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Advancing the efficiency and reliability of wireless sensor networks is a paramount pursuit in modern networking research. In this context, we introduce a groundbreaking approach based on Hidden Markov Chain (HMC) modeling with opportunistic routing, harnessed by the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) mechanism. Our innovative algorithm addresses key challenges in Wireless Body Area Networks, including delay, throughput, and bandwidth utilization. By strategically integrating the HMC model, which captures intricate state transitions and emission probabilities, our proposed method introduces a robust solution for optimizing node connections and routing decisions. This fusion of HMC and opportunistic routing capitalizes on the strengths of both paradigms, enhancing the network's ability to make intelligent decisions in dynamic scenarios. Through rigorous simulations, we showcase the algorithm's prowess in achieving efficient data transmission. The empirical evidence from these simulations underscores the algorithm's superiority when juxtaposed against state-of-the-art mechanisms such as Simple Multi-Packet Access (SMPA) and Priority-based Congestion-avoidance Routing Protocol (PCRP). Our algorithm not only outperforms existing solutions in terms of delay, throughput, and bandwidth utilization, but it also showcases its ability to adapt to varying network conditions, making it a versatile tool for diverse applications.

In the last few decades, poor prognosis of pancreatic tumor has been an issue of concern in spite of the recent advancements in the different imaging modalities. Small size, similar attenuation to normal sized pancreas or concealed position of pancreas during CT scans are the factors that leads to failure in early diagnosis of pancreatic tumor. In this research, an organized framework is proposed for monitoring, classifying and diagnosing of pancreatic tumor. The suggested model integrates the technique of Deep Neural Network (DNN) and optimistic aspects of nature-inspired algorithms; this model aims to achieve a harmonious combination of both the techniques. The proposed model examines the medical images obtained from CT scans for the presence of pancreatic tumor using SSA-ML image segmentation on CT dataset. Evaluation of suggested model in comparison to other contemporary models IDLDMS, ODL, weighted KLM, Kernel-ELM, and ELM models is also performed in reference to sensitivity, specificity, accuracy and F1 score. The classification accuracy of our model is 99.44 % which reflects its supremacy over other recent models.

Abstract This article studies a distributed consensus‐based estimation problem for discrete time‐varying non‐linear systems with missing measurements and Denial of Service (DoS) attacks. The probability of missing measurements is independent for each sensor. The communication link between sensor nodes is unreliable and subjected to DoS attacks. To achieve accurate state estimation against missing measurements, a local estimator with compensation mechanism is designed for each sensor node. A stochastic event‐triggered mechanism is used to lessen additional information transfer. Based on this, a distributed consensus‐based estimator is constructed by continually fusing local neighbours information matrixs and vectors. Moreover, the analysis of the designed estimator boundedness is presented. Finally, the effectiveness of the proposed algorithm is verified by three numerical examples.

Polar codes have garnered substantial research attention due to their impressive performance characteristics and have found applications in recent technologies, including 5G New Radio (NR) systems, Internet of Things (IoT) communications, and cyber-physical systems that utilize sensor and actuator networks. However, the existing SC decoders suffer from lengthy processing latencies due to their sequential processing steps, thereby restricting the practical applicability of polar codes. To address this latency issue, this paper introduces a Compound Pipeline Processing Unit (CPPU) and its simplified counterpart, a crucial step in realizing tree-level compound pipelining. In contrast to sequential circuitry, the previously described combinational architecture lacks internal storage elements, with the clock period defined by the longest path delay. This strategy conserves hardware resources by avoiding memory usage, but it inevitably decelerates the decoder’s performance. Notably, implementation results underline the efficiency of the proposed CPPU-based SC polar decoder using a fully unrolled encoder and decoder on the targeted platform of a Virtex UltraScale - XCVU190 Field Programmable Gate Array (FPGA), using a parametric approach in the Very High-Speed Integrated Circuit Hardware Description Language (VHDL). The assessment of error-correction performance involves examining various combinations of integral and fractional bits in LLR quantized representations. This approach achieves a throughput of about 2672 Mbps, accompanied by a substantial reduction of 17% in Lookup Table (LUT) usage. Furthermore, the decoder’s speed is enhanced by approximately 17.34% for a code length of 128 bits and LLR quantization of 5 bits.

Ever-increasing dynamic surges in renewable-based electric power systems, notably wind and photovoltaic farms bring adverse impacts and challenges in terms of reliability and stability. The intermittency of renewable sources imposes significant deviations in frequency due to variations in demand. Wind power induces instability in the grid due to its vulnerable nature, and reduction in system inertia. To mitigate these dynamics issues, an optimal control technique based on flatness-based Active disturbance rejection control (FADRC) and utilizing an enhanced Beluga Whale optimization algorithm (EBWO) for a multi-area interconnected power system with photovoltaic generation. The proposed LFC model addresses the load perturbation and the deviation of tie-line power, with system uncertainties considered as lumped disturbances that are approximated by extended state observers. To achieve optimal performance, the Enhanced Beluga Whale optimization algorithm is adopted and integrated with the suggested controller to fine-tune the controller. To validate the formidable performance of the suggested scheme, different cases have been studied with the existing approaches. The simulation results reveal the supremacy and robustness of the dynamic response of the Flatness-based active disturbance rejection control as compared to other approaches under load variations and parameter uncertainty.

The graphical user interface (GUI) in mobile applications plays a crucial role in connecting users with mobile applications. GUIs often receive many UI design smells, bugs, or feature enhancement requests. The design smells include text overlap, component occlusion, blur screens, null values, and missing images. It also provides for the behavior of mobile applications during their usage. Manual testing of mobile applications (app as short in the rest of the document) is essential to ensuring app quality, especially for identifying usability and accessibility that may be missed during automated testing. However, it is time-consuming and inefficient due to the need for testers to perform actions repeatedly and the possibility of missing some functionalities. Although several approaches have been proposed, they require significant performance improvement. In addition, the key challenges of these approaches are incorporating the design guidelines and rules necessary to follow during app development and combine the syntactical and semantic information available on the development forums. In this study, we proposed a UI bug identification and localization approach called Mobile-UI-Repair (M-UI-R). M-UI-R is capable of recognizing graphical user interfaces (GUIs) display issues and accurately identifying the specific location of the bug within the GUI. M-UI-R is trained and tested on the history data and also validated on real-time data. The evaluation shows that the average precision is 87.7% and the average recall is 86.5% achieved in the detection of UI display issues. M-UI-R also achieved an average precision of 71.5% and an average recall of 70.7% in the localization of UI design smell. Moreover, a survey involving eight developers demonstrates that the proposed approach provides valuable support for enhancing the user interface of mobile applications. This aids developers in their efforts to fix bugs.

In an islanded hybrid microgrid, frequency deviation is caused by the intermittent nature of renewable resources and load variations. Maintaining nominal frequency requires the secondary controller to send a signal to the controllable energy sources so that supply and demand are synchronized. Time delays in the system's communication channels, however, can reduce its efficiency. To compensate for the system uncertainties, modeling error and variant nature of renewable resources, an optimized flatness based active disturbance rejection controller have been proposed. Mitigating the effects of communication delay in the system and to synchronize the inputs to the extended state observer, a smith predictor based low pass filter is employed. Furthermore, an improved beluga whale optimization (IBWO) technique is employed to tune the proposed controller gains. The comparative analysis of the proposed IBWO is performed with existing algorithms. Moreover, the proposed controller superiority over the standard PID and LADRC approach has been investigated by perturbation in renewable resources, uncertainty in parameters, variation and mismatch in communication delay. Simulation results provide the testimony for the efficient performance of the designed controller. Lastly, the proposed controller is further evaluated in multi microgrid environment.

Emotion detection (ED) involves the identification and understanding of an individual’s emotional state through various cues such as facial expressions, voice tones, physiological changes, and behavioral patterns. In this context, behavioral analysis is employed to observe actions and behaviors for emotional interpretation. This work specifically employs behavioral metrics like drawing and handwriting to determine a person’s emotional state, recognizing these actions as physical functions integrating motor and cognitive processes. The study proposes an attention-based transformer model as an innovative approach to identify emotions from handwriting and drawing samples, thereby advancing the capabilities of ED into the domains of fine motor skills and artistic expression. The initial data obtained provides a set of points that correspond to the handwriting or drawing strokes. Each stroke point is subsequently delivered to the attention-based transformer model, which embeds it into a high-dimensional vector space. The model builds a prediction about the emotional state of the person who generated the sample by integrating the most important components and patterns in the input sequence using self-attentional processes. The proposed approach possesses a distinct advantage in its enhanced capacity to capture long-range correlations compared to conventional recurrent neural networks (RNN). This characteristic makes it particularly well-suited for the precise identification of emotions from samples of handwriting and drawings, signifying a notable advancement in the field of emotion detection. The proposed method produced cutting-edge outcomes of 92.64% on the benchmark dataset known as EMOTHAW (Emotion Recognition via Handwriting and Drawing).

This paper addresses the multi-task allocation problem with complex constraints in the multi-UAV system by presenting an extension to consensus-based bundle algorithm(CBBA). The presented algorithm termed dynamic clustering consensus-based bundle algorithm(DCCBBA) provides an improved bundle construction, a novel consensus strategy and a systematic way of grouping. DCCBBA features different consensus methods for inner conflict resolution and outer conflict resolution. The inner conflict resolution aims at handling the violated task assignment that belongs to the same cluster, while outer conflict resolution guarantees conflict-free assignments in the overall UAVs. The proposed DCCBBA coupled with Pythagorean Hodograph curves can significantly reduce the number of communication events required to achieve consensus while preserving the robust convergence property of the baseline CBBA. Numerical simulations on various cooperative reconnaissance and strike tasks demonstrate the reduction of communication events compared with the baseline CBBA, the cluster-formed CBBA, the grouped CBBA and Two-layer CBBA.

Hot topic trends have become increasingly important in the era of social media, as these trends can spread rapidly through online platforms and significantly impact public discourse and behavior. As a result, the scope of distributed representations has expanded in machine learning and natural language processing. As these approaches can be used to effectively identify and analyze hot topic trends in large datasets. However, previous research has shown that analyzing sequential periods in data streams to detect hot topic trends can be challenging, particularly when dealing with large datasets. Moreover, existing methods often fail to accurately capture the semantic relationships between words over different time periods, limiting their effectiveness in trend prediction and relationship analysis. This paper aims to utilize a distributed representations approach to detect hot topic trends in streaming text data. For this purpose, we build a sequential evolution model for a streaming news website to identify hot topic trends in streaming text data. Additionally, we create a visual display model and knowledge graph to further enhance our proposed approach. To achieve this, we begin by collecting streaming news data from the web and dividing it chronologically into several datasets. In addition, word2vec models are built in different periods for each dataset. Finally, we compare the relationship of any target word in sequential word2vec models and analyze its evolutionary process. Experimental results show that the proposed method can detect hot topic trends and provide a graphical representation of any raw data that cannot be easily designed using traditional methods.

This paper is concerned with the joint state and input estimation problem for linear discrete time-varying systems over peer-to-peer sensor networks, in which deception attacks are taken into account. By resorting to singular value decomposition, a local estimator structure is proposed to jointly estimate the system states and unknown inputs. Then, a distributed state estimator is constructed by fusing local estimates and covariance matrices. In addition, a sufficient condition is provided to ensure the uniformly bounded estimation error (in mean square) in each node. Finally, a numerical example is provided to show the effectiveness of the proposed estimation algorithm.

Semiconductor lasers with a wavelength of 2 µm, composed of antimonide materials, find important applications in trace gas detection, laser medicine, and free-space optical communication, among others. In this paper, a more suitable microlens shape for 2 µm gallium antimonide semiconductor lasers is designed. Based on the fiber coupling efficiency model, the parameters of the designed slanting wedge-shaped microlens fiber are optimized to improve laser beam quality. The large tangent angle on both sides of the slanted, wedge-shaped microlens fiber is calculated using Snell’s law, and the fiber core diameter and small wedge angle are determined through space fiber coupling experiments. After packaging the fiber coupling module with the chip, the laser output beam exhibits good overall symmetry in the spot with a uniform intensity distribution. The maximum output power is approximately 210 mW, demonstrating good power stability.

This paper establishes a novel holistic T-shape roundabout coordination system (TCS) that addresses the connected and automated vehicles (CAVs) flow at a typically isolated T-shape roundabout. The TCS optimizes the states of each approaching vehicle to the roundabout in a receding horizon control framework that aims to minimize the total delay time, and ensures the smooth crossing. The optimized states are broadcasted immediately by TCS, enabling the CAVs to form a cluster and tune their speed to transit the roundabout with negligible stop-delay. We evaluate the effectiveness of the proposed TCS method under the various traffic flow demand. From the executed simulation, it is observed that the optimization process usually improves the vehicle’s average speed and reduces both the traffic density and idling. As a consequence, the fuel consumption of each vehicle around the roundabout is also reduced. Finally, these outputs are compared with the traditional method, showing that the roundabout performance significantly improves in the case of the proposed scheme, and vehicles have rapid optimization for a smooth crossing.

Abstract Background Obesity among women of childbearing age has becoming an important public health concern. We aimed to describe the trends of central obesity among Chinese women of childbearing age from 2004 to 2011 and to examine its associations with nutrients intake and daily behaviors. Methods Longitudinal data were derived from the China Health and Nutrition Survey. Participants consisted of 2481 women aged 15–44 years old. WC (Waist circumference) and WHtR (Waist to height ratio) were adopted as indicators of central obesity. Generalized linear mixed model was performed to analyze the associations of nutrients intake and daily behaviors with central obesity. Results From 2004 to 2011, the prevalence of central obesity among Chinese women of childbearing age increased from 21.6 to 30.7% (WC as indice) or from 22.8 to 32.6% (WHtR as indice) (both p

Power electronic equipment to miniaturization, high integration direction while facing the problem of high heat flow density and uneven temperature distribution. The large temperature fluctuations and uneven temperature distribution in the operation of power electronic equipment will lead to thermal stress, and excessive thermal stress or uneven distribution will cause fatigue failure of the packaging material, resulting in reduced reliability, module failure, and reduced life of power electronic equipment. In this paper, we propose two kinds of liquid-cooled flow channel non-linear reinforced heat transfer homogeneous liquid-cooled plate, which is theoretically derived to achieve the design requirement of uniform temperature on the heating side wall surface. The thermal design of the cold plate is carried out using ANSYS Workbench, and the indicators of various structures of the cold plate are analyzed and compared in terms of their ability to dissipate heat and maintain temperature uniformity. Based on the original runner structure, the heat dissipation performance of the rod structure and fin structure was evaluated, and the form of the internal heat dissipation structure of the runner was determined. The results show that the integrated liquid cooling plate temperature uniformity and flow resistance of both factors, the fin column heat sink is significantly better than the cylindrical fin column heat sink in the case of the same thermal resistance, the fin column heat sink flow resistance is significantly lower than the cylindrical fin column heat sink.

With the continuous progress of automotive new energy technology, the motor has become an important part of the power system, and the heat dissipation of insulated-gate bipolar transistors (IGBT) determines the reliability of the power system. Minichannel structure can be added to the thermal management system of new energy vehicles to improve the heat transfer capacity. Due to the growth of the boundary layer in the smooth minichannel flow channel, the cooling performance improvement was limited. Pin-fins and rib structures were used to break the boundary layer and increased the heat transfer area to enhance the heat transfer capacity. In this study, a numerical simulation model of minichannel with triangular pin-fins with different rotation angles was established and calculated using the SST k-omega method. The temperature field, velocity field, pressure, and vortex distribution under different configurations were discussed in detail. The jet area formed by the prism wall and the side wall of the minichannel would impact the wall and reduce the growth of the boundary layer. However, the stagnation area generated in the center and corner will reduce the improvement of heat transfer capacity. The thermo-hydraulic characteristics of different configurations at different Reynolds numbers (Re), such as Nusselt number (Nu), Darcy friction resistance coefficient (f), and performance evaluation criterion (PEC), were analyzed. As Re increased, the best and worst configurations changed, the best configuration changed from the 90°–120° structure to the 120°–120° structure, and the worst configuration changed from the 75°–60° to the 60°–60° structure. When the Re = 663, the influence of the front and rear rotation angle on the cooling performance was explored. When the rotation angle was closer to 60°, the cooling performance of the minichannel was better. And the closer the rotation angle was to 120°, the cooling performance was better. This has a reference effect on the design of minichannel heat sinks.

This article compares the conventional model predictive control (MPC) and active disturbance rejection control (ADRC) with a novel MPADRC technique for controlling a non-minimum phase behavior in the DC–DC boost converter. The control of the boost converter is challenging as it is nonlinear, and it shows non-minimum phase behavior in a continuous conduction mode (CCM). Moreover, in this article, the comparison is presented for the boost converter and the two-phase interleaved boost converter using MPC and ADRC, and the effectiveness of the interleaving technique is shown. Finally, it is proved that the interleaving method has much more efficiency and less output ripple than the simple boost converter. To conclude, a novel technique has been introduced that combines both the techniques, that is, MPC and ADRC, in the outer and inner loop with a boost converter, respectively, and the response is clearly the best when compared to the said techniques individually. The overall impact of this technique includes the advantages of both the techniques, that is, the use of MPC allows us to optimize the current value by predicting the future values, and the use of ADRC ensures that the disturbance factor is well tackled and cancels the effect caused by all the disturbances including ignored quantities as well.

Abstract This paper studies trajectory tracking control problem of quadrotor in the presence of external disturbances. A new control scheme integrating adaptive sliding mode disturbance observer (ASMDO) with backstepping funnel control is designed in both position loop and attitude loop. Adaptive sliding mode disturbance observer is utilized to estimate and compensate the external disturbances of quadrotor system, which relaxed the upper bound requirement in conventional sliding mode disturbance observer (SMDO) whilst adapting the gain of discontinuous term to a level as small as possible for consideration of chattering mitigation. The modified funnel function not only guarantees the dynamic quality in the tracking process but also avoids derivative operation of Euclidian norm of tracking error in the backstepping design. To guarantee stabilization of closed‐loop system, Lyapunov stability criterion is utilized in the overall controller design process. Finally, the effectiveness and superiority of the proposed scheme are illustrated by simulation results.

The quadrotor Unmanned Aerial Vehicle (UAV) belongs to an open-loop unstable nonlinear system, which also has the characteristics of underdrive, strong coupling and external disturbance. In the height control of quadrotor UAVs, the traditional sliding mode control (SMC) and PID methods cannot quickly and effectively eliminate disturbance effects caused by gust, aerodynamic drag and other factors, which indicates that the quadrotor UAV cannot return to its predetermined trajectory. To this end, this paper proposes a dual closed-loop finite-time height control method for the quadrotor UAV. The proposed method is able to estimate and compensate for the disturbance in the height control and make up for the lack of anti-disturbance ability in the control process. More specifically, a finite-time Extended State Observer (ESO) and a finite-time super-twisting controller are designed for the velocity control system to compensate for the total disturbance and track the rapidly changing expected signal. An integral sliding mode controller is designed for the height control system. Simulation results show that the proposed method can reduce the chattering phenomenon of traditional SMC and improve both control accuracy and convergence speed.

Abstract This manuscript proposes a model predictive control for collision avoidance for the regulation problem of deterministic linear systems, which provides a priori guarantees of strong system theoretic properties, such as positive invariance and asymptotic stability, and high computational efficiency. Notion of safe distance sets is introduced, and also utilized as a novel approach to ensure collision avoidance via suitably defined convex constraints. The proposed convex model predictive control for collision avoidance is obtained by employing interactive strategic‐tactical structure for overall decision‐making. The strategic stage of the overall algorithm employs direct algebraic manipulations in order to construct safe distance sets that ensure collision avoidance. The tactical stage of the overall algorithm employs strictly convex quadratic programs for the optimization of local finite horizon predicted control processes. The dynamically compatible interaction of strategic and tactical stages of the overall algorithm is ensured by construction, which guarantees structural and computational benefits. These novel and unique features effectively enable both real time implementation and real life utilization of model predictive control for collision avoidance.

Abstract Background Early life exposure in the uterus had a long-term effect on children’s health. As the prevalence of allergies is increasing with a remarkable sex difference, very few studies have traced back to their early origins. We sought to investigate if maternal behavioral exposure, herein sleep, physical activity, and screen time during pregnancy is associated with childhood respiratory allergies. The sex difference would be examined. Methods Six thousand two hundred thirty-six mother-child pairs from Shanghai Children Allergy Study (SCAS) were enrolled, The International Study of Asthma and Allergies in Childhood questionnaire was adopted to evaluate respiratory allergic diseases. Results 14.6, 16.2, and 21.0% of children had asthma, wheeze, and allergic rhinitis, respectively. Maternal short sleep duration, lack of physical activity, and too much screen exposure during pregnancy could increase the risk of childhood respiratory allergies, however, the significance was found only in males. Moreover, a dose-response trend was clearly shown, any two of the three combined could increase the risk (OR,1.921; 95% CI,1.217–3.033), and the coexistence of all three further amplified the risk (OR,2.412; 95% CI,1.489–3.906). The findings can be verified in allergen test subgroup and each single type of respiratory allergies in most cases. Conclusions Maternal unhealthy behaviors during pregnancy could increase the risk of childhood respiratory allergies with a dose-response pattern. Males were more susceptible to the association. The identification of modifiable maternal risk behaviors lies in the emphasis of intervention in early life to face up increasing childhood allergies.

Novel generalized predictive control (GPC) is proposed to deal with the problems of cumulative deviation of the predicted value, long control process and large noise that often encountered in conventional GPC. First, the objective function of GPC is improved based on integral time square error (ITSE) criterion, and the output expression under soft constraint is deduced. Then, the predicted value is corrected by adopting the integral weighting method, the convergence of which is demonstrated. Finally, both the simulations and hardware experiments are conducted in a practical photoelectric tracking system. It turns out that the superiority of the improved GPC in control precision, dynamic response and robustness is verified.

In this article, a modified scheme of nonlinear active disturbance rejection control (ADRC) is proposed by correcting the disturbance compensation and improving the speed observer. The stability, disturbance rejection and tracking performance of the modified ADRC scheme are analyzed and compared with that of the typical ADRC scheme utilizing the describing function method and numerical method, which shows that the dynamic stiffness and tracking performance are significantly improved while the stability almost remains invariant. Both the simulations and experiments are conducted in inertial stabilized platform (ISP) control system. It turns out that the simulation and experimental results further support the analysis.

Uncoordinated traffic flows at the traditional roundabouts, especially with a small circumference and fewer lanes, are often heavily affected by congestion, which escalates fuel consumption, CO2 emissions, idling, and travel delay. An intriguing way to mitigate such uncoordinated flows at junctions would be facilitated through optimal traffic signalization. For this purpose, this paper presents a novel holistic Three-Leg Signalized Roundabout (TLSR) model based on two signalized stop lines (2SL). The first stop line is placed at each entry curve of a roundabout with effectual lane markings as usual. Hereafter, the second stop line is set exclusively in the circulatory roadway to improve left-turning mobility with an additional “short-lane model” to deal with heavy traffic, following specific patterns for smooth vehicle merging. The capacity and optimal signal cycle relationships are derived to evaluate the performance of the proposed TLSR-2SL, considering the internal space constraints of the roundabout. Under the various scenarios, the parameters’ sensitivity tests demonstrate that signal cycle and central radius play a significant role in enhancing the roundabout’s operational performance. From the executed simulation, the proposed framework improves the traffic flow by 15% and controls the relative error within 10% compared to benchmark methods.

An efficient control of large-scale unmanned aerial vehicle (UAV) swarm to establish a complex formation is one of the most challenging tasks. This paper investigates a novel multi-layer topology network and consensus control approach for a large-scale UAV swarm moving under a stable configuration. The proposed topology can make the swarm remain robust in spite of the large number of UAVs. Then a potential function-based controller is developed to control the UAVs in realizing autonomous configuration swarming under the consideration of mutual collision, and the stability of the controller from the individual UAV to the entire swarm system is analyzed by a Lyapunov approach. Afterwards, a yaw angle adjustment approach for the UAVs to reach consensus is developed for the multi-layer swarm, then the direction state of each UAV converges with a fast rate. Finally, simulations are performed on the large-scale UAV swarm system to demonstrate the effectiveness of the proposed scheme.

Fertilization stability is an important index for evaluating the operational performance of variable fertilizer dischargers. To study the influence law of the combination of fertilizer discharge wheel rotational speed n and opening L on the fertilizer discharge performance, this paper firstly constructs a fertilizer amount prediction model based on a radial basis function neural network (RBFNN) through a calibration test, and after verification, its determination coefficient reaches 0.99965 with a mean relative error (MRE) of 3.88%. Then the discrete element simulation software (EDEM) was used to simulate the fertilizer discharge process under different control sequences for each of the three target fertilizer application amounts. The simulation results show that at the target fertilizer discharge rate of 944.92 g/min, when the control sequence is 18.3 r/min and 25 mm, the uniformity coefficient of variation (CV) of fertilizer discharge is the smallest. In the other control sequences, σ was higher than 20%, the stability of fertilizer discharge was poor, and the phenomenon of broken strips appeared; under the target fertilizer discharge rate of 2101.47 g/min, σ was the smallest at (24.2 r/min, 45 mm) 4.34%; under the target fertilizer discharge rate of 3842.87 g/min, σ was less than 4% in all cases, and at the control sequence (44.7 r/min, 45 mm), σ reached a minimum of 2.01%. Finally, using the simulation results and the prediction model of fertilizer amount based on RBFNN, the optimization model of fertilizer discharge control sequence based on the differential evolutionary (DE) algorithm was established, and a bench test was conducted to verify the optimization results, which showed that the accuracy and uniformity of fertilizer discharge met the operational requirements.

Abstract Background Previous studies suggested that maternal subjective feeling of stress seemed to be involved in the incidence of congenial heart disease in offspring. To better understand the findings, our study would discuss the relationships of maternal exposure to stressful life event and social support, which are more objective and comprehensive indicators of stress, around periconceptional period with the risk of ventricular septal defect (VSD), the most popular subtype of congenital heart disease. Methods A hospital-based case-control study was conducted through June, 2016 to December, 2017. We collected maternal self-reports of 8 social support questions in 3 aspects and 8 stressful life events among mothers of 202 VSD cases and 262 controls. Social support was categorized into low, medium high, and high (higher is better), and stressful life event was indexed into low, medium low, and high (higher is worse). Logistic regression models were applied to estimate adjusted odds ratios and 95% confidence intervals (95% CI). Results The adjusted odds ratio of high stressful life event was 2.342 (95% CI: 1.348, 4.819) compared with low stressful life event. After crossover analysis, compared with low event & high support, the adjusted odds ratio of low event & low support, high event & high support, and high event & low support were 2.059 (95% CI: 1.104, 3.841), 2.699 (95% CI: 1.042, 6.988) and 2.781 (95% CI: 1.033, 7.489), respectively. Conclusions In summary, we observed an increased risk of VSD when pregnant women exposed to stressful life events, however, social support could, to some extent, reduce the risk of stressful life event.

The rapid and accurate detection of soil nutrient content through spectral technology is one of the requisite technologies for precision fertilization, which, however, is an unsolved issue. In order to achieve this purpose, a more robust and accurate model is established in this study. The regression algorithm is integrated with effective wavelength selection to construct the prediction model for total nitrogen, available phosphorus, and available potassium (N, P, and K), which removes the need for complex pretreatment and algorithm constraints. According to the research results, with regard to the prediction of soil nitrogen, phosphorus, and potassium contents, the joint interval support vector regression (Si-SVR) model performed best in modeling, with the root mean square error of prediction (RMSEP) limited to 0.0231%, 1.0554%, and 3.4225%, respectively. In addition, the relative percentage deviation (RPD) values were restricted to 2.68, 2.12, and 2.37, respectively. As indicated by the prediction results obtained for the above three nutrient contents, the RPD values of the Si-SVR model prediction accuracy evaluation indicators exceeded 2.0, which evidences a high level of prediction accuracy. This method makes it possible for spectral data to be applied in practical production, and these results provide a valuable reference for the effective detection of major soil nutrients.

The COVID-19 pandemic has been a global threat. Through rapid and effective surveillance and control, the newly confirmed patients have been fluctuated at a very low level and imported case explained most of them through March, 2020 to the present, indicating China’s response has achieved a stage victory. By contrast, the epidemic of COVID-19 in other countries out of China is bursting. Different countries are adopting varied response strategy in terms of their public health system to prevent the spread. Herd immunity has been a hot topic since the outbreak of COVID-19 pandemic. Can it be a possible strategy to combat COVID-19? To fully interpret the knowledge regarding the term upon the background of COVID-19-related health crisis, we aim to systematically review the definition, describe the effective measures of acquiring herd immunity, and discuss its feasibility in COVID-19 prevention. Findings from this review would promote and strengthen the international cooperation and joint efforts when confronting with COVID-19.

Purpose. We conducted this study to add the evidence regarding the gender-specific association between sleep duration and blood pressure (BP) in children. Methods. A secondary analysis was performed among 1000 children aged 7–13 years, who had at least two rounds of survey records in China Health and Nutrition Survey through 2004–2015. Generalized estimating equation was used to explore the gender-specific association of sleep duration with BP. The subgroup analysis was applied in those participants with normal weight. Results. The time trend of decreasing sleep duration, along with increasing BP level, was observed in each age group during the survey period. Short sleepers (

A robust derivative unscented Kalman filter is proposed for a nonlinear system with non-Gaussian noise and outliers based on Huber function. In this paper, the time update process can be performed using a Kalman filter (KF), and measurement update process can be carried out utilizing an unscented transform. This novel filtering algorithm differs from the traditional unscented Kalman filter (UKF) which is sensitive to non-Gaussian noise and outliers. Besides, for a nonlinear system with linear state equation, the presented filtering has the merits of both KF and UKF, and it has better performance under non-Gaussian noise and large outliers. Furthermore, the proposed filtering algorithm can not only track the target effectively but also suppress the effects of the non-Gaussian noise and large outliers. Finally, numerical simulations are conducted to verify estimation accuracy and effectiveness of the proposed filtering algorithm.

The ordered structures of polynomial idempotent algebras over max-plus algebra are investigated in this paper. Based on the antisymmetry, the partial orders on the sets of formal polynomials and polynomial functions are introduced to generate two partially ordered idempotent algebras (POIAs). Based on the symmetry, the quotient POIA of formal polynomials is then obtained. The order structure relationships among these three POIAs are described: the POIA of polynomial functions and the POIA of formal polynomials are orderly homomorphic; the POIA of polynomial functions and the quotient POIA of formal polynomials are orderly isomorphic. By using the partial order on formal polynomials, an algebraic method is provided to determine the upper and lower bounds of an equivalence class in the quotient POIA of formal polynomials. The criterion for a formal polynomial to be the minimal element of an equivalence class is derived. Furthermore, it is proven that any equivalence class is either an uncountable set with cardinality of the continuum or a finite set with a single element.

Community structure, one of the most popular properties in complex networks, has long been a cornerstone in the advance of various scientific branches. Over the past few years, a number of tools have been used in the development of community detection algorithms. In this paper, by means of fusing unsupervised extreme learning machines and the k-means clustering techniques, we propose a novel community detection method that surpasses traditional k-means approaches in terms of precision and stability while adding very few extra computational costs. Furthermore, results of extensive experiments undertaken on computer-generated networks and real-world datasets illustrate acceptable performances of the introduced algorithm in comparison with other typical community detection algorithms.

This paper studies denial-of-services (DoS) attacks against industrial cyber-physical systems (ICPSs) for which we built a proper ICPS model and attack model. According to the impact of different attack rates on systems, instead of directly studying the time delay caused by the attacks some security zones are identified, which display how a DoS attack destroys the stable status of the ICPS. Research on security zone division is consistent with the fact that ICPSs’ communication devices actually have some capacity for large network traffic. The research on DoS attacks’ impacts on ICPSs by studying their operation conditions in different security zones is simplified further. Then, a detection method and a mimicry security switch strategy are proposed to defend against malicious DoS attacks and bring the ICPS under attack back to normal. Lastly, practical implementation experiments have been carried out to illustrate the effectiveness and efficiency of the method we propose.

Recently, network attacks launched by malicious attackers have seriously affected modern life and enterprise production, and these network attack samples have the characteristic of type imbalance, which undoubtedly increases the difficulty of intrusion detection. In response to this problem, it would naturally be very meaningful to design an intrusion detection system (IDS) to effectively and quickly identify and detect malicious behaviors. In our work, we have proposed a method for an IDS-combined incremental extreme learning machine (I-ELM) with an adaptive principal component (A-PCA). In this method, the relevant features of network traffic are adaptively selected, where the best detection accuracy can then be obtained by I-ELM. We have used the NSL-KDD standard dataset and UNSW-NB15 standard dataset to evaluate the performance of our proposed method. Through analysis of the experimental results, we can see that our proposed method has better computation capacity, stronger generalization ability, and higher accuracy.

Shipboard is not an absolute rigid body. Many factors could cause deformations which lead to large errors of mounted devices, especially for the navigation systems. Such errors should be estimated and compensated effectively, or they will severely reduce the navigation accuracy of the ship. In order to estimate the deformation, an unscented particle filter method for estimation of shipboard deformation based on an inertial measurement unit is presented. In this method, a nonlinear shipboard deformation model is built. Simulations demonstrated the accuracy reduction due to deformation. Then an attitude plus angular rate match mode is proposed as a frame to estimate the shipboard deformation using inertial measurement units. In this frame, for the nonlinearity of the system model, an unscented particle filter method is proposed to estimate and compensate the deformation angles. Simulations show that the proposed method gives accurate and rapid deformation estimations, which can increase navigation accuracy after compensation of deformation.

In most practical applications, the tracking process needs to update the data constantly. However, outliers may occur frequently in the process of sensors’ data collection and sending, which affects the performance of the system state estimate. In order to suppress the impact of observation outliers in the process of target tracking, a novel filtering algorithm, namely a robust adaptive unscented Kalman filter, is proposed. The cost function of the proposed filtering algorithm is derived based on fading factor and maximum correntropy criterion. In this paper, the derivations of cost function and fading factor are given in detail, which enables the proposed algorithm to be robust. Finally, the simulation results show that the presented algorithm has good performance, and it improves the robustness of a general unscented Kalman filter and solves the problem of outliers in system.