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Abstract Echinococcosis poses a significant health burden in China, yet previous studies have mainly concentrated on its prevalence rather than incidence trends. This study analyzes echinococcosis incidence from 2010 to 2021 and projects future trends to enhance prevention and control strategies. The annual percentage change (APC) and average annual percentage change (AAPC) were calculated to determine temporal trends, while the time series was decomposed to assess seasonal patterns. During this period, China documented 53,141 new echinococcosis cases (excluding Hong Kong, Macao, and Taiwan), averaging 0.320 cases per 100,000 annually. The AAPC indicated a non-significant decline of -2.718%. The analysis identified two critical inflection points in 2014 and 2017, leading to three distinct trends: a non-significant decline from 2010 to 2014, a significant rise from 2014 to 2017, and a significant decrease from 2017 to 2021. Cases peaked in December, and predictions from the Seasonal Autoregressive Integrated Moving Average (SARIMA) model suggest a slight rise in incidence from September 2022 to August 2025, advising intensified efforts in preventive measures to prevent resurgence.

Microplastics (MPs) pollution is emerging as a significant environmental concern, threatening human and animal health. Liver, as an important organ, plays an important role in the metabolism and detoxification of pollutants. Many studies have suggested that the liver is a potential target organ for MPs. However, the extent and consequences of the impacts of MPs on the liver reported in studies remain inconsistent. We categorized vertebrates into fish, mammals, and birds and performed a meta–analysis to comprehensively examine the effects of MPs on the liver. Hedges’ g values were calculated to evaluate effect sizes. To further explore the sources of heterogeneity among the studies, we conducted subgroup analyses focusing on life stage, MPs type, MPs size, and exposure duration. Additionally, we normalized the MPs concentrations and conducted meta-regression analyses to explore the relationship between MPs concentrations and their hepatotoxic effects. A total of 118 studies were included in this meta-analysis. By quantifying 19 indicators, the results showed that MPs could damage the liver by altering liver morphology, inducing oxidative stress, producing intracellular toxicity, altering biotransformation processes, and disturbing lipid metabolism. Intracellular toxicity, followed by oxidative stress, had the greatest impact. Organisms are more sensitive to MPs under the following conditions: longer exposure duration, smaller MPs sizes, and earlier life stages. As the concentration of MPs increases, the levels of several liver indicators, including catalase, glutathione S-transferase, reactive oxygen species, and alkaline phosphatase, progressively increase. This study provides a comprehensive understanding of the effects of MPs on the liver and suggests the underlying mechanisms of MPs hepatotoxicity.

Abstract High-speed GaN-based lasers play a pivotal role in visible light communication (VLC) systems; however, the causes of the limited modulation response of our fabricated laser diode (LD) are not fully understood. Accordingly, we constructed an equivalent circuit model for both the LD and its packaging. This model enabled us to analyze the series resistance and parallel capacitance of the LD at different injection currents. Experiments and simulations were performed to investigate the intrinsic responses of the LD. The series resistance and parallel capacitance are responsible for S21 roll-off at low frequencies. Determination of the packaging design parameters on the modulation response of a transistor outline (TO)-can packaged LD was investigated which is important to achieve the impedance match in the future. The value of each discrete component was determined by fitting the scattering parameters of the equivalent circuit model to the packaged LD. Reducing the series resistance and parallel capacitance improved the modulation response. Our study firstly illustrates the design and manufacture of violet-blue-green laser transmitters with a large modulation bandwidth for ultra-high-speed VLC from the point of the impedance influence.

Abstract Due to its unique intensity distribution, self-acceleration, and beam self-healing properties, Airy beam holds great potential for optical wireless communications in challenging channels, such as underwater environments. As a vital part of 6G wireless network, the Internet of Underwater Things requires high-stability, low-latency, and high-capacity underwater wireless optical communication (UWOC). Currently, the primary challenge of UWOC lies in the prevalent time-varying and complex channel characteristics. Conventional blue Gaussian beam-based systems face difficulties in underwater randomly perturbed links. In this work, we report a full-color circular auto-focusing Airy beams metasurface transmitter for reliable, large-capacity and long-distance UWOC links. The metasurface is designed to exhibits high polarization conversion efficiency over a wide band (440-640 nm), enabling an increased data transmission rate of 91% and reliable 4 K video transmission in wavelength division multiplexing (WDM) based UWOC data link. The successful application of this metasurface in challenging UWOC links establishes a foundation for underwater interconnection scenarios in 6G communication.

Given the burgeoning necessity for high-speed, efficient, and secure wireless communication in 6G, visible light communication (VLC) has emerged as a fervent subject of discourse within academic and industrial circles alike. Among these considerations, it is imperative to construct scalable multi-user VLC systems, meticulously addressing pivotal issues such as power dissipation, alignment errors, and the safeguarding of user privacy. However, traditional methods like multiplexing holography (MPH) and multiple focal (MF) phase plates have shown limitations in meeting these diverse requirements. Here, we propose a novel spatial multiplexing holography (SMH) theory, a comprehensive solution that overcomes existing hurdles by enabling precise power allocation, self-designed power coverage, and secure communication through orbital angular momentum (OAM). The transformative potential of SMH is demonstrated through simulations and experimental studies, showcasing its effectiveness in power distribution within systems of VR glasses users, computer users, and smartphone users; enhancing power coverage with an 11.6 dB improvement at coverage edges; and securing data transmission, evidenced by error-free 1080P video playback under correct OAM keys. Our findings illustrate the superior performance of SMH in facilitating seamless multi-user communication, thereby establishing a new benchmark for future VLC systems in the 6G landscape.

IntroductionPersistent infections caused by certain viruses and parasites have been associated with multiple diseases and substantial mortality. Heavy metals are ubiquitous environmental pollutants with immunosuppressive properties. This study aimed to determine whether heavy metals exposure suppress the immune system, thereby increasing the susceptibility to persistent infections.MethodsUsing data from NHANES 1999–2016, we explored the associations between heavy metals exposure and persistent infections: Cytomegalovirus (CMV), Epstein–Barr Virus (EBV), Hepatitis C Virus (HCV), Herpes Simplex Virus Type–1 (HSV–1), Toxoplasma gondii (T. gondii), and Toxocara canis and Toxocara cati (Toxocara spp.) by performing logistic regression, weighted quantile sum (WQS) and Bayesian kernel machine regression (BKMR) models. Mediation analysis was used to determine the mediating role of host immune function in these associations.ResultsLogistic regression analysis revealed positive associations between multiple heavy metals and the increased risk of persistent infections. In WQS models, the heavy metals mixture was associated with increased risks of several persistent infections: CMV (OR: 1.58; 95% CI: 1.17, 2.14), HCV (OR: 2.94; 95% CI: 1.68, 5.16), HSV–1 (OR: 1.25; 95% CI: 1.11, 1.42), T. gondii (OR: 1.97; 95% CI: 1.41, 2.76), and Toxocara spp. (OR: 1.76; 95% CI: 1.16, 2.66). BKMR models further confirmed the combined effects of heavy metals mixture and also identified the individual effect of arsenic, cadmium, and lead. On mediation analysis, the systemic immune inflammation index, which reflects the host’s immune status, mediated 12.14% of the association of mixed heavy metals exposure with HSV–1 infection.DiscussionThe findings of this study revealed that heavy metals exposure may increase susceptibility to persistent infections, with the host’s immune status potentially mediating this relationship. Reducing exposure to heavy metals may have preventive implications for persistent infections, and further prospective studies are needed to confirm these findings.

Abstract Multimode fiber (MMF) which supports parallel transmission of spatially distributed information is a promising platform for remote imaging and capacity-enhanced optical communication. However, the variability of the scattering MMF channel poses a challenge for achieving long-term accurate transmission over long distances, of which static optical propagation modeling with calibrated transmission matrix or data-driven learning will inevitably degenerate. In this paper, we present a self-supervised dynamic learning approach that achieves long-term, high-fidelity transmission of arbitrary optical fields through unstabilized MMFs. Multiple networks carrying both long- and short-term memory of the propagation model variations are adaptively updated and ensembled to achieve robust image recovery. We demonstrate >99.9% accuracy in the transmission of 1024 spatial degree-of-freedom over 1 km length MMFs lasting over 1000 seconds. The long-term high-fidelity capability enables compressive encoded transfer of high-resolution video with orders of throughput enhancement, offering insights for artificial intelligence promoted diffusive spatial transmission in practical applications.

Underwater Wireless Optical Communication (UWOC) holds significant implications in the realms of marine scientific observation, marine military security, and marine resource utilization. Overcoming the challenges associated with cross-medium information transmission represents a pivotal research frontier within the domain of underwater Internet of Things (IoT). This paper investigates the across water-air-interface ultraviolet light uplink communication in the presence of waves. Different degrees of diffuse-line-of-sight and different sizes of waves are studied in the communication link to assess their impact on transmission performance. Using non-return-to-zero on-off keying (NRZ-OOK) modulation, a data transmission of 17.4 Mbit/s is achieved with a lateral displacement of 5 cm in still water, and 17.1 Mbit/s data transmission is achieved under aligned 12 mm wave amplitude condition. A method is proposed to reduce the dependence on relay in across water-air-interface communication when the optical transmitter and receiver are not aligned, and the effectiveness of using ultraviolet light communication under large wave conditions is demonstrated. Bit error rate and received optical power indicate that increasing wave size and deviation of the transceivers both result in channel attenuation in the water-air-interface channel, thus degrading link performance.

Maritime transportation plays a crucial role in facilitating global trade, and navigation lights, which serve as traffic lights for ships, are key devices for nighttime navigation. Beyond lighting, light-emitting diode navigation lights can also be used as a transmitter to enable visible light communication (VLC) for broadcasting and smart navigation. To this end, a long-distance transmission model under weak light conditions has been developed. The transmitter uses a navigation light and employs an on-off keying modulation for data transmission. A spinal code is used to guarantee data integrity in the long-distance channel. The receiver relies on a photomultiplier tube for signal reception and successfully achieves 1600-bit/s information transmission over 1.5 km. This accomplishment illustrates the viability of employing VLC technology in navigation lights an represents a significant milestone in establishing the foundation for long-range and omnidirectional optical communication within the VLC domain.

In fiber optic communication systems, the dispersion and nonlinear interaction of optical signals are critical to modeling fiber optic communication, and the regular perturbation (RP) model is a simplified modeling method composed of parallel branches, which has obvious advantages in deep learning backpropagation. In this paper, we propose a simplified single-mode fiber signal transmission model based on the RP model, which significantly improves the fitting accuracy of the model for dispersion and nonlinear interactions at the same complexity by adding trainable parameters to the standard RP model. We explain in the paper that this improvement is applicable to dual-polarization systems and still effective under the conditions of large launch power, without dispersion management, and containing amplified spontaneous emission (ASE) noise. The model uses the standard split-step Fourier method (SSFM) to generate labels and updates parameters through gradient descent method. When transmitting a dual-polarization signal with a launch power of 13 dBm, the modified regular perturbation (MRP) model proposed in the paper can reduce the fitting errors by more than 75% compared to the standard RP model after transmitting through a 120 km standard single-mode fiber.

Abstract Silicon microring modulators (Si-MRM) are critical components for high-performance electro-optical (E-O) signal conversion at optical interconnections due to their ultrawide bandwidth. However, the current transmission speed at the interconnections is still limited to 240 Gbps because of the low spectral-efficiency, as a result of the inherent modulation nonlinearity of Si-MRMs. Here, we theoretically analyse the modulation nonlinearity of a depletion-mode Si-MRM. Based on the analytical results, we further propose a physics-inspired neural network, named as bidirectional gate recurrent unit (Bi-GRU) to mitigate the signal distortion in Si-MRMs. Bi-GRU matches the analytical E-O modulation dynamics within Si-MRMs, thus can accurately capture the impairment features and accelerate the data transmission speed. We then fabricate a Si-MRM with −3dB E-O bandwidth of 42.5 GHz, achieving an ultrahigh speed optical interconnection with a data rate of 302 Gbps. The maximum spectral-efficiency of modulated signals is improved to 5.20 bit/s/Hz. The results provide insights to develop ultrahigh-speed Si-MRM using emerging AI techniques.

Although the 5G wireless network has made significant advances, it is not enough to accommodate the rapidly rising requirement for broader bandwidth in post-5G and 6G eras. As a result, emerging technologies in higher frequencies including visible light communication (VLC), are becoming a hot topic. In particular, LED-based VLC is foreseen as a key enabler for achieving data rates at the Tb/s level in indoor scenarios using multi-color LED arrays with wavelength division multiplexing (WDM) technology. This paper proposes an optimized multi-color LED array chip for high-speed VLC systems. Its long-wavelength GaN-based LED units are remarkably enhanced by V-pit structure in their efficiency, especially in the “yellow gap” region, and it achieves significant improvement in data rate compared with earlier research. This work investigates the V-pit structure and tries to provide insight by introducing a new equivalent circuit model, which provides an explanation of the simulation and experiment results. In the final test using a laboratory communication system, the data rates of eight channels from short to long wavelength are 3.91 Gb/s, 3.77 Gb/s, 3.67 Gb/s, 4.40 Gb/s, 3.78 Gb/s, 3.18 Gb/s, 4.31 Gb/s, and 4.35 Gb/s (31.38 Gb/s in total), with advanced digital signal processing (DSP) techniques including digital equalization technique and bit-power loading discrete multitone (DMT) modulation format.

OBJECTIVES The magnesium depletion score (MDS) is considered more reliable than traditional approaches for predicting magnesium deficiency in humans. We explored the associations of MDS and dietary magnesium intake with diabetes. METHODS We obtained data from 18,853 participants in the National Health and Nutrition Examination Survey 2011-2018. Using multivariate regression and stratified analysis, we investigated the relationships of both MDS and magnesium intake with diabetes. To compute prevalence ratios (PRs), we employed modified Poisson or log-binomial regression. We characterized the non-linear association between magnesium intake and diabetes using restricted cubic spline analysis. RESULTS Participants with MDS ≥2 exhibited a PR of 1.26 (95% confidence interval [CI], 1.19 to 1.34) for diabetes. Per-standard deviation (SD) increase in dietary magnesium intake was associated with a lower prevalence of diabetes (PR, 0.91; 95% CI, 0.87 to 0.96). Subgroup analyses revealed a positive association between MDS ≥2 and diabetes across all levels of dietary magnesium intake, including the lowest (PR, 1.35; 95% CI, 1.18 to 1.55), middle (PR, 1.23; 95% CI, 1.12 to 1.35), and highest tertiles (PR, 1.25; 95% CI, 1.13 to 1.37; pinteraction

Signals are susceptible to nonlinear effects in the process of underwater visible light communication (UVLC) channel transmission.In order to improve the communication performance of the system, it is very important to recover the received signals.A bidirectional gated recurrent unit model based on Bayesian optimization algorithm (BO-BiGRU) was proposed as post equalizer in UVLC system, which could automatically find the optimal hyperparameters.BO-BiGRU model was applied to the 1.2 m underwater experimental platform.64 quadrature amplitude modulation (QAM)-carrierless amplitude-and-phase modulation (CAPM) was adopted, and 3.10 Gbit/s rate of data transfer was achieved under the condition that the bit error ratio (BER) of the system was less than 3.8×10-37% forward error correction (FEC), which is 128 Mbit/s higher than the traditional post equalization method.

Extreme multi-label text classification (XMTC) is an emerging and essential task in natural language processing. Its objective is to retrieve the most relevant labels for a text from a large set of labels while balancing time and accuracy. Although large-scale pre-trained models have brought new perspectives to this task, more attention should be given to valuable fine-tuned methods and the significant semantic gap between texts and labels. In this paper, we propose a novel guide network (GUDN) with a label reinforcement strategy based on label semantics to help fine-tune pre-trained models for classification. Experimental results demonstrate that GUDN outperforms state-of-the-art methods on Eurlex-4k and achieves competitive results on other popular datasets. In addition, we find that meaningless tokens can harm the Transformer-based model’s classification accuracy in another experiment. We conclude that GUDN is effective in the presence of solid semantics. Our source code is available at https://t.hk.uy/aFSH.

Mobile visible light communication (VLC) is key for integrating lighting and communication applications in the 6G era, yet there exists a notable gap in experimental research on mobile VLC. In this study, we introduce a mobile VLC system and investigate the impact of mobility speed on communication performance. Leveraging a laser-based light transmitter with a wide coverage, we enable a light fidelity (LiFi) system with a mobile receiving end. The system is capable of supporting distances from 1 m to 4 m without a lens and could maintain a transmission rate of 500 Mbps. The transmission is stable at distances of 1 m and 2 m, but an increase in distance and speed introduces interference to the system, leading to a rise in the Bit Error Rate (BER). The mobile VLC experimental system provides a viable solution to the issue of mobile access in the integration of lighting and communication applications, establishing a solid practical foundation for future research.

An equalizer based on a recurrent neural network (RNN), especially with a bidirectional gated recurrent unit (biGRU) structure, is a good choice to deal with nonlinear damage and inter-symbol interference (ISI) in optical communication systems because of its excellent performance in processing time series information. However, its recursive structure prevents the parallelization of the computation, resulting in a low equalization rate. In order to improve the speed without compromising the equalization performance, we propose a minimalist 1D convolutional neural network (CNN) equalizer, which is reconverted from a biGRU with knowledge distillation (KD). In this work, we applied KD to regression problems and explain how KD helps students learn from teachers in solving regression problems. In addition, we compared the biGRU, 1D-CNN after KD and 1D-CNN without KD in terms of Q-factor and equalization velocity. The experimental data showed that the Q-factor of the 1D-CNN increased by 1 dB after KD learning from the biGRU, and KD increased the RoP sensitivity of the 1D-CNN by 0.89 dB with the HD-FEC threshold of 1 × 10−3. At the same time, compared with the biGRU, the proposed 1D-CNN equalizer reduced the computational time consumption by 97% and the number of trainable parameters by 99.3%, with only a 0.5 dB Q-factor penalty. The results demonstrate that the proposed minimalist 1D-CNN equalizer holds significant promise for future practical deployments in optical wireless communication systems.

We propose and demonstrate a hybrid communication architecture that combines millimeter-wave (MMW) in the radio frequency (RF) domain and free-space-optics (FSO) technologies using adaptive combining and hybrid automatic repeat request (HARQ) techniques. At the receiving end, we employed joint signal processing with an adaptive diversity combining technique (ADCT) based on a maximum ratio combining (MRC) algorithm. We derived closed-form expressions for the outage probability and throughput of the hybrid RF and FSO (RF/FSO) system, considering various characteristics of atmospheric turbulence in the FSO link. Experimental testing with 10-Gbaud quadrature phase shift keying (QPSK) data was conducted under different simulated atmospheric turbulence intensities, FSO and MMW speed-ratios, and forward error correction (FEC) overheads. Additionally, we validated improvements in terms of bit error ratio (BER), outage probability, and throughput performance.

Cattleyak, the interspecies hybrid from cattle (males) and yak (females), displays a faster growth rate and higher feeding efficiency than yak with usually one-year shorter raising cycle. This study aimed to assess the carcass characteristics and meat quality attributes of cattleyak by compared to yak at a similar bodyweight. Results showed that cattleyak exhibited an elevated dressing percentage (58.33%) and a significantly higher content of healthy fatty acids, including eicosapentaenoic acid (5.64%) and docosahexaenoic acid (0.46%) (P < 0.05). Compared to yak meat, the tenderness of cattleyak meat was significantly enhanced (P < 0.05), with the lower Warner-Bratzler shear force (59.53 N) and hardness value (580.92 g), as well as the significant reduction of more than 20% of muscle fiber diameter (P < 0.05). Additionally, myofibrillar proteins in cattleyak, including desmin, tropomyosin, and troponin-T, degraded faster than those in yak (P < 0.05), indicating their role in tenderization improvement in cattleyak. These findings suggest that, in comparison to yaks of the similar bodyweight, cattleyaks with less than one year of the raising cycle have improved meat quality and economic efficiency.

Since visible-light communication (VLC) has become an increasingly promising candidate for 6G, the field of underwater visible-light communication (UVLC) has also garnered significant attention. However, the impairments introduced by practical systems and the time-varying underwater channels always limit the performance of underwater visible-light communication. In this paper, we propose and experimentally demonstrate an autoencoder-based geometric shaping model (AEGSM) framework to jointly optimize quadrature amplitude modulation (QAM) signals at the symbol-wise and bit-wise levels for underwater visible-light communication. Unlike traditional geometric shaping (GS) methods, which only give theoretically optimal shaping solutions, our framework can always obtain the globally optimal shaping scheme for a specific channel condition or different application scenarios. In our AEGSM framework, an autoencoder is used to find the optimal shaping scheme at the symbol-wise level and a revised pairwise optimization (RPO) algorithm is applied to achieve bit-wise optimization. In a real UVLC system, 2.05 Gbps transmission is achieved under the hard decision–forward error correction (HD-FEC) threshold of 3.8 × 10−3 by employing the autoencoder-based 8QAM (AE-8QAM) optimized by the AEGSM, which is 103 Mbps faster than the Norm-8QAM. The AE-8QAM also shows its resistance to nonlinearity and enables the UVLC system to operate within a larger dynamic range of driving voltages. The results substantiate the potential and practicality of the proposed AEGSM framework in the realm of underwater visible-light communication.

As 6G research progresses, both visible light communication (VLC) and artificial intelligence (AI) become important components, which makes them appear to converge. Neural networks (NN) as equalizers are gradually occupying an increasingly important position in the research of the physical layer of VLC, especially in nonlinear compensation. In this paper, we will propose three categories of neural network equalizers, including input data reconfiguration NN, network reconfiguration NN and loss function reconfiguration NN. We give the definitions of these three neural networks and their applications in VLC systems. This work allows the reader to have a clearer understanding and future trends of neural networks in visible light communication, especially in terms of equalizers.

The introduction of visible light communication (VLC) technology could increase the capacity of existing wireless communication systems towards 6G networks. In practice, VLC can make good use of lighting system infrastructures to transmit data using light fidelity (Li-Fi). The use of semiconductor light sources, including light-emitting diodes (LEDs) and laser diodes (LDs) are essential to VLC technology because these devices are energy-efficient and have long lifespans. To achieve high-speed VLC links, various technologies have been utilized, including injection locking. Optical injection locking (OIL) is an optical frequency and phase synchronization technique that has been implemented in semiconductor laser systems for performance enhancement. High-performance optoelectronic devices with narrow linewidth, wide tunable emission, large modulation bandwidth and high data transmission rates are desired for advanced VLC. Thus, the features of OIL could be promising for building high-performance VLC systems. In this paper, we present a comprehensive review of the implementation of the injection-locking technique in optical communication systems. The enhancement of characteristics through OIL is elucidated. The applications of OIL in VLC systems are discussed. The prospects of OIL for future VLC systems are evaluated.

Optimal control of aluminum electrolysis production process (AEPP) has long been a challenging industrial issue due to its inherent difficulty in establishing an accurate dynamic model. In this paper, a novel robust optimal control algorithm based on adaptive dynamic programming (ADP) is proposed for the AEPP, where the system subjects to input constraints. First, to establish an accurate dynamic model for the AEPP system, recursive neural network (RNN) is employed to reconstruct the system dynamic using the input-output production data. To ensure input constraints are not to exceed the bound of the actuator, the optimal control problem of the AEPP is formulated under a new nonquadratic form performance index function. Then, considering the perturbation of the AEPP, the robust control problem is effectively converted to the constrained optimal control problem via system transformation. Furthermore, a single critic network framework is developed to obtain the approximate solution of the Hamilton-Jacobi-Bellman (HJB) equation. Finally, the proposed ADP controller is applied to the AEPP system to validate the effectiveness and performance.

Post-equalization using neural network (NN) is a promising technique that models and offsets the nonlinear distortion in visible light communication (VLC) channels, which is recognized as an essential component in the incoming 6G era. NN post-equalizer is good at modeling complex channel effects without previously knowing the law of physics during the transmission. However, the trained NN might be weak in generalization, and thus consumes considerable computation in retraining new models for different channel conditions. In this paper, we studied transfer learning strategy, growing DNN models from a well-trained ‘stem model’ instead of exhaustively training multiple models from randomly initialized states. It extracts the main feature of the channel first whose signal power balances the signal-to-noise ratio and the nonlinearity, and later focuses on the detailed difference in other channel conditions. Compared with the exhaustive training strategy, stem-originated DNN models achieve 64% of the working range with five times the training efficiency at most or more than 95% of the working range with 150% higher efficiency. This finding is beneficial to improving the feasibility of DNN application in real-world UVLC systems.

Nowadays, underwater visible light communication (UVLC) has become one of the key technologies for high-speed underwater wireless communication. Because of the limited modulation bandwidth and nonlinearity of the optoelectronic devices in the UVLC system, the combination of inter-symbol interference and nonlinear impairment will inevitably degrade the transmission performance. Advanced digital signal processing methods including equalization and decoding are required. In the past few years, Support vector machine (SVM) has been widely investigated in quadrature amplitude modulation (QAM) for soft decision in the decoding process. However, previous works only consider 2-dimensional (2-D) separate symbol, ignoring the correlation between consecutive symbols. In this paper, we propose to use SVM for soft decision with a 4-dimensional (4-D) constellation by concatenating two consecutive symbols. To deal with the increasing computational complexity in the SVM training phase, bit-based binary SVM multi-class strategy and an edge-detection-based data pre-processing method are employed. In this paper, we demonstrate a carrierless amplitude and phase (CAP) 16-QAM UVLC system. Experimental results indicate that the performance is greatly improved when using consecutive-symbol-expanded 4-D constellation with SVM for soft decision.

Visible light communication (VLC) is a highly promising complement to conventional wireless communication for local-area networking in future 6G. However, the extra electro-optical and photoelectric conversions in VLC systems usually introduce exceeding complexity to communication channels, in particular severe nonlinearities. Artificial intelligence (AI) techniques are investigated to overcome the unique challenges in VLC, whereas considerable obstacles are found in practical VLC systems applied with intelligent learning approaches. In this paper, we present a comprehensive study of the intelligent physical and network layer technologies for AI-empowered intelligent VLC (IVLC). We first depict a full model of the visible light channel and discuss its main challenges. The advantages and disadvantages of machine learning in VLC are discussed and analyzed by simulation. We then present a detailed overview of advances in intelligent physical layers, including optimal coding, channel emulator, MIMO, channel equalization, and optimal decision. Finally, we envision the prospects of IVLC in both the intelligent physical and network layers. This article lays out a roadmap for developing machine learning-based intelligent visible light communication in 6G.

In this paper, a low-density parity check (LDPC) coded GFDM with high-level modulation scheme is proposed for more flexible and advanced multicarrier modulation in intensity-modulated/direct-detection (IM/DD) optical systems. Then, we experimentally demonstrate an LDPC-coded GFDM with 16-quadrature amplitude modulation (16QAM) system transmitted over 20-km standard single-mode fiber at different symbol rates to verify the applicability of the proposed scheme. Furthermore, the proposed scheme with trellis-coded 32-quadrature amplitude modulation (TC-32QAM) has also been investigated and compared with the bit error rate (BER) performance with 16QAM-modulated condition. The results indicate that TC-32QAM-modulated GFDM method has a better performance than the 16QAM-modulated case. But with LDPC coding module added, the performance of the proposed system with TC-32QAM gets worse than that with 16QAM modulation. We also experimentally compare the proposed LDPC-coded scheme and the Turbo-coded scheme in the QAM-modulated GFDM IM/DD optical system. The results show the LDPC-coded optical (LCO-) GFDM scheme has lower power penalties and better BER performance. Moreover, the proposed LCO-GFDM scheme is applied in multiband transmission by optical simulation software, and the results show that multiband LCO-GFDM scheme has a better performance than that of LCO-OFDM scheme.

Fault diagnosis of pumping unit system has long been a challenging issue owing to the system that exhibits nonlinearity, coupled parameters and time-varying in the production process. In this paper, a novel fault diagnosis method based on dynamic axis nucleation kernel partial least squares (DANKPLS) is proposed for pumping unit. First, the multiple dichotomous regression (MDR) model is established to reflect the hidden dynamic relationship between variables efficiently. Then, a novel axis nucleation kernel partial least squares method is proposed to map the principal axis into a high-dimensional space. In particular, the correlation of the data can be further and clearly represented. Finally, the proposed method is applied to the pumping unit system. Experimental results show the effectiveness and favorable diagnosis rate in false alarm and missing alarm.

Desulfurization control of natural gas has long been a challenging industrial issue owing to its inherent difficulty in establishing accurate mathematical model for the nonlinear and strong coupling process. In this paper, a data-based adaptive dynamic programming (ADP) algorithm is presented to solve optimal control for natural gas desulfurization. First, neural network (NN) is used to reconstruct the dynamics of the desulfurization system via the input and output production data. Then, an improved unscented Kalman filter (IUKF) aided ADP method is presented to solve optimal control problem for desulfurization system, where IUKF algorithm is developed as a new weight-updating strategy for the action network and the critic network. The IUKF aided algorithm can improve the convergence speed as well as the anti-interference ability of the ADP controller. Furthermore, the proposed IUKF-ADP algorithm is implemented using the heuristic dynamic programming (HDP) structure. Finally, the effectiveness of the proposed IUKF-ADP algorithm is demonstrated through experiments of the natural gas desulfurization system.

Abstract Background Gray leaf spot (GLS), which is caused by the necrotrophic fungi Cercospora zeae-maydis and Cercospora zeina, is one of the most impactful diseases in maize worldwide. The aim of the present study is to identify the resistance genes and understand the molecular mechanisms for GLS resistance. Results Two cultivars, ‘Yayu889’ and ‘Zhenghong532,’ which are distinguished as resistant and susceptible cultivars, respectively, were challenged with the GLS disease and a RNA-seq experiment was conducted on infected plants at 81, 89, 91, and 93 days post planting (dap). Compared with the beginning stage at 81 dap, 4666, 1733, and 1166 differentially expressed genes (DEGs) were identified at 89, 91, and 93 dap, respectively, in ‘Yayu889,’ while relatively fewer, i.e., 4713, 881, and 722 DEGs, were identified in ‘Zhenghong532.’ Multiple pathways involved in the response of maize to GLS, including ‘response to salicylic acid,’ ‘protein phosphorylation,’ ‘oxidation-reduction process,’ and ‘carotenoid biosynthetic process,’ were enriched by combining differential expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA). The expression of 12 candidate resistance proteins in these pathways were quantified by the multiple reaction monitoring (MRM) method. This approach identified two candidate resistance proteins, a calmodulin-like protein and a leucine-rich repeat receptor-like protein kinase with SNPs that were located in QTL regions for GLS resistance. Metabolic analysis showed that, compared with ‘Zhenghong532,’ the amount of salicylic acid (SA) and total carotenoids in ‘Yayu889’ increased, while peroxidase activity decreased during the early infection stages, suggesting that increased levels of SA, carotenoids, and reactive oxygen species (ROS) may enhance the defense response of ‘Yayu889’ to GLS. Conclusion By combining transcriptome and proteome analyses with comparisons of resistance QTL regions, calmodulin-like protein and leucine-rich repeat receptor-like protein kinase were identified as candidate GLS resistance proteins. Moreover, we found that the metabolic pathways for ROS, SA, and carotenoids are especially active in the resistant cultivar. These findings could lead to a better understanding of the GLS resistance mechanisms and facilitate the breeding of GLS-resistant maize cultivars.

Visible light communication (VLC) is a promising technology for next-generation high-speed optical wireless data links. Among various transmitters, GaN-based superluminescent diodes (SLDs) show interesting characteristics, including a large modulation bandwidth, droop free and low speckle noise, which makes them attractive for VLC applications. In this work, we design and fabricate a blue-emitting SLD utilizing tilted facet configuration. Using SLD as the light source, a VLC system is experimentally demonstrated. A record data rate of 4.57 gigabit per second (Gbps) is achieved with adaptive bit-loading discrete multiple tone (DMT) modulation, while the highest modulation format reaches 256 quadrature amplitude modulation (QAM). The corresponding bit error rate (BER) is ~3.5 × 10−3, which is below the forward error correction (FEC) threshold of 3.8 × 10−3.

Visible light communication (VLC) has become an alternative candidate in next-generation indoor wireless local area network. However, it is a great challenge for a receiver to obtain the signals from multiple LED sources that transmit different information. In this paper, we explore the BER degradation due to the overlapping of multiple LED sources in the visible light communication network. We experimentally demonstrate a Multiple Input Signal Output (MISO) VLC system utilizing space–time block coding (STBC) to overcome the signal interference due to multiple inputs. A throughput of 1.6 Gbit/s is successfully achieved, which reveals great improvement in the robustness and compatibility of LED based network system.

In this paper, for the first time, we propose the use of a hybrid post equalizer in a high-order carrierless-amplitude-and-phase-modulation-based visible light communication (VLC) system. The hybrid equalizer consists of a linear equalizer, a Volterra-series-based nonlinear equalizer, and a decision-directed least mean squares equalizer to simultaneously mitigate the linear and nonlinear distortions of the VLC system. A commercially available red-blue-green-yellow light-emitting diode (RBGY LED) is utilized for four-wavelength multiplexing. By the hybrid equalizer, an aggregate data rate of 8 Gb/s is experimentally achieved over a 1-m indoor free-space transmission with the bit error rate (BER) below the 7% forward error correction (FEC) limit of 3.8 × 10-3. To the best of our knowledge, this is the highest data rate ever reported in high-speed VLC systems.

In this paper, we present a high-speed visible light communication (VLC) system based on a single commercially available phosphorescent white light-emitting diode (LED). In this system, a preequalization circuit is used to extend the modulation bandwidth, and a differential output receiver is utilized to reduce the system noise. With adaptive bit and power allocation and orthogonal frequency-division multiplexing (OFDM), we experimentally demonstrated a 2.0-Gb/s visible light link over 1.5-m free-space transmission, and the BER is under a preforward error correction limit of 3.8×10-3. To the best of our knowledge, this is the highest white-light VLC data rate using a single phosphorescent white LED.

The light-emitting diode nonlinearity in visible light communication (VLC) systems is considered to be a major problem that deteriorates system performance. In this paper, we experimentally demonstrate a high-speed WDM CAP64 VLC system employing a Volterra series-based nonlinear equalizer to mitigate the nonlinear effect. A modified cascaded multimodulus algorithm (M-CMMA) is utilized to calculate the error function and update the weights of the nonlinear equalizer without using training symbols. An aggregate data rate of 4.5 Gb/s is successfully achieved over 2-m indoor free-space transmission with a bit error rate (BER) below the 7% forward error correction limit of 3.8 × 10-3. With the Volterra nonlinear equalizer, the Q factor of the VLC system is 1. 6 dB better than that without using the nonlinear equalizer, and the transmission distance is also increased by about 110 cm at the BER of 3.8 × 10-3. To the best of our knowledge, this is the first time that the Volterra nonlinear equalizer is utilized for high-speed carrierless amplitude and phase (CAP) modulation-based VLC systems.

Exploiting the increasingly wide use of light emitting diodes (LEDs) lighting, in this paper we propose a reversed indoor positioning system (IPS) based on LED visible light communication (VLC) in order to improve indoor positioning accuracy. Unlike other VLC positioning systems, we employ two annular receivers with multi-photodiodes installed on the ceiling to locate the persons who carry LEDs. The basic idea is using multi-photodiodes to calculate the angle while using the received signal strength (RSS) method to calculate the distance. The experiment results show that the effective positioning range of the proposed system is 1.8 m when the distance between two receivers is 1.2 m. Moreover, a positioning error less than 0.2 m can be achieved under the condition that the radius of the PIN circle is between 0.16 m and 0.2 m, and the distance of the transmitter-receiver plane is less than 1.8 m, which will be effective in practice.