Search Results (47)

Urban–rural integration realises the coordinated development and prosperity of urban and rural areas as a whole by optimising the allocation of resources and the flow of factors, and its connotations have been extended from a single dimension to multiple dimensions such as people, land and industry. The Beijing–Tianjin–Hebei Region has a typical “Core–Periphery Structure”, and this paper took the 187 county units within the region as the research object, taking into account indicators of development and coordination to construct an evaluation index system of urban–rural integration of the Beijing–Tianjin–Hebei region counties in the dimensions of “people–land–industry”. Global principal component analysis was used to measure the evolutionary pattern of the urban–rural integration level between 2005 and 2020, and its spatiotemporal drivers were analysed by using the Geographical and Temporal Weighted Regression model (GTWR). The results of the study show that (1) the level of urban–rural integration in the Beijing–Tianjin–Hebei region showed an increasing trend during the 15-year study period, the high-value areas of urban–rural integration were mainly distributed in Beijing and the Bohai Rim region in the eastern part of the Tianjin–Hebei region, and the level of urban–rural integration of the peri-urban county units of the city was better than that of the remote counties and cities as a whole. (2) In terms of spatial agglomeration, all dimensions were characterised by significant spatial agglomeration. The degree of agglomeration was categorised as urban–rural comprehensive integration (U-RCI) > urban–rural industry integration (U-RII) > urban–rural land integration (U-RLI) > urban–rural people integration (U-RPI). (3) In terms of spatial and temporal driving factors for urban–rural integration, the driving role of U-RPI, U-RLI and U-RII for U-RCI has gradually weakened during the past 15 years, and urban–rural integration in the counties shifted from a single role to a more central coordinated and multidimensional driving role. Full article

Fast urbanization and economic development lead to a prosperous high-rise building industry with high material handling efficiency (MHE). However, the integrated material handling (IMH) framework optimization and empirical studies on Chinese high-rise buildings are not in-depth. Here, the IMH practice in Chinese Chongqing high-rise building projects (CHBPs) was researched, and the effect factors of MHE were discussed to propose improvement strategies. A questionnaire survey (191 participants), qualitative topic analysis, quantitative descriptive statistics, reliability/correlation analysis, an independent sample t-test, analysis of variance (ANOVA), and regression analysis were performed. As a result, the understanding of the IMH concept, effectiveness of training projects, and positive effect of regulations were found to favor an improved MHE. Moreover, a weak positive correlation between work experience and MHE was found. Through the proposed model development framework, the combination of theoretical analysis and empirical research can provide comprehensive tools and knowledge resources for IMH practices in CHBP to improve MHE. Through quantitative indicators such as the material handling efficiency index (MHEI), the training project impact score (TPIS) and the regulation perception index (RPI), this framework offers an objective basis for continuous monitoring and improvement. Full article

Large language models (LLMs) have revolutionized natural language processing (NLP), yet their potential in Internet of Things (IoT) and embedded systems (ESys) applications remains largely unexplored. Traditional IoT interfaces often require specialized knowledge, creating barriers for non-technical users. We present Vega, a modular system that leverages LLMs to enable intuitive, natural language control and interrogation of IoT devices, specifically, a Raspberry Pi (RPi) connected to various sensors, actuators, and devices. Our solution comprises three key components: a physical circuit with input and output devices used to showcase the LLM’s ability to interact with hardware, an RPi integrating a control server, and a web application integrating LLM logic. Users interact with the system through natural language, which the LLM interprets to remotely call appropriate commands for the RPi. The RPi executes these instructions on the physically connected circuit, with outcomes communicated back to the user via LLM-generated responses. The system’s performance is empirically evaluated using a range of task complexities and user scenarios, demonstrating its ability to handle complex and conditional logic without additional coding on the RPi, reducing the need for extensive programming on IoT devices. We showcase the system’s real-world applicability through physical circuit implementation while providing insights into its limitations and potential scalability. Our findings reveal that LLM-driven IoT control can effectively bridge the gap between complex device functionality and user-friendly interaction, and also opens new avenues for creative and intelligent IoT applications. This research offers insights into the design and implementation of LLM-integrated IoT interfaces. Full article

Recently, studies on accelerator-based boron neutron capture therapy (AB-BNCT) systems for cancer treatment have attracted the attention of researchers around the world. A neutron source can be obtained through the impingement of high-intensity proton beams emitted from the accelerator onto the target. This process would deposit a large amount of heat within this target. A thermal management system design is needed for AB-BNCT systems to prevent the degradation of the target due to thermal/mechanical loading. However, there are few studies that investigate this topic. In this paper, a cooling channel with a boiling heat transfer mechanism is numerically designed for thermal management in order to remove heat deposited in the Be target of the AB-BNCT system of Heron Neutron Medical Corp. A three-dimensional (3D) CFD methodology with a two-fluid model and an RPI wall boiling model is developed to investigate its availability. Two subcooled boiling experiments from previous works are adopted to validate the present CFD boiling model. This validated model can be confidently applied to assist in thermal management design for the AB-BNCT system. Based on the simulation results under the typical operating conditions of the AB-BNCT system set by Heron Neutron Medical Corp., the present coolant channel employing the boiling heat transfer mechanism can efficiently remove the heat deposited in the Be target, as well as maintain its integrity during long-term operation. In addition, compared with the channel with the single-phase convection traditionally designed for an AB-BNCT system, the boiling heat transfer mechanism can result in a lower peak temperature in the Be target and its corresponding deformation. Full article

Particle dispersion models (PDMs) are essential to capture the influence of unresolved turbulent eddies on particle transport in computational fluid dynamics (CFD) simulations. However, the validation of these models remains challenging, especially when relying on experimental data or CFD simulations that are based on turbulence models. In this work, we use time-averaged data obtained in a direct numerical simulation (DNS) instead of relying on turbulence models to model particle dispersion. In addition, a new particle dispersion model is presented, referred to as the limited particle–eddy interaction time (LPI) model. For a detailed and systematic evaluation of the new LPI model, we compare its performance with that of other commonly used models, such as the mean particle–eddy interaction time (MPI) model implemented in OpenFOAM^® and the randomized particle–eddy interaction time (RPI) model from the literature. The MPI model shows good agreement with the DNS for the largest particles tested (Stokes number, St = 0.2) but exhibits erratic and unphysical trajectories for smaller particles (St ≤ 0.05). To mitigate this erratic behavior, we have adjusted the eddy interaction time in the new LPI model. Full article

Advancements in embedded systems and Artificial Intelligence (AI) have enhanced the capabilities of Unmanned Aircraft Vehicles (UAVs) in computer vision. However, the integration of AI techniques o-nboard drones is constrained by their processing capabilities. In this sense, this study evaluates the deployment of object detection models (YOLOv8n and YOLOv8s) on both resource-constrained edge devices and cloud environments. The objective is to carry out a comparative performance analysis using a representative real-time UAV image processing pipeline. Specifically, the NVIDIA Jetson Orin Nano, Orin NX, and Raspberry Pi 5 (RPI5) devices have been tested to measure their detection accuracy, inference speed, and energy consumption, and the effects of post-training quantization (PTQ). The results show that YOLOv8n surpasses YOLOv8s in its inference speed, achieving 52 FPS on the Jetson Orin NX and 65 fps with INT8 quantization. Conversely, the RPI5 failed to satisfy the real-time processing needs in spite of its suitability for low-energy consumption applications. An analysis of both the cloud-based and edge-based end-to-end processing times showed that increased communication latencies hindered real-time applications, revealing trade-offs between edge (low latency) and cloud processing (quick processing). Overall, these findings contribute to providing recommendations and optimization strategies for the deployment of AI models on UAVs. Full article

Recent research has demonstrated the effectiveness of convolutional neural networks (CNN) in assessing the health status of bee colonies by classifying acoustic patterns. However, developing a monitoring system using CNNs compared to conventional machine learning models can result in higher computation costs, greater energy demand, and longer inference times. This study examines the potential of CNN architectures in developing a monitoring system based on constrained hardware. The experimentation involved testing ten CNN architectures from the PyTorch and Torchvision libraries on single-board computers: an Nvidia Jetson Nano (NJN), a Raspberry Pi 5 (RPi5), and an Orange Pi 5 (OPi5). The CNN architectures were trained using four datasets containing spectrograms of acoustic samples of different durations (30, 10, 5, or 1 s) to analyze their impact on performance. The hyperparameter search was conducted using the Optuna framework, and the CNN models were validated using k-fold cross-validation. The inference time and power consumption were measured to compare the performance of the CNN models and the SBCs. The aim is to provide a basis for developing a monitoring system for precision applications in apiculture based on constrained devices and CNNs. Full article

In this paper, we present the development and evaluation of a contextually relevant, cost-effective, multihop cluster-based agricultural Internet of Things (MCA-IoT) network. This network utilizes commercial off-the-shelf (COTS) Bluetooth Low-Energy (BLE) and LoRa communication technologies, along with the Raspberry Pi 3 Model B+ (RPi 3 B+), to address the challenges of climate change-induced global food insecurity in smart farming applications. Employing the lean engineering design approach, we initially implemented a centralized cluster-based agricultural IoT (CA-IoT) hardware testbed incorporating BLE, RPi 3 B+, STEMMA soil moisture sensors, UM25 m, and LoPy low-power Wi-Fi modules. This system was subsequently adapted and refined to assess the performance of the MCA-IoT network. This study offers a comprehensive reference on the novel, location-independent MCA-IoT technology, including detailed design and deployment insights for the agricultural IoT (Agri-IoT) community. The proposed solution demonstrated favorable performance in indoor and outdoor environments, particularly in water-stressed regions of Northern Ghana. Performance evaluations revealed that the MCA-IoT technology is easy to deploy and manage by users with limited expertise, is location-independent, robust, energy-efficient for battery operation, and scalable in terms of task and size, thereby providing a versatile range of measurements for future applications. Our results further demonstrated that the most effective approach to utilizing existing IoT-based communication technologies within a typical farming context in sub-Saharan Africa is to integrate them. Full article

A kettle reboiler uses the latent heat from the condensation of high-temperature and high-pressure steam in the tube to produce low-pressure saturated steam in the outer shell. The deposition of particles on the tube may change the boiling heat transfer mode from nucleate boiling to natural convection, thereby deteriorating the heat transfer performance of the kettle reboiler. Therefore, it is very important to explore the deposition characteristics of particles in the kettle reboiler. In this study, the RPI boiling model based on the Euler–Euler method is used to analyze the water boiling process on the surface of the tube bundle. The DRW model and critical adhesion velocity model based on the Euler–Lagrangian method are used to calculate the motion of particles during the boiling process and the deposition (rebound) behavior. The results show that the boiling of liquid water enhances the local flow velocity of the fluid, so that the maximum flow velocity appears around the near-wall region. The local high-speed flow disperses the particles in the wake flow of the tube bundle, which inhibits the impact of particles on the wall. As the particle size increases, the wall adhesion and fluid drag on the particles are weakened, and the gravity effect gradually becomes dominant, increasing the residence time of the particles in the tube bundle and the frequency of particle impact on the wall. The particle deposition ratio first decreases and then increases. Ultimately, most particles will be deposited in the low-speed area at the end of the tube bundle. Full article

Electric vehicles are increasingly appearing on Polish roads due to a number of technical, legal and marketing conditions. However, electromobility is developing primarily in urban areas, mainly due to the unevenly developed infrastructure for charging vehicle batteries and the power grid. Therefore, solutions should be created that use the existing power infrastructure, including the use of railway power infrastructure (RPI). The railway power network covers a significant part of the country, including forest areas, and, above all, it very often intersects with road infrastructure or runs along roads. This paper raises issues related to the possibility of using RPI to charge the batteries of electric vehicles. After characterizing the technical, operational and legal requirements related to these technical systems, a concept of an electric vehicle charging system using RPI was developed, along with a demonstration of the possibility of its implementation, which was simulated using mathematical models developed by the authors. Full article

This study aims to investigate alterations in effective connectivity (EC) within the fronto-thalamic circuit and their associations with motor and cognitive declines in pontine infarction (PI). A total of 33 right PI patients (RPIs), 38 left PI patients (LPIs), and 67 healthy controls (HCs) were recruited. The spectral dynamic causal modeling (spDCM) approach was used for EC analysis within the fronto-thalamic circuit, including the thalamus, caudate, supplementary motor area (SMA), medial prefrontal cortex (mPFC), and anterior cingulate cortex (ACC). The EC differences between different sides of the patients and HCs were assessed, and their correlations with motor and cognitive dysfunctions were analyzed. The LPIs showed increased EC from the mPFC to the R-SMA and decreased EC from the L-thalamus to the ACC, the L-SMA to the R-SMA, the R-caudate to the R-thalamus, and the R-thalamus to the ACC. For RPIs, the EC of the R-caudate to the mPFC, the L-thalamus and L-caudate to the L-SMA, and the L-caudate to the ACC increased obviously, while a lower EC strength was shown from the L-thalamus to the mPFC, the LSMA to the R-caudate, and the R-SMA to the L-thalamus. The EC from the R-caudate to the mPFC was negatively correlated with the MoCA score for RPIs, and the EC from the R-caudate to the R-thalamus was negatively correlated with the FMA score for LPIs. The results demonstrated EC within the fronto-thalamic circuit in PI-related functional impairments and reveal its potential as a novel imaging marker. Full article

Environmental Sound Recognition (ESR) plays a crucial role in smart cities by accurately categorizing audio using well-trained Machine Learning (ML) classifiers. This application is particularly valuable for cities that analyzed environmental sounds to gain insight and data. However, deploying deep learning (DL) models on resource-constrained embedded devices, such as Raspberry Pi (RPi) or Tensor Processing Units (TPUs), poses challenges. In this work, an evaluation of an existing pre-trained model for deployment on Raspberry Pi (RPi) and TPU platforms other than a laptop is proposed. We explored the impact of the retraining parameters and compared the sound classification performance across three datasets: ESC-10, BDLib, and Urban Sound. Our results demonstrate the effectiveness of the pre-trained model for transfer learning in embedded systems. On laptops, the accuracy rates reached 96.6% for ESC-10, 100% for BDLib, and 99% for Urban Sound. On RPi, the accuracy rates were 96.4% for ESC-10, 100% for BDLib, and 95.3% for Urban Sound, while on RPi with Coral TPU, the rates were 95.7% for ESC-10, 100% for BDLib and 95.4% for the Urban Sound. Utilizing pre-trained models reduces the computational requirements, enabling faster inference. Leveraging pre-trained models in embedded systems accelerates the development, deployment, and performance of various real-time applications. Full article

Flow boiling within conventional, mini and micro-scale channels is encountered in a wide range of engineering applications such as nuclear reactors, steam engines and cooling of electronic devices. Due to the high complexity and importance of the boiling process, several numerical and experimental investigations have been conducted for the better understanding of the underpinned physics and heat transfer characteristics. One of the most widely used numerical approaches that can analyse such phenomena is the Eulerian–Eulerian two-fluid method in conjunction with the RPI model. However, according to the current state-of-the-art methods this modelling approach heavily relies on empirical closure relationships derived for conventional channels, limiting its applicability to mini- and micro-scale channels. The present paper aims to give further insights into the applicability of this modelling approach for non-conventional channels. For this purpose, a numerical investigation utilising the Eulerian–Eulerian two-fluid model and the RPI wall heat flux partitioning model in OpenFOAM 8.0 is conducted. Initially the parameters comprising the empirical closure relationships used in the RPI sub-models are tuned against the DEBORA experiments on conventional channels, through an extensive sensitivity analysis. In the second part of the investigation, numerical simulations against flow boiling experiments within micro-channels are performed, utilising the previously optimised and validated model setup. Furthermore the importance of including a bubble coalescence and break-up sub-model to capture parameters such as the radial velocity profiles, is also illustrated. However, when the optimal model setup, in conventional tubes, is used against micro-channel experiments, the need to develop new correlations from data obtained from mini and micro-scale channel studies, not from experimental data on conventional channels, is revealed. Full article

This paper studies the hybrid flow shop scheduling problem with blocking constraints (BHFSP). To better understand the BHFSP, we will construct its mixed integer linear programming (MILP) model and use the Gurobi solver to demonstrate its correctness. Since the BHFSP exists parallel machines in some processing stages, different decoding strategies can obtain different makespan values for a given job sequence and multiple decoding strategies can assist the algorithm to find the optimal value. In view of this, we propose a hybrid decoding strategy that combines both forward decoding and backward decoding to select the minimal objective function value. In addition, a hybrid decoding-assisted variant iterated greedy (VIG) algorithm to solve the above MILP model. The main novelties of VIG are a new reconstruction mechanism based on the hybrid decoding strategy and a swap-based local reinforcement strategy, which can enrich the diversity of solutions and explore local neighborhoods more deeply. This evaluation is conducted against the VIG and six state-of-the-art algorithms from the literature. The 100 tests showed that the average makespan and the relative percentage increase (RPI) values of VIG are 1.00% and 89.6% better than the six comparison algorithms on average, respectively. Therefore, VIG is more suitable to solve the studied BHFSP. Full article

In a fast-growing global economy, there is much debate in the socio-economic models about the sharing economy, which is a digital platform that benefits society and improves people’s quality of life. A significant benchmark of the sharing economy is that it enables individuals to monetize their assets that need to be used fully. This highlights an individual’s ability and perhaps their preference to either rent or borrow goods rather than own them. This study investigated Saudi Arabian students’ User Satisfaction (US) and their Re-Purchase Intentions (RPI) in the context of the sharing economy. We employed a deductive approach that utilized cross-sectional data collected through online sampling. The results were derived from 324 acceptable completed questionnaires. We used a Structural Equation Model (SEM) to confirm the positive and significant predictive power of Trust, Economic Benefits (EBs), Sharing Economy Philosophy (SEP), Service Quality and Net Benefits (NBs) on US and RPI. The results also demonstrated a positive and significant effect of concerns such as Lack of Trust (LoT) and Expected Effort (EE) on US. Finally, among Saudi Arabian students, US is a positive and significant predictor of RPI. In the context of a developing country such as Saudi Arabia, this study’s insights to the practical and theoretical spheres contribute to operational management and the literature about online digital learning. Full article