Search Results (26,024)

This paper presents a dual-purpose LED driver system that functions as both a lighting source and a Visible Light Communication (VLC) transmitter integrated with a Powerline Communication (PLC) network under the PRIME G3 standard. The system decodes PLC messages from the powerline grid and transmits the information via LED light to an optical receiver under a binary phase shift keying (BPSK) modulation. The load design targets a light flux of 800 lumens, suitable for LED light bulb applications up to 10 watts, ensuring practicality and energy efficiency. The Universal Asynchronous Receiver-Transmitter (UART) module enables communication between the PLC and VLC systems, allowing for an LED driver with dynamic control and real-time operation. Key signal processing stages are commented and developed, including a hybrid buck converter with modulation capabilities and a nonlinear optical receiver to regenerate the BPSK reference signal for VLC. Results show a successful prototype working under a laboratory environment. Experimental validation shows successful transmission of bit streams from the PLC grid to the VLC setup. A design guideline is presented in order to dictate the design of the electronic devices involved in the experiment. Finally, this research highlights the feasibility of integrating PLC and VLC technologies, offering an efficient and cost-effective solution for data transmission over existing infrastructure. Full article

Copper-containing diamine oxidases are ubiquitous enzymes that participate in many important biological processes. These processes include the regulation of cell growth and division, programmed cell death, and responses to environmental stressors. Natural substrates include, for example, putrescine, spermidine, and histamine. Enzymatic activity is typically assayed using spectrophotometric, electrochemical, or fluorometric methods. The aim of this study was to develop a method for measuring activity using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry based on the intensity ratio of product to product-plus-substrate signals in the reaction mixtures. For this purpose, an enzyme purified to homogeneity from pea (Pisum sativum) seedlings was used. The method employed α-cyano-4-hydroxycinnamic acid as a matrix with the addition of cetrimonium bromide. Product signal intensities with pure compounds were evaluated in the presence of equal substrate amounts to determine intensity correction factors for data processing calculations. The kinetic parameters k_cat and K_m for the oxidative deamination of selected substrates were determined. These results were compared to parallel measurements using an established spectrophotometric method, which involved a coupled reaction of horseradish peroxidase and guaiacol, and were discussed in the context of data from the literature and the BRENDA database. It was found that the method provides accurate results that are well comparable with parallel spectrophotometry. This method offers advantages such as low sample consumption, rapid serial measurements, and potential applicability in assays where colored substances interfere with spectrophotometry. Full article

Reconfigurable intelligent surfaces (RISs) are a promising technology for sixth-generation (6G) wireless networks. However, a fully passive RIS cannot independently process signals. Wireless systems equipped with it often encounter the challenge of large channel matrix dimensions when acquiring channel state information using pilot-assisted algorithms, resulting in high pilot overhead. To address this issue, this article proposes a semi-blind joint channel and symbol estimation receiver without a pilot training stage for RIS-aided multiple-input multiple-output (MIMO) (including massive MIMO) communication systems. In a semi-blind system, a transmission symbol matrix and two channel matrices are coupled within the received signals at the base station (BS). We decouple them by building two parallel factor (PARAFAC) tensor models. Leveraging PARAFAC tensor decomposition, we transform the joint channel and symbol estimation problem into least square (LS) problems, which can be solved by Alternating Least Squares (ALSs). Our proposed scheme allows duplex communication. Compared to recently proposed pilot-based methods and semi-blind receivers, our results demonstrate the superior performance of our proposed algorithm in estimation accuracy and speed. Full article

Sprinkle formulations represent an interesting genre of medicinal products. A frequent problem, however, is the need to mask the unpleasant taste of these drug substances. In the present work, we propose the use of a novel sensor array based on solid-state ion-selective electrodes to evaluate the taste-masking efficiency of rosuvastatin (ROS) sprinkle formulations. Eight Multiple Unit Pellet Systems (MUPSs) were analyzed at two different doses (API_50) and (API_10), as well as pure Active Pharmaceutical Ingredient (API) as a bitter standard. Calcium phosphate-based starter pellets were coated with the mixture containing rosuvastatin. Some of them were additionally coated with hydroxypropyl methylcellulose, which was intended to separate the bitter substance and prevent it from coming into contact with the taste buds. The sensor array consisted of 16 prepared sensors with a polymer membrane that had a different selectivity towards rosuvastatin calcium. The main analytical parameters (sensitivity, selectivity, response time, pH dependence of potential, drift of potential, lifetime) of the constructed ion-selective electrodes sensitive for rosuvastatin were determined. The signals from the sensors array recorded during the experiments were processed using Principal Component Analysis (PCA). The results obtained, i.e., the chemical images of the pharmaceutical samples, indicated that the electronic tongue composed of the developed solid-state electrodes provided respective attributes as sensor signals, enabling both of various kinds of ROS pellets to be distinguished and their similarity to ROS bitterness standards to be tested. Full article

The mining face under the close coal seam group is affected by the superposition of the concentrated stress of the overlying residual diagonally intersecting coal pillar and the mining stress, which can easily cause the instability and damage of the section coal pillars during the process of mining back to the downward face. Additionally, the traditional methods of monitoring such as numerical simulation, drilling peeping, and acoustic emission fail to realize the real-time and accurate deformation monitoring of the internal deformation of the section coal pillars. The introduction of the drill-hole-implanted fiber-optic grating monitoring method can realize real-time deformation monitoring for the whole area inside the coal pillar, which solves the short board problem of coal pillar deformation monitoring. However, fiber-optic monitoring is easily disturbed by the external environment, which is especially sensitive to the background noise of the complex underground mining environment. Therefore, taking the live chicken and rabbit well of Shaanxi Daliuta Coal Mine as the engineering background, the ensemble empirical modal decomposition (EEMD) is introduced for primary noise reduction and signal reconstruction by the threshold determination (DE) algorithm, and then the singular matrix decomposition (SVD) is introduced for secondary noise reduction. Finally, a machine learning algorithm is combined with the noise reduction algorithm for the prediction of the fiber grating strain signals of coal pillar in a zone, and DBO-LSTM-BP is constructed as the prediction model. The experimental results demonstrate that compared with the other two noise reduction prediction models, the SNR of the EEMD-DE-SVD-DBO-LSTM-BP model is improved by 0.8–2.3 dB on average, and the prediction accuracy is in the range of 88–99%, which realizes the over-advanced prediction of the deformation state of the coal column in the section. Full article

This research paper describes the development of a low-cost device for measuring wastewater flow rates in open channels, a significant advancement enabled by the evolution of microcomputers and processing techniques. A laboratory setup was constructed to validate the device’s accuracy against a standard flow measurement method, optimizing key parameters to achieve a linear relationship between detected and set flow rates, while considering hardware limitations and energy efficiency. The central focus of the research was developing a method to measure the velocity of contaminated fluid using ultrasonic signals, employing the cross-correlation method for signal delay analysis in a stochastic environment. This was complemented by a procedure to measure fluid levels, also based on ultrasonic signals. The device’s reliability was assessed through repeatability and uncertainty measurements, confirming its accuracy with an extended uncertainty not exceeding an average of 3.47% for flows above 40 L/min. The device has potential to provide valuable data on the operational dynamics of sanitary networks, crucial for developing and calibrating simulation models. Full article

Increased levels of reactive oxygen species (ROS) produced during aerobic metabolism in animals can negatively affect the intracellular redox status, cause oxidative stress and interfere with physiological processes in the cells. The antioxidant defence regulates ROS levels by interplaying diverse enzymes and non-enzymatic metabolites. The thioredoxin system, consisting of the enzyme thioredoxin reductase (TrxR), the redox-active protein thioredoxin (Trx) and NADPH, represent a crucial component of antioxidant defence. It is involved in the signalling and regulation of multiple developmental processes, such as cell proliferation or apoptotic death. Insects have evolved unique variations of TrxR, which resemble mammalian enzymes in overall structure and catalytic mechanisms, but the selenocysteine–cysteine pair in the active site is replaced by a cysteine–cysteine pair typical of bacteria. Moreover, the role of the thioredoxin system in insects is indispensable due to the absence of glutathione reductase, an essential enzyme of the glutathione system. However, the functions of the Trx system in insects are still poorly characterised. In the present review, we provide a critical overview of the current knowledge on the insect Trx system, focusing mainly on TrxR’s role in the antioxidant and immune system of model insect species. Full article

This paper presents a single-input multiple-output (SIMO) cascode low-noise amplifier with inductive degeneration for inter- and intra-band carrier aggregation. The proposed low-noise amplifier has two output ports for flexible operation in carrier aggregation combinations for band 30 and band 7. However, during inter- and intra-band operation, gain variation occurs depending on the output mode. To compensate for this, a switching circuit is proposed to adjust the degeneration inductor, optimizing gain performance for both modes. The switching operation can minimize the control for the dynamic range in the receiver system to support carrier aggregation. The designed low-noise amplifier was fabricated using a 65 nm CMOS process, occupying an area of 2.1 mm². In inter-band operation, the small-signal gain was measured by 18.9 dB for band 30 and 18.6 dB for band 7, with the noise figures of 1.03 dB and 1.07 dB, respectively. For intra-band operation, the small-signal gain was 17.3 dB and 17.2 dB, with the noise figures of 1.3 dB and 1.41 dB. The IIP₃ values were measured by −7.6 dBm and −6.7 dBm for inter-band, and −6.3 dBm and −6.2 dBm for intra-band. Power consumption was 8.04 mW and 7.68 mW in inter-band, and 17.04 mW and 17.64 mW in intra-band depending on the output configuration. Full article

Accurately measuring the moisture content (MC) of square timber is crucial for ensuring the quality and performance of wood products in wood processing. Traditional MC detection methods have certain limitations. Therefore, this study developed a one-dimensional convolutional neural network (1D-CNN) model based on the first 8 nanoseconds of ground-penetrating radar (GPR) signals to predict the MC of square timber. The study found that the mixed-species model exhibited effective predictive performance (R² = 0.9864, RMSE = 0.0393) across the tree species red spruce, Dahurian larch, European white birch, and Manchurian ash (MC range 0%–133.1%), while single-species models showed even higher accuracy (R² ≥ 0.9876, RMSE ≤ 0.0358). Additionally, the 1D-CNN model outperformed other algorithms in automatically capturing complex patterns in GPR full-waveform amplitude data. Moreover, the algorithms based on full-waveform amplitude data demonstrated significant advantages in detecting wood MC compared to those based on a traditional time–frequency feature parameter. These results indicate that the 1D-CNN model can be used to optimize the drying process and detect the MC of load-bearing timber in construction and bridge engineering. Future work will focus on expanding the dataset, further optimizing the algorithm, and validating the models in industrial applications to enhance their reliability and applicability. Full article

Image classification is increasingly being utilized on construction sites to automate project monitoring, driven by advancements in reality-capture technologies and artificial intelligence (AI). Deploying real-time applications remains a challenge due to the limited computing resources available on-site, particularly on remote construction sites that have limited telecommunication support or access due to high signal attenuation within a structure. To address this issue, this research proposes an efficient edge-computing-enabled image classification framework for support of real-time construction AI applications. A lightweight binary image classifier was developed using MobileNet transfer learning, followed by a quantization process to reduce model size while maintaining accuracy. A complete edge computing hardware module, including components like Raspberry Pi, Edge TPU, and battery, was assembled, and a multimodal software module (incorporating visual, textual, and audio data) was integrated into the edge computing environment to enable an intelligent image classification system. Two practical case studies involving material classification and safety detection were deployed to demonstrate the effectiveness of the proposed framework. The results demonstrated the developed prototype successfully synchronized multimodal mechanisms and achieved zero latency in differentiating materials and identifying hazardous nails without any internet connectivity. Construction managers can leverage the developed prototype to facilitate centralized management efforts without compromising accuracy or extra investment in computing resources. This research paves the way for edge “intelligence” to be enabled for future construction job sites and promote real-time human-technology interactions without the need for high-speed internet. Full article

This paper explores an application of 3D printing technology on the food industry. Since its inception in the 1980s, 3D printing has experienced a huge rise in popularity. This study uses cost-effective, flexible, and sustainable components that enable specific features of certain gas chromatography. This study aims to optimize the process of gas detection using a 3D printed separation column and the MiCS-6814 sensor. The principle of the entire device is based on the idea of utilizing a simple capillary chromatographic column manufactured by 3D printing for the separation of samples into components prior to their measurement using inexpensive chemiresistive sensors. An optimization of a system with a 3D printed PLA block containing a capillary, a mixing chamber, and a measuring chamber with a MiCS-6814 sensor was performed. The optimization distributed the sensor output signal in the time domain so that it was possible to distinguish the peak for the two most common alcohols, ethanol and methanol. The paper further describes some optimization types and their possibilities. Full article

The development of astronomy relies heavily on advances in high-resolution imaging techniques. With the growing demand for high-resolution astronomical observations, conventional optical interferometry has gradually revealed various limitations, especially in coping with atmospheric phase fluctuations and long baseline observations. However, intensity interferometry is becoming an important method to overcome these challenges due to its high robustness to atmospheric phase fluctuations and its excellent performance in long-baseline observations. In this paper, the basic principles and key technologies of intensity interferometry are systematically described, and the remarkable potential of this technique for improving angular resolution and detection sensitivity is comprehensively discussed in light of the recent advances in modern photon detector and signal processing techniques. The results show that the intensity interferometry technique is capable of realizing high-precision observation of long-range and low-brightness targets, especially in the field of exoplanet detection, which shows a wide range of application prospects. In the future, with the continuous development of telescope arrays and adaptive optics, the intensity interferometry technique is expected to further promote the precision and breadth of astronomical observations, and provide new opportunities for revealing the mysteries of the universe. Full article

Accurately estimating the modulation parameters of pseudorandom binary code–pulse amplitude modulation (PRBC–PAM) signals damaged by strong noise poses a significant challenge in emitter identification and countermeasure. Traditionally, weak signal detection methods based on chaos theory can handle situations with low signal-to-noise ratio, but most of them are developed for simple sin/cos waveform and cannot face PRBC–PAM signals commonly used in ultra-low altitude performance equipment. To address the issue, this article proposes a novel adaptive detection and estimation method utilizing the in-depth analysis of the Duffing oscillator’s behaviour and output characteristics. Firstly, the short-time Fourier transform (STFT) is used for chaotic state identification and ternary processing. Then, two novel approaches are proposed, including the adjusting zero value (AZV) method and the chaotic state ratio (CSR) method. The proposed weak signal detection system exhibits unique capability to adaptively modify its internal periodic driving force frequency, thus altering the difference frequency to estimate the signal parameters effectively. Furthermore, the accuracy of the proposed method is substantiated in carrier frequency estimation under varying SNR conditions through extensive experiments, demonstrating that the method maintains high precision in carrier frequency estimation and a low bit error rate in both the pseudorandom sequence and carrier frequency, even at an SNR of −30 dB. Full article

Over the past 40 years, research has heavily emphasized stroke treatments that directly target ischemic cascades after stroke onset. Much attention has focused on studying neuroprotective drugs targeting one aspect of the ischemic cascade. However, the single-target therapeutic approach resulted in minimal clinical benefit and poor outcomes in patients. Considering the ischemic cascade is a multifaceted and complex pathophysiological process with many interrelated pathways, the spotlight is now shifting towards the development of neuroprotective drugs that affect multiple aspects of the ischemic cascade. Phosphatidylserine (PS), known as the “eat-me” signal, is a promising candidate. PS is involved in many pathophysiological changes in the central nervous system after stroke onset, including apoptosis, inflammation, coagulation, and neuronal regeneration. Moreover, PS might also exert various roles in different phases after stroke onset. In this review, we describe the synthesis, regulation, and function of PS under physiological conditions. Furthermore, we also summarize the different roles of PS after stroke onset. More importantly, we also discuss several treatment strategies that target PS. We aim to advocate a novel stroke care strategy by targeting PS through a translational perspective. Full article

The improvement of information sciences promotes the utilization of data for process monitoring. As the core of modern automation, time-stamped signals are used to estimate the system state and construct the data-driven model. Many recent studies claimed that the effectiveness of data-driven methods relies greatly on data quality. Considering the complexity of the operating environment, process data will inevitably be affected. This poses big challenges to estimating faults from data and delivers feasible strategies for electrical systems of industry. This paper addresses the missing data problem commonly in traction systems by designing a martingale posterior-based data generation method for the state-space model. Then, a data-driven approach is proposed for fault detection and estimation via the subspace identification technique. It is a general scheme using the Bayesian framework, in which the Dirichlet process plays a crucial role. The data-driven method is applied to a pilot-scale traction motor platform. Experimental results show that the method has good estimation performance. Full article