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Over the past few decades, the ground-based global navigation satellite systems (GNSSs) tropospheric sounding technique has undergone rapid development and has proven to be highly effective in sensing atmospheric variables. Through the utilization of the maturing data assimilation technique, GNSS products, e.g., precipitable water vapor (PWV), can be effectively integrated into a numerical weather prediction (NWP) model, thereby significantly bolstering its performance. In this study, to further refine this practice, a comprehensive investigation of the optimal spatial resolutions for assimilating near real-time PWV into the Weather Research and Forecasting model to improve the accuracy of atmospheric humidity field was conducted under different weather conditions in the context of Victoria, Australia. Results revealed that the optimal spatial resolutions under the heavy rainfall and normal conditions were 46.40 and 55.10 km, respectively. Therefore, the overall optimal spatial resolution for assimilating PWV was ultimately determined as 46.40 km, which can capture the necessary details and improve the accuracy of humidity field across different weather scenarios. Specifically, by incorporating PWV into an NWP model using the optimal spatial resolution, the accuracy of atmospheric humidity fields under the heavy rainfall and normal conditions were significantly improved by 26.0% and 24.7%, respectively. Therefore, the findings have considerable implications for further advancing the assimilation technique and offer valuable insights for the construction and deployment of GNSS ground infrastructure in future scenarios.

A remote-control method is proposed for healthcare services involving auditory assessment and therapy through video-conferencing software. Different video-conferencing software platforms have different sound responses, and the user computer also has variations in sound performance because of its hardware and software configuration. These restrictions affect the ability of video-conferencing software to deliver health services that require accurate, reliable, and repeatable sound. In this paper, a remote-control system board is developed wherein sound for auditory assessment or therapy is stored. A low-cost processor is used to remotely deliver sound by sound pattern recognition. Service providers play a sound pattern locally to remotely control the system board. The desired audio files can be delivered to the client side without suffering from sound loss in terms of frequency range and loudness. Experiments were also conducted to verify the performance of the proposed method. The results show that the remote-control method for audio playback can be realized with a low-cost processor, such as a microcontroller unit for sound pattern recognition and that it accurately delivers audio files in terms of frequency and amplitude.

Accurate tropical cyclone (TC) intensity and size estimation are key in disaster management and prevention. While great breakthroughs have been made in TC intensity estimation research, there is currently a lack of research on TC size reflecting TC influence radius. Therefore, we propose a lightweight multi-task learning model (TC-MTLNet) with adaptive loss balance to simultaneously estimate TC intensity and size. Adaptive loss balance is utilized to solve the problem of inconsistent convergence speed of TC intensity and size estimation tasks. The model based on four 2-D convolutions, four 3-D convolutions and three fully connected layers takes up less computational and storage space and improves the accuracy of TC intensity and size estimation by sharing knowledge among multiple tasks. In addition, due to the imbalanced distribution of TC samples, with significantly few low-intensity and high-intensity TC satellite data, this phenomenon poses a great challenge to TC intensity and size estimation. So, we utilize the influence of nearby samples to calibrate the sample density to weight the loss function to enable the model to be generalized to all samples. The result shows that the root-mean-square error (RMSE) of TC intensity estimation is $\text{8.40}\,\text{kts}$, which is 33.5% lower than that of the Advanced Dvorak Technique (ADT) and 11.4% lower than that of the deep learning method (3DAttentionTCNet). The mean absolute error (MAE) of the TC size estimation is $\text{20.89}\,\text{nmi}$, which is a 16% reduction compared to the Multi-Platform Tropical Cyclone Surface Winds Analysis (MTCSWA).

Full-color display is a primary challenge for the commercialization of quantum dots (QDs). In this study, we utilize the spectral narrowing phenomenon of microcavities to fabricate the red, green and blue quantum dot light-emitting diodes (QLEDs) with a single QD layer. This work theoretically analyses the role of microcavities in adjusting the emitting color of QLEDs. By enhanced microcavity and properly chosen spacer thickness, the spectral selectivity shifts, realizing the full-color-tunability of QLEDs. The tunable experimental spectra of microcavity QLEDs are observed, in excellent agreement with our theoretical design. Benefiting from the spectral narrowing of microcavity and the narrow spectra of QDs, a high color purity with full width at half maximum (FWHM) of 18 to 25 nm is realized, leading to a color gamut ratio of 104.8% compared to National Television System Committee (NTSC) standard. The light extraction is also enhanced by constructive interference and the Purcell effect in the microcavity. Moreover, in the fabrication of red, green, and blue pixels, patterning the transparent cathode has better feasibility and lower damage relative to patterning the light-emitting layer.

The use of Stereoscopic 3D (S3D) videos has been popular in commercial markets with ongoing developments in the field of visual entertainment in recent years. A wide variety of projection methods of 3D video content is currently available, such as projection to a panoramic screen and projection of omnidirectional video content from head mounted displays using Virtual Reality (VR) technology. This article investigates the Quality of Experience (QoE) and associated Visually Induced Motion Sickness (VIMS) caused by the viewing of S3D videos. The investigations used three different projection screens: a 3D flat screen, a 3D panoramic screen in a hemispherical shaped room and a VR headset. Several assessment methods including a Simulator Sickness Questionnaire (SSQ), ElectroEncephaloGraphy (EEG), and measurement tools for eye blink rate detection were applied to measure the QoE experienced by viewers. The SSQ scores were also compared with the behavioral data such as attention and meditation levels and enjoyment ratings acquired from different video content and projection screens. The results indicate that the projection screen is a key factor affecting the level of visual fatigue, VIMS and QoE assessments, which are discussed in-depth in the article.

Environmental concerns and energy efficiency have been driving the transportation innovation in the past decades. In this framework, hybrid city transport has been receiving the attention of the industry. The challenges for this application are the required high-power density, lower maintenance cost and the high-temperature environment due to the integration with the internal combustion engine. A multiport converter is proposed to realise the requirements of the automotive industry to achieve greater performance in driveline electrification. The platform suggests the combination of previously discrete power electronics converter modules into one physical package commonising parts such as the microcontroller, heatsink and busbars. This paper provides an overview of design considerations for engine start-stop within a series-parallel topology and how it is achieved using the multiport converter. Finally, a comparison between the multiport and discrete converter approach is made compared and findings presented, highlighting the advantages of the integrated solution.

The diagnosis of functional gastrointestinal disorders and chronic digestive system diseases such as irritable bowel syndrome relies heavily on macroscopic examination of human stool specimens. However, traditional manual stool analysis processes are time-consuming and prone to human subjectivity errors that may lead to incorrect judgments. In this study, we employed deep convolutional neural networks (CNN) to automatically recognize and classify stools in macroscopic images. This approach is advantageous because it reduces the amount of direct interaction required by patients or medical staff, lowers the risk of cross-infection, and removes subjectivity in stool analysis. The U-Net segmentation model is applied to define the region of stools by generating a mask image. After combining the mask and the corresponding original image, the ResNeXt-50 classifies the pre-processed stool image according to the modified Bristol Stool Form Scale (BSFS). Overall, the U-Net model yielded a mean intersection-over-union (mIoU) of 93.75% and an F-score of 0.9570, as the ResNeXt-50 classifier had a classification accuracy of 94.35% and showed decent performance in terms of predictive power. Our study may help to improve the quality of diagnosis and monitoring of diseases associated with bowel movement habits by providing reliable measurements in terms of stool form and consistency.

An effective strategy based on optical tunneling is developed to significantly improve the efficiency of quantum dots light-emitting diodes (QLEDs) and perovskite light-emitting diodes (PeLEDs). Contrary to the common impression that the hole transport layers (HTL) with low refractive index will severely confine lights according to Snell's law, optical tunneling unlocks a channel to extract the lights through HTL. In this work, we demonstrate the light beyond the critical angle still has a chance to penetrate into the substrate and finally emit outside the device. An optical tunneling model is also presented here to quantitatively describe the light extraction through optical tunneling and reveal related factors. Applying a thin HTL (thickness = 10 nm) and a high-index substrate (refractive index = 2) in the typical device architecture (Glass/ITO/PEDOT:PSS/EML/TPBi/LiF/Al), substrate modes can be improved from 18% to 60%. Therefore, optical tunneling followed by EES is believed to be a cost-effective strategy to enhance light extraction efficiency. This work unravels the potential to further significantly improve the efficiency of QLEDs and PeLEDs.

Food traceability has been one of the emerging blockchain applications in recent years, for improving the areas of anti-counterfeiting and quality assurance. Existing food traceability systems do not guarantee a high level of system reliability, scalability, and information accuracy. Moreover, the traceability process is time-consuming and complicated in modern supply chain networks. To alleviate these concerns, blockchain technology is promising to create a new ontology for supply chain traceability. However, most consensus mechanisms and data flow in blockchain are developed for cryptocurrency, not for supply chain traceability; hence, simply applying blockchain technology to food traceability is impractical. In this paper, a blockchain-IoT-based food traceability system (BIFTS) is proposed to integrate the novel deployment of blockchain, IoT technology, and fuzzy logic into a total traceability shelf life management system for managing perishable food. To address the needs for food traceability, lightweight and vaporized characteristics are deployed in the blockchain, while an integrated consensus mechanism that considers shipment transit time, stakeholder assessment, and shipment volume is developed. The data flow of blockchain is then aligned to the deployment of IoT technologies according to the level of traceable resource units. Subsequently, the decision support can be established in the food supply chain by using reliable and accurate data for shelf life adjustment, and by using fuzzy logic for quality decay evaluation.

Visible light communication (VLC) is a kind of high-efficiency wireless communication technology. As one of the key issues in VLC, the modulation bandwidth of light-conversion materials based light-emitting diodes (LEDs) has gained considerable attention in recent years. However, few people focus on theoretical analyses of the light-conversion process during modulation, which show significant influence on the modulation bandwidth of a white LED. In this work, we proposed low-pass filter based models of light-conversion materials of YAG:Ce phosphor and CdSe/ZnS quantum dots, to build the relationship between fluorescence lifetime of these light-conversion materials and their modulation bandwidth. Furthermore, the modulation bandwidth of light source with polychromatic light was obtained and analyzed based on the proposed model. The experimental results of the modulation bandwidth showed a highly consistency with the results from simulation models. These analyses were believed to help realizing white-light illumination with high bandwidth. To the best of our knowledge, this is the first time the modulation mechanisms of light-conversion materials in VLC have been comprehensively discussed in literature.

Closed-loop stimulation of many neurological disorders, such as epilepsy, is an emerging technology and regarded as a promising alternative for surgical and drug treatment. In this paper, a real-time seizure detection algorithm based on STFT and support vector machine (SVM) and its field-programmable gate array (FPGA) implementation are proposed. With a two-stage patient-specific channel selection and feature selection mechanism, those redundant and uncorrelated spectral features are removed from the entire feature set. The evaluation results on CHB-MIT epilepsy database show that the mean detection latency of the proposed algorithm is 6 s, the sensitivity is 98.4%, and the false detection rate is 0.356/h. The performance of our proposed algorithm is comparable to other existing seizure detection algorithms. Moreover, we implement the proposed seizure detection algorithm on Xilinx Zynq-7000 XC7Z020 with high level synthesis. Each classification of the input electroencephalography signal can be finished within 313 $\mu \text{s}$ , and the power consumption of the programmable logic is only 380 mW at 100 MHz. In hardware implementation, an optimization strategy for the nested-loop structure within nonlinear SVM is proposed to improve pipeline efficiency. Compared with existing method, the experimental result shows that our method can speed up the nonlinear SVM by $1.70\times $ , $1.53\times $ , $1.37\times $ , and $1.26\times $ with the unroll factor equal to 1–4 at the same DSP utilization rate. The evaluation results affirm the possibility of integrating the proposed algorithm and FPGA implementation into a wearable seizure control device.

Localization capability is a challenging task in global navigation satellite system-degraded or denied environments. Alternatively, today’s smartphones have an increased number of integrated sensors that can act as terminals for indoor personal positioning solutions such as pedestrian dead reckoning (PDR). However, magnetic interference, poor sensor measurements, and diverse handling of smartphone quickly decrease the performance for indoor PDR. This paper proposes a comprehensive and novel pedestrian indoor positioning solution in which heading estimation is improved by using simplified magnetometer calibration, by calculating projected acceleration along the moving direction using frequency-domain features and by applying direction constrains to indoor accessible paths. Moreover, compared with an ordinary particle filter (OPF) and a Kalman filter, this paper proposes a multidimensional particle filter (MPF) algorithm, namely MPF, which includes high-dimensional variables such as position, heading, step length parameters, motion label, lifetime, number of current particles, and factor. An MPF can handle more uncertain parameters than the OPF. Therefore, positioning with an MPF can achieve lower errors using low-quality sensors, mitigate interference introduced from surrounding environments, and reduce heading ambiguities due to different modes of carrying a smartphone. Consequently, field tests show that the proposed algorithm obtains robust performance for heading estimation and positioning.

Neuron-on-chip (NoC) systems—microfluidic devices in which neurons are cultured—have become a promising alternative to replace or minimize the use of animal models and have greatly facilitated in vitro research. Here, we review and discuss current developments in neuron-on-chip platforms, with a particular emphasis on existing biological models, culturing techniques, biomaterials, and topologies. We also discuss how the architecture, flow, and gradients affect neuronal growth, differentiation, and development. Finally, we discuss some of the most recent applications of NoCs in fundamental research (i.e., studies on the effects of electrical, mechanical/topological, or chemical stimuli) and in disease modeling. [ABSTRACT FROM AUTHOR]

Stroke is a serious neurological disease that may lead to long-term disabilities and even death for stroke patients worldwide. The acute period, ($\le$ 1 mo post-stroke), is crucial for rehabilitation but the current standard clinical practice may be ineffective for patients with severe motor impairment, since most rehabilitation programs involve physical movement. Imagined movement – the so-called motor imagery (MI) – has been shown to activate motor areas of the brain without physical movement. MI therefore offers an opportunity for early rehabilitation of stroke patients. MI, however, is not widely employed in clinical practice due to a lack of evidence-based research. Here, we review MI-based approaches to rehabilitation of stroke patients and immersive virtual reality (VR) technologies to potentially assist MI and thus, promote recovery of motor function. [ABSTRACT FROM AUTHOR]

A recently proposed theoretical methodology for the assessment of the electromigration (EM) induced IR-drop degradation in on-chip power/ground grids has been validated by means of measurements performed on real silicon. A voltage tapping technique was employed for the direct measurement of voltage variations at 162 nodes of the power net, stressed with 10 mA constant source current at an elevated temperature of 350 °C. A voltage drop between cathode and anode pads exceeding a specified threshold was considered as a failure. Times-to-failure (TTF) was measured on 19 packaged test grids and used for computing the mean TTF (MTTF). The EM-induced voltage degradation in this grid was also analyzed with an assessment methodology based on a simulation of stress evolution everywhere in the grid, resulting in a voiding in some of grid branches and corresponding resistance increase. A set of voiding compact models for different grid segments was developed and used in the simulations. The stochastic nature of the EM phenomenon was captured by introducing random distributions of atomic diffusivities and critical stresses across the grid and iterating them with Monte Carlo loops. A good fit between the measured voltage evolution kinetics at different grid nodes and that predicted by simulation, and the good agreement between measured and simulated failure distributions can be considered as the ever first experimental validation of this EM assessment methodology for on-chip power/ground (p/g) grids. [ABSTRACT FROM AUTHOR]

Virtual manufacturing (VM) is an emerging manufacturing practice that is increasingly popular among manufacturers. The success of VM requires an in-depth understanding and innovative applications of operations strategies, supply chain management techniques, and business dynamics. In this article, we identify the critical capabilities of VM firms and their organizational performance implications. We first compiled two lists of potential critical capabilities from the academic and practical perspectives. From the lists, we identified nine and six initial constructs of capabilities and organizational performance measures, respectively. Based on the identified constructs, we designed a structured survey questionnaire and conducted an organizational-level field study. Obtaining 150 valid responses, we analyzed the data by exploratory factor analysis to ascertain the critical capabilities and organizational performance measures and by multiple regression analysis to examine their relationships. The data analysis yielded four critical capabilities, namely technological capability, marketing capability, dynamic capability, and relationship capability, and four organizational performance measures, namely personnel performance, corporate social responsibility performance, relationship performance, and marketing performance. We found that relationship capability was the most prominent among the four critical capabilities. Finally, we conducted a case study of a pioneering VM firm to validate the empirical findings from the practical perspective. [ABSTRACT FROM AUTHOR]