Capability Of System Research Articles

Development and implementation of a raspberry Pi-based IoT system for real-time performance monitoring of an instrumented tractor

The tractor serves as a crucial power source in agricultural operations. However, the tractor's power often remains underutilized due to a mismatch between the tractor and implement, considering specific field conditions. To enhance system output, it becomes vital to acquire data on performance-related parameters for the tractor-implement combination. In this study we develop a real-time Instrumented Tractor Performance Monitoring System (ITPMS) using the Internet-of-Things (IoT). This system consists of a Raspberry Pi, a GPS sensor, a proximity sensor, a rotary potentiometer, and a three-point hitch dynamometer. The rotary potentiometer measures tillage depth, while the three-point hitch dynamometer used to measure data on draft force. Proximity sensors are installed on a two-wheel drive (2WD) tractor to measure forward speed and drive-wheel slip. We establish a dedicated web server using a Google® Firebase® project to store data from all sensors through Raspberry Pi. Additionally, we design a web interface and a mobile application to provide real-time data generated from the sensors. Field experiments were done to evaluate and monitor the performance parameters of the tractor-implement combination utilising the developed ITPMS. The results demonstrate that the system effectively monitors the performance parameters necessary for tractor-implement combination. Furthermore, the system's capability to update data to the IoT server in real-time is validated. Overall, the development and implementation of this Raspberry Pi based IoT system, provides a reliable and efficient solution for real-time performance monitoring of instrumented tractors.

Techno-economic assessment and transient modeling of a solar-based multi-generation system for sustainable/clean coastal urban development

To ensure the health of vulnerable coastal ecosystems, a transition to sustainable energy solutions is essential. Environmentally friendly systems powered by renewable sources offer not only a reduction in pollution but also the adaptability needed for a flexible and resilient energy future. This study proposes and comprehensively evaluates an integrated solar-based system designed to meet the daily needs of coastal cities. The proposed system incorporates key components such as dual-loop power cycles, parabolic trough solar collectors, liquefied natural gas (LNG) regasification, reverse osmosis, and proton exchange membrane electrolysis. To optimize energy utilization, the inclusion of a thermoelectric generator (TEG) is considered, harnessing the thermal gradient among the LNG stream and the power cycle fluid. We conduct transient modeling, incorporating comprehensive scenarios that account for both thermal and economic aspects. The performance evaluation of the system focuses specifically on coastal regions, with San Francisco serving as a case study. The dynamic simulation results demonstrate the capability of the integrated system in fulfilling the urban needs for one year, delivering 1,134,207 cubic meters of potable water and generating 11,306 MWh of electricity. Financial analysis reveals that the solar unit accounts for over 46 % of the total cost, with an hourly cost rate of $69.61. The levelized cost of electricity is predicted at 4.61 cents/kWh, while the levelized cost of water is calculated at 30.54 cents/m3. These findings provide valuable insights into the cost-effectiveness and competitive advantage of the system in terms of energy and water production.

αvβ3 Integrin and Folate-Targeted pH-Sensitive Liposomes with Dual Ligand Modification for Metastatic Breast Cancer Treatment.

The advent of pH-sensitive liposomes (pHLips) has opened new opportunities for the improved and targeted delivery of antitumor drugs as well as gene therapeutics. Comprising fusogenic dioleylphosphatidylethanolamine (DOPE) and cholesteryl hemisuccinate (CHEMS), these nanosystems harness the acidification in the tumor microenvironment and endosomes to deliver drugs effectively. pH-responsive liposomes that are internalized through endocytosis encounter mildly acidic pH in the endosomes and thereafter fuse or destabilize the endosomal membrane, leading to subsequent cargo release into the cytoplasm. The extracellular tumor matrix also presents a slightly acidic environment that can lead to the enhanced drug release and improved targeting capabilities of the nano-delivery system. Recent studies have shown that folic acid (FA) and iRGD-coated nanocarriers, including pH-sensitive liposomes, can preferentially accumulate and deliver drugs to breast tumors that overexpress folate receptors and αvβ3 and αvβ5 integrins. This study focuses on the development and characterization of 5-Fluorouracil (5-FU)-loaded FA and iRGD surface-modified pHLips (FA-iRGD-5-FU-pHLips). The novelty of this research lies in the dual targeting mechanism utilizing FA and iRGD peptides, combined with the pH-sensitive properties of the liposomes, to enhance selective targeting and uptake by cancer cells and effective drug release in the acidic tumor environment. The prepared liposomes were small, with an average diameter of 152 ± 3.27 nm, uniform, and unilamellar, demonstrating efficient 5-FU encapsulation (93.1 ± 2.58%). Despite surface functionalization, the liposomes maintained their pH sensitivity and a neutral zeta potential, which also conferred stability and reduced aggregation. Effective pH responsiveness was demonstrated by the observation of enhanced drug release at pH 5.5 compared to physiological pH 7.4. (84.47% versus 46.41% release at pH 5.5 versus pH 7.4, respectively, in 72 h). The formulations exhibited stability for six months and were stable when subjected to simulated biological settings. Blood compatibility and cytotoxicity studies on MDA-MB-231 and SK-BR3 breast cancer cell lines revealed an enhanced cytotoxicity of the liposomal formulation that was modified with FA and iRGD compared to free 5-FU and minimal hemolysis. Collectively, these findings support the potential of FA and iRGD surface-camouflaged, pH-sensitive liposomes as a promising drug delivery strategy for breast cancer treatment.

Performance Analysis of an Aerial Remote Sensing Platform Based on Real-Time Satellite Communication and Its Application in Natural Disaster Emergency Response

The frequency of natural disasters has increased recently, posing a huge threat to human society. Rapid, accurate, authentic, and comprehensive acquisition and transmission of disaster information are crucial in emergency response. In this paper, we propose a design scheme for an aerial remote sensing platform based on real-time satellite communication. This platform mainly includes a civilian heavy-duty unmanned aerial vehicle, ground observation system with the self-developed orthographic image stabilization device, wireless communication system with an airborne mobile communication device using Ku band, ground satellite information receiving station, and data processing and application analysis system. The image stabilization capability of the ground observation system and the communication capability of the wireless communication system were verified through ground and flight tests respectively. The results showed that the stability accuracy of the platform was better than the theoretical threshold, the system transmission rate was not less than 2 M bandwidth, the data packet loss rate was low, and the time delay was not more than 2 s. The images captured in the experiment were clear, with a resolution of less than 1cm and an overlap rate of more than 70%. These all results meet the emergency observation requirement, which indicates that the aerial remote sensing platform based on real-time satellite communication has great potential for application in natural disaster emergency response.

AI-based automatic identification and processing techniques for agricultural safety information

In the context of globalization, agricultural safety is directly linked to food security and human health. Modern agriculture’s challenge is effectively monitoring and processing vast agricultural safety information in the digital era. This study aims to achieve the automatic identification and intelligent processing of agricultural safety information within a digital media environment by applying artificial intelligence (AI) technologies. Initially, the background of AI applications in agriculture is explored, followed by an analysis of the urgent need for automated processing of agricultural safety information and an overview of current research in this field. It is demonstrated that, despite progress, existing methods still lack in-depth feature extraction and real-time capabilities of the data processing systems. A method based on multi-level feature fusion for identifying agricultural product safety is proposed to address these limitations alongside a decentralized AI Internet of Things (IoT) system for processing agricultural safety information. The multi-level feature fusion method can extract and integrate essential details on agricultural products from different dimensions and levels, thus enhancing the identification accuracy. Meanwhile, the decentralized AIoT system strengthens the efficiency and reliability of data processing, ensuring timely responses to agricultural safety information. Through deep neural networks, efficient recognition and classification of agricultural product images were achieved, enabling the automatic identification of agricultural safety information in a digital media environment. Utilizing deep learning algorithms, the system could learn and understand the characteristics of different agricultural products and accurately identify potential safety issues, such as pests, diseases, or spoilage, providing timely monitoring and management for agricultural production. The innovation of this research lies in the comprehensive utilization of multi-level data features and advanced Internet of Things (IoT) technology, significantly improving the level of intelligence in agricultural safety supervision.

A Deep Learning Approach to Investigating Clandestine Laboratories Using a GC-QEPAS Sensor

Illicit drug production in clandestine laboratories involves the use of large quantities of different chemicals that can be obtained for legitimate purposes. The identification of these chemicals, including reagents, catalyzers and solvents, is crucial for forensic investigations. From a legal point of view, a drug precursor is a material that is specific and critical to the production of a finished chemical and that constitutes a significant portion of the final molecular structure of the drug. In this study, a gas chromatography quartz-enhanced photoacoustic spectroscopy (GC-QEPAS) sensor—in conjunction with a deep learning model—was evaluated for its effectiveness in the detection and identification of interesting compounds for the production of amphetamine, methamphetamine, 3,4-methylenedioxymethamphetamine (MDMA), phenylcyclohexyl piperidine (PCP), and cocaine. The GC-QEPAS sensor includes a gas sampler, a fast GC for separation, and a QEPAS detector, which excites molecules exiting the GC column using a quantum cascade laser to provide the infra-red (IR) spectrum. The on-site capability of the GC-QEPAS system offers significant advantages over the current instruments employed in this field, including rapid analysis, which is crucial in field operations. This allows law enforcement to quickly identify specimens of interest on site. The system’s performance was validated by taking into account the limit of detection, repeatability, and within-laboratory reproducibility. The results showed excellent repeatability and reproducibility for both the GC and QEPAS modules. The deep learning model, a multilayer perceptron neural network, was trained using IR spectra and retention times, achieving very high classification accuracy in the testing conditions. This study demonstrated the efficacy of the GC-QEPAS sensor combined with a deep learning model for the reliable identification of drug precursors, providing a robust tool for law enforcement during criminal investigations in clandestine laboratories.

Nanomaterials that Aid in the Diagnosis and Treatment of Alzheimer's Disease, Resolving Blood-Brain Barrier Crossing Ability.

As a form of dementia, Alzheimer's disease (AD) suffers from no efficacious cure, yet AD treatment is still imperative, as it ameliorates the symptoms or prevents it from deteriorating or maintains the current status to the longest extent. The human brain is the most sensitive and complex organ in the body, which is protected by the blood-brain barrier (BBB). This yet induces the difficulty in curing AD as the drugs or nanomaterials that are much inhibited from reaching the lesion site. Thus, BBB crossing capability of drug delivery system remains a significant challenge in the development of neurological therapeutics. Fortunately, nano-enabled delivery systems possess promising potential to achieve multifunctional diagnostics/therapeutics against various targets of AD owing to their intriguing advantages of nanocarriers, including easy multifunctionalization on surfaces, high surface-to-volume ratio with large payloads, and potential ability to cross the BBB, making them capable of conquering the limitations of conventional drug candidates.This review, which focuses on the BBB crossing ability of the multifunctional nanomaterials in AD diagnosis and treatment, will provide an insightful vision that is conducive to the development of AD-related nanomaterials.

The middle-income trap and the middle-technology trap in Latin America–practice comparison based on East Asian perspective

Most Latin American countries entered the ranks of middle-income levels almost simultaneously with Japan and South Korea. However, while Japan and South Korea embarked on the path of economic development through technological innovation and industrial upgrading, Latin America fell into the middle-income trap. By examining the history of industrial and technological development in Brazil, Argentina, and Mexico, three representative countries in Latin America, this paper finds that the fundamental reasons for Latin America falling into the middle-income trap lie in the tension between the choice of industrialization path and primitive accumulation, as well as the institutional deficiencies in technology and industrial development. Latin America, which has fallen into the middle-income trap, finds it hard to upgrade its technology and is trapped in premature industrialization and the middle-technology trap. It can be said that Latin America has fallen into a double trap, which reinforces each other and forms a solid trap pattern. The warning from the Latin American experience is that late-developing countries need to maintain an open attitude during the process of technological catching up, build a complete technological innovation system, enhance industrial and technological governance capabilities, and seize the window of opportunity for technological and industrial upgrading. Otherwise, there is a significant risk of falling into the middle-income trap and even the double trap.

Wave-driven current and vortex patterns at an open beach: Insights from phase-resolving numerical computations and Lagrangian measurements

Wave-driven currents have a substantial impact on local circulation patterns in and across the surf zone, and are responsible for cross-shore and longshore exchange of mass and momentum over a broad range of spatial and temporal scales. Nearshore currents may drive sediment transport, lead to beach erosion, and also affect the spread of bacteria and other marine microorganisms, as well as the distribution of pollutants such as chemicals and microplastics. In addition, surf zone currents can cause hazardous conditions for beach-goers in the form of rip currents.It is known from previous work (Chen et al., 2003; Feddersen et al., 2011; Hally-Rosendahl and Feddersen, 2016) that Boussinesq-type models in combination with appropriate boundary conditions and wave breaking capabilities can function as powerful tools for the analysis of circulation patterns in the surf zone. In the present work, data from a recent field campaign reported on in Bjørnestad et al. (2021) are used to further validate the capability of Boussinesq systems to simulate nearshore dynamics.The numerical model is then used to study the influence of tidal elevation, peak direction and directional spread of the incoming wavefield on the quantity, extent, and circulatory magnitude of the nearshore circulation. In addition, fundamental features such as horizontal eddies are investigated, and comparisons are made to solid-body rotation and irrotational vortices.Overall, it is observed that local variations in the bathymetry across the surf zone are the controlling factor regarding the size of these circulations, and an increasing tidal level, which can be seen as a uniform offset to the bathymetry, favors the generation of larger vortex patterns. For a given tidal stage, the directional spread of the incoming wavefield has the most pronounced influence on the size and strength of the nearshore eddies while the peak direction has the strongest effect on the total number of circulations.

Numerical study of a novel battery thermal management system coupled with heat pipe and cold plate

This study addresses the critical issue of thermal management in lithium-ion batteries, presenting an innovative battery thermal management system that integrates heat pipes and a cold plate (IHPCP). Effective thermal management is crucial for enhancing the performance and longevity of lithium-ion batteries. Using three-dimensional numerical simulations, the study evaluates the impact of various parameters, including the number of heat pipes, the ratio of the plate runner’s width to the heat pipe’s thickness (ω), and the ratio of the cold plate’s thickness to the battery’s thickness (ζ), on battery temperature regulation. The results demonstrate that incorporating heat pipes significantly reduces the maximum battery temperature and improves temperature uniformity. Optimal heat transfer efficiency was achieved with seven heat pipes, resulting in a 21.7% reduction in maximum dimensionless temperature and a 16.4% decrease in temperature variance at a Reynolds number of 100. The Performance Evaluation Criterion (PEC) reached 3.1, indicating superior thermal management performance. This work surpasses previous efforts in the literature by demonstrating the enhanced thermal regulation capabilities of the IHPCP system, offering a compact and efficient solution for battery thermal management.

Adaptive neighborhood triplet loss: enhanced segmentation of dermoscopy datasets by mining pixel information.

The integration of deep learning in image segmentation technology markedly improves the automation capabilities of medical diagnostic systems, reducing the dependence on the clinical expertise of medical professionals. However, the accuracy of image segmentation is still impacted by various interference factors encountered during image acquisition. To address this challenge, this paper proposes a loss function designed to mine specific pixel information which dynamically changes during training process. Based on the triplet concept, this dynamic change is leveraged to drive the predicted boundaries of images closer to the real boundaries. Extensive experiments on the PH2 and ISIC2017 dermoscopy datasets validate that our proposed loss function overcomes the limitations of traditional triplet loss methods in image segmentation applications. This loss function not only enhances Jaccard indices of neural networks by 2.42 and 2.21 for PH2 and ISIC2017, respectively, but also neural networks utilizing this loss function generally surpass those that do not in terms of segmentation performance. This work proposed a loss function that mined the information of specific pixels deeply without incurring additional training costs, significantly improving the automation of neural networks in image segmentation tasks. This loss function adapts to dermoscopic images of varying qualities and demonstrates higher effectiveness and robustness compared to other boundary loss functions, making it suitable for image segmentation tasks across various neural networks.

Transfer learning based cross-process fault diagnosis of industrial robots

In the actual industrial application of robots, the characteristics of robot malfunctions change accordingly as the working environment becomes increasingly diverse and complex. Utilizing the original fault diagnosis models in new working environments correspondingly leads to a decline in the performance and the generalization capability of the model. Moreover, the monitoring data collected in new working processes often has limited or no labels, making the diagnosis models trained with this data unable to identify faults accurately. In this paper, we propose a Domain adaptive Cross-process Fault Diagnosis method (DCFD) to leverage knowledge from existing working processes for diagnosing faults in new working processes. DCFD uses Multi-Kernel Maximum Mean Discrepancy (MK-MMD) to measure the difference between the current working processes and the previous working processes, enhancing the fault diagnosis capability of the robotic system in cross-process scenarios. DCFD achieves an average fault classification accuracy of 98% on 12 types of migration tasks, which demonstrates the effectiveness of DCFD on cross-process fault diagnosis classification tasks in real-time industrial application scenarios.

Design of a multichannel vacuum ultraviolet spectroscopy system for SPARC.

The design of a vacuum ultraviolet spectroscopy system has been performed to monitor and provide feedback for impurity control in SPARC. The spectrometer, covering a wavelength range of 10-2000Å through a flat-field configuration with diffraction gratings, incorporates five survey lines of sight. This allows for comprehensive impurity analysis across the core and four divertor regions (inner/outer and upper/lower). Its compact modular design facilitates vertical stacking of each spectrometer unit, significantly reducing space in the tokamak hall, where a dedicated radiation shielding bunker will be built. Safety features include a secondary helium enclosure to mitigate tritium permeation risks during deuterium-tritium (D-T) operations and shielding within the beamlines for enhanced radiation protection. The silicon carbide mirror design for divertor observation ensures its survivability in the in-vessel environment of SPARC, validated by thermal and electromagnetic analysis. Signal modeling and data acquisition testing results show that an exposure time of a few milliseconds is appropriate considering photon flux reaching the detector, demonstrating the system's capability for discharge control that includes disruption avoidance.

Total-Body Dynamic Imaging and Kinetic Modeling of [18F]F-AraG in Healthy Individuals and a Non-Small Cell Lung Cancer Patient Undergoing Anti-PD-1 Immunotherapy.

Immunotherapies, especially checkpoint inhibitors such as anti-programmed cell death protein 1 (anti-PD-1) antibodies, have transformed cancer treatment by enhancing the immune system's capability to target and kill cancer cells. However, predicting immunotherapy response remains challenging. 18F-arabinosyl guanine ([18F]F-AraG) is a molecular imaging tracer targeting activated T cells, which may facilitate therapy response assessment by noninvasive quantification of immune cell activity within the tumor microenvironment and elsewhere in the body. The aim of this study was to obtain preliminary data on total-body pharmacokinetics of [18F]F-AraG as a potential quantitative biomarker for immune response evaluation. Methods: The study consisted of 90-min total-body dynamic scans of 4 healthy subjects and 1 non-small cell lung cancer patient who was scanned before and after anti-PD-1 immunotherapy. Compartmental modeling with Akaike information criterion model selection was used to analyze tracer kinetics in various organs. Additionally, 7 subregions of the primary lung tumor and 4 mediastinal lymph nodes were analyzed. Practical identifiability analysis was performed to assess the reliability of kinetic parameter estimation. Correlations of the SUVmean, the tissue-to-blood SUV ratio (SUVR), and the Logan plot slope (K Logan) with the total volume of distribution (V T) were calculated to identify potential surrogates for kinetic modeling. Results: Strong correlations were observed between K Logan and SUVR with V T, suggesting that they can be used as promising surrogates for V T, especially in organs with a low blood-volume fraction. Moreover, practical identifiability analysis suggested that dynamic [18F]F-AraG PET scans could potentially be shortened to 60 min, while maintaining quantification accuracy for all organs of interest. The study suggests that although [18F]F-AraG SUV images can provide insights on immune cell distribution, kinetic modeling or graphical analysis methods may be required for accurate quantification of immune response after therapy. Although SUVmean showed variable changes in different subregions of the tumor after therapy, the SUVR, K Logan, and V T showed consistent increasing trends in all analyzed subregions of the tumor with high practical identifiability. Conclusion: Our findings highlight the promise of [18F]F-AraG dynamic imaging as a noninvasive biomarker for quantifying the immune response to immunotherapy in cancer patients. Promising total-body kinetic modeling results also suggest potentially wider applications of the tracer in investigating the role of T cells in the immunopathogenesis of diseases.

Machine learning and physics-driven modelling and simulation of multiphase systems

We highlight the work of a multi-university collaborative programme, PREMIERE (PREdictive Modelling with QuantIfication of UncERtainty for MultiphasE Systems), which is at the intersection of multi-physics and machine learning, aiming to enhance predictive capabilities in complex multiphase flow systems across diverse length and time scales. Our contributions encompass a variety of approaches, including the Design of Experiments for nanoparticle synthesis optimisation, Generalised Latent Assimilation models for drop coalescence prediction, Bayesian regularised artificial neural networks, eXtreme Gradient Boosting for microdroplet formation prediction, and a sub-sampling based adversarial neural network for predicting slug flow behaviour in two-phase pipe flows. Additionally, we introduce a generalised latent assimilation technique, Long Short-Term Memory networks for sequence forecasting mixing performance in stirred and static mixers, active learning via Bayesian optimisation to recover coalescence model parameters for high current density electrolysers, Gaussian process regression for drop size distribution predictions for sprays, and acoustic emission signal inversion using gradient boosting machines to characterise particle size distribution in fluidised beds. We also offer perspectives on the development of a shape optimisation framework that leverages the use of a multi-fidelity multiphase emulator. The results presented have applications in chemical synthesis, microfluidics, product manufacturing, and green hydrogen generation.

FRT Capability Enhancement of DFIG-based Wind Turbine with Coordination Control of MSVC and FCL

Objective: To address the problems of various FRT (fault ride through) schemes in DFIG (doubly fed induction generator) systems, especially their applicability under different voltage sags, a combination scheme of an MSVC (minimized series voltage compensator) and FCL (fault current limiter) is proposed. Methods: Based on the analysis of the mathematical model of the DFIG and considering the capacity and volume in the process of practical engineering, the application structure and specific control strategy of an MSVC in DFIG systems are designed on the stator side, and the application effect is analyzed theoretically. Simultaneously, the application structure and control strategy of the FCL is proposed on the rotor side, and the application effect of the combination scheme is theoretically deduced and analyzed. Moreover, the simulation model is built on the MATLAB/Simulink platform. Results: The simulation results show that the scheme can quickly and effectively recover the fault voltage of the DFIG under different voltage sag degrees and has better dynamic performance. At the same time, it can effectively limit the fault current and suppress the DC-link voltage of the rotor side, and the transition process is relatively stable. Conclusion: The purpose of improving the FRT capability of the DFIG system is realized.

Multi-rate measurement fusion for power system state estimation

Abstract In recent years, the estimation of power systems has become increasingly critical. The introduction of Synchronized PMU has significantly enhanced the dynamic estimation capabilities of power systems. However, given the widespread deployment and usage of SCADA systems in the power grid, it is challenging to entirely replace SCADA systems with PMU systems in a short period. Therefore, utilizing a hybrid approach that combines the two systems for power state estimation is of utmost importance. This article achieves the integration of multi-rate and multi-sensor information in the power system by employing predictive interpolation on SCADA to match the sampling rate of PMU. The performance of power grid state estimation surpasses that of a single SCADA deployment.

Early-stage diagnosis of ovarian cancer via digital immunoassay on a SlipChip

Ovarian cancer (OC) is one of the three major gynecologic malignancies and has the highest mortality rate because of the late diagnosis. Liquid biopsy based on serum protein biomarkers has demonstrated great potential for early diagnosis but remains limited by the analysis performance of conventional immunoassay technologies, such as chemiluminescence, and biomarkers, such as CA125. To address this challenge and achieve accurate early-stage diagnosis of OC, we developed a digital immunoassay on a SlipChip (DiSC) for quantitative analysis of a potential serum protein biomarker, Spondin-1 (SPON1). The DiSC system achieved a limit of detection (LoD) of 23 fg/μL for digital quantification of SPON1. The DiSC system was utilized to quantify the serum level of SPON1 in 357 clinical serum samples, including 63 from patients with benign ovarian tumors and 294 from patients with malignant ovarian cancer, ranging from stages I to IV. SPON1 concentrations were significantly different in samples from patients with malignant ovarian cancer. Notably, significantly different SPON1 levels were observed in early stages (I and II), in lymph node-negative cases (N0), and before metastasis (M0), suggesting that SPON1 could serve as a sensitive diagnostic biomarker for early-stage OC. The differential diagnostic model based on SPON1 levels quantified using DiSC demonstrated an area under the curve (AUC) of 0.8187 for early-stage OC, a significant improvement over CA-125 (AUC = 0.6967). For OC of all stages, the AUC was 0.8225, which could be further increased to 0.8750 when combined with CA-125. This showcases the potential of SPON1 as a novel biomarker for sensitive early-stage diagnosis of ovarian cancer and the capability of the DiSC system in discovering low-abundance biomarkers for disease diagnosis.

Digital age: The path choice of government-citizen value co-creation

This study explores the relationship between government and citizen in the digital age and proposes a specific path to realize the value co-creation of government and citizen in the digital age. Quantitative analysis of 40 policy texts on “digital government construction” of 30 provincial and municipal governments in China before November 2022 was conducted using NVivo12. The case of Minsheng Cloud revealed that provincial and municipal government departments have not paid sufficient attention to citizen participation. Furthermore, citizens' participation ability and enthusiasm are not high. The study found that in the process of strengthening the construction of digital government, “government optimisation”, “citizen participation”, “governance guarantee”, “data governance” and “government digitization” are the key elements. We discuss the feasibility, necessity and coupling of the value co-creation theory to establish the benign interaction between government and citizens and realize co-creation. We find that the “DART” model can be co-created based on value, and that the continuous Improving the four aspects of “dialogue”, “access”, “transparency” and “risk assessment” will undoubtedly help realize the value co-creation of government and citizens in the digital era, and form a governance pattern of co-construction and sharing. It is therefore of great significance to promote the government's digital construction and digital governance capability to accelerate the construction of digital China and promote the modernisation of the national governance system and governance capability.

CNN-LSTM for MFCC-based Speech Recognition on Smart Mirrors for Edge Computing Command

Smart mirrors are conventional mirrors that are augmented with embedded system capabilities to provide comfort and sophistication for users, including introducing the speech command function. However, existing research still applies the Google Speech API, which utilizes the cloud and provides sub-optimal processing time. Our research aim is to design speech recognition using Mel-frequency cepstral coefficients (MFCC) and convolutional neural network–long short-term memory (CNN-LSTM) to be applied to smart mirror edge devices for optimum processing time. Our first step was to download a synthetic speech recognition dataset consisting of waveform audio files (WAVs) from Kaggle, which included the utterances “left,” “right,” “yes,” “no,” “on,” and “off. ” We then designed speech recognition by involving Fourier transformation and low-pass filtering. We benchmark MFCC with linear predictive coding (LPC) because both are feature extraction methods on speech datasets. Then, we benchmarked CNN-LSTM with LSTM, simple recurrent neural network (RNN), and gated recurrent unit (GRU). Finally, we designed a smart mirror system complete with GUI and functions. The test results show that CNN-LSTM performs better than the three other methods with accuracy, precision, recall, and an f1-score of 0.92. The speech command with the best precision is "no," with a value of 0.940. Meanwhile, the command with the best recall is "off," with a value of 0.963. On the other hand, the speech command with the worst precision and recall is "other," with a value of 0.839. The contribution of this research is a smart mirror whose speech commands are carried out on the edge device with CNN-LSTM.