Guest Editorial on 10th Anniversary of Technologies—Recent Advances and Perspectives

1. Introduction

In 2022, Technologies (ISSN: 2227-7080) celebrated its 10th anniversary. This international, peer-reviewed, open access journal is published by MDPI (Basel, Switzerland) and indexed by ESCI, Inspec, and INSPIRE, among others. It ranks 46/170 (Q2) in “Engineering and Multidisciplinary” in the Journal Citation Indicator (2021) and has received its first Impact Factor. The journal released its inaugural issue in 2013 and published its 500th paper in 2021.

To commemorate this milestone, a Special Issue titled “10th Anniversary of Technologies—Recent Advances and Perspectives” was launched. The Special Issue welcomed high-quality original research articles and reviews on topics like quantum technologies, innovations in materials processing, construction technologies, environmental technologies, biotechnologies, medical technologies, and computer and information technologies. Contributors were invited to submit papers on trendy or emerging topics for peer review and possible publication. A total of 36 papers were published in this Special Issue.

2. Overview of Contributions

In the contribution by Yeh and Zhu, titled “Forecasting by Combining Chaotic PSO and Automated LSSVR”, a novel automatic least square support vector regression (LSSVR) optimization method, using mixed kernel chaotic particle swarm optimization (CPSO), was introduced to tackle regression problems [item 1 in the List of Contributions]. The LSSVR model consisted of the following three steps: chaotic sequence positioning for randomness and ergodicity, binary particle swarm optimization (PSO) for selecting potential input feature combinations, and a chaotic search to refine the input features. These steps were combined to form the CP-LSSVR model. The method was evaluated using datasets from UCI, showing a strong predictive capability and efficient model building with a limited number of features.

The contribution by Brischetto et al., titled “A Layer-Wise Coupled Thermo-Elastic Shell Model for Three-Dimensional Stress Analysis of Functionally Graded Material Structures”, presented a coupled 3D thermo-elastic shell model for analyzing thermal stress in one-layered and sandwich plates and shells with functionally graded material (FGM) layers [item 2 in the List of Contributions]. The model combined three-dimensional (3D) equilibrium equations and the Fourier heat conduction equation for spherical shells into four coupled equations. Solved using the exponential matrix method, the model assumed simply supported boundary conditions. Static responses were evaluated in terms of displacements and stresses. The model’s accuracy, showing less than 0.5% difference from uncoupled models, was validated for various thickness ratios, geometries, and temperatures. The FGM layers were metallic at the bottom and ceramic at the top.

The contribution by Rehman et al., titled “FogTrust: Fog-Integrated Multi-Leveled Trust Management Mechanism for Internet of Things”, introduced FogTrust, which is a lightweight trust management mechanism designed to enhance security in the Internet of Things (IoT) [item 3 in the List of Contributions]. With a multi-layer architecture, it includes edge nodes, a trust agent, and a fog layer. The trust agent acts as an intermediary, calculating trust degrees and transmitting encrypted values to the fog layer for computation, reducing node burden and maintaining a trustworthy environment. FogTrust was tested against various attacks, such as on–off, good mouthing, and bad mouthing. The simulation results showed its effectiveness in assigning low trust degrees to malicious nodes, even with varying percentages of malicious actors in the network.

The contribution by Manjarrez et al., titled “Estimation of Energy Consumption and Flight Time Margin for a UAV Mission Based on Fuzzy Systems”, presented a method for estimating the energy required for UAV missions to ensure safety and efficient operation [item 4 in the List of Contributions]. A fuzzy Takagi–Sugeno system, optimized using fuzzy C-means and particle swarm optimization, was implemented to estimate power requirements during mission stages. Additionally, a fuzzy model of a battery’s equivalent circuit was used to determine the state of charge, combined with an extended Kalman filter. A methodology was developed to calculate the minimum allowable battery charge and the available flight time margin. A physical experiment with a hexarotor UAV showed a maximum prediction error of 7 s, or 2% of the total mission time.

The contribution by Bozorgpanah et al., titled “Privacy and Explainability: The Effects of Data Protection on Shapley Values”, explored the impact of privacy methods on explainability techniques, based on Shapley values in machine learning models [item 5 in the List of Contributions]. Explainability is crucial for understanding model behavior, while privacy is essential for protecting sensitive data. The study examined how privacy-preserving methods influenced Shapley values across four machine learning models. The results suggested that, while some degree of protection could result in the maintenance of valuable Shapley information, linear models were the most affected by privacy measures. The paper highlighted the balance between ensuring data privacy and maintaining effective model explainability, particularly when using Shapley-based methods.

The contribution by Carneiro et al., titled “Simulation Analysis of Signal Conditioning Circuits for Plants’ Electrical Signals”, discussed plant electrophysiology and presented low-cost signal conditioning circuits for acquiring electrical signals generated by plants in response to environmental stimuli like touch, light, and heat [item 6 in the List of Contributions]. These signals informed the entire plant structure almost instantly. Two specific signal conditioning circuits, depending on the signal type, were detailed, with electrical simulations performed using OrCAD Capture Software. Monte Carlo simulations were also conducted to assess the impact of component variations on circuit accuracy. The results showed that, despite variations, the filters’ cut-off frequencies deviated by no more than 4% from the mean, indicating reliable performance.

In the contribution by Tarasov et al., titled “Friction Stir Welding of Ti-6Al-4V Using a Liquid-Cooled Nickel Superalloy Tool”, the authors introduced friction stir welding (FSW) of titanium alloy, which was performed using a heat-resistant nickel superalloy tool cooled by circulating water [item 7 in the List of Contributions]. The FSW joints were analyzed for microstructures and mechanical strength. The results showed that the mechanical strength of the welded joints exceeded that of the base metal, demonstrating the effectiveness of liquid cooling in improving the quality and strength of FSW joints in titanium alloys.

The contribution by Lin et al., titled “Modelling the Trust Value for Human Agents Based on Real-Time Human States in Human-Autonomous Teaming Systems”, proposed a multi-evidence human trust model to address the challenges of calibrating human trust in human–autonomous teaming (HAT) systems [item 8 in the List of Contributions]. Human trust was influenced by dynamic cognitive states, making it harder to estimate than robotic trust. The model used real-time data from eye trackers, heart rate monitors, and human awareness to assess attention, stress, and perception abilities. Fuzzy reinforcement learning fused these data and handled uncertainty in physiological signals. Simulations showed that the model improved human trust estimation and boosted HAT system efficiency by over 50%. These findings suggested that the model could enhance future HAT systems through real-time adaptation based on human states.

In a competitive global market, construction companies can enhance their competitiveness by selecting qualified personnel for construction engineering manager roles. Traditional selection methods often rely on qualitative techniques, leading to suboptimal decisions. The contribution by Phan and Nguyen, titled “Evaluation Based on the Distance from the Average Solution Approach: A Derivative Model for Evaluating and Selecting a Construction Manager”, introduced a new model using the Evaluation Based on the Distance from the Average Solution Approach (EDASA) for selecting construction managers [item 9 in the List of Contributions]. EDASA effectively addresses personnel evaluation by incorporating quantitative criteria, improving decision-making. The research findings demonstrated that EDASA was efficient, particularly when the number of evaluation criteria or alternatives increased, offering a faster and more reliable selection process for construction managers.

The contribution by Yurova et al., titled “Design and Implementation of an Anthropomorphic Robotic Arm Prosthesis”, discussed the development of a low-cost, anthropomorphic prosthetic arm with twenty-one degrees of freedom (DOFs), for use in robotic research and education [item 10 in the List of Contributions]. This robotic hand replicated human hand functions, with four degrees of freedom per finger, three for the thumb, and two for hand positioning. It was designed using open-source mechanical components, closely mimicking human hand dimensions and motor parameters. The prosthesis can operate autonomously via battery power and supports various control systems, including computer interfaces, electroencephalographs, and touch gloves. The study highlighted the practical implementation of this artificial hand and its control system.

The contribution by Saeidi Aminabadi et al., titled “An Automatic, Contactless, High-Precision, High-Speed Measurement System to Provide In-Line, As-Molded Three-Dimensional Measurements of a Curved-Shape Injection-Molded Part”, presented a high-precision, high-speed, contactless 3D measurement system for inspecting piano-black injection-molded parts [item 11 in the List of Contributions]. The system, capable of ±5 µm precision and measuring a part in 24 s, operated in real time to enable closed-loop and predictive quality control. A multicolor confocal sensor, along with a linear and cylindrical moving axis, performed measurements on the part’s glossy, curved surface. A six DOF robot handled part transfer, while communication was managed via OPC UA protocol. Repeatability tests confirmed an accuracy within ±5 µm at speeds under 60 mm/s, with increased error (up to ±10 µm) from fixture and suction effects.

The contribution by Almeida et al., titled “Extraction and Characterization of β-Viginin Protein Hydrolysates from Cowpea Flour as a New Manufacturing Active Ingredient”, investigated the antimicrobial potential of cowpea (Vigna unguiculata L.) vicilin (7S) protein against antibiotic-resistant pathogens [item 12 in the List of Contributions]. Due to genetic similarities between vicilins from soybean and vicilins from adzuki beans, cowpea was chosen for its high protein content. The beta viginin protein from cowpea was isolated, characterized, and hydrolyzed, both in silico and in vitro, using pepsin and chymotrypsin. The resulting hydrolysate fractions were tested for antimicrobial activity against Staphylococcus aureus and Pseudomonas aeruginosa, showing promising inhibitory effects. These findings suggested that cowpea-derived peptides could be used as potential innovative agents for combating antibiotic resistance.

The contribution by Algredo-Badillo et al., titled “Analysis and Hardware Architecture on FPGA of a Robust Audio Fingerprinting Method Using SSM”, addressed the rise in the unauthorized use of digital media, particularly in audio applications, due to increased digital sharing during the pandemic [item 13 in the List of Contributions]. To secure audio content, acoustic fingerprint technology was employed to identify the unique properties of audio files. The paper presented two hardware architectures for audio fingerprinting, utilizing spectrogram saliency maps (SSM) and a brute-force search. The first system processed 33 maps of 32 × 32 pixels. A second, optimized architecture reduced the map size to 27 × 25 pixels, cutting hardware usage by 75.67%, power consumption by 64.58%, and improving efficiency by 3.19 times through a 22.29% throughput reduction.

The contribution by Hadi et al., titled “Efficient Supervised Machine Learning Network for Non-Intrusive Load Monitoring”, addressed the challenge of energy disaggregation, or non-intrusive load monitoring (NILM), which estimated individual appliance energy consumption from a home’s overall electrical usage [item 14 in the List of Contributions]. While AI-based models were effective for NILM, they often required significant computational resources, making them impractical for devices with limited capabilities. The study proposed an efficient non-parametric supervised machine learning (ENSML) architecture, designed to reduce size and computational costs while maintaining high performance. The ENSML model allowed for fast inference and accurately predicted appliance-level consumption. The results demonstrated that the model improved energy prediction accuracy in 99% of cases, offering a resource-efficient solution for NILM.

The contribution by Stopka et al., titled “Optimization of the Pick-Up and Delivery Technology in a Selected Company: A Case Study” examined pick-up and delivery processes in a company distributing gastronomic products and suggested improvements for efficiency [item 15 in the List of Contributions]. It began by defining key logistics optimization concepts, followed by an analysis of current delivery routes. The article then applied operations research methods, including the Hungarian method, Vogel approximation method, nearest neighbor method, and the Routin route planner (based on the Greedy algorithm), to minimize the total distance traveled. The findings were technically and economically evaluated, comparing the results of each method. Ultimately, optimized delivery routes were selected, aiming to streamline the company’s distribution activities and reduce costs.

The contribution by Gabbar et al., titled “Demonstration of Resilient Microgrid with Real-Time Co-Simulation and Programmable Loads”, presented a real-time simulation of a micro energy grid (MEG) system designed for resilience and sustainability, aimed at reducing fossil fuel dependence and enhancing grid stability [item 16 in the List of Contributions]. The system ensured reliable energy flow by backing up renewable energy sources, mitigating peak demand effects, and providing fail-safe operation through redundant control. It integrated real hardware components like inverters, battery chargers, and controllers with emulated components, via OPAL-RT OP4510, for real-time testing. The setup supported modular, expandable, and flexible scenarios, including fault imitations, using various energy sources like solar panels, wind turbines, and energy storage systems to optimize energy management and grid operation.

The multivehicle routing problem (MVRP) is a variation of the vehicle routing problem (VRP), focusing on finding optimal routes for multiple vehicles to serve multiple customers at minimal cost, while tolerating traffic delays. This NP problem is typically solved using metaheuristics like evolutionary algorithms. The contribution by Li et al., titled “Distribution Path Optimization by an Improved Genetic Algorithm Combined with a Divide-and-Conquer Strategy”, proposed an optimal distribution path optimization method using a divide-and-conquer strategy inspired by dynamic programming [item 17 in the List of Contributions]. An improved genetic algorithm (GA) was employed, incorporating preprocessing, elitist strategy, two-point crossover, and reversion mutation operators. The improved GA outperformed the simple GA in cost, route feasibility, and efficiency, benefiting logistics, transportation, and manufacturing enterprises for flow-shop scheduling.

The contribution by Gradov, titled “Exciting of Strong Electrostatic Fields and Electromagnetic Resonators at the Plasma Boundary by a Power Electromagnetic Beam”, explored the interaction of an electromagnetic beam with the sharp boundary of a dense cold semi-limited plasma under normal wave incidence [item 18 in the List of Contributions]. It revealed the possibility of an electrostatic field forming outside the plasma, with its intensity diminishing, according to a power law with distance from the plasma and beam center. The study also identified the potential to form cavities with reduced electron density, which act as electromagnetic resonators that penetrate deep into the plasma. These cavities can exist in a stable state for extended periods, offering insights into plasma behavior and electromagnetic interactions.

The contribution by Yeh et al., titled “Solving Dual-Channel Supply Chain Pricing Strategy Problem with Multi-Level Programming Based on Improved Simplified Swarm Optimization”, addressed the pricing strategy in capital-constrained dual-channel supply chains, where companies sell through both traditional and online third-party platforms [item 19 in the List of Contributions]. Using game theory, specifically Stackelberg game theory, they modeled the pricing negotiations between manufacturers and other parties. The study proposed a multi-level improved simplified swarm optimization (MLiSSO) method to solve the multi-level programming problem (MLPP) associated with supply chain pricing strategies. The method was tested on three MLPPs from previous studies, demonstrating its effectiveness, stability, and applicability to other multi-level optimization problems. The results confirmed MLiSSO’s capability in solving complex supply chain decision problems.

The contribution by Zimeras, titled “Patterns Simulations Using Gibbs/MRF Auto-Poisson Models”, focused on pattern analysis in big data, particularly in image recognition, using spatial models like Markov random fields (MRFs) [item 20 in the List of Contributions]. It highlighted auto-Poisson models, which leveraged local characteristics of images to improve pattern recognition. By employing advanced statistical techniques such as Monte Carlo Markov Chains (MCMC), specifically the Gibbs sampler, the study aimed to define an MRF model under Poisson distribution and demonstrate its effectiveness through simulations. The results illustrated the model’s performance on both simulated and real pattern data, showcasing its ability to accurately capture and explain underlying data structures.

The contribution by Jarfors et al., titled “An a Priori Discussion of the Fill Front Stability in Semisolid Casting”, reviewed the filling front behavior in metal casting, particularly focusing on semisolid casting processes, which offer design flexibility, productivity, and cost-effectiveness while addressing filling-related defects [item 21 in the List of Contributions]. It emphasized the importance of solid fraction and gate design, providing a fresh perspective on gate configurations in semisolid processing compared to conventional high-pressure die-casting. The study highlighted that optimizing gate widths and managing solid fractions were crucial to preventing instability and issues like spraying during the casting process, ultimately enhancing the quality and reliability of cast products.

The contribution by Bauer et al., titled “Palachandran, M.; Wadehn, F.O.; Wolfschmidt, C.; Grothe, T.; Güth, U.; Ehrmann, A. Electrospinning for the Modification of 3D Objects for the Potential Use in Tissue Engineering”, explored the use of electrospinning in biotechnological applications, particularly for tissue engineering and cell growth, by investigating the influence of 3D-printed substrates on the orientation and diameter of electrospun nanofiber mats [item 22 in the List of Contributions]. It examined how conductive and insulating 3D-printed objects affected fiber characteristics, using 3D-printed ear models as a case study. The research highlighted the impact of shadowing on fiber formation and demonstrated the potential of integrating electrospun nanofibers with 3D-printed scaffolds to create tissue structures in desired shapes, advancing applications in tissue engineering.

The contribution by Khan et al., titled “Study of Joint Symmetry in Gait Evolution for Quadrupedal Robots Using a Neural Network”, investigated the impact of joint symmetry on the gait of bio-inspired legged robots, focusing on their ability to navigate uneven terrains efficiently [item 23 in the List of Contributions]. Using a spider-like robot morphology simulated in PyroSim, the study tested various joint symmetries, including diagonal, adjacent, and random configurations. Each robot, equipped with eight joints and controlled by an artificial neural network optimized through a genetic algorithm, underwent simulations on a flat surface. The results indicated that joint symmetry enhanced gait optimization, producing stable and effective movements reminiscent of natural gaits. Certain symmetries demonstrated superior performance in stability, speed, and distance traveled.

The contribution by Papachristou and Anastassiu, titled “Application of 3D Virtual Prototyping Technology to the Integration of Wearable Antennas into Fashion Garments”, addressed the integration of wearable antennas into everyday clothing, highlighting a gap in the existing literature, which focuses primarily on antenna efficiency without considering garment design [item 24 in the List of Contributions]. Utilizing two-dimensional pattern and 3D virtual prototyping technology, the study developed market-available clothing with embedded antennas, ensuring that the garment’s elegance and comfort were maintained. The paper detailed the functionality of various commercial software modules used in this automated design process and presented specific design examples that demonstrated the effectiveness of the approach. This work paved the way for creating more complex configurations of wearable antennas within garments.

The contribution by Haque et al., titled “Comparative Analysis on Suicidal Ideation Detection Using NLP, Machine, and Deep Learning”, focused on detecting suicidal ideation through social media analysis, specifically using Twitter data [item 25 in the List of Contributions]. It addressed the challenges of identifying early symptoms of suicidal thoughts by comparing various machine learning and deep learning models. The research aimed to improve model performance by enhancing its accuracy when recognizing suicidal indicators, in order to potentially save lives. Using a dataset of 49,178 instances derived from live tweets, the study employed text preprocessing and feature extraction techniques. The results showed that the random forest (RF) model achieved a 93% accuracy, while the BiLSTM deep learning model, enhanced by word embedding, reached 93.6% accuracy and an F1 score of 0.93.

The contribution by Kodakkal et al., titled “An Optimized Enhanced Phase Locked Loop Controller for a Hybrid System”, addressed the need for efficient control algorithms in hybrid power systems that integrate renewable energy sources, specifically wind and solar energy [item 26 in the List of Contributions]. It proposed a controller, based on the enhanced phase locked loop (EPLL) algorithm, to maintain power quality and manage load fluctuations without affecting source current. EPLL overcomes the double-frequency error common in standard phase locked loops and offers simplicity, precision, and stability. The paper employed optimization techniques to tune the proportional-integral (PI) controller gains, with the salp swarm algorithm yielding the best results. Additionally, maximum power point tracking (MPPT) was implemented using the perturb and observe method to enhance solar power efficiency.

The contribution by Ma et al., titled “Aging Mechanism and Models of Supercapacitors: A Review”, explored electrochemical supercapacitors as a promising energy storage technology with diverse applications [item 27 in the List of Contributions]. It outlined their fundamental working principles and applications, while analyzing aging mechanisms that affect performance. The study reviewed existing supercapacitor models, evaluating their characteristics and application scopes. By assessing the current state and limitations of supercapacitor modeling research, the paper highlighted the need for more accurate models to enhance rational utilization, performance optimization, and system simulation. It also identified future development trends and key research focuses in the field of supercapacitor modeling, emphasizing the significance of improving these models for better energy storage solutions.

The Internet of Medical Things (IoMT) has significantly transformed healthcare by enabling efficient patient monitoring and data management. However, security and privacy concerns arise from the increased connectivity and potential cyber threats to sensitive data. The contribution by Pritika et al., titled “Risk Assessment of Heterogeneous IoMT Devices: A Review”, reviewed existing IoT and IoMT applications, risks, and common attacks, emphasizing the inadequacy of current risk assessment frameworks for heterogeneous IoMT devices [item 28 in the List of Contributions]. It analyzed established frameworks like NIST, ISO 27001, and TARA, highlighting the need for new methodologies to address diverse risks. The proposed framework aimed to enhance risk assessment for IoMT devices, ensuring better security and privacy for users in healthcare settings.

The contribution by Barbosa et al., titled “Production Technologies, Regulatory Parameters, and Quality Control of Vaccine Vectors for Veterinary Use”, examined the impact of the Internet of Medical Things (IoMT) on healthcare, emphasizing its ability to facilitate remote patient monitoring and streamline hospital data management [item 29 in the List of Contributions]. However, it also addressed significant security and privacy concerns arising from the increasing number of cyber threats targeting sensitive user information. The study reviewed existing risk assessment frameworks for IoMT devices, including NIST, ISO 27001, TARA, and IEEE213-2019, noting their limitations in addressing the heterogeneous risks associated with IoMT. It advocated for new methodologies to improve risk assessment and proposed a comprehensive framework based on NIST and ISO 27001 to enhance security for IoMT users.

The contribution by Uddin et al., titled “Thermal Inkjet Printing: Prospects and Applications in the Development of Medicine”, discussed the significant advancements in inkjet printing technologies over the past decade, particularly their applications in the pharmaceutical and biomedical sectors [item 30 in the List of Contributions]. Thermal inkjet printing was highlighted for its versatility in developing bioinks for cell printing and biosensors, as well as its potential for fabricating personalized medications, including films and tablets. The paper provided an overview of the principles underlying inkjet printing, detailing its advantages and limitations. Additionally, it presented a variety of case studies showcasing the use of inkjet printing in precision medicine, emphasizing its growing relevance in tailored healthcare solutions.

The contribution by Guzmán and Maestro, titled “Synthetic Micro/Nanomotors for Drug Delivery”, focused on synthetic micro/nanomotors (MNMs), which are self-propelled devices that convert chemical energy into motion, making them promising tools for biomedical applications, particularly in drug delivery [item 31 in the List of Contributions]. MNMs offer advantages over conventional drug carriers by enhancing drug transport to specific targets, thereby improving bioavailability in tissues. However, to ensure safe in vivo applications, further research is needed to address biocompatibility and biodegradability of these systems. The review provided an updated perspective on the potential of synthetic MNMs in drug delivery, while discussing key performance factors and biosafety considerations necessary for their clinical use.

The contribution by Rizzoli et al., titled “Multimodal Semantic Segmentation in Autonomous Driving: A Review of Current Approaches and Future Perspectives”, addressed the challenges of achieving accurate semantic scene representation for autonomous driving systems using only RGB information [item 32 in the List of Contributions]. The lack of geometric details and sensitivity to weather and lighting conditions necessitates the use of multiple sensors, such as color, depth, thermal cameras, LiDARs, and RADARs. The paper presented commonly employed acquisition setups and datasets, followed by a review of various deep learning architectures for multimodal semantic segmentation. It discussed techniques for integrating color, depth, and LiDAR data at different stages of learning architectures, highlighting how effective fusion strategies can enhance performance compared to relying on a single data source.

The contribution by Madanu et al., titled “Explainable AI (XAI) Applied in Machine Learning for Pain Modeling: A Review”, reviewed the role of artificial intelligence (AI) in pain assessment, highlighting its potential to enhance understanding of patient discomfort through physiological and behavioral changes [item 33 in the List of Contributions]. Pain, which varies in intensity and can arise from injuries, illnesses, or medical procedures, is often reflected in facial expressions, providing valuable information for clinicians. Recent advancements in machine learning and deep learning have improved the automatic assessment of pain. The review focused on explainable AI (XAI) and its applications for evaluating different types of pain, emphasizing the growing importance of AI in biomedical and healthcare settings for better patient outcomes.

The contribution by Ali et al., titled “Advanced Security Framework for Internet of Things (IoT)”, aimed to propose a secure framework for the Internet of Things (IoT) in response to the vulnerabilities posed by the widespread interconnectivity of IoT devices [item 34 in the List of Contributions]. Utilizing a systematic literature review (SLR) approach, the study analyzed around 568 articles, ultimately focusing on 260 articles and 54 reports to identify key constructs and themes related to data security, confidentiality, and integrity. The analysis was conducted using MAXQDA (MAXQDA11), leading to the development of a qualitative model. This model, grounded in existing literature, was designed to assist IT managers, developers, and users in enhancing IoT security.

The contribution by Pearce, titled “Strategic Investment in Open Hardware for National Security”, explored the potential of free and open-source hardware (FOSH) development to enhance national security by undermining imports and exports from targeted countries posing threats [item 35 in the List of Contributions]. A formal methodology was proposed for selecting strategic national investments in FOSH, which included identifying the threatening country, quantifying key imports, and identifying hardware that could reduce reliance on these imports. The methodology was illustrated through a case study of a current military aggressor and fossil-fuel exporter, revealing opportunities for FOSH development in energy conservation and renewable energy. The widespread adoption of FOSH could mitigate pollution and decrease financing for military activities.

The contribution by Yu et al., titled “Developments and Applications of Artificial Intelligence in Music Education”, explored the integration of artificial intelligence (AI) in music education, highlighting its advantages and applications [item 36 in the List of Contributions]. With advancements in information technology, AI introduces innovative elements that enhance traditional teaching methods. By addressing the lack of personalization in conventional music education, AI facilitates a more individualized learning experience, fostering greater student engagement and interest. The paper systematically analyzed various AI applications in music education and discussed future development prospects, emphasizing the potential of intelligent technology to revolutionize the educational landscape in music. Overall, AI serves as a valuable tool for improving teaching effectiveness and student outcomes in music learning.

3. Conclusions

This Special Issue presents 36 groundbreaking research findings on Recent Advances and Perspectives in Technologies. It is expected that the insights shared here will help in further development and research in future technologies.