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Automatic dependent surveillance-broadcast (ADS-B) is the foundational technology of the next generation air transportation system defined by Federal Aviation Authority and is one of the most precise ways for tracking aircraft position. ADS-B is intended to provide greater situational awareness to the pilots by displaying the traffic information like aircraft ID, altitude, speed and other critical parameters on the Cockpit Display of Traffic Information displays in the cockpit. Unfortunately, due to the initial proposed nature of ADS-B protocol, it is neither encrypted nor has any other innate security mechanisms, which makes it an easy target for malicious attacks. The system is vulnerable to various active and passive attacks like message ingestion, message deletion, eavesdropping, jamming, etc., which has become an area of concern for the aviation industry. The purpose of this study is to propose a method based on modified advanced encryption standard (AES) algorithm to secure the ADS=B messages and increase the integrity of ADS-B data transmissions.

Though there are various cryptographic and non-cryptographic methods proposed to secure ADS-B data transmissions, it is evident that most of these systems have limitations in terms of cost, implementation or feasibility. The new proposed method implements AES encryption techniques on the ADS-B data on the sender side and correlated decryption mechanism at the receiver end. The system is designed based on the flight schedule data available from any flight planning systems and implementing the AES algorithm on the ADS-B data from each aircraft in the flight schedule.

The suitable hardware was developed using Raspberry pi, ESP32 and Ra-02. Several runs were done to verify the original message, transmitted data and received data. During transmission, encryption algorithm was being developed, which has got very high secured transmission, and during the reception, the data was secured. Field test was conducted to validate the transmission and quality. Several trials were done to validate the transmission process. The authors have successfully shown that the ADS-B data can be encrypted using AES algorithm. The authors are successful in transmitting and receiving the ADS-B data packet using the discussed hardware and software methodology. One major advantage of using the proposed solution is that the information received is encrypted, and the receiver ADS-B system can decrypt the messages on the receiving end. This clearly proves that when the data is received by an unknown receiver, the messages cannot be decrypted, as the receiver is not capable of decrypting the AES-authenticated messages transmitted by the authenticated source. Also, AES encryption is highly unlikely to be decrypted if the encryption key and the associated decryption key are not known.

Implementation of the developed solution in actual onboard avionics systems is not within the scope of this research. Hence, assessing in the real-time distances is not covered.

The authors propose to extend this as a software solution to the onboard avionics systems by considering the required architectural changes. This solution can also bring in positive results for unmanned air vehicles in addition to the commercial aircrafts. Enhancement of security to the key operational and navigation data elements is going to be invaluable for future air traffic management and saving lives of people.

The proposed solution has been practically implemented by developing the hardware and software as part of this research. This has been clearly brought out in the paper. The implementation has been tested using the actual ADS-B data/messages received from using the ADS-B receiver. The solution works perfectly, and this brings immense value to the aircraft-to-aircraft and aircraft-to-ground communications, specifically while using ADS-B data for communicating the position information. With the proposed architecture and minor software updates to the onboard avionics, this solution can enhance safety of flights.

Virtual wine experiences (VWEs) replaced in-person wine experiences during the Covid-19 pandemic and continue to be offered by some actors. This study aims to investigate the factors driving interest in VWEs and identify relevant traits of potential consumers to help assess VWEs long-term potential.

A representative sample of 399 Oregon and California wine consumers answered a structured online survey. The authors combine ordered logistic regression and qualitative techniques to analyze the data.

VWEs may effectively attract potential wine consumers and tourists. High interest in VWEs is associated with strong wine involvement and intentions to visit wine regions. Digitization, aversion to travel-related risk and convenience are other relevant drivers of VWE interest. The segmentation analysis revealed that consumers with a potentially higher interest in VWE have distinct traits.

Wineries and wine tourism destinations could leverage VWEs to attract wine tourists and consumers. The authors discuss specific characteristics of high-interest consumers.

Participants in VWEs interact with hosts and explore products in real time. This engagement has long-term marketing potential for attracting them as customers or visitors. The study provides strategic information for practitioners and academics on VWE interest drivers and potential demand, which is currently missing from the literature.

With the advancement of digital transformation, it is important for e-retailers to use artificial intelligence (AI) for customer engagement (CE), as CE enables e-retail brands to succeed. Essentially, AI e-marketing (AIeMktg) is the use of AI technological approaches in e-marketing by blending customer data, and Retail 4.0 is the digitisation of the physical shopping experience. Therefore, in the era of Retail 4.0, this study investigates the factors influencing the use of AIeMktg for transforming CE.

The primary data were collected from 305 e-retailer customers, and the analysis was performed using a quantitative methodology.

The results reveal that AIeMktg has tremendous applications in Retail 4.0 for CE. First, it enables marketers to swiftly and responsibly use data to anticipate and predict customer demands and to provide relevant personalised messages and offers with location-based e-marketing. Second, through a continuous feedback loop, AIeMktg improves offerings by analysing and incorporating insights from a 360-degree view of CE.

The main contribution of this study is to provide theoretical underpinnings of CE, AIeMktg, factors influencing the use of AIeMktg, and customer commitment in the era of Retail 4.0. Subsequently, it builds and validates structural relationships among such theoretical underpinning variables in transforming CE with AIeMktg, which is important for customers to expect a different type of shopping experience across digital channels.

Many practical control problems require achieving multiple objectives, and these objectives often conflict with each other. The existing multi-objective evolutionary reinforcement learning algorithms cannot achieve good search results when solving such problems. It is necessary to design a new multi-objective evolutionary reinforcement learning algorithm with a stronger searchability.

The multi-objective reinforcement learning algorithm proposed in this paper is based on the evolutionary computation framework. In each generation, this study uses the long-short-term selection method to select parent policies. The long-term selection is based on the improvement of policy along the predefined optimization direction in the previous generation. The short-term selection uses a prediction model to predict the optimization direction that may have the greatest improvement on overall population performance. In the evolutionary stage, the penalty-based nonlinear scalarization method is used to scalarize the multi-dimensional advantage functions, and the nonlinear multi-objective policy gradient is designed to optimize the parent policies along the predefined directions.

The penalty-based nonlinear scalarization method can force policies to improve along the predefined optimization directions. The long-short-term optimization method can alleviate the exploration-exploitation problem, enabling the algorithm to explore unknown regions while ensuring that potential policies are fully optimized. The combination of these designs can effectively improve the performance of the final population.

A multi-objective evolutionary reinforcement learning algorithm with stronger searchability has been proposed. This algorithm can find a Pareto policy set with better convergence, diversity and density.

This paper aims to compare the surface roughness and hardness of three commercially available self-curing acrylic resins for dental use, under different polymerization conditions.

A comparative in vitro study was conducted using a convenience sample of 12 × 5 × 2 mm blocks with n = 40 for each material (Nic Tone, Arias Plus and Orthocryl®), with subgroups according to the polymerization method: conventional (C) and polymerization under ambient conditions (A). The surface roughness of the materials was measured using a profilometer; hardness was measured with a portable hardness tester. Additionally, surface morphology as well as particles size and morphology were evaluated with scanning electron microscopy.

There were significant differences in roughness and hardness values between the three self-curing acrylic resins (p < 0.05), as well as within each self-curing acrylic resin according to the polymerization method used (p < 0.05). The samples polymerized with the conventional method presented lower surface roughness and hardness values.

This study provides scientific evidence of values not provided by manufacturers in relation to the surface roughness and hardness of these materials, and all of them met the ideal minimum values of surface roughness, regardless of the polymerization technique used.

This study aims to investigate the impact of trust in the metaverse on the Islamic banking sector, particularly in facilitating market success. Additionally, it seeks to explore the relationship between metaverse-driven brand image, product features, service quality and overall performance in the market.

Data were collected from 187 participants in Jordan, with the SmartPLS software used to test the hypotheses.

The findings reveal a significant impact of metaverse-enhanced brand image, product features and service quality on Islamic banking market performance. Furthermore, customer trust in the metaverse plays a significant role in shaping the relationship between product features, service quality and Islamic banking market performance.

The study’s practical implications still suggest the need for a more holistic metaverse-driven approach. Investing in service quality initiatives alone may not adequately build and sustain customer trust in the metaverse. Instead, transparent communication on ethical practices in the metaverse is required to reinforce trust and magnify the positive influence of superior service quality in the metaverse.

The purpose of this paper is to explore this process through reviewing key reports and literature through an Indigenous standpoint lens. We identify three key challenges facing the Yoorrook Commission in its journey. First, the continued resistance of influential sections of the Australian community to look backwards and accept responsibility for the violence of the colonial project. Second, the trauma facing those who speak out and remember and the real danger of expectations dashed. Third, the continuance of the colonial pandemic and underlying and invisible racism that infects and poisons all Australians.

This paper has drawn on key literature and secondary data through an Indigenous Lens.

We identify three challenges facing Yoorrook. First, the resistance of influential sections of the Australian community to accept responsibility for the violence of the colonial project. Second, the trauma facing those who speak out and remember and the danger of expectations dashed. Third, the continuance of underlying and invisible racism that infects and poisons the hearts and minds of non-Indigenous Australia. Despite these challenges we argue that the ability of Yoorrook to capture the lived experience of First Peoples in Victoria and the ability to hold key government officials to account presents a unique opportunity to advance the self determination of all First Peoples in Australia.

This is the first Treaty in Victoria and there has been no study of it before.

The increasing prevalence of mobile phones and the ease of cheaper Internet access have accelerated the growth of mobile-based payments in India. The study aims to identify must-have and should-have factors affecting consumers’ usage intention toward “Near-Field Communication (NFC)” based mobile payments.

The research integrates diffusion innovation theory and the technology acceptance model. Employing a mixed-methods approach, combining partial least square structural equation modeling and Necessary Condition Analysis (NCA). Thus, the study draws from qualitative interviews and a quantitative survey of 218 respondents.

The study results indicate that the relative advantage of NFC-based mobile payments over conventional payment modes and security concerns directly affect the intention to use it. However, complexity, transaction speed and ease of use do not affect the intention to use. Moreover, perceived usefulness mediates the relationship between complexity, relative advantage and transaction speed with the intention to use NFC-based mobile payments. Less complexity and ease of use, despite being insignificant determinants as per PLS-SEM, must be focused on as the NCA results indicate its necessity for future market growth.

This research addresses a significant gap by focusing on Indian consumers' intention to use NFC-based mobile payments, an area largely unexplored despite the global and availability of such services. The study distinguishes itself by examining factors influencing adoption within the nascent stage of mobile-based NFC payments in India, where digital growth and technology uptake are gradually unfolding.

Multiple encapsulated phase change materials (PCMs) are used in a wide range of applications, including convective drying, electronic cooling, waste heat recovery and air conditioning. Therefore, it is important to understand the performance of multiple PCMs in channels with flow separation and develop methods to increase their effectiveness. The aim of the study is to analyze the phase transition dynamics of multiple encapsulated PCMs mounted in a U-shaped tube under inclined magnetic field by using ternary nanofluid.

The PCMs used in the upper horizontal channel, vertical channel and lower horizontal channel are denoted by M1, M2 and M3. Magnetic field is uniform and inclined while finite element method is used as the solution technique. Triple encapsulated-PCM system study is carried out taking into account different values of Reynolds number (Re, ranges from 300 to 1,000), Hartmann number (Ha ranges from 0 and 60), magnetic field inclination (between 0 and 90) and solid volume fraction of ternary nanofluid (between 0 and 0.03). The dynamic response of the liquid fraction is estimated for each PCM with varying Re, Ha and t using an artificial neural network.

It is observed that for PCMs M2 and M3, the influence of Re on the phase transition is more effective. For M2 and M3, entire transition time (t-F) lowers by approximately 47% and 47.5% when Re is increased to its maximum value, whereas it only falls by 10% for M1. The dynamic characteristics of the phase transition are impacted by imposing MGF and varying its strength and inclination. When Ha is raised from Ha = 0 to Ha = 50, the t-F for PCM-M2 (PCM-M3) falls (increases) by around 30% (29%). For PCMs M1, M2 and M3, the phase transition process accelerates by around 20%, 30% and 28% when the solid volume fraction is increased to its maximum value.

Outcomes of this research is useful for understanding the phase change behavior of multiple PCMs in separated flow and using various methods such as nano-enhanced magnetic field to improve their effectiveness. Research outputs are beneficial for initial design and optimization of using multiple PCMs in diverse energy system technologies, including solar power, waste heat recovery, air conditioning, thermal management and drying.

The rise of artificial intelligence (AI) applications has driven enterprises to provide many intelligent services to consumers. For instance, customers can use chatbots to make relevant inquiries and seek solutions to their problems. Despite the development of customer service chatbots years ago, they require significant improvements for market recognition. Many customers have reported negative experiences with customer service chatbots, contributing to resistance toward their use. Therefore, this study adopts the innovation resistance theory (IRT) perspective to understand customers’ resistance to using chatbots. It aims to integrate customers’ negative emotions into a predictive behavior model and examine users’ functional and psychological barriers.

In this study, we collected data from 419 valid individuals and used structural equation modeling to analyze the relationships between resistance factors and negative emotions.

The results confirmed that barrier factors affect negative emotions and amplify chatbot resistance influence. We discovered that value and risk barriers directly influence consumer use. Moreover, both functional and psychological barriers positively impact negative emotions.

This study adopts the innovation resistance theory perspective to understand customer resistance to using chatbots, integrates customer negative emotions to construct a predictive behavior model and explores users’ functional and psychological barriers. It can help in developing online customer service chatbots for e-commerce.