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This study aims to present a new rapid enhancement digital elevation model (DEM) framework using Google Earth Engine (GEE), machine learning, weighted interpolation and spatial interpolation techniques with ground control points (GCPs), where high-resolution DEMs are crucial spatial data that find extensive use in many analyses and applications.

First, rapid-DEM imports Shuttle Radar Topography Mission (SRTM) data and Sentinel-2 multispectral imagery from a user-defined time and area of interest into GEE. Second, SRTM with the feature attributes from Sentinel-2 multispectral imagery is generated and used as input data in support vector machine classification algorithm. Third, the inverse probability weighted interpolation (IPWI) approach uses 12 fixed GCPs as additional input data to assign the probability to each pixel of the image and generate corrected SRTM elevations. Fourth, gridding the enhanced DEM consists of regular points (E, N and H), and the contour interval is 5 m. Finally, densification of enhanced DEM data with GCPs is obtained using global positioning system technique through spatial interpolations such as Kriging, inverse distance weighted, modified Shepard’s method and triangulation with linear interpolation techniques.

The results were compared to a 1-m vertically accurate reference DEM (RD) obtained by image matching with Worldview-1 stereo satellite images. The results of this study demonstrated that the root mean square error (RMSE) of the original SRTM DEM was 5.95 m. On the other hand, the RMSE of the estimated elevations by the IPWI approach has been improved to 2.01 m, and the generated DEM by Kriging technique was 1.85 m, with a reduction of 68.91%.

A comparison with the RD demonstrates significant SRTM improvements. The suggested method clearly reduces the elevation error of the original SRTM DEM.

This study aims to fill a knowledge gap about the best ways to execute waste management regulations that support sustainability and environmental responsibility. However, the study seeks to investigate and assess how well waste management systems support sustainability and environmentally friendly behaviors.

Since the quantitative technique was used to discover and generate hypotheses about relationships between variables, it was chosen for the entirety of the study. The data collection from ready-made garments (RMG) in Bangladesh was carried out using a survey strategy that involved the distribution of questionnaires using a cluster sampling approach. The partial least squares structural equation model (PLS-SEM) was used for quantitative analysis with SmartPLS.

However, the results show the importance of waste management, green practice methods and sustainability for the company's goal of maintainable performance. On the other hand, PLS-SEM demonstrated that based on the provided t-statistics and p-values, all of the routes in the structural model are statistically significant at the level of p < 0.05. These routes also appeared to have far broader and more powerful effects. Principles of the circular economy are highlighted, as are the importance of holistic methods, awareness and education, stakeholder participation and fusing these concepts. Furthermore, it seems to affect significantly both short-term and long-term success due to its distinctiveness in sustained performance.

Much time will be required to assess waste management methods and their long-term effects. A healthcare manager may adapt research findings to practice, ensuring the study enhances management and patient care.

This research addresses a significant literature gap by providing managers and policymakers with concrete ideas on integrating sustainability into operational and strategic frameworks. In today's competitive environment, integrating stakeholder engagement and green practices into core company operations is an innovative way to achieve sustainable excellence.

Autonomous flight of unmanned aerial vehicles (UAVs) in global position system (GPS)-denied environments has become an increasing research hotspot. This paper aims to realize the indoor fixed-point hovering control and autonomous flight for UAVs based on visual inertial simultaneous localization and mapping (SLAM) and sensor fusion algorithm based on extended Kalman filter.

The fundamental of the proposed method is using visual inertial SLAM to estimate the position information of the UAV and position-speed double-loop controller to control the UAV. The motion and observation models of the UAV and the fusion algorithm are given. Finally, experiments are performed to test the proposed algorithms.

A position-speed double-loop controller is proposed, by fusing the position information obtained by visual inertial SLAM with the data of airborne sensors. The experiment results of the indoor fixed-points hovering show that UAV flight control can be realized based on visual inertial SLAM in the absence of GPS.

A position-speed double-loop controller for UAV is designed and tested, which provides a more stable position estimation and enabled UAV to fly autonomously and hover in GPS-denied environment.

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.

The purpose of this study is to determine propeller damage based on acoustic recordings taken from unmanned aerial vehicle (UAV) propellers operated at different thrust conditions on a test bench. Propeller damage is especially critical for fixed-wing UAVs to sustain a safe flight. The acoustic characteristics of the propeller vary with different propeller damages.

For the research, feature extraction methods and machine learning techniques were used during damage detection from propeller acoustic data. First of all, sound recordings were obtained by operating five different damaged propellers and undamaged propellers under three different thrusts. Afterwards, the harmonic-to-noise ratio (HNR) feature extraction technique was applied to these audio recordings. Finally, model training and validation were performed by applying the Gaussian Naive Bayes machine learning technique to create a diagnostic approach.

A high recall value of 96.19% was obtained in the performance results of the model trained according to damaged and undamaged propeller acoustic data. The precision value was 73.92% as moderate. The overall accuracy value of the model, which can be considered as general performance, was obtained as 81.24%. The F1 score has been found as 83.76% which provides a balanced measure of the model’s precision and recall values.

This study include provides solid method to diagnose UAV propeller damage using acoustic data obtain from the microphone and allows identification of differently damaged propellers. Using that, the risk of in-flight failures can be reduced and maintenance costs can be lowered with addressing the occurred problems with UAV propeller before they worsen.

This study introduces a novel method to diagnose damaged UAV propellers using the HNR feature extraction technique and Gaussian Naive Bayes classification method. The study is a pioneer in the use of HNR and the Gaussian Naive Bayes and demonstrates its effectiveness in augmenting UAV safety by means of propeller damages. Furthermore, this approach contributes to UAV operational reliability by bridging the acoustic signal processing and machine learning.

The purpose of this study is to examine how the implementation of edge computing can enhance the progress of the circular economy within supply chains and to address the challenges and best practices associated with this emerging technology.

This study utilized a streamlined evaluation technique that employed Latent Dirichlet Allocation modeling for thorough content analysis. Extensive searches were conducted among prominent publishers, including IEEE, Elsevier, Springer, Wiley, MDPI and Hindawi, utilizing pertinent keywords associated with edge computing, circular economy, sustainability and supply chain. The search process yielded a total of 103 articles, with the keywords being searched specifically within the titles or abstracts of these articles.

There has been a notable rise in the volume of scholarly articles dedicated to edge computing in the circular economy and supply chain management. After conducting a thorough examination of the published papers, three main research themes were identified, focused on technology, optimization and circular economy and sustainability. Edge computing adoption in supply chains results in a more responsive, efficient and agile supply chain, leading to enhanced decision-making capabilities and improved customer satisfaction. However, the adoption also poses challenges, such as data integration, security concerns, device management, connectivity and cost.

This paper offers valuable insights into the research trends of edge computing in the circular economy and supply chains, highlighting its significant role in optimizing supply chain operations and advancing the circular economy by processing and analyzing real time data generated by the internet of Things, sensors and other state-of-the-art tools and devices.

Online food delivery service has evolved swiftly and stretched the bounds of the catering business. In the gig economy, being a food delivery rider draws employees with the promise of flexibility and independence. To this end, the purpose of this paper is to explore the main themes shared in online reviews by food delivery riders and which of these themes are linked to positive and negative ratings.

The authors used 729 employee reviews posted on the Glassdoor website. In addition, they used content analysis to examine reviews of Uber Eats online food ordering company shared by food delivery riders on an online platform.

The results of this study include seven main themes: “earning,” “customers,” “orders,” “tips,” “car,” “flexible schedule” and “app” (navigation). Positive concepts are associated with “earning,” “orders,” “tips” and “flexible schedule.” Negative themes are linked to the “app” (navigation), “car” and “customers.”

Management should consider online reviews as employees’ opinions and voices. Specifically, management should provide financial support to employees for car maintenance, offer insurance for income stability and arrange training programs to enable them to use several tip-enhancing behaviors.

No research has been conducted using online reviews from an employment search engine to investigate employees’ experiences of online food delivery. To the best of the authors’ knowledge, this study is one of the first attempts using user-generated content from an employment search engine to explore employees’ experiences.

Built environments are highly vulnerable to climatic disasters such as extreme floods, droughts and storms. Inaccurate decisions in adopting emerging construction technologies can result in missed opportunities to improve the resilience of built environments. Therefore, understanding the effectiveness of emerging construction technologies in improving built environment resilience can help in making better strategic decisions at the national and organizational levels. This study aims to evaluate the effectiveness of Construction 4.0 technologies in improving built environment resilience.

A list of Construction 4.0 technologies was adopted from a national strategic plan. Then, the data were collected using the fuzzy technique for order preference by similarity to ideal solution technique from selected built environment experts to determine the relative effectiveness of Construction 4.0 technologies in improving built environment resilience.

Six Construction 4.0 technologies are critical in improving built environment resilience (in rank order): building information modeling, autonomous construction, advanced building materials, big data and predictive analytics, internet of Things and prefabrication and modular construction. In addition, adopting Construction 4.0 technologies collectively is crucial, as moderate to strong connections exist among the technologies in improving built environment resilience.

To the best of the authors’ knowledge, this is one of the first papers that evaluate the effectiveness of Construction 4.0 technologies in improving built environment resilience. Industry professionals, researchers and policymakers can use the study findings to make well-informed decisions on selecting Construction 4.0 technologies that improve built environment resilience to climatic disasters.

The purpose of this study is to examine various data sources to identify trends and themes in humanitarian supply chain management (HSCM) in the digital age.

In this study, various data sources such as published literature and social media content from Twitter, LinkedIn, blogs and forums are used to identify trending topics and themes on HSCM using topic modelling.

The study examined 33 published literature and more than 94,000 documents, including tweets and expert opinions, and identified eight themes related to HSCM in the digital age namely “Digital technology enabled global partnerships”, “Digital tech enabled sustainability”, “Digital tech enabled risk reduction for climate changes and uncertainties”, “Digital tech enabled preparedness, response and resilience”, “Digital tech enabled health system enhancement”, “Digital tech enabled food system enhancement”, “Digital tech enabled ethical process and systems” and “Digital tech enabled humanitarian logistics”. The study also proposed a framework of drivers, processes and impacts for each theme and directions for future research.

Previous research has predominantly relied on published literature to identify emerging themes and trends on a particular topic. This study is unique because it examines the ability of social media sources such as blogs, websites, forums and published literature to reveal evolving patterns and trends in HSCM in the digital age.

This paper aims to extend insights about the relationship between inter-organizational collaboration and approaches to control from the perspective of decision-makers. We investigate the relationship between approaches to control and intended forms of integration between actors responsible for solving the complex problem of integrated person-centered care for elderly with diverse and significant needs.

The empirical study is based on a content analysis of contractual agreements. We have analyzed a total of 118 collaboration agreements and associative documents between all Swedish regions and municipalities.

The study shows that intended integration is subject to remarkable variation in intended forms of inter-organizational collaboration in this Swedish case. The paper illustrates that decision-makers’ intentions with proposed collaboration in each given context are important for the chosen approach to control. Regardless of intended forms of integration, our study suggests that an imminent soft approach to control is expressed alongside limited signs of hard control. Various forms of intended integration can be managed by the two approaches simultaneously insofar as the agreements appear to have a two-sided purpose.

Our paper proposes an empirically driven taxonomy of intended forms of integration initiatives. The taxonomy provides resources for studies about how collaboration can be managed when it is stipulated by national legislation but local self-governance gives actors considerable freedom to decide on how to organize and manage services. By presenting the taxonomy and relating this to approaches of control, our iterative study builds on and adds to a recent stream of research arguing that the relationship between collaboration and approaches to control may by fuzzier and more complex than originally thought.