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The development of intrusion detection technology has contributed greatly to industrial Internet of Things (IIoT) security. However, intrusion detection system (IDS) for IIOT anomaly detection suffer from limited computational costs. In addition, the problem of class imbalance in IIOT network traffic is a challenge for IDS. To this end, this paper proposes a deep residual SConv1D-Attention model, which improves the accuracy of detecting minority classes, increases the speed of model anomaly detection, and reduces the computational cost of the model. Specifically, we use a binary Particle Swarm Optimization (bPSO) algorithm to select the features of the samples and remove the redundant features of the samples, which improves the performance of the model. We design a novel SConv1D-Attention module that employs a one-dimensional version of depth separable convolution and self-attention for information integration, the computational cost is reduced while information loss is effectively minimized. In response to data imbalances, during training, we design a robust model loss function to increase the weight of the minority class and balance attention to learning in a few categories. We used the ACC, DR, FPR, Precision and F1_score indicators based on CICDDoS2019, NSL-KDD and X-IIoTID datasets to evaluate our model. The experimental results show that the binary and multiclassification results of our model reached 99.86%–99.99% and 99.42%–99.91%, respectively, on the basis of the ACC, DR, Precision and F1_score indicators of each dataset, and 0.03%–0.16% and 0.02%–0.42%, respectively, on the FPR indicators, which are superior to those of traditional deep learning methods and state-of-the-art models. High evaluation results show that our model can improve the efficiency of network intrusion detection. [ABSTRACT FROM AUTHOR]

This study examines the shift in China's seafood production, revealing that mariculture has surpassed marine capture as the primary source of marine food. The research aims to assess the roles of marine capture and mariculture in meeting rising food demand and ensuring nutrition security, given the limitations of land-based food production. Using data from 2003 to 2021, the study highlights several key trends: mariculture, focused on shellfish and algae, now outpaces marine capture, which remains centered on fish and crustaceans. Significant regional disparities are observed, with mariculture expanding rapidly in several coastal provinces as marine capture declines. Nutrient supply from marine capture has followed a three-phase pattern of growth, stagnation, and decline, while mariculture's nutrient output has steadily increased, particularly in protein. By 2019-2020, mariculture surpassed marine capture in energy and protein supply, though fat supply remains lower. Regional differences in nutrient supply show mariculture leading in multiple provinces. The study concludes by recommending strategies to promote sustainable, diverse, and environmentally friendly practices for China's marine food systems. [ABSTRACT FROM AUTHOR]

Given the weak convergence, limited balance capacity, and optimization limitations observed in the Marine Predators Algorithm (MPA), which draws inspiration from the predatory behavior of marine organisms during evolutionary processes, this study introduces a Reinforcement Learning Marine Predators Algorithm (RLMPA). Firstly, based on the predatory characteristics at different stages, we have designed three location update strategies for search agents aimed at creating high-quality candidate solutions from three perspectives. In particular, ranking paired mutually beneficial learning is specifically designed to expand the scope of exploration to generate as many high-quality candidate solutions as possible for future generations. The Gaussian random walk learning is specifically designed to achieve better optimization in the transitional phase by adjusting the step-size control parameters, successfully completing the transition from exploration to local exploitation phase. Additionally, modified somersault foraging strategy is introduced to accelerate local convergence and perform more extensive local exploitation. Secondly, we integrate reinforcement learning into MPA and use Q-learning mechanism to adaptively select location update strategies. Agents fully utilize the collected information to evaluate the next action of the agents, coordinate the exploration phase and exploitation phase, and enhance the global optimization ability. Finally, compared with 10 competitive algorithms, RLMPA achieves better comprehensive performance in global optimization ability, search efficiency and convergence speed on 41 test functions and 5 practical engineering problems. In the Friedman rank sum tests, RLMPA achieves a preferable overall ranking, and has certain ascendant preponderances in solving practical problems with stability, effectiveness and robustness. [ABSTRACT FROM AUTHOR]

Due to factors such as high temperatures, elevated pressures, and severe high-frequency shocks in the bore, there is considerable noise interference in acceleration test signals. This makes it challenging to accurately measure projectile motion acceleration using existing methods. To address this issue, we propose an advanced measurement approach that uses bottom pressure correction. Our model suggests a significant correlation between projectile motion acceleration and thrust. By utilizing the correlations between bottom pressure and motion acceleration in both temporal and frequency domains, we can improve the accuracy of acceleration measurements. Based on these insights, we have developed a novel testing system that synchronously measures bottom pressure and acceleration, using bottom pressure as a corrective mechanism for the measured acceleration signals. Empirical results show that the maximum relative error in peak motion acceleration is only 4.86%, demonstrating the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of SiC dosage on the high-temperature mechanical properties (compressive strength, flexural strength, and bond flexural strength) of geopolymer mortar. The findings reveal that appropriate amount of SiC was found to improve the high-temperature mechanical properties of geopolymer mortar. Compared to the specimen without SiC, the addition of 3 wt. % SiC to the geopolymer mortar resulted in a noteworthy 14.59% increase in compressive strength at 300 °C, along with respective augmentations of 24.72% and 22.54% in flexural strength and bond flexural strength at 200 °C. Utilizing the experimental data as a foundation, a thermodynamic mathematical model was formulated, linking the strength and temperature parameters of SiC-modified geopolymer mortar. Furthermore, FTIR (Raman and Fourier transform infrared) analysis indicated that the polycondensation reaction of geopolymer mortar treated at 300 °C was promoted, and the products of displacement reaction increased, which provides a plausible explanation for the observed enhancement in compressive strength at 300 °C. Consequently, it is found that the incorporation of SiC can enhance the high-temperature mechanical properties of geopolymer mortar. [ABSTRACT FROM AUTHOR]

This study applies data envelopment analysis (DEA) to gauge technical efficiency and allocative efficiency in China's beef cattle-fattening industry using survey data. The Tobit model considers the salient determinants that drive these efficiencies. The results indicate that (1) large-scale farms exhibit robust TE and pure technical efficiencies (PTE), whereas scale efficiencies (SE) diverge significantly between large and medium-sized operations. The cost efficiency (CE) of smaller farms lags behind their larger counterparts, with the latter displaying greater revenue efficiencies (RE) and profit efficiency (PE). (2) The influence of identical factors on the efficiency of beef cattle fattening production can vary, sometimes antithetically, across different scales. Local policy interventions must be differentiated according to farm type and size. (3) The unique context of China's national conditions and the status quo of livestock farming render the dual implementation of environmental regulations and technological subsidies less viable for Chinese beef cattle farms. These entities should prioritize production over technological innovation and advancement. Policymakers should adopt strategies such as targeted skill/technological training for farm managers at particular scales of operation. This could represent a critical trajectory to augment the efficiency of beef cattle production and increase beef yield in China. [ABSTRACT FROM AUTHOR]

The multi-beam array stitching test system (MASTS) based on the Hartmann principle is employed to measure the aberrations in large-aperture optical systems. As each small-aperture and ideal parallel beam traverses the optical system, it is converged into a spot at the focal plane of the optical system. The centroid position of the spot contains the information about the wavefront slope of the sub-aperture at that specific location in the optical system. Scanning the optical system with this small-aperture beam across the entire aperture of the optical system, we can yield the aberration information to be tested. To mitigate pointing errors induced by scanning motion and accurately obtain the aberration signals of the optical system, nine beams are integrated into a 3 × 3 multi-beam array system, and their directions are aligned to be identical. However, achieving complete alignment in the same direction for all nine beams is a challenging task, resulting in errors due to their pointing differences within the array. This paper introduces an iterative algorithm designed to obtain high-precision multi-beam pointing errors and to reconstruct the wavefront of the optical system under test. This enables a more accurate measurement of wavefront aberrations in the optical system to be tested. Firstly, simulation models were implemented to validate the algorithm's feasibility. Additionally, a scanning optical measurement system with a multi-beam array was developed in our lab, and the iterative algorithm was applied to process our experimental data. The results were then compared with interferometer data, demonstrating that our algorithm is feasible for MASTS to measure aberrations in large-aperture optical systems with high accuracy. [ABSTRACT FROM AUTHOR]

In recent years, due to the growing complexity of real-world problems, researchers have been favoring stochastic search algorithms as their preferred method for problem solving. The slime mould algorithm is a high-performance, stochastic search algorithm inspired by the foraging behavior of slime moulds. However, it faces challenges such as low population diversity, high randomness, and susceptibility to falling into local optima. Therefore, this paper presents an enhanced slime mould algorithm that combines multiple strategies, called the ESMA. The incorporation of selective average position and Lévy flights with jumps in the global exploration phase improves the flexibility of the search approach. A dynamic lens learning approach is employed to adjust the position of the optimal slime mould individual, guiding the entire population to move towards the correct position within the given search space. In the updating method, an improved crisscross strategy is adopted to reorganize the slime mould individuals, which makes the search method of the slime mould population more refined. Finally, the performance of the ESMA is evaluated using 40 well-known benchmark functions, including those from CEC2017 and CEC2013 test suites. It is also recognized by Friedman's test as statistically significant. The analysis of the results on two real-world engineering problems demonstrates that the ESMA presents a substantial advantage in terms of search capability. [ABSTRACT FROM AUTHOR]

Yu, H.; Wang, Y., and Li, D., 2023. Using the AHP-entropy weight method to study regional marine economy resilience in China. Journal of Coastal Research, 39(5), 960–969. Charlotte (North Carolina), ISSN 0749-0208. Marine economy has become an important part of China's national economy, but it is facing many constraints and disturbances from within and outside the region. Marine economy resilience can reflect to what extent the region will overcome these problems and achieve sustainable development. Nevertheless, evaluating marine economy resilience is a difficult task requiring considerable effort that is missing in the literature. This paper constructed the evaluation index system from the four dimensions of resistance, recovery, reorganization, and renewal and comprehensively used the analytic hierarchy process and entropy method to make a quantitative assessment of marine economy resilience in 11 coastal regions of China. Based on this, the paper then explored the spatial and temporal evolution of regional marine economy resilience. The evaluation results reveal that the overall level of China's marine economy resilience from 2006 to 2016 shows a fluctuating and rising trend. The resistance ability is generally in an upward fluctuating trend, the recovery ability fluctuates little but has no significant improvement, the reorganization ability also shows a fluctuating and rising trend, and the renewal ability has the largest increase in the four dimensions. This paper puts forward three suggestions for the coastal province governments to improve regional marine economy resilience in terms of technological innovation, green development, and coordinated opening up. [ABSTRACT FROM AUTHOR]

Marine fishery is an important part of China's maritime power strategy. Improving the ecological efficiency of marine fishery is the inevitable way to achieve the sustainable development of fishery. Based on the perspective of industrial sustainable development, this study used the Super-SBM model to evaluate the ecological efficiency of marine fishery in 11 coastal provinces of China from 2011 to 2020. Combined with Malmquist index, Moran index and other methods, the spatial and temporal evolution characteristics were analyzed. On this basis, the Tobit panel model was used to explore the influencing factors of marine fishery eco-efficiency. The results show that: (1) From 2011 to 2020, the marine fishery eco-efficiency in the 10 coastal provinces and cities of China shows a clear trend of improvement, and the efficiency values in high-efficiency areas remain basically stable. The relative gap between efficient and inefficient regions remains significant. (2) From the perspective of spatial distribution characteristics, the ecological efficiency of marine fishery in coastal provinces and cities in China had no obvious spatial correlation, and showed a trend of cross-distribution between high-efficiency regions and low-efficiency regions. (3) The change of marine fishery eco-efficiency is the result of a variety of influencing factors. Fishery industrial structure, scientific and technological support levels and environmental regulation play a role in promoting the improvement of marine fishery eco-efficiency. Therefore, optimizing the structure of the fishery industry, improving environmental regulation and increasing investment in science and technology are all effective measures for local governments to improve the eco-efficiency of marine fisheries. [ABSTRACT FROM AUTHOR]

Due to defects of relatively slow convergence and being easily trapped in local optimal of flower pollination algorithm (FPA), an enhanced lens learning and traction mutation based flower pollination algorithm (LMFPA) is proposed in this paper. First, LMFPA applies a modified lens learning mechanism to improve the distribution of population. Second, LMFPA uses the observation factor based traction mutation strategy to further accelerate the convergence and increase the probability to jumping out of local optimal. 12 benchmark functions from CEC2013 are selected as testbed to evaluate performance of LMFPA with original FPA algorithm and another 3 improved FPA algorithms, including TMFPA (T-distribution mutation-based flower pollination algorithm), t-GSSA(improved sparrow search algorithm based on adaptive t-distribution and golden sine and its application) and PCSPSO (particle compaction and scheduling based particle swarm optimization). Experimental result shows that LMFPA can achieve superior performance both in convergence speed and accuracy. At last, LMFPA is also used to solve 3D path planning for UAVs. Experimental result illustrates that LMFPA can also find better 3D paths for UAVs. [ABSTRACT FROM AUTHOR]

Coal combustion remains the primary source of thermal power generation, contributing to approximately half of China's electricity supply. As China strives towards the goals of "carbon peaking and carbon neutrality", the issue of carbon emissions ascends to critical importance in the thermal power industry. The delicate balance between preserving electricity production capacity and curbing carbon emissions presents a considerable challenge to thermal power enterprise management. This paper models the incentive and organizational dilemmas arising from these conflicting tasks in thermal power generation enterprises, and compares the advantages and disadvantages of single-agent and multi-agent systems. Two practical scenarios are examined: (1) the "Two Mountains" theory propounded by the Chinese government, which attaches equal importance to thermal power production and carbon emission control, and (2) the 2022 Chinese summer electricity crisis, in which thermal power production takes on a dominant role. Through comparative static analysis of both single-agent and multi-agent models under these circumstances, this study concludes that, in the first scenario, the multi-agent model outperforms the single-agent model by enhancing agent effort levels and bolstering government revenue. However, when power supply emerges as the chief concern of thermal power enterprises, centralized management of a single-agent is more effective. [ABSTRACT FROM AUTHOR]

Over the past 20 years, as the depth and diameter of shaft lines increased in China, the cracking and water leakage of the inner walls of frozen shafts have become increasingly severe, resulting in significant safety threats and economic losses. Understanding the stress variation patterns of cast-in-place inner walls under the combined effects of temperature and constraint during construction is a prerequisite for evaluating the crack resistance performance of inner walls and preventing water leakage in frozen shafts. The temperature stress testing machine is an important instrument for studying the early-age crack resistance performance of concrete materials under the combined effects of temperature and constraint. However, existing testing machines have shortcomings in terms of applicable specimen cross-sectional shapes, temperature control methods for concrete structures, and axial loading capacity. In this paper, a novel temperature stress testing machine suitable for the inner wall structure shape, capable of simulating the hydration heat of the inner walls, was developed. Then, a reduced-scale model of the inner wall according to similarity criteria was manufactured indoors. Finally, preliminary investigations of the temperature, strain, and stress variations of the inner wall under 100% end constraint conditions were conducted by simulating the actual hydration heating and cooling process of the inner walls. Results show that the hydration heating and cooling process of the inner wall can be accurately simulated. After approximately 69 h of concrete casting, the accumulated relative displacement and strain of the end-constrained inner wall model were −244.2 mm and 187.8 με, respectively. The end constraint force of the model increased to a maximum value of 1.7 MPa and then rapidly unloaded, causing the model concrete to crack in tension. The temperature stress testing method presented in this paper provides a reference for scientifically formulating technical approaches to prevent cracking in cast-in-place concrete inner walls. [ABSTRACT FROM AUTHOR]

Aiming at the deficiencies of the sparrow search algorithm (SSA), such as being easily disturbed by the local optimal and deficient optimization accuracy, a multi-strategy sparrow search algorithm with selective ensemble (MSESSA) is proposed. Firstly, three novel strategies in the strategy pool are proposed: variable logarithmic spiral saltation learning enhances global search capability, neighborhood-guided learning accelerates local search convergence, and adaptive Gaussian random walk coordinates exploration and exploitation. Secondly, the idea of selective ensemble is adopted to select an appropriate strategy in the current stage with the aid of the priority roulette selection method. In addition, the modified boundary processing mechanism adjusts the transgressive sparrows' locations. The random relocation method is for discoverers and alerters to conduct global search in a large range, and the relocation method based on the optimal and suboptimal of the population is for scroungers to conduct better local search. Finally, MSESSA is tested on CEC 2017 suites. The function test, Wilcoxon test, and ablation experiment results show that MSESSA achieves better comprehensive performance than 13 other advanced algorithms. In four engineering optimization problems, the stability, effectiveness, and superiority of MSESSA are systematically verified, which has significant advantages and can reduce the design cost. [ABSTRACT FROM AUTHOR]

A simple but effective method for increasing the viewing angle of the integral imaging system was proposed. Unlike the existing methods, the viewing angle was extended through the fully effective utilization of the pixels within the elemental image array rather than the modifications to the system structures or the importing of extra devices. The relationships between the pixel positions and the viewing zone distributions were analyzed, the algorithm model for replacing the invalid pixels was built and then a method for generating full‐effective‐pixel elemental image array (FEP‐EIA) was proposed. Experiment results demonstrated that the viewing angle of the integral imaging system was significantly increased with the proposed method. [ABSTRACT FROM AUTHOR]

Marine development and eco-environmental management have received increasing attention over the past two decades, however, no effective universal approach has been established to achieve marine development without destroying marine ecosystems. This study discusses the integrated ocean management (IOM) for meeting the sustainable development goal (SDG14) through the following four aspects: the marine eco-environment foundation, market mechanism, management support, and space consideration. Our findings highlight how to enhance the coastal and marine areas management efficiency to achieve ecological and socioeconomic values for sustainable development through the benign interaction of marine ecosystem and socioeconomic systems. The presented case study examines the IOM framework for achieving SDG14 in the Bohai Sea. Furthermore, content analysis and semi-structured interviews were conducted. The framework is theoretically and empirically explored in light of the Bohai Sea's management, focusing on the role of the government and incentive. Further, issues preventing effective IOM are highlighted and a framework for optimizing the IOM implementation to better balance the interests of various industries is suggested. When implementing the IOM framework, each region should fully play to its own advantages and push forward with some focused aspects first. The long-term effect of the Bohai Sea's management may need time to be verified, and the role of the market mechanism and multi-interest coordination mechanism need more special attention for the Bohai Sea in the future. [ABSTRACT FROM AUTHOR]

There exists a defect of the narrow viewing angle in the conventional integral imaging system. One reason for this is that only partial pixels of each elemental image contribute to the viewing angle and the others cause image flips. In this paper, a wide-viewing-angle integral imaging system with a full-effective-pixels elemental image array (FEP-EIA) was proposed. The correspondence between viewpoints and pixel coordinates within the elemental image array was built up, and effective pixel blocks and pixels leading to flipping images were deduced. Then, a pixel replacement method was proposed to generate the FEP-EIAs, which adapt to different viewing distances. As a result, the viewing angle of the proposed integral imaging system was effectively extended through the replacement of the pixels, which caused the image flips. Experiment results demonstrated that wide viewing angles are available for the proposed integral imaging system regardless of the viewing distances. [ABSTRACT FROM AUTHOR]

Lenses play an important role in imaging systems. Having an effective way to test the aberrations of imaging lenses is important. However, the existing methods cannot satisfy the requirements in some conditions. To overcome these difficulties, wavefront aberration measurement deflectometry (WAMD) is proposed in this paper, which can reconstruct the wavefront aberrations of imaging lenses by measuring the angular aberrations. The principle of WAMD is analyzed in detail, and the correctness and feasibility of the proposed method are verified by both a simulation and an experiment. A telephoto lens and a single imaging lens were tested in an experiment, and the RMS errors were 166.8 nm (5.71%) and 58.9 nm (4.74%), respectively, as compared with the interferometer's results. This method is widely applicable with relatively reasonable accuracy. It has potential to be applied in the lens manufacturing and alignment process. [ABSTRACT FROM AUTHOR]

In order to solve the problems of low accuracy, wrong detection and missing detection of traditional target detection algorithms in blood cell detection task, a target detection algorithm YOLO-Att based on improved YOLO framework is proposed. Based on the YOLO framework, a multi-scale residual enhancement module is added to the backbone network to improve the utilization rate of feature information by combining with the feature level of low-level informationrich network. An attentional gating structure embedding model is designed to obtain more high quality information of main features. At the same time, Focal loss is used to replace the cross entropy in the original loss function to improve the weight of positive and negative samples and accelerate the convergence rate of the model. Finally, K -means++ clustering algorithm is used to optimize the anchor frame of the target to further improve the detection accuracy. Compared with the existing target detection algorithms such as Faster-RCNN, SSD and YOLOv4, YOLO-Att increased mAP to 66.32% in the BCCD detection task of the universal blood cell data set, and the detection rate reaches 85.4 ms, which is more in line with the real-time detection task of blood cell. [ABSTRACT FROM AUTHOR]

Science and technology innovation is an important driving force to promote the development of fishery industry, and is very important to improve the quality of fishery development. In this study, the Super-SBM model was used to evaluate the fishery science and technology innovation efficiency of 30 provinces and cities in China (excluding Hong Kong, Macao, Taiwan and Tibet) from 2011 to 2020. Combined with the kernel density estimation, the spatial and temporal differentiation characteristics were analyzed. Then, from the two dimensions of investment scale and innovation efficiency, the sustainable development types of fishery science and technology innovation were classified. The results show the following: (1) From the perspective of efficiency change, the overall efficiency of fishery science and technology innovation in China increased first and then decreased during 2011–2020, but the overall efficiency level was low, and the efficiency difference between regions gradually widened, and the eastern coastal regions became the development core of fishery science and technology innovation. (2) From the perspective of spatial differentiation characteristics, there was a large gap between the coastal and inland areas in China. The high-efficiency areas were mainly concentrated in the coastal provinces and cities, such as Guangdong, Jiangsu, Shandong, Shanghai and Tianjin, showing a decreasing trend from east to west. (3) From the perspective of investment scale and innovation efficiency, the study regions can be divided into four types: leading area, breakthrough area, catch-up area and backward area. This paper mainly calculates the efficiency of fishery science and technology innovation in various regions, and divides the type areas of fishery science and technology innovation and development. According to the advantages and problems of different types of areas, different development strategies and correction measures are proposed, which can effectively improve the efficiency of resource utilization, avoid resource waste and realize the sustainable development of fishery. [ABSTRACT FROM AUTHOR]

Phase measuring deflectometry (PMD) is a competitive method for specular surface measurement that offers the advantages of a high dynamic range, non-contact process, and full field measurement; furthermore, it can also achieve high accuracy. Camera calibration is a crucial step for PMD. As a result, a method based on the calibration of the entrance pupil center is introduced in this paper. Then, our proposed approach is compared with the most popular photogrammetric method based on Zhang's technique (PM) and Huang's modal phase measuring deflectometry (MPMD). The calibration procedures of these three methods are described, and the measurement errors introduced by the perturbations of degrees of freedom in the PMD system are analyzed using a ray tracing technique. In the experiment, a planar window glass and an optical planar element are separately measured, and the measurement results of the use of the three methods are compared. The experimental results for the optical planar element (removing the first 6 terms of the Zernike polynomial) show that our method's measurement accuracy reached 13.71 nm RMS and 80.50 nm PV, which is comparable to accuracy values for the interferometer. [ABSTRACT FROM AUTHOR]

The combination of deep learning and intrusion detection has become a hot topic in today's network security. In the face of massive, high-dimensional network traffic with uneven sample distribution, how to be able to accurately detect anomalous traffic is the primary task of intrusion detection. Most research on intrusion detection systems based on network anomalous traffic detection has focused on supervised learning; however, the process of obtaining labeled data often requires a lot of time and effort, as well as the support of network experts. Therefore, it is worthwhile investigating the development of label-free self-supervised learning-based approaches called BYOL which is a simple and elegant framework with sufficiently powerful feature extraction capabilities for intrusion detection systems. In this paper, we propose a new data augmentation strategy for intrusion detection data and an intrusion detection model based on label-free self-supervised learning, using a new data augmentation strategy to introduce a perturbation enhancement model to learn invariant feature representation capability and an improved BYOL self-supervised learning method to train the UNSW-NB15 intrusion detection dataset without labels to extract network traffic feature representations. Linear evaluation on UNSW-NB15 and transfer learning on NSK-KDD, KDD CUP99, CIC IDS2017, and CIDDS_001 achieve excellent performance in all metrics. [ABSTRACT FROM AUTHOR]

This article proposes a new eye-in-hand 3-D scanner-robot calibration approach to realize low data stitching errors during long-term continuous measurement. Eye-in-hand 3-D scanner–robot systems are commonly used for the complete measurement of an object under test (OUT) from multiple fields of view (FOVs). To align the multiple FOVs into a single coordinate system, marker-free stitching assisted by robot's positioning is attractive since it bypasses the cumbersome traditional fiducial marker-based method. Based on periodically capturing calibration images from a 2-D calibration target, scanner's and robot kinematic model's parameters are optimized. The challenges overcome in this article include: how to compensate for the center-detection error in scanner calibration; how to avoid the dependence of hand-eye calibration on DH parameters; and how to calculate an accurate world-to-robot transformation. These challenges were tackled by several new techniques including accurate scanner calibration with iterative refinement of control points, virtual arm-based scanner-robot kinematic modeling, and trajectory-based world-to-robot transformation calculation. Experimental results demonstrated a low-initial stitching error similar to the fiducial marker-based method (0.0446 mm versus 0.0542 mm) was achieved. The mean stitching error was effectively maintained to be < 0.1 mm during the continuous measurement with an average intermittent downtime of 78 s for recalibration. [ABSTRACT FROM AUTHOR]

Based on the assumption that the long-term value of a venture capital satisfies the algebraic Brownian motion, we develop a continuous-time exit model of venture capital under different exit modes, namely, initial public offering (IPO) and mergers and acquisitions (M&A). The employee incentive problem is analyzed jointly with the exit decision of the firm in terms of the exit timing and the exit mode. Further, the problem of capital exit is considered from two perspectives, namely, optimal venture capital and social welfare maximization, and the differences between these exit decisions are compared. Our model predicts that the timing of an IPO, the purpose of which is to maximize the utility of the capitalists, lags behind the exit timing, whose purpose is to maximize social welfare. Using a numerical analysis, this paper also proves that increasing the production efficiency, lowering the interest rates, and improving risk management can make the exit decision of venture capitalists converge with that of maximizing social welfare. [ABSTRACT FROM AUTHOR]

Featured Application: In this paper, the dual-view integral imaging 3D display can be used in head-up display, augmented reality and so on. We propose a dual-view integral imaging 3D display based on a multiplexed lens-array holographic optical element (MHOE). A MHOE is a volume holographic optical element obtained by multiplexing technology, which can be used for dual-view integral imaging 3D display due to the angle selectivity of the volume HOE. In the fabrication of the MHOE, two spherical wavefront arrays with different incident angles are recorded using photopolymer material. In the reconstruction, two projectors are used to project the elemental image arrays (EIA) with corresponding angles for two viewing zones. We have developed a prototype of the dual-view integral imaging display. The experimental results demonstrate the correctness of the theory. [ABSTRACT FROM AUTHOR]

The present study aimed to investigate the effects of chitinase-3-like protein 1 (YKL-40) gene RNA interference on the biological behaviors and enhanced chemosensitivity of endometrial cancer (EC) HEC-1A cells. YKL-40 small interfering (si)RNA was transduced into EC HEC-1A cells using a lentivirus. The experiment was divided into three groups: The experimental group was transfected with YKL-40 siRNA (si-YKL-40); the mock-treatment group was transfected with transfection reagent only; and the blank control group was left untreated. A reverse transcription-quantitative polymerase chain reaction was performed to investigate the mRNA expression levels of YKL-40. The biological behaviors, including cell proliferation, migration, invasion and apoptosis, were detected by MTT and Transwell assays, and flow cytometry (FCM) analysis, respectively. The results of the present study demonstrated that the mRNA expression levels of YKL-40 were downregulated within HEC-1A cells upon transfection with si-YKL-40 (P<0.05). The proliferative, migratory and invasive abilities of HEC-1A cells were inhibited by si-YKL-40 (P<0.05). The mRNA expression levels of YKL-40 were upregulated within HEC-1A cells following treatment with cisplatin (P<0.05). FCM analysis revealed that the average cellular apoptosis rate increased following the inhibition of YKL-40 gene expression via siRNA (P<0.05). Therefore, the YKL-40 gene may be associated with the proliferative, migratory, invasive and anti-apoptotic ability of HEC-1A cells. YKL-40 downregulation may enhance the sensitivity of human EC HEC-1A cells to chemotherapy. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is a simple yet efficient stochastic search approach for numerical optimization. However, it tends to suffer from slow convergence when tackling complicated problems. In addition, its search ability is significantly influenced by its control parameters. To improve the performance of the basic DE, this paper proposes a self-adaptive differential evolution with global neighborhood search (NSSDE). In the proposed NSSDE, its control parameters are self-adaptively tuned according to the feedback from the search process, while the global neighborhood search strategy is incorporated to accelerate the convergence speed. To evaluate the performance of the proposed NSSDE, we compare it with several DE variants on a set of benchmark test functions. The experimental results show that NSSDE can achieve better results than its competitors on the majority of the benchmark test functions. [ABSTRACT FROM AUTHOR]

In this paper, a novel evolutionary strategy-based particle swarm optimisation (PSO) approach is presented. The evolutionary strategy is dependent on a BP neural proportional integral derivative (PID) controller, which is intentionally placed between the position and the global best position in a closed loop of their relationship. The BP neural PID controller is designed to aid the position to well track the global best position. Furthermore, it can be utilised to improve the evolutionary dynamics of the PSO algorithm. The experiments for performance evaluation are tried on both analytical benchmark functions and an automatic control system of pulp consistency in pulping and paper-making engineering. The experimental results illustrate that the proposed approach enhances the diversity of swarms, considerably improves the global convergence efficiency and outperforms the PSO algorithm. In addition, it is also easily used in real industrial practice and offers a novel and convenient solution to engineering optimisation design of industrial systems. [ABSTRACT FROM AUTHOR]

The article discusses the Lubei cupronickel (Cu-Ni) deposition in West of East Tianshan and North Xinjiang, China. It states that the Lubei complex contains gabbro or hornblende-gabbro blocks and ultramafic phases due to lateral zonation. It is notes the possibility of discovering the Cu-Ni ore deposit in East Tianshan.

The article discusses a study on the Cupronickel (Cu-Ni) sulphide deposition in East Tianshan of Central Asian Orogenic Belt, Northwest China. It states that zircon was also part of the deposition, however were separated using density and magnetic separation techniques. It is noted that zircon crystals are transparent and light brown in color. Further analysis states that deposition of Cu-Ni was mainly found in the Huangshan-Jing'erquan area during the mineralization ages of 298-269 Ma.

In traditional phase measurement deflectometry (PMD), a number of sinusoidal fringe patterns are displayed on the screen in two orthogonal directions, which is time-consuming and not suitable for dynamic measurements. A phase-extraction algorithm based on the spatial-carrier phase-shifting technology for a single-shot spatial-carrier orthogonal fringe pattern is proposed. The phase increment of each pixel in two orthogonal directions is obtained by the least squares method and then the amount of spatial phase shift of all pixels relative to the probe pixel in the rectangular neighborhood centered on the probe pixel can be obtained. The number of fringe patterns required for the PMD is reduced to one by displaying a spatial-carrier orthogonal fringe pattern. Finally, the feasibility of the algorithm is verified by simulation and experiment. [ABSTRACT FROM AUTHOR]

Aiming at the problem of wireless sensor network node coverage optimization with obstacles in the monitoring area, based on the grey wolf optimizer algorithm, this paper proposes an improved grey wolf optimizer (IGWO) algorithm to improve the shortcomings of slow convergence, low search precision, and easy to fall into local optimum. Firstly, the nonlinear convergence factor is designed to balance the relationship between global search and local search. The elite strategy is introduced to protect the excellent individuals from being destroyed as the iteration proceeds. The original weighting strategy is improved, so that the leading wolf can guide the remaining grey wolves to prey in a more reasonable way. The design of the grey wolf's boundary position strategy and the introduction of dynamic variation strategy enrich the population diversity and enhance the ability of the algorithm to jump out of local optimum. Then, the benchmark function is used to test the convergence performance of genetic algorithm, particle swarm optimization, grey wolf optimizer, and IGWO algorithm, which proves that the convergence performance of IGWO algorithm is better than the other three algorithms. Finally, the IGWO algorithm is applied to the deployment of wireless sensor networks with obstacles (rectangular obstacle, trapezoidal obstacle and triangular obstacles). Simulation results show that compared with GWO algorithm, IGWO algorithm can effectively improve the coverage of wireless sensor network nodes and obtain higher coverage rate with fewer nodes, thereby reducing the cost of deploying the network. [ABSTRACT FROM AUTHOR]