on firm performance. The study emphasizes the importance of testing for parallel trends to ensure the validity of the ATET analysis and it presents a generalized DID model to examine the relationship between ESG scores and company performance outcomes.

Our study's main conclusions show that, in a world with some degree of stability, ESG not only does not improve but, in some situations, also hurts firms' success. On the other hand, at times of notable worldwide unrest, like the COVID-19 pandemic, firms with better ESG ratings demonstrate exceptional stock market success and a noteworthy ability to rebound from a crisis. Moreover, we note that investors truly prioritize sustainable investments as a risk mitigation strategy in addition to their environmental and social duties only when companies face sufficiently significant risks. The results will highlight the significance of sustainable and responsible investment for investors and provide management with more knowledge to create effective ESG strategies for their companies.

By incorporating sustainability and responsibility into their operations, businesses may reduce risk, perform better over the long run and benefit society and the environment. As investors come to understand the importance of ESG issues in their decision-making, the global landscape is experiencing a transformation. Therefore, in the era when stakeholders, such as consumers, workers and shareholders, want more responsibility and transparency when it comes to ESG practises, it is crucial that companies should devote their priority to their ESG performance in order to reduce the danger of slipping behind, especially in light of the increasing importance of sustainability issues and changing laws. However, in the case of small-sized firms, investment policies to improve companies’ governance need to be controlled in moderation during the period of stability because it will create financial pressure and leave them without enough resources to cope with negative impacts during uncertain period. In sum, sustainable and ethical investment is not only a fad; rather, it is a vital and unavoidable route for companies looking to prosper in an unpredictable and complicated global environment.

This research study significantly enhances the existing academic discourse surrounding the relationship between ESG factors and firm performance, particularly, in periods of uncertainty. The findings underscore the critical importance for real estate companies to place a greater emphasis on ESG practices in order to not only benefit themselves but also to improve their overall performance and sustainability in the long term. By shedding light on the positive outcomes associated with prioritizing ESG considerations, this study offers valuable insights for real estate firms seeking to enhance their competitive advantage and stakeholder value in today's dynamic business landscape.

This study explores the complex impact of COVID-19 on India's financial sector, moving beyond simplistic public health vs. economy views. We assess market vulnerabilities and analyze how public sentiment, measured through Google Trends, can predict stock market fluctuations. We propose a novel framework using Google Trends for financial sentiment analysis, aiming to improve understanding and preparedness for future crises.

Hybrid approach leverages Google Trends as sentiment tool, market data, and momentum indicators like Rate of Change, Average Directional Index and Stochastic Oscillator, to deliver accurate, market insights for informed investment decisions during pandemic.

Our study reveals that the pandemic significantly impacted the Indian financial sector, highlighting its vulnerabilities. Capitalizing on this insight, we built a ground-breaking predictive model with an impressive 98.95% maximum accuracy in forecasting stock market values during such events.

To the best of authors knowledge this model's originality lies in its focus on short-term impact, novel data fusion and methodology, and high accuracy.• Focus on short-term impact: Our model uniquely identifies and quantifies the fleeting effects of COVID-19 on market behavior.• Novel data fusion and framework: A novel framework of sentiment analysis was introduced in the form of Trend Popularity Index. Combining trend popularity index with momentum offers a comprehensive and dynamic approach to predicting market movements during volatile periods.• High predictive accuracy: Achieving the prediction accuracy (98.93%) sets this model apart from existing solutions, making it a valuable tool for informed decision-making.

This study aims to introduce an image-based method to determine the processing window for a given alloy system using laser powder bed fusion equipment based on achieving the desired melting mode across multiple materials for powder-free specimens. The method uses a convolutional neural network trained to classify different track morphologies across different alloy systems to select appropriate printing settings. This method is intended for the development of new alloy systems, where the powder feedstock may be unavailable, or prohibitively expensive to manufacture.

A convolutional neural network is designed from scratch to identify the 4 key melting modes that are observed in laser powder bed fusion additive manufacturing across different alloy systems. To increase the prediction accuracy and generalisation accuracy across different materials, the network is trained using a novel hybrid data set that combines fully unsupervised learning with semi-supervised learning.

This study demonstrates that our convolutional network with a novel hybrid training approach can be generalised across different materials, and k-fold validation shows that the model retains good accuracy with changing training conditions. The model can predict the processing maps for the different alloys with an accuracy of up to 96% in some cases. It is also shown that powder-free single-track experiments are a useful indicator for predicting the final print quality of a component.

The “invariant information clustering” (IIC) approach is applied to process optimisation for additive manufacturing, and a novel hybrid data set construction approach that accounts for uncertainty in the ground truth data, enables the trained convolutional model to perform across a range of different materials and most importantly, generalise to materials outside of the training data set. Compared to the traditional cross-sectioning approach, this method considers the whole length of the single track when determining the melting mode.

This study aims to examine the relationship between corporate social responsibility (CSR) and job pursuit intention (JPI), and the role of job seekers’ perception on employer prosocial orientation, value congruence and employer attractiveness in this relationship. CSR is measured based on internal and external CSR.

By adopting quantitative approach, data was obtained through survey questionnaire from 420 bachelor’s degree university fresh graduates from five universities in Malaysia who are actively seeking for jobs. Data was analysed using structural equation modelling technique.

Research findings show that internal and external CSR positively impact job seekers’ perception of employer prosocial orientation. Job seekers’ perception towards employer prosocial orientation has a significant positive impact on value congruence. Value congruence has a significant positive influence on employer attractiveness. Finally, employer attractiveness has a significant positive impact on JPI.

The findings are useful for human resources management. Organisations (employers) should focus on effective internal and external CSR practices through a prosocial orientation approach to attract the best talents and create a strong position in the job market.

This study extends the Signalling Theory and P-O Fit theory by applying them to an entirely different context of CSR and JPI, by incorporated the holistic job seekers’ psychological processes of the recruitment signals (internal and external CSR), signalling process and person-organisation fit (perception on employer prosocial orientation, value congruence and employer attractiveness) thoroughly.

In this paper, the seismic behavior of the gusset plate moment connection (GPMC) exposed to the post-earthquake fire (PEF) is investigated.

For this purpose, for the sake of verification, first, a numerical model is built using ABAQUS software and then exposed to earthquakes and high temperatures. Afterward, the effects of a series of parameters, such as gusset plate thickness, gap width, steel grade, vertical load value and presence of the stiffeners, are evaluated on the behavior of the connection in the PEF conditions.

Based on the results obtained from the parametric study, all parameters effectively played a role against the seismic loads, although, when exposed to fire, it was found that the vertical load value and presence of the stiffener revealed a great contribution and the other parameters could not significantly affect the connection performance. Finally, to develop the modeling and further study the performance of the connection, the 4 and 8-story frames are subjected to 11 accelerograms and 3 different fire scenarios. The findings demonstrate that high temperatures impose rotations on the structure, such that the story drifts were changed compared to the post-earthquake drift values.

The obtained results can be used by engineers to design the GPMC for the combined action of earthquake and fire.

Although separate studies on the influence of corrosion and fire exposure on the constitutive relationship of concrete and steel have been done, there is still a gap in knowledge on the influence of corrosion-temperature superimposition as nonlinear phenomenon. The current study is focused to investigate the response of hot-rolled steel bars subjected to corrosion-temperature superimposition.

Using the accelerated corrosion-impressed-current technique, hot-rolled specimens with different levels of corrosion were obtained. The hot-rolled rebars were first corroded to target levels such as (6, 12, 18, 24, 30 and 36%) and subsequently subjected to target temperatures (250 °C, 400 °C, 550 °C, 800 °C and 950 °C), before tensile tests were carried out to evaluate the residual mechanical response.

The outcomes showed a significant decline in the parameters governing the mechanical properties of steel reinforcement due to the combined damage due to corrosion and fire. Corroded reinforcement still showed ductile failure after exposure to fire. Moreover, the combined loss of load-bearing characteristics due to corrosion and fire has little influence on the modulus of elasticity. The outcomes of this investigation provide a theoretical database for the assessment of aged structural elements exposed to combination after exposure to fire.

The information concerning structural material's response to corrosion-temperature combined damage is still limited. The cover of the reinforcement is designed to safeguard the reinforcing bars from foreign agencies but is often damaged and spalled off due to corrosion, rendering the reinforcing bars directly exposed. The study aims at the experimental production of fire conditions in a corrosion-damaged infrastructure to cover the aforementioned research gap. The effects of corrosion being superimposed by exposure to elevated temperatures on key parameters affecting mechanical behavior were examined.

1. Influence of corrosion-temperature superimposition on the mechanical properties of hot-rolled rebars.

2. Influence of corrosion-temperature superimposition on the macro and microstructure properties of hot-rolled rebars.

3. Influence of corrosion-temperature superimposition on stress-strain curves of hot-rolled rebars.

4. Influence of corrosion-temperature superimposition on tensile strength, modulus of elasticity and elongation of hot-rolled rebars.

Influence of corrosion-temperature superimposition on the mechanical properties of hot-rolled rebars.

Influence of corrosion-temperature superimposition on the macro and microstructure properties of hot-rolled rebars.

Influence of corrosion-temperature superimposition on stress-strain curves of hot-rolled rebars.

Influence of corrosion-temperature superimposition on tensile strength, modulus of elasticity and elongation of hot-rolled rebars.

This paper explores current teaching and learning practices, benefits and challenges in the implementation of Internationalization at Home (IaH) in higher education.

The study follows a systematic review (SR) protocol in accordance with the PRISMA Statement, covering published research from 2018 to 2022. Through this process, we identified 58 peer-reviewed manuscripts meeting our inclusion criteria. We examined disciplines, locations of IaH, objectives pursued, modality of the IaH implementation, activities and resources used. Benefits and challenges were also analysed.

The SR reveals a growing adoption of IaH, employing various technologies and interdisciplinary methods to foster cross-cultural competence. It emphasizes diverse teaching activities and resources, aligning with digitalization trends. While IaH brings benefits like improved intercultural sensitivity, collaboration and skills development, it also faces challenges in language, technical, personal, pedagogical and organizational aspects, highlighting its complexity.

Our search focused on research from 2018 to 2022, potentially missing earlier trends, and excluded grey literature due to quality concerns. The SR emphasizes online collaborative efforts in IaH, signalling a shift to digital internationalization. Institutions should invest in supporting such practices aided by strategic university alliances. A critical approach to “Global-North” collaborations is urged, promoting geographically inclusive IaH initiatives.

This study responds to the call for critical analysis on concrete examples of IaH. Through a systematic review, it explores recent teaching and learning practices, with a particular focus on the latest technological advancements. The study specifies learning objectives and identifies relevant tools for implementing IaH initiatives.