Search Results (2,196)

In recent years, mobility as a service (MaaS) has been thought as one of the opportunities for shifting towards shared travel solutions with respect to private transport modes, particularly owned cars. Although many MaaS aspects have been explored in the literature, there are still issues, such as platform implementations, travel solution generation, and the user’s role for making an effective system, that require more research. This paper extends and improves a previous study carried out by the authors by providing more details and experiments. The paper proposes a diachronic network model for representing travel services available in a given MaaS platform by using an agent-based approach to simulate the interactions between travel operators and travelers. Particularly, the diachronic network model allows the consideration of both the spatial and temporal features of the available transport services, while the agent-based framework allows the representation of how shared services might be used and which effects, in terms of modal split, could be expected. The final aim is to provide insights for setting the architecture of an agent-based MaaS platform where transport operators would share their data for providing seamless travel opportunities to travelers. The results obtained for a simulated test case are promising. Particularly, there are interesting findings concerning the traffic congestion boundary values that would move users towards shared travel solutions. Full article

This study presents the design and implementation of a wire-driven, multi-joint robotic arm equipped with a cutting and gripping mechanism for harvesting delicate strawberries, with the goal of reducing labor and costs. The arm is mounted on a lifting mechanism and linked to a laterally movable module, which is affixed to the tube cultivation shelf. The trained deep learning model can instantly detect strawberries, identify optimal picking points, and estimate the contour area of fruit while the mobile platform is in motion. A two-stage fuzzy logic control (2s-FLC) method is employed to adjust the length of the arm and bending angle, enabling the end of the arm to approach the fruit picking position. The experimental results indicate a 90% accuracy in fruit detection, an 82% success rate in harvesting, and an average picking time of 6.5 s per strawberry, reduced to 5 s without arm recovery time. The performance of the proposed system in harvesting strawberries of different sizes under varying lighting conditions is also statistically analyzed and evaluated in this paper. Full article

This paper explores the ways in which the digital divide affects labor in the context of sustainable development within the digital economy. It discusses the effects of major indicators such as digital infrastructure construction, digital industry development, and digital-inclusive finance on labor mobility. Although existing research has analyzed the ways in which the digital economy enhances economic vitality, there is insufficient research that investigates how the divide between digital access and usage can be effectively reduced to promote sustainable development. Therefore, through empirical analysis and mechanism research, this study used quantitative measurement and regression analysis methods to conduct an in-depth analysis of the dual effects of digital access and usage divides on the long-term marginal impact for labor. The results show that improving digital infrastructure such as broadband and fiber optic networks not only significantly boosts the economic vitality of underdeveloped areas, but also enhances their ability to participate in sustainable development. This enables more laborers to access new job opportunities and resources provided by the digital economy. While narrowing the digital use divide initially increases labor mobility, uneven dissemination may create barriers to information access, thus limiting mobility. Our research indicates that the development of the digital economy promotes cross-regional labor mobility, which is particularly prominent in the digital platform economy, facilitating more sustainable economic growth. After controlling for variables such as the level of economic development, this positive impact remains robust. This paper suggests that digital infrastructure construction and training in digital skills should be strengthened to narrow the digital divide and promote sustainable, balanced regional development and increased economic vitality. Full article

Provincial mobile government service platforms, represented by ‘Zheliban’ and ‘Yueshengshi’, have transformed the traditional way governments provide public services to citizens. Maintaining user engagement with these platforms has become a critical challenge in promoting the digitalization of public services. Despite the widespread adoption of mobile services, the characteristics influencing citizens’ intentions for continued usage of mobile government service platforms have not received sufficient attention in the academic literature. This study, based on the Information Systems Success Model (IS Theory) and Expectation Confirmation Theory (ECT), constructs a Push-Pull-Mooring (PPM) model from a dynamic perspective to examine factors influencing citizens’ continued usage intentions. The research findings indicate that the quality of mobile government service platforms has a significant positive impact on citizens’ continued usage intentions, with citizen satisfaction mediating the relationship between platform quality and continued usage intention. Furthermore, digital exclusion and platform user stickiness negatively moderate the mediating role of satisfaction. This study provides a comprehensive framework for explaining the pathways influencing citizens’ continued usage of mobile government service platforms, extends the theoretical boundaries of the PPM model, and contributes to research in related fields. The findings offer valuable insights for the government in optimizing and promoting mobile government services. Full article

In contemporary times, a significant portion of the population experiences symptoms of temporomandibular joint (TMJ) dysfunction. The objective of this study was to evaluate the effects of a single-session TMJ soft tissue therapy on the TMJ and cervical spine mobility as well as on body balance and the foot load distribution. This study was a parallel-group, randomized, controlled trial with a 1:1 allocation ratio. Fifty women aged 20–30 years diagnosed with myofascial pain in the TMJ area were included in the study and divided into two groups. The experimental group received TMJ soft tissue therapy. The following research tools were used: a Hogetex electronic caliper, a CROM Deluxe, and a FreeMed Base pedobarographic platform. In the experimental group, an increase in mobility within all assessed jaw and cervical spine movements was observed. This change was statistically significant (p < 0.05) for lateral movement to the left, abduction, and protrusion of the jaw (an increase of 10.32%, 7.07%, and 20.92%, respectively) and for extension, lateral bending to the right and left, and rotation to the right and left, of the cervical spine (an increase of 7.05%, 7.89%, 10.44%, 4.65%, and 6.55%, respectively). In the control group, no significant differences were observed. No significant changes were observed in the load distribution and body balance assessment. A single session of TMJ soft tissue therapy increases jaw and cervical spine mobility but does not impact body balance or foot load distribution in static conditions in women diagnosed with myofascial pain in the TMJ area. Full article

Unmanned aerial vehicles (UAVs) and drones are considered to represent a flexible mobile aerial platform to collect data in various applications. However, the existing data collection methods mainly consider uplink communication. The burgeoning development of integrated sensing and communication (ISAC) provides a new paradigm for data collection. A diverse data collection framework is established where the uplink communication and sensing functions are both considered, which can also be referred to as the uplink ISAC system. An optimization is formulated to minimize the data freshness indicator for communication and the detection freshness indicator for sensing by optimizing the UAV paths, the transmitted power of IoT devices and UAVs, and the transmission allocation indicators. Three state-of-the-art deep reinforcement learning (DRL) algorithms are utilized to solve this optimization. Experiments are conducted in both single-UAV and multi-UAV scenarios, and the results demonstrate the effectiveness of the proposed algorithms. In addition, the proposed algorithms outperform the benchmark in terms of accuracy and efficiency. Moreover, the effectiveness of the data collection mode with only communication or sensing functions is also verified. Also, the numerical Pareto front between communication and sensing performance is obtained by adjusting the importance parameter. Full article

The robotization of the non-destructive inspection of aircraft is essential for improving the accuracy and duration of performed inspections, being an integral part of inspection and data management systems within the currently developed NDT 4.0 concept. In this paper, the authors presented the design and testing of a universal mobile platform with interchangeable sensing systems for the non-destructive inspection of aircraft structures with various angles of inclination. As a result of the performed studies, a low-cost approach of automation of existing measurement devices used for inspection was proposed. The constructed prototype of the mobile platform was equipped with eddy current testing probe and successfully passed both laboratory and environmental tests, demonstrating its performance in various conditions. The presented approach confirms the effectiveness of the automation of the inspection process using climbing robots and defining the directions of possible development of automation in non-destructive testing in aviation. Full article

Outdoor environments provide excellent teaching and learning experiences in science education. However, many teachers find outdoor teaching challenging. In this study, we investigated factors supporting skill development and learning among pre-service teacher during a blended science didactics course that includes mobile interaction in outdoor environments. Available WhatsApp mobile application was used as an interaction platform between the pre-service teachers’ and the teacher educator. Based on the findings, the pre-service teachers learned easy ways of using outdoor environments with pupils. They also identified challenges that may arise in outdoor teaching and upskilled on how to overcome them. From the perspective of interaction, submitting learning tasks, especially visual observations, through mobile messaging and reviewing tasks of other students in the application were perceived as important. However, the most crucial benefit of mobile interaction was considered to be the teacher’s real-time feedback. Full article

The consumer profile has undergone evolutions and transformations over the years due to the evolution of new generations of individuals, such as Generation Y. Social media has revolutionized the way in which consumers can search and find information about products in general, which has impacted how brands relate to their consumers. In this context, this study tries to understand how digital influencers are being used to influence Generation Y in the consumption of banking services from fintechs in Brazil via social media platforms. The specific objectives include profiling these consumers, identifying the most relevant influencers, and measuring the impact of influencer marketing. The results pointed to Generation Y’s preference for mobile applications and personal recommendations when making decisions to purchase financial products. Fintechs stood out for the agility and autonomy they offered, as well as for being on the forefront in leading practices, innovations, and product offerings that drive sustainability forward. The study concluded that digital influencers play a crucial role in the awareness phase, but additional factors influence Millennial consumption decisions, highlighting the complexity of the decision process. Full article

Recently, as the types of imported food and the design of their packaging become more complex and diverse, digital recognition technologies such as barcodes, QR (quick response) codes, and OCR (optical character recognition) are attracting attention in order to quickly and easily check safety information (e.g., food ingredient information and recalls). However, consumers are still exposed to inaccurate and inconvenient situations because legacy technologies require dedicated terminals or include information other than safety information. In this paper, we propose a deep learning-based packaging recognition system which can easily and accurately determine food safety information with a single image captured through a smartphone camera. The detection algorithm learned a total of 100 kinds of product images and optimized YOLOv7 to secure an accuracy of over 95%. In addition, a new SUS (system usability scale)-based questionnaire was designed and conducted on 71 consumers to evaluate the usability of the system from the individual consumer’s perspective. The questionnaire consisted of three categories, namely convenience, accuracy, and usefulness, and each received a score of at least 77, which confirms that the proposed system has excellent overall usability. Moreover, in terms of task completion rate and task completion time, the proposed system is superior when it compared to existing QR code- or Internet-based recognition systems. These results demonstrate that the proposed system provides consumers with more convenient and accurate information while also confirming the sustainability of smart food consumption. Full article

This work presents a novel fully parallel decoder architecture designed for high-throughput decoding of Quasi-Cyclic Low-Density Parity-Check (QC-LDPC) codes within the context of 5G New-Radio (NR) communication. The design uses the layered Min-Sum (MS) algorithm and focuses on increasing throughput to meet the strict needs of enhanced Mobile BroadBand (eMBB) applications. We incorporated a Sub-Optimal Low-Latency (SOLL) technique to enhance the critical check node processing stage inherent to the MS algorithm. This technique efficiently computes the two minimum values, rendering the architecture well-suited for specific Ultra-Reliable Low-Latency Communication (URLLC) scenarios. We design the decoder to be reconfigurable, enabling efficient operation across all expansion factors. We rigorously validate the decoder’s effectiveness through meticulous bit-error-rate (BER) performance evaluations using Hardware Description Language (HDL) co-simulation. This co-simulation utilizes a well-established suite of tools encompassing MATLAB/Simulink for system modeling and Vivado, a prominent FPGA design suite, for hardware representation. With 380,737 Look-Up Tables (LUTs) and 32,898 registers, the decoder’s implementation on a Virtex-7 XC7VX980T FPGA platform by AMD/Xilinx shows good hardware utilization. The architecture attains a robust operating frequency of 304.5 MHz and a normalized throughput of 49.5 Gbps, marking a 36% enhancement compared to the state-of-the-art. This advancement propels decoding capabilities to meet the demands of high-speed data processing. Full article

To address the challenges of suboptimal remote detection and significant computational burden in existing multi-sensor information fusion 3D object detection methods, a novel approach based on Bird’s-Eye View (BEV) is proposed. This method utilizes an enhanced lightweight EdgeNeXt feature extraction network, incorporating residual branches to address network degradation caused by the excessive depth of STDA encoding blocks. Meantime, deformable convolution is used to expand the receptive field and reduce computational complexity. The feature fusion module constructs a two-stage fusion network to optimize the fusion and alignment of multi-sensor features. This network aligns image features to supplement environmental information with point cloud features, thereby obtaining the final BEV features. Additionally, a Transformer decoder that emphasizes global spatial cues is employed to process the BEV feature sequence, enabling precise detection of distant small objects. Experimental results demonstrate that this method surpasses the baseline network, with improvements of 4.5% in the NuScenes detection score and 5.5% in average precision for detection objects. Finally, the model is converted and accelerated using TensorRT tools for deployment on mobile devices, achieving an inference time of 138 ms per frame on the Jetson Orin NX embedded platform, thus enabling real-time 3D object detection. Full article

Recent advancements in Unmanned Aerial Vehicle (UAV) technology have made them effective platforms for data capture in applications like environmental monitoring. UAVs, acting as mobile data ferries, can significantly improve ground network performance by involving ground network representatives in data collection. These representatives communicate opportunistically with accessible UAVs. Emerging technologies such as Software Defined Wireless Sensor Networks (SDWSN), wherein the role/function of sensor nodes is defined via software, can offer a flexible operation for UAV data-gathering approaches. In this paper, we introduce the “UAV Fuzzy Travel Path”, a novel approach that utilizes Deep Reinforcement Learning (DRL) algorithms, which is a subfield of machine learning, for optimal UAV trajectory planning. The approach also involves the integration between UAV and SDWSN wherein nodes acting as gateways (GWs) receive data from the flexibly formulated group members via software definition. A UAV is then dispatched to capture data from GWs along a planned trajectory within a fuzzy span. Our dual objectives are to minimize the total energy consumption of the UAV system during each data collection round and to enhance the communication bit rate on the UAV-Ground connectivity. We formulate this problem as a constrained combinatorial optimization problem, jointly planning the UAV path with improved communication performance. To tackle the NP-hard nature of this problem, we propose a novel DRL technique based on Deep Q-Learning. By learning from UAV path policy experiences, our approach efficiently reduces energy consumption while maximizing packet delivery. Full article

Active mobility and light vehicles, such as e-bikes, are gaining increasing attention as sustainable transportation alternatives to internal combustion solutions. In this context, collecting comprehensive data on environmental conditions, vehicle performance, and user interaction is crucial for improving system efficiency and user experience. This paper presents a data acquisition system designed to collect data from multiple sensor platforms. The architecture is optimized to maintain low power consumption and operate within limited computational resources, making it suitable for real-time data acquisition on light vehicles. To achieve this, a data acquisition module was developed using a single-board computer integrated with a custom shield, which also captures data related to the assistance of an e-bike motor through a wireless interface. The paper provides an in-depth discussion of the architecture and software development, along with a detailed overview of the sensors used. A demonstrator was created to verify the system architecture idea and prove the potentialities of the system overall. The demonstrator has been qualified by professional and semi-professional riders in the framework of the Giro-E, a cyclist event which took place in May 2024, on the same roads of the Giro d’Italia. Finally, some preliminary analyses on the data acquired are provided to show the performance of the system, particularly in reconstructing the user behavior, the environmental parameters, and the type of road. Full article

This paper presents the design, implementation, and characterization of a digital IoT platform capable of generating brain rhythm frequencies using synchronous digital logic. Designed with the Google SkyWater 130 nm open-source process design kit (PDK), this platform emulates Alpha, Beta, and Gamma rhythms. As a proof of concept and the first of its kind, this device showcases its potential applications in both industrial and academic settings. The platform was integrated with an IoT device to optimize and accelerate research and development efforts in embedded systems. Its cost-effective and efficient performance opens opportunities for real-time neural signal processing and integrated healthcare. The presented digital platform serves as a valuable educational tool, enabling researchers to engage in hands-on learning and experimentation with IoT technologies and system-level hardware–software integration at the device level. By utilizing open-source tools, this research demonstrates a cost-effective approach, fostering innovation and bridging the gap between theoretical knowledge and practical application. Furthermore, the proposed system-level design can be interfaced with various serial devices, Wi-Fi modules, ARM processors, and mobile applications, illustrating its versatility and potential for future integration into broader IoT ecosystems. This approach underscores the value of open-source solutions in driving technological advancements and addressing skills shortages. Full article