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Abstract Codeword position index based sparse code multiple access (CPI‐SCMA) is an effective scheme that expands codeword positions in the time domain and carries extra information by their index. This paper proposes a dual‐mode CPI‐SCMA (DCPI‐SCMA) scheme to improve the error propagation caused by the mismatch between SCMA codewords and information bits. Furthermore, a DCPI‐SCMA with transmit diversity scheme is proposed where index bits are repeatedly transmitted to obtain a transmit diversity gain; hence, a higher reliability can be achieved with a slight loss of spectral efficiency. An optimal approach for the codebook index pattern is derived where the decision of index bits is involved in the demodulation of SCMA. It can be seen from simulation results and system analysis that the proposed schemes achieve better bit error rate performance with a decrease in complexity and obtain better robustness and flexibility compared with the existing CPI‐based SCMA schemes under the same spectral efficiency.

Electrochemical biosensors are characterized by rapid response, miniaturization, portability, and ease of operation. With tunable nanostructure, DNA has been comprehensively combined with electrochemical devices to design high-sensitivity and selectivity biosensors for the achievement of disease diagnosis, food safety, and environmental monitoring. In recent years, DNA-functionalized electrochemical biosensors have made significant research progress. In this article, the recent research progress of DNA-functionalized electrochemical biosensors for in vitro and in vivo disease diagnosis was reviewed. The structure and sensing principles of DNA-functionalized electrochemical biosensors were first summarized. The preparation of DNA-functionalized electrochemical biosensors based on nanomaterials was introduced in detail. Meanwhile, the latest evolution of integrated and portable DNA-functionalized electrochemical biosensors for in vitro disease diagnosis was summarized. For a further step, the construction of implantable DNA-functionalized electrochemical biosensors for in vivo and real-time disease monitoring was overviewed. Finally, the challenges and outlook of DNA-functionalized electrochemical biosensors were discussed to provide a guideline for the future development of DNA-functionalized electrochemical biosensors.

This paper focuses on a class of generalized nonlinear wave equations with doubly dispersive over equation whole lines. By employing the potential well theory, we classify the initial profile such that the solution blows up or globally exists.

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.

Aiming at the problem that in the hybrid visible light and RF network, such as the traditional user switching scheme leveraging received signal strength and ignoring the user switching error rate, a location-based user switching scheme was proposed.Specifically, an integrated communication and positioning system built upon low-cost IoT chip ESP32 was designed, which could fuse both visible light communication downlink and Wi-Fi uplink signals for accurate user location.A communication and positioning integration framework utilizing time-frequency multiplexing was designed, which used a neural network to estimate the user’s position, and the user completed the switching based on the estimated position.In terms of communication, the throughput of the system can reach up to 35 kbit/s.In terms of positioning, the positioning accuracy of the system can reach 2.8 cm, making a 33% improvement over traditional system.Compared with the switching scheme relying on received signal strength, user switching error rate of the proposed scheme is reduced by 4%.

With the help of traffic lights and street cameras, optical camera communication (OCC) can be adopted in Internet of Vehicles (IoV) applications to realize communication between vehicles and roadside units. However, the encoded light emitted by these OCC transmitters (LED infrastructures on the roadside and/or LED-based headlamps embedded in cars) will generate stripe patterns in image frames captured by existing license-plate recognition systems, which seriously degrades the accuracy of the recognition. To this end, we propose and experimentally demonstrate a method that can reduce the interference of OCC stripes in the image frames captured by the license-plate recognition system. We introduce an innovative pipeline with an end-to-end image reconstruction module. This module learns the distribution of images without OCC stripes and provides high-quality license-plate images for recognition in OCC conditions. In order to solve the problem of insufficient data, we model the OCC strips as multiplicative noise and propose a method to synthesize a pairwise dataset under OCC using the existing license-plate dataset. Moreover, we also build a prototype to simulate real scenes of the OCC-based vehicle networks and collect data in such scenes. Overall, the proposed method can achieve a recognition performance of 81.58% and 79.35% on the synthesized dataset and that captured from real scenes, respectively, which is improved by about 31.18% and 24.26%, respectively, compared with the conventional method.

Abstract The immune system is closely associated with the pathogenesis of polycystic ovary syndrome (PCOS). Macrophages are one of the important immune cell types in the ovarian proinflammatory microenvironment, and ameliorate the inflammatory status mainly through M2 phenotype polarization during PCOS. Current therapeutic approaches lack efficacy and immunomodulatory capacity, and a new therapeutic method is needed to prevent inflammation and alleviate PCOS. Here, octahedral nanoceria nanoparticles with powerful antioxidative ability were bonded to the anti-inflammatory drug resveratrol (CeO2@RSV), which demonstrates a crucial strategy that involves anti-inflammatory and antioxidative efficacy, thereby facilitating the proliferation of granulosa cells during PCOS. Notably, our nanoparticles were demonstrated to possess potent therapeutic efficacy via anti-inflammatory activities and effectively alleviated endocrine dysfunction, inflammation and ovarian injury in a dehydroepiandrosterone (DHEA)-induced PCOS mouse model. Collectively, this study revealed the tremendous potential of the newly developed nanoparticles in ameliorating the proinflammatory microenvironment and promoting the function of granulosa cells, representing the first attempt to treat PCOS by using CeO2@RSV nanoparticles and providing new insights in combating clinical PCOS.

Abstract Microbial communication can drive coordinated functions through sensing, analyzing and processing signal information, playing critical roles in biomanufacturing and life evolution. However, it is still a great challenge to develop effective methods to construct a microbial communication system with coordinated behaviors. Here, we report an electron transfer triggered redox communication network consisting of three building blocks including signal router, optical verifier and bio-actuator for microbial metabolism regulation and coordination. In the redox communication network, the Fe3+/Fe2+ redox signal can be dynamically and reversibly transduced, channeling electrons directly and specifically into bio-actuator cells through iron oxidation pathway. The redox communication network drives gene expression of electron transfer proteins and simultaneously facilitates the critical reducing power regeneration in the bio-actuator, thus enabling regulation of microbial metabolism. In this way, the redox communication system efficiently promotes the biomanufacturing yield and CO2 fixation rate of bio-actuator. Furthermore, the results demonstrate that this redox communication strategy is applicable both in co-culture and microbial consortia. The proposed electron transfer triggered redox communication strategy in this work could provide an approach for reducing power regeneration and metabolic optimization and could offer insights into improving biomanufacturing efficiency.

IntroductionPaphiopedilum barbigerum is currently the rarest and most endangered species of orchids in China and has significant ornamental value. The mature seeds of P. barbigerum are difficult to germinate owing to the absence of an endosperm and are highly dependent on mycorrhizal fungi for germination and subsequent development. However, little is known about the regulation mechanisms of symbiosis and symbiotic germination of P. barbigerum seeds.MethodsHerein, transcriptomics and proteomics were used to explore the changes in the P. barbigerum seeds after inoculation with (FQXY019 treatment group) or without (control group) Epulorhiza sp. FQXY019 at 90 days after germination.ResultsTranscriptome sequencing revealed that a total of 10,961 differentially expressed genes (DEGs; 2,599 upregulated and 8,402 downregulated) were identified in the control and FQXY019 treatment groups. These DEGs were mainly involved in carbohydrate, fatty acid, and amino acid metabolism. Furthermore, the expression levels of candidate DEGs related to nodulin, Ca2+ signaling, and plant lectins were significantly affected in P. barbigerum in the FQXY019 treatment groups. Subsequently, tandem mass tag-based quantitative proteomics was performed to recognize the differentially expressed proteins (DEPs), and a total of 537 DEPs (220 upregulated and 317 downregulated) were identified that were enriched in processes including photosynthesis, photosynthesis-antenna proteins, and fatty acid biosynthesis and metabolism.DiscussionThis study provides novel insight on the mechanisms underlying the in vitro seed germination and protocorm development of P. barbigerum by using a compatible fungal symbiont and will benefit the reintroduction and mycorrhizal symbiotic germination of endangered orchids.

Human activity recognition widely exists in applications such as sports management and activity classification.The current human activity recognition applications are mainly divided into three types: camera-based, wearable device-based, and Wi-Fi awareness-based.Among them, the camera-based human activity recognition application has the risk of privacy leakage, and the wearable device-based human activity recognition application has problems such as short battery life and poor accuracy.Human activity recognition based on Wi-Fi sensing generally uses Wi-Fi network cards or software-defined radio devices to identify the rules of channel state information changes, so as to infer user activity.It does not have the problems of privacy leakage and short battery life.But Wi-Fi network cards need to rely on computers and software-defined radio platforms are expensive, which greatly limit the application scenarios of Wi-Fi sensing.Aiming at the above problems, a human activity recognition system based on the low-cost IoT chip ESP32 was proposed.Specifically, the Hampel filter and Gaussian filter were used to preprocess the channel state information obtained by ESP32.Then, the principal component analysis and discrete wavelet transform were utilized to reduce the dimension of the data.Finally, the K-nearest neighbor (KNN) algorithm was applied to classify data.The experimental results show that the system can achieve a recognition accuracy which close to the current mainstream Wi-Fi perception system (Intel 5300 network card) when only two ESP32 nodes are deployed, and the average accuracy rate for the six activities is 98.6%.

Joint localization and synchronization (JLAS) is a technology that simultaneously determines the spatial locations of user nodes and synchronizes the clocks between user nodes (UNs) and anchor nodes (ANs). This technology is crucial for various applications in wireless sensor networks. Existing solutions for JLAS are either computationally demanding or not resilient to noise. This paper addresses the challenge of localizing and synchronizing a mobile user node in broadcast-based JLAS systems using sequential one-way time-of-arrival (TOA) measurements. The AN position uncertainty is considered along with clock offset and skew. Two redundant variables that couple the unknowns are introduced to pseudo-linearize the measurement equation. In projecting the equation to the nullspace spanned by the coefficients of the redundant variables, the affection of them can be eliminated. While the closed-form projection solution provides an initial point for iteration, it is suboptimal and may not achieve the Cramér-Rao lower bound (CRLB) when noise or AN position error is relatively large. To improve performance, we propose a novel robust iterative solution (RIS) formulated through factor graphs and developed via message passing. The RIS outperforms the common Gauss–Newton iteration, especially in high-noise scenarios. It exhibits a lower root mean-square error (RMSE) and a higher probability of converging to the optimal solution, while maintaining manageable computational complexity. Both analytical results and numerical simulations validate the superiority of the proposed solution in terms of performance, resilience, and computational load.

Grain sorghum is an exceptional source of dietary nutrition with outstanding economic values. Breeding of grain sorghum can be slowed down by the occurrence of genotype × environment interactions (GEI) causing biased estimation of yield performance in multi-environments and therefore complicates direct phenotypic selection of superior genotypes. Multi-environment trials by randomized complete block design with three replications were performed on 13 newly developed grain sorghum varieties at seven test locations across China for two years. Additive main effects and multiplicative interaction (AMMI) and genotype + genotype × environment (GGE) biplot models were adopted to uncover GEI patterns and effectively identify high-yielding genotypes with stable performance across environments. Yield (YLD), plant height (PH), days to maturity (DTM), thousand seed weight (TSW), and panicle length (PL) were measured. Statistical analysis showed that target traits were influenced by significant GEI effects (p < 0.001), that broad-sense heritability estimates for these traits varied from 0.40 to 0.94 within the medium to high range, that AMMI and GGE biplot models captured more than 66.3% of total variance suggesting sufficient applicability of both analytic models, and that two genotypes, G3 (Liaoza No.52) and G10 (Jinza 110), were identified as the superior varieties while one genotype, G11 (Jinza 111), was the locally adapted variety. G3 was the most stable variety with highest yielding potential and G10 was second to G3 in average yield and stability whereas G11 had best adaptation only in one test location. We recommend G3 and G10 for the production in Shenyang, Chaoyang, Jinzhou, Jinzhong, Yulin, and Pingliang, while G11 for Yili.

Abstract In real life, either the subjective factors of speakers or the objective environment degrades the performance of automatic speech recognition (ASR). This study focuses on one of the subjective factors, accented speech, and attempts to build a multi‐accent ASR system to solve the degradation caused by different accents, one of whose characteristic is the low resource. To deal with the challenge of the low‐resource data and the different speech styles, a wav2vec‐MoE (mixture of experts) is proposed to adapt the wav2vec 2.0 for multi‐accent ASR. In the wav2vec‐MoE, a domain MoE is developed by introducing pseudo‐domain information in the pre‐training stage, where the domain denotes a collection of speech varied by the same influence factors. The MoE is trained with two strategies according to the proposed domain mismatch assessment between unlabeled speech and target speech, without requiring any explicit domain information. Experiments show that the wav2vec‐MoE achieves a 14.69% relative word error rate reduction (WERR) on the AESRC2020 accent dataset and an 8.79% relative WERR on the Common Voice English dataset.

Abstract Nano‐biosensors that are composed of recognition molecules and nanomaterials have been extensively utilized in disease diagnosis, health management, and environmental monitoring. As a type of nano‐biosensors, molecular specificity field‐effect transistor (FET) biosensors with signal amplification capability exhibit prominent advantages including fast response speed, ease of miniaturization, and integration, promising their high sensitivity for molecules detection and identification. With intrinsic characteristics of high stability and structural tunability, aptamer has become one of the most commonly applied biological recognition units in the FET sensing fields. This review summarizes the recent progress of FET biosensors based on aptamer functionalized nanomaterials in medical diagnosis and environmental monitoring. The structure, sensing principles, preparation methods, and functionalization strategies of aptamer modified FET biosensors were comprehensively summarized. The relationship between structure and sensing performance of FET biosensors was reviewed. Furthermore, the challenges and future perspectives of FET biosensors were also discussed, so as to provide support for the future development of efficient healthcare management and environmental monitoring devices.

Background: Hepatocellular carcinoma (HCC) is one of the most common aggressive malignancies with increasing incidence worldwide. The oncogenic roles of transcription factors (TFs) were increasingly recognized in various cancers. This study aimed to develop a predicting signature based on TFs for the prognosis and treatment of HCC.Methods: Differentially expressed TFs were screened from data in the TCGA-LIHC and ICGC-LIRI-JP cohorts. Univariate and multivariate Cox regression analyses were applied to establish a TF-based prognostic signature. The receiver operating characteristic (ROC) curve was used to assess the predictive efficacy of the signature. Subsequently, correlations of the risk model with clinical features and treatment response in HCC were also analyzed. The TF target genes underwent Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, followed by protein-protein-interaction (PPI) analysis.Results: A total of 25 differentially expressed TFs were screened, 16 of which were related to the prognosis of HCC in the TCGA-LIHC cohort. A 2-TF risk signature, comprising high mobility group AT-hook protein 1 (HMGA1) and MAF BZIP transcription factor G (MAFG), was constructed and validated to negatively related to the overall survival (OS) of HCC. The ROC curve showed good predictive efficiencies of the risk score regarding 1-year, 2-year and 3-year OS (mostly AUC >0.60). Additionally, the risk score independently predicted OS for HCC patients both in the training cohort of TCGA-LIHC dataset (HR = 2.498, p = 0.007) and in the testing cohort of ICGC-LIRI-JP dataset (HR = 5.411, p < 0.001). The risk score was also positively correlated to progressive characteristics regarding tumor grade, TNM stage and tumor invasion. Patients with a high-risk score were more resistant to transarterial chemoembolization (TACE) treatment and agents of lapatinib and erlotinib, but sensitive to chemotherapeutics. Further enrichment and PPI analyses demonstrated that the 2-TF signature distinguished tumors into 2 clusters with proliferative and metabolic features, with the hub genes belonging to the former cluster.Conclusion: Our study identified a 2-TF prognostic signature that indicated tumor heterogeneity with different clinical features and treatment preference, which help optimal therapeutic strategy and improved survival for HCC patients.

BackgroundEarly-stage amyloidosis of the heart is prone to be underdiagnosed or misdiagnosed, increasing the risk of early heart failure and even death of the patient. To ensure timely intervention for cardiac light-chain amyloidosis (AL CA), it is vital to develop an effective tool for early identification of the disease. Recently, multiparameter cardiovascular magnetic resonance (CMR) has been used as a comprehensive tool to assess myocardial tissue characterization. We aimed to investigate the difference in left ventricular (LV) strain, native T1, extracellular volume (ECV), and late gadolinium enhancement (LGE) between AL CA patients, hypertrophic cardiomyopathy patients (HCM), and healthy control subjects (HA). Moreover, we explored the value of multiparameter CMR for differential diagnosis of the early-stage AL CA patients from HCM patients, who shared similar imaging characteristics under LGE imaging.MethodsA total of 38 AL CA patients, 16 HCM patients, and 17 HA people were prospectively recruited. All subjects underwent LGE imaging, Cine images, and T1 mapping on a 3T scanner. The LV LGE pattern was recorded as none, patchy or global. LV strain, native T1, and ECV were measured semi-automatically using dedicated CMR software. According to clinical and biochemical markers, all patients were classified as Mayo stage I/II and Mayo stage IIIa/IIIb. Univariable and multivariable logistic regression models were utilized to identify independent predictors of early-stage AL CA from HCM patients. Receiver operator characteristic (ROC) curve analysis and Youden’s test were done to determine the accuracy of multiparameter CMR in diagnosing Mayo stage I/II AL CA and establish a cut-off value.ResultsFor Mayo stage I/II AL CA patients, the global longitudinal strain (GLS) absolute value (11.9 ± 3.0 vs. 9.5 ± 1.8, P < 0.001) and the global circumferential strain (GCS) absolute value (19.0 ± 3.6 vs. 9.5 ± 1.8, P < 0.001) were significantly higher than in HCM patients. The native T1 (1334.9 ± 49.9 vs. 1318.2 ± 32.4 ms, P < 0.0001) and ECV values (37.8 ± 5.7 vs. 31.3 ± 2.5%, P < 0.0001) were higher than that of HCM patients. In multiparameter CMR models, GCS (2.097, 95% CI: 1.292–3.403, P = 0.003), GLS (1.468, 95% CI: 1.078–1.998, P = 0.015), and ECV (0.727, 95% CI: 0.569–0.929, P = 0.011) were the significant variables for the discrimination of the early-stage AL CA patients from HCM patients. ROC curve analysis and Youden’s test were used on GCS, GLS, ECV, and pairwise parameters for differentiating between Mayo stage I/II AL CA and HCM patients, respectively. The combination of GLS, GCS, and ECV mapping could distinguish Mayo stage I/II AL amyloidosis patients from hypertrophic cardiomyopathy with excellent performance (AUC = 0.969, Youden index = 0.813).ConclusionIn early-stage AL CA patients with atypical LGE, who had similar imaging features as HCM patients, ECV mapping, GCS, and GLS were correlated with the clinical classification of the patients. The combination of GCS, GLS, and ECV could differentiate early-stage AL CA from HCM patients. Multiparameter CMR has the potential to provide an effective and quantitative tool for the early diagnosis of myocardial amyloidosis.

Visible light communication (VLC) has emerged as a promising technology for wireless communication due to its advantages of the vast optical spectrum, high energy efficiency, and no electromagnetic interference radiation. With the widespread adoption of LED infrastructure and camera-equipped smart devices, optical camera communication (OCC) has gained momentum as a pragmatic version of VLC based on commercial off-the-shelf (COTS) devices. Compared with VLC systems based on photodiodes (PD), the information-carrying capability of OCC enables it to provide a wide range of services in the areas of intelligent transportation, indoor positioning, underwater communication, and the Internet of Things (IoT). This paper presents a brief overview of the OCC system, focuses on the constraints affecting OCC performance, and offers feasible solutions for dependable data transmission in complex and diverse scenarios. Finally, this paper summarizes the potential extended applications of OCC, hoping to push this advanced form of optical wireless communication toward practical deployments in our daily lives.

A three-party authenticated key agreement (3PAKA) protocol allows two protocol entities to authenticate and agree on a common session key over common communication channels with the help of a trust server. Due to the better robustness and high computation efficiency, Chebyshev chaotic maps technology has been widely used in the authenticated key agreement protocol. In recent years, many researches of the 3PAKA protocol based on Chebyshev chaotic maps have been well-documented. However, cryptanalysis of previous schemes showcased that existing 3PAKA protocols commonly could not resist known session specific temporary attack and could not guarantee users anonymity. In addition, they assumed that the communication channels were absolutely secure during the registration process, which is impracticable in major applications. To overcome the existing weaknesses, we propose a security-enhanced protocol. Finally yet importantly, the security of our proposed scheme has been validated with Strand Space technology and ProVerif tool. Contrary to the existing schemes, the proposed protocol showcased improved security with lower computation and communication cost.

To support multiple users in an indoor multiple-input multiple-output visible light communication (MIMO-VLC) system adopting spatial multiplexing, orthogonal frequency division multiple access (OFDMA) is usually adopted, where the overall modulation bandwidth is shared by all the users. In this paper, by fully exploiting the spatial distributions of light-emitting diode (LED) transmitters in the ceiling and end users around the receiving plane, we propose a space division multiple access (SDMA) technique for indoor spatial multiplexing-based MIMO-VLC systems. When applying SDMA, users within the MIMO-VLC system are divided into different user groups (UGs) based on their spatial locations with respect to different LED transmitters. Specifically, each UG can use the overall modulation bandwidth of the system. For efficient implementation of SDMA, two distributed user grouping (DUG) approaches are proposed, including basic DUG and transmit diversity-enhanced DUG (TD-DUG), and a two-step resource allocation algorithm is further designed. The achievable rates of the MIMO-VLC system employing SDMA with both basic DUG and TD-DUG are derived accordingly. To verify the superiority of SDMA over conventional OFDMA, detailed analytical and simulation results are presented. Moreover, a proof-of-concept experiment is conducted to demonstrate the advantage of SDMA in a practical spatial multiplexing-based MIMO-VLC system.

As an essential requirement of pervasive smart devices, free hand gestural input considered as necessary for user interactions has attracted lots of research attention for nearly decades. Nevertheless, existing proposals heavily rely on either expensive pre-deployed equipment or user on-body sensors, thus confine their application scenarios. In this paper, we propose a novel hand gesture recognition system which purely relies on ubiquitous ambient light and low-cost photodiodes. The proposed system does not need any modification to existing lighting infrastructure. While without complex signal pre-processing for modulated light, very low-cost photodiodes and processors can capture and process the light variations caused by hand gesture. To produce accurate hand gesture recognition, we design efficient algorithms based on recurrent neural network to process sensing data collected by a photodiode array. We implement a prototype consisting of an array of 8 photodiodes and extensive experiments demonstrate that the proposed solution can achieve a very high overall recognition accuracy of 99.31%.

Optical camera communication (OCC), enabled by light-emitting diodes (LEDs) and embedded cameras on smartphones, has drawn considerable attention thanks to the pervasive adoption of LED lighting and mobile devices. However, most existing studies do not consider the performance bottleneck of Region of Interest (RoI) extraction during decoding, making it challenging to improve communication capacity further. To this end, we propose a fast grid virtual division scheme based on pixel grayscale values, which extracts RoI quickly without sacrificing computational complexity, thereby reducing the decoding delay and improving the communication capacity of OCC. Essentially, the proposed scheme uses a grid division strategy to divide the received image into blocks and randomly sample several pixels within different blocks to quickly locate the RoI with high grayscale values in the original image. By implementing the lightweight RoI extraction algorithm, we experimentally verify its effectiveness in reducing decoding latency, demonstrating its superior performance in terms of communication capacity. The experimental results clearly show that the decoding delay of the proposed scheme is 70% lower than that provided by the Gaussian blur scheme for the iPhone receiver at a transmission frequency of 5 kHz.

Visible light positioning (VLP) has attracted intensive attention from both academic and industrial communities thanks to its high accuracy, immunity to electromagnetic interference, and low deployment cost. In general, the receiver in a VLP system determines its own position by exploring the received signal strength (RSS) from the transmitter according to a pre-built RSS attenuation model. In such model-based methods, the LED’s emission power and the receiver’s height are usually required known and constant parameters to obtain reasonable positioning accuracy. However, the LED’s emission power is normally time-varying due to the fact that the LED’s optical output power is prone to changing with the LED’s temperature, and the receiver’s height is random in a realistic application scenario. To this end, we propose a height-independent three-dimensional (3D) VLP scheme based on the RSS ratio (RSSR), rather than only using RSS. Unlike existing RSS-based VLP methods, our method is able to independently find the horizontal coordinate, i.e., two-dimensional (2D) position, without a priori height information of the receiver, and also avoids the negative effect caused by fluctuation of the LED’s emission power. Moreover, we can further infer the height of the receiver to achieve three-dimensional (3D) positioning by iterating the 2D results back into positioning equations. To quickly verify the proposed scheme, we conduct theoretical analysis with mathematical proof and experimental results with real data, which confirm that the proposed scheme can achieve high position accuracy without known information of the receiver’s height and LED’s emission power. We also implement a VLP prototype with five LED transmitters, and experimental results show that the proposed scheme can achieve very low average errors of 2.73 cm in 2D and 7.20 cm in 3D.

This study investigated the effects of foxtail millet whole grain flours obtained through different processing methods on alleviating symptoms and gut microbiota dysbiosis in a dextran sulfate sodium (DSS)-induced murine colitis model. Sixty C57BL/6 mice were divided into six groups (n = 10 in each group), including one control group (CTRL) without DSS treatment and five DSS-treated groups receiving one of the following diets: AIN-93M standard diet (93MD), whole grain foxtail millet flour (FM), fermented (F-FM), germinated (G-FM), and fermented-germinated foxtail millet flour (FG-FM). A comparison of the disease activity index (DAI) demonstrated that foxtail millet whole grain-based diets could alleviate the symptoms of enteritis to varying degrees. In addition, 16S rRNA gene sequencing revealed that FG-FM almost completely alleviated DSS-induced dysbiosis. Mice on the FG-FM diet also had the lowest plasma IL-6 levels and claudin2 expression levels in the colon, indicating reduced systemic inflammation and improved gut barrier function. This study suggested that foxtail millet whole grain is an attractive choice for the intervention of IBD and gut microbiota dysbiosis, and its prebiotic properties are highly affected by the processing methods.

In this paper, a 3D indoor visible light positioning (VLP) system with fast computation time using received signal strength (RSS) is proposed and experimentally demonstrated. Assisted by the deep learning techniques, the complexity of the trilateration problem is greatly reduced, and the trilateration problem can be formulated as a linear mapping leading to faster position estimation than the conventional estimation. Moreover, a new method of off-line preparation is adopted to minimize the workload of the VLP system deployment for more practical usage. The proposition is implemented on an atto-cellular VLP unit, through which the real-time performance and positioning accuracy are demonstrated and validated in a 3D positioning experiment performed in a space of 1.2 × 1.2 × 2 m3. The experimental results show that a positioning accuracy of 11.93 cm in confidence of 90% is achieved with 50 times faster the computation time compared to the conventional scheme.

We study an initial boundary value problem for Kirchhoff-type parabolic equation with the fractional p-Laplacian. We first discuss the blow up of solutions in finite time with three initial energy levels: subcritical, critical and supercritical initial energy levels. Then we estimate an upper bound of the blowup time for low and for high initial energies.

In this paper, for the first time, we propose and experimentally demonstrate a novel pairwise coding (PWC)-based multiband carrierless amplitude and phase (mCAP) modulation with chaotic dual-mode index modulation (DM) for secure bandlimited visible light communication (VLC) systems. The combination of mCAP and DM can sustain a higher spectral efficiency (SE) compared with mCAP with conventional index modulation (IM), while PWC can be employed to efficiently mitigate the signal-to-noise ratio (SNR) imbalance caused by the low-pass frequency response of light emitting diodes (LEDs). Moreover, the DM is enhanced by a two-dimensional (2D) chaotic encryption scheme to guarantee the security of the useful information in VLC systems. Simulation and experimental results successfully verify the superiority of the proposed PWC-based mCAP-DM scheme with two-level chaotic encryption over other benchmark schemes.

This article studies the existence and nonexistence of global solutions to the initial boundary value problems for semilinear wave and heat equation, and for Cauchy problem of nonlinear Schrodinger equation. This is done under three possible initial energy levels, except the NLS as it does not have comparison principle. The most important feature in this article is a new hypothesis on the nonlinear source terms which can include at least eight important and popular power-type nonlinearities as special cases. This article also finds some kinds of divisions for the initial data to guarantee the global existence or finite time blowup of the solution of the above three problems.

As a key component of building management and security, occupancy inference through smart sensing has attracted a lot of research attention for nearly two decades. Recently, a cutting edge technique visible light sensing (VLS) that utilizes the LED luminaires as light sensors has shown its promising application potentials in occupancy inference as it piggybacks on pervasive lighting infrastructure without extra equipment deployment. Although existing inference algorithms based on the VLS data set can achieve high accuracy, the performance degrades when the occupants are moving. This paper focuses on the occupancy inference issue and presents an ensemble learning algorithm to improve the inference accuracy. We use heterogeneous learning algorithms to generate diverse learners. Consequently, we adopt forward sequential pruning to enhance the ensemble that pursues inference error minimization. We conduct extensive experiments based on the field data. The experiment results show that the proposed algorithm is able to improve inference accuracy, especially for highly dynamic occupancy data set.

In the Industrial Internet, computing- and power-limited mobile devices (MDs) in the production process can hardly support the computation-intensive or time-sensitive applications. As a new computing paradigm, mobile edge computing (MEC) can almost meet the requirements of latency and calculation by handling tasks approximately close to MDs. However, the limited battery capacity of MDs causes unreliable task offloading in MEC, which will increase the system overhead and reduce the economic efficiency of manufacturing in actual production. To make the offloading scheme adaptive to that uncertain mobile environment, this paper considers the reliability of MDs, which is defined as residual energy after completing a computation task. In more detail, we first investigate the task offloading in MEC and also consider reliability as an important criterion. To optimize the system overhead caused by task offloading, we then construct the mathematical models for two different computing modes, namely, local computing and remote computing, and formulate task offloading as a mixed integer non-linear programming (MINLP) problem. To effectively solve the optimization problem, we further propose a heuristic algorithm based on greedy policy (HAGP). The algorithm achieves the optimal CPU cycle frequency for local computing and the optimal transmission power for remote computing by alternating optimization (AP) methods. It then makes the optimal offloading decision for each MD with a minimal system overhead in both of these two modes by the greedy policy under the limited wireless channels constraint. Finally, multiple experiments are simulated to verify the advantages of HAGP, and the results strongly confirm that the considered task offloading reliability of MDs can reduce the system overhead and further save energy consumption to prolong the life of the battery and support more computation tasks.

The western region of China is rich in mineral resources. The vigorous development of mineral resources has exacerbated the environmental and safety problems in the region. One of the important links to solve this problem is to control the development laws and distribution characteristics of the overburdened cracks in the mining of this area. In this paper, the Xiashijie coal mine 3-2 coal seam and 4-2 coal seam are examples of repeated mining, and are examined as the background, through theoretical analysis to optimize the size of the coal pillars in the lower section, using the 3DEC numerical simulation experiment method and the rise of the cracks in the short-distance coal seam. Repeated mining monitoring and analysis of the development law are used to ascertain distribution characteristics of overburdened cracks caused by the repeated mining process of the working face. The results show that: (1) By establishing a mechanical model of the overlying strata structure under short-distance coal seam group mining, and carrying out the force analysis of the double section coal pillar under repeated mining, the reasonable size of a lower section coal pillar was determined to be 70 m. (2) As the development height of a fracture progresses with the working face, its expansion rate undergoes four obvious changes: fluctuations within a certain range, the expansion rate reaches the peak after the rock formation is concentrated and broken, the cyclical change gradually decreases, and the expansion rate is zero after complete mining. (3) The fracture zone height of 222 and 224 face under repeated mining in the 4-2 coal seam was 19.56–22.31 times and 22.38–24.54 times larger, respectively, and the post-mining fracture extension of the face with larger width and deeper burial under repeated mining was higher than that of the adjacent face. This study provides scientific guidance for the rational division of coal pillars and the solution of the problem of water conservation mining under repeated mining in the adjacent face of a short-distance coal seam.

The heterogeneous distribution of soil salinity across the rhizosphere can moderate salt injury and improve sorghum growth. However, the essential molecular mechanisms used by sorghum to adapt to such environmental conditions remain uncharacterized. The present study evaluated physiological parameters such as the photosynthetic rate, antioxidative enzyme activities, leaf Na+ and K+ contents, and osmolyte contents and investigated gene expression patterns via RNA sequencing (RNA-seq) analysis under various conditions of nonuniformly distributed salt. Totals of 5691 and 2047 differentially expressed genes (DEGs) in the leaves and roots, respectively, were identified by RNA-seq under nonuniform (NaCl-free and 200 mmol·L-1 NaCl) and uniform (100 mmol·L-1 and 100 mmol·L-1 NaCl) salinity conditions. The expression of genes related to photosynthesis, Na+ compartmentalization, phytohormone metabolism, antioxidative enzymes, and transcription factors (TFs) was enhanced in leaves under nonuniform salinity stress compared with uniform salinity stress. Similarly, the expression of the majority of aquaporins and essential mineral transporters was upregulated in the NaCl-free root side in the nonuniform salinity treatment, whereas abscisic acid (ABA)-related and salt stress-responsive TF transcripts were more abundant in the high-saline root side in the nonuniform salinity treatment. In contrast, the expression of the DEGs identified in the nonuniform salinity treatment remained virtually unaffected and was even downregulated in the uniform salinity treatment. The transcriptome findings might be supportive of the increased photosynthetic rate, reduced Na+ levels, increased antioxidative capability in the leaves and, consequently, the growth recovery of sorghum under nonuniform salinity stress as well as the inhibited sorghum growth under uniform salinity conditions. The increased expression of salt resistance genes activated in response to the nonuniform salinity distribution implied that the cross-talk between the nonsaline and high-saline sides of the roots exposed to nonuniform salt stress is potentially regulated.

In this paper, we propose and evaluate a novel light-emitting diode (LED) nonlinearity estimation and compensation scheme using probabilistic Bayesian learning (PBL) for spectral-efficient visible light communication (VLC) systems. The nonlinear power-current curve of the LED transmitter can be accurately estimated by exploiting PBL regression and hence the adverse effect of LED nonlinearity can be efficiently compensated. Simulation results show that, in a 80-Mbit/s orthogonal frequency division multiplexing (OFDM)-based nonlinear VLC system, comparable bit-error rate (BER) performance can be achieved by the conventional time domain averaging (TDA)-based LED nonlinearity mitigation scheme with totally 20 training symbols (TSs) and the proposed PBL-based scheme with only a single TS. Therefore, compared with the conventional TDA scheme, the proposed PBL-based scheme can substantially reduce the required training overhead and hence greatly improve the overall spectral efficiency of bandlimited VLC systems. It is also shown that the PBL-based LED nonlinearity estimation and compensation scheme is computational efficient for the implementation in practical VLC systems.

Evolutionary spatial game is a promising way to unravel the mystery of cooperation, and it has been well recognized that spatial structure could enable cooperation to persist. Schweitzer et al.’s lattice model provides an innovative method to solve the problem. In this paper, we conduct simulations using the same von Neumann neighborhood as in Schweitzer et al.’s study (2002) and observe the effect of initial population and lattice size on the evolution of cooperation. Then, we extend the model with a more complicated Moore neighborhood and self-playing rule for each central player. Simulation results not only provide new evidence for the persistence of cooperation in the evolution with spatial structures, but also investigate critical conditions for the spatial coexistence or the invasion of cooperators and defectors with the more complicated neighborhood.