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As a novel building forms, prefabricated buildings offer unique advantages in terms of safety and speed. Focusing on the problems of target tracking in the threaded sleeve production line of prefabricated buildings, such as low accuracy rate, slow recognition speed, and difficulty in dealing with object occlusion and environment changes, a target tracking and detection approach based on the improved DeepSort algorithm is proposed. The real-time tracking of the equipment of the prefabricated building threaded sleeve production line is realized. The improved YOLOv5 replaces the detection algorithm in the track phase of the original DeepSort algorithm, which is used for real-time tracking and detecting the equipment. The experimental results show that the tracking accuracy of the improved algorithm is 88%, 70.5%, and 64.8% for three videos of different lengths, respectively. The tracking detection frame rate reached 71.95 fps in the three experiments of video tracking. The improvement of the frame rate of the improved algorithm is over 100% compared with the traditional tracking algorithms. The improved algorithm enhances the accuracy of target tracking in prefabricated building threaded sleeve production line and improves the production efficiency of prefabricated building threaded sleeve production line effectively.

In the present research, a method to model the pose error of the machine tool feed system is proposed based on the principle of minimum potential energy. Firstly, the force-moment equilibrium equation of the feed system is established by mechanical analysis. Secondly, the force of the single slider is analyzed based on Hertzian contact theory, and the force state of the slider under the action of the guideway error is obtained. The elastic displacements of the single slider in the horizontal and vertical directions are solved based on the principle of the minimal potential energy. Subsequently, the pose error of the feeding system is obtained by using the plane fitting principle. Finally, the correctness of the proposed method is verified through experiments, and the experimental results are in good agreement with the theoretical results.

The light field display technology can reconstruct the virtual three-dimensional scene with high precision, providing users with a highly immersive viewing experience without any auxiliary equipment. However, one of the main challenges faced by this technology is the narrow field of view (FOV). In this paper, we present a novel autostereoscopic display utilizing Composite Lenticular Lens Array (CLLa) and Saddle Lens Array (SLa). By utilizing time-division quadruplexing, distinct contents are assigned to different pairs of viewing zones. The optical model of this scheme is simulated by ZEMAX. The FOV is expanded horizontally to 100 degrees and the crosstalk level is reduced to 0.8%, ideally. It is worth noting that both viewing zones share the same set of pixels, which theoretically results in a spatial resolution one times higher than traditional schemes. Furthermore, this scheme has been partially verified by a 7.9-inch TFT-LCD screen, and experimental results demonstrate an effective expansion of the field of view angle by more than 16.2% compared to traditional schemes.

Phantoms are used to evaluate, calibrate, and compare the performance of electrical impedance tomography (EIT) systems. This paper presents a dynamic thorax-like mesh phantom, which mimics the changes in electrical conductivity distribution within a human thorax at different time frames. Furthermore, element merging and contour smoothing, electrode placement techniques, and PCB design automation methods are proposed to simplify, optimize the mesh phantom, and reduce the hardware implementation cost. To verify the accuracy of the mesh phantom and its PCB, SPICE simulations and experimental testings are performed to obtain the conductance of the mesh phantom and reconstruct the images through the EIDORS software. These images achieve an image correlation coefficient (ICC) of 0.680 (model versus SPICE simulation) and 0.9098 (SPICE simulation versus measurement result), respectively. This demonstrates the validity of our proposed dynamic thorax-like mesh phantom.

With the growing usage of renewable energy resources, the fluctuation and randomness of solar and wind power output have a significant influence on the safe and stable operation of a power system. Deciding how to deal with these uncertainties has become a hot topic for researchers and practitioners. In this paper, a hybrid improved marine predators algorithm is proposed to solve the optimal reactive power dispatch problem with load demand, and wind-solar power uncertainties. The $\varepsilon $ -constraint handling technique is adopted to deal with the constraints in the optimization problem. The uncertainty factors in the system are modeled using appropriate probability density functions. The backward reduction method is used to determine a representative set of scenarios from a large number of scenarios produced by a Monte Carlo simulation method. IEEE 30-bus test system is utilized to verify the proposed algorithm. The simulation results demonstrate that the algorithm can produce better results than other advanced metaheuristic algorithms.

In an increasingly open electricity market environment, short-term load forecasting (STLF) can ensure the power grid to operate safely and stably, reduce resource waste, power dispatching, and provide technical support for demand-side response. Recently, with the rapid development of demand side response, accurate load forecasting can better provide demand side incentive for regional load of prosumer groups. Traditional machine learning prediction and time series prediction based on statistics failed to consider the non-linear relationship between various input features, resulting in the inability to accurately predict load changes. Recently, with the rapid development of deep learning, extensive research has been carried out in the field of load forecasting. On this basis, a feature selection algorithm based on random forest is first used in this paper to provide a basis for the selection of the input features of the load forecasting model. After the input features are selected, a hybrid neural network STLF algorithm based on multi-model fusion is proposed, of which the main structure of the hybrid neural network is composed of convolutional neural network and bidirectional gated recurrent unit (CNN-BiGRU). The input data is obtained by using long sliding time windows of different steps, then multiple CNN-BiGRU models are trained respectively. The forecasting results of multiple models are averaged to get the final forecasting load value. The load datasets come from a region in New Zealand and a region in Zhejiang, China, are used as load forecast examples. Finally, a variety of load forecasting algorithms are introduced for comparison. The experimental results show that our method has a higher accuracy than comparison models.

With the development of distribution network, high elastic distribution system with multiple integration needs more response resources from the user-side to participate in the more flexible distribution network interaction. In the face of emergency or accident, the traditional power grid operating control system often takes part in the response in the form of cutting off the whole line load, which will greatly affect the power consumption of users. In this context, this paper proposes a temperature-controlled load shedding method for high elastic distribution network with real-time response. Taking the temperature-controlled load aggregate as the response resource, the temperature-controlled load control method suitable for system frequency and voltage recovery is analyzed, and the temperature-controlled load dispatching strategy participating in the emergency control of high elastic distribution network is formed. Specifically, according to the energy storage characteristics and service time flexibility of temperature-controlled load, the aggregate response model of temperature-controlled load is established to determine the control mode of temperature-controlled load. Then, a temperature-controlled load disaster reduction method based on the real-time response of high elastic load is proposed to realize the participation of temperature-controlled load in system disaster reduction. Finally, the voltage and frequency recovery characteristics of the system are analyzed in a 10 machine 39 bus system.

The power supply and distribution network of prosumer group is equipped with contact switch and section switch. After feeder fault occurs, the topological structure of distribution network can be adjusted by changing the switch state of each switch in the system, so as to realize the reconstruction after the fault, so as to restore the power supply in the fault free area. The optimal configuration of different types of switchgear in the appropriate position of the distribution network can reduce the time of fault location and isolation, and further realize the reconfiguration of the distribution network after the fault occurs, which greatly improves the reliability of the power supply and distribution network for prosumer group. This paper introduces a series of Boolean expressions to describe the existence of switches in distribution network, and then determines the corresponding load interruption time according to different fault types. On this basis, a distribution network fault reconfiguration model is established. By minimizing the investment cost and network loss, a multi-objective optimization model with power supply reliability as the index is established. Finally, the proposed model is tested through the RBIs bus 4 network. The test results show that the solution can effectively reduce the investment cost, improve the reliability of the distribution network, and maximize the investment income.

Line joint temperature monitoring is used in high-voltage transmission lines to improve the reliability and safety of power lines. However, traditional monitoring methods (e.g., manual inspection, UAV inspection, short-range wireless technology, etc.) cannot realize real-time monitoring of line joint temperature in remote areas. In this paper, we proposed a novel real-time monitoring system, which adopted a communication method combining ZigBee and LPWAN technology (LoRa and NB-IoT) for long-distance transmission. In this system, each power tower was regarded as a communication node that contained a main node and multiple subnodes. First, each node collected temperature data of the transmission line junctions by ZigBee and microprocessor processed and analyzed the data. Second, the LoRa multihop communication technology was used for transmission of the data between adjacent nodes to achieve wider coverage and a longer transmission distance. Finally, the end of the network was a main node, i.e., an LoRa-NB-IoT gateway. The temperature data of all nodes were transmitted to the “USR Cloud” by the NB-IoT technology. By conducting a system test, the system can meet the requirements of long-distance communication outside and reduce power consumption. After calculation, the system communication packet loss rate is approximately 3.3%, and the average power consumption of the system is about 21.19 mA. The PC can monitor the transmission line junction temperature in real time, transmission is stable, and the alarm function can be also realized. Therefore, the system is more suitable for temperature monitoring of line junctions in remote areas, achieving long-distance data acquisition and transmission through low power consumption and high transmission rate.

With the increasing demand for transparent/flexible displays, healthcare sensors, and robotics, there is a need to advance the development of thin-film transistors (TFTs) manufacturing and large-scale circuits. This paper has proposed an IGZO-TFT-PDK to aid the educators and research community to explore the circuit design space of dual-gate IGZO-TFT devices. To solve the tensile force-induced current variation problem in IGZO-TFT devices, an omni-directional device and its layout template with a compensation methodology that mitigates its variation are proposed. A layout template is also proposed to speed up the design development flow of both analog and digital IGZO-TFT circuits. Based on the proposed layout template, a tensile force-insensitive standard cell library is proposed. We have implemented a 32-bit carry select adder to validate the usability of the standard cell library.

Automatic route planning and tracking control are important for unmanned surface vehicles (USVs). This study presents a final-optimized A* (FO A*) algorithm for automatic route planning of USVs and designs a nonlinear tracking controller to ensure that the ship navigates on the route. Ship domain theory is employed in the FO A* algorithm to ensure that the planning route is far from shorelines and reefs. Meanwhile, water depth is used to determine the safety area in the search process. The nonlinear feedback algorithm is also applied to reduce the rudder angle when the ship tracks the planning route in wind and waves. Finally, the proposed algorithms are well validated on the ship-based Electronic Chart Display and Information System (ECDIS). The experiment shows that the FO A* algorithm and the nonlinear tracking controller are effective and deterministic for route planning offline. It can be put into navigation practice, which will make some contribution in accelerating the USV development.

EHB (electro-hydraulic brake system) can provide active and decoupled brake for electric intelligent vehicles. However, modelling and control of automotive electro-hydraulic brake systems are always challenging due to the complex and highly nonlinear dynamic characteristics of the braking process. In this paper, a nonlinear backstepping control algorithm for EHB based on the bond graph model is introduced. A nonlinear, single-wheel brake system model is constructed using the bond graph method, in which the capacitive effect of the hydraulic fluid and the damping and inertia effects of calipers are considered. Based on this nonlinear model, a backstepping controller with good robustness is designed, and the control signals of the inlet and outlet valves are described by a unified expression. Moreover, the effects of the controller parameters on brake-pressure control are discussed in details, and suitable design parameters are chosen. Finally, the proposed algorithm is verified by the simulation and hardware-in-the-loop tests. The results show that valves are properly regulated by the backstepping controller, and the brake pressure follows the desired value rapidly and accurately.

Emerging computing paradigm edge computing expects to store and process data at the network edge with reduced latency and improved network bandwidth. To the best of our knowledge, key performance issues such as coding performance of erasure-coded storage systems haven't been investigated for edge computing. In this paper, we present an erasure-coded storage system for edge computing. Unlike the data center and cloud storage systems, it employs edge devices to perform encoding and decoding operations, which can be a performance bottleneck of the whole storage system due to limited computing power. Hence, we present a comprehensive study of the performance of erasure coding to see if it can match the network performance of 5G and Wi-Fi 6 at the network edge. We use the popular edge device Jetson Nano and two state-of-the-art coding libraries: Jerasure and G-CRS. Our evaluation results reveal unsatisfied performance for Jerasure and high variance for G-CRS. To obtain better and stable performance, we accelerate erasure code with OpenMP on a multi-core CPU. Our work demonstrates our acceleration can bring stable performance and match the network bandwidth of 5G and Wi-Fi 6 for some commonly used cases. Besides, our work offers a better understanding of erasure-coded storage systems for edge computing and can be served as a reference to any further optimization for such kind of systems at the network edge.

To solve the problem of intercepting a moving target by a multirotor unmanned aerial vehicle (UAV) swarm, an optimal guidance strategy is proposed. The proposed guidance law is based on the integration of the classic pure pursuit guidance law and Kuhn-Munkres (KM) optimal matching algorithm, and virtual force potential functions are used to avoid collision. The proposed optimal guidance strategy is demonstrated by simulation experiments. The simulation results indicate that with the proposed optimal guidance strategy, a UAV swarm can intercept a moving target while maintaining the predetermined formation, and during the formation flight, the collisions between UAVs or the target can be avoided. Through a comparative experiment, the proposed optimal matching algorithm is proven to significantly reduce the average per-sampling-period total flight distance of all the UAVs and accelerate the interception process, and the formation completion degree is improved.

A multi-arm robot for mandible reconstruction assisted surgery is developed. It has three manipulators and eighteen degrees of freedom. In order to prevent the collision of the manipulator caused by the overlap of the motion space of the manipulator during the space positioning of the whole mandible reconstruction operation, a collision avoidance path planning method for manipulator based on C-space is proposed. In order to obtain the space non-interference posture of one manipulator, and the interference posture of the other manipulators is taken as the obstacle mapping in C-space to obtain the free space after removing obstacles. At the same time, combining with the space position of the system manipulator, the C-space mapping mathematical model of feature points and line segments is studied, and the mapping mathematical model of any point and line segment in the plane is obtained. By introducing admissibility proof lemma to weigh the evaluation function, the improved heuristic A* search algorithm is used to search the best path for collision avoidance. The collision avoidance experiment is carried out through simulation and robot platform, and finally the animal experiment is carried out. These experimental results verify the feasibility and validity of the collision avoidance path planning method for the space manipulator.

Lysine acetylation (Kace) is a conservative protein posttranslational modification (PTM) closely related to various metabolic diseases. Therefore, Kace sites identification is of great significance for investigating metabolic disease treatments. Existing studies have shown that protein structural properties contain highly useful structural information, which provides a strong basis for identifying PTMs. During the feature learning process, features at different levels are complementary, and taking them into consideration can effectively improve the quality of the features. However, existing deep-learning methods used protein sequence-level information as input without considering the protein structural properties. Furthermore, only high-level features were focused on, resulting in considerable information loss and weakening the prediction results. Therefore, we propose a novel deep-learning model based on modular densely connected convolutional networks (MDC) for Kace sites prediction, called MDC-Kace. MDC-Kace introduces the protein structural properties and combines them with the original protein sequence and the amino acid physicochemical properties to construct the site's feature space. Then, modular densely connected convolutional networks are used to capture the information of features at different levels and reduce information loss and crosstalk during the feature learning step. We add a squeeze-excitation layer to evaluate the importance of different features and improve the network abstraction ability to identify potential Kace sites. The experimental results of ten-fold cross-validation and independent testing in human, Mus musculus and Escherichia coli datasets showed that our MDC-Kace model outperforms the existing Kace site predictors and can predict potential Kace sites effectively. MDC-Kace can be available at https://github.com/lianglianggg/MDC-Kace.

We present a novel design of few-mode fiber (FMF) with a uniform differential mode group delay (U-DMGD) among propagating modes and apply the FMF to a single-fiber delay line module for implementing microwave photonic finite impulse response (FIR) filters. By optimizing key parameters such as the core grading exponent and the dimension of the trench of FMF, a U-DMGD between adjacent modes among four modes (LP01, LP11, LP02 and LP31) over the entire C band is achieved. Wavelength dependence is entirely removed. An FIR microwave photonic filter (MPF) implemented using the designed 1-km FMF is investigated through numerical simulations. The free spectral range (FSR) of the MPF is 5.7 GHz, the 3-dB bandwidth is 1.26 GHz, and the main lobe-to-side lobe ratio (MSR) is 10.42 dB. Discussions on fabrication aspects have also been presented. The proposed single-fiber delay line structure based on FMF can significantly reduce the system complexity of microwave photonic signal processing.

Ice doline is a particular kind of ice morphology, usually scattered on ice streams which are mostly far from the existing research bases. For this reason, glaciologists rarely have opportunities to document its developments in detail. Satellite observations are too coarse to capture such fine features, whereas unmanned aerial vehicle (UAV)-based structure-from-motion (SfM) and light detection and ranging (LiDAR) technologies have revolutionized geosciences research, especially in less accessed polar regions. We developed two bespoke UAV systems for glaciological investigation and carried out four campaigns during two consecutive Chinese Antarctic expeditions in 2017 and 2018. Founded on manual coregistration and accuracy assessment, a successful application to characterize a doline's spatio-temporal evolution is presented in this article. The overlying weight of surface melting directly triggered the collapse event on Jan 30, 2017 near the Dalk Glacier, and then the newborn doline grew for another 8135.6 m2 in area and 280 303.38 m3 in volume by early 2018. The UAV-based results revealed the doline's changes during a year, showing a maximum horizontal extension of 50 m and vertical subsidence of more than 10 m. Furthermore, we evaluate the photogrammetry and LiDAR systems and find the former is cost-effective and time-efficient on a large-scale survey, while the latter enjoys a better capability to characterize ice morphological details. Based on systematic comparisons, other pros and cons of the two techniques are discussed. To achieve the best performance for relevant applications in similar scenarios, we recommend adopting an integrated approach, in which the LiDAR restores the fine features on the basis of extensive SfM coverage.

Permanent magnet synchronous motors are the core components of electric vehicles and widely used in the field of electric vehicles. The existence of vibration will reduce the operating efficiency and service life of the motor, which is the key factor to determine whether the motor is running normally. In this article, a certain type of permanent magnet synchronous motor is taken as the test object. The permanent magnet synchronous motor is divided into three substructures: stator, rotor and shell. The finite element modal results are obtained respectively. The modal parameters of the whole PMSM are calculated by using the substructure modal synthesis method. The weak links of the motor are found out and the structure optimization is carried out. Through the calculation of the modal analysis, it can be seen that each vibration mode of the optimized motor has been reduced, and the amplitude difference between the 2nd order and the 5th order is the largest. Finally, the modal test is carried out on the motor using the exciter method, the test results are analyzed, and the modal parameters of the motor are obtained through the frequency domain analysis method, the FEA result is confirmed by the modal test of the motor.

In this paper, we present an optical structure for two-stage beam combination to realize a high-power fiber-coupled diode laser source. In the first stage, dense spectral combination based on reflecting volume Bragg gratings is implemented for combining five diode laser blocks with a spectral separation of 1.5 nm, to a high output power submodule. In the second stage, submodules are further coaxially multiplexed by polarization beam combination and coarse spectral combination to obtain a higher output power. In the process of the beam combination, thermal effects of combining elements are also investigated. By using a temperature control, the diode laser source can steadily produce 3120-W power from an optical fiber with 105-μm core diameter and 0.2 numerical aperture. This paper demonstrates the benefits of this combination structure and the effectiveness of the temperature control.

We report the design of 12-LP-mode (21-spatial-mode) fiber with a low differential mode group delay (DMGD), a large effective area, and a low bending loss that is adapted to a mode-division-multiplexing system over the C+L band. Based on the designed fiber parameters, we characterize the few-mode fiber (FMF) with the DMGD, an effective area, and a bending loss. Over the C+L band, the maximum DMGD is 0.106 ps/m, and the effective area is in the range of 150~485 μm2. The bending loss of the designed FMF reduces to zero when the bending radius is greater than 9 mm, and the DMGD is below 0.0413 ps/m at the wavelength of 1550 nm.

Based on the polarization interference technique, we propose a modified method for simultaneously measuring the beat length of all guided modes in a polarization-maintaining few-mode fiber (PM-FMF). We experimentally demonstrate the effectiveness of our proposed method on a 3-mode PANDA-type PM-FMF. The simultaneous measurement results agree well with the results obtained by one-by-one measurement process. What's more, the modified measurement method is easier to be implemented and more scalable to a larger mode number.

In this paper, high images quality switchable two-dimensional (2-D)/3-D display is achieved by carefully matching the high-resolution liquid crystal display (LCD) pixel structure and liquid crystal graded index (GRIN) lens. A parallelogram LCD pixel structure is proposed, in which the edges of each sub-pixels are slanted. At the top of the LCD pixels, the liquid crystal GRIN lens is also tilted to reduce the moiré effect and the tilt angle is carefully aligned with the edges of subpixels of LCD. This accurate alignment is useful to eliminate the crosstalk and improves the imaging quality of 3-D display. The 2-D/3-D switchable display is realized by tuning the liquid crystal in the GRIN lens. Based on this, a 3-D display based on 32-inch 8K4K resolution LCD display and Liquid crystal GRIN lens was designed.

We propose a hybrid computational near-eye light field display based on micro-lens arrays (MLAs) and structure parameters prioritization method (SPPM) that can render correct accommodation and expand the depth of field (DOF). The proposed display consists of two MLAs and two micro-displays. Two sets of depth-sliced images are superimposed to reconstruct the object's light field. The system's ray resolution and the spectra difference between the proposed light field and the conventional one are discussed. The DOF of the proposed system is extended without the requiring of high refresh rate display. Furthermore, SPPM is proposed to extend the spatial frequency of the light field. A prototype is demonstrated and the experimental result shows that the depth field of the reconstructed images is significantly increased compared with conventional near-eye light field display based on single MLA.

With the change of epoch, the standard of male attractiveness is also changing. Analyzing the trend of facial features changes over time in different environments can reveal the influence of social development on facial attractiveness. In this paper, we propose a method to analyze the trend of facial features of Chinese male. First, a face database with Chinese male of different times is established and rated by raters of different ages. Second, the machine learning method is used to rate face images of different times for verifying the change of male aesthetic. Then, the retrained Inception v3 model is used to realize facial shape classification. After that, the change trend of face shape is analyzed by using massive data. Finally, the change characteristics of the eyebrow are analyzed by calculating the geometric parameters of the eyebrow model, including the area, length, and average width of the eyebrow. Compared with other researches on facial attractiveness, the proposed method can deeply understand the guiding trend of popular culture on facial attractiveness in different times.

From a sketch image or text description, generating a semantic and photographic face image has always been an extremely important issue in computer vision. Sketch images generally contain only simple profile information but not the detail of the face. Therefore, it is difficult to generate facial attributes accurately. In this paper, we treat the sketch to face the problem as a face hallucination reconstruction problem. In order to solve this problem, we propose an image translation network by exploiting attributes with the generated adversarial network. And it can significantly contribute to the authenticity of the generated face by supplementing sketch image with the additional facial attribute feature. The generator network is composed of a feature extracting network and downsampling-upsampling network, both networks use skip-connection to reduce the number of layers without affecting network performance. The discriminator network is designed to examine whether the generated faces contain the desired attributes or not. In the underlying feature extraction phase, our network is different from most attribute-embedded networks, we fuse the sketch images and attributes perceptually. We set the network sub-Branch A and B, which receive a sketch image and attribute vector in order to extract low-level profile information and high-level semantic features. Compared with the state-of-the-art methods of image translation, the performance of the proposed network is excellent.

In this paper, we study a relay-aided multiuser amplify-and-forward relaying system where a multi-antenna base station (BS) transmits downlink to multiple single-antenna mobile stations (MSs) via a multi-antenna relay station (RS). We propose a novel precoder design scheme by using the joint integer-forcing method to achieve high rate with the algebraic structure of lattice codes. Inspired by the concept of decoding the linear equations in compute-and-forward relay systems, the linear precoding matrix is generalized as an integer matrix that is approximate to the channel matrix, thereby mitigating the multiuser interference (MUI) at the MSs. The power allocation optimization problem at the BS is taken into account to improve the performance of the scheme. The simulation results show the outstanding performance of the proposed scheme.

By means of additional specific heat capacity and momentum source terms methods, the physical and mathematical models of the heat transfer process of waxy crude oil during its static cooling in the vault tank are established. Through the numerical simulations for the cooling process of waxy crude oil in the vault tank, the detailed information about the flow field formed by natural convection together with the evolution of the temperature field is presented synchronously. Furthermore, more concern is taken on the evolution characteristics of the gelatinization structure of waxy crude oil and the relation between the distribution of the temperature field and flow field. Some conclusions include that it presents the significant coupling relationship between the natural convection and temperature field during the cooling. And the transition of the heat transfer mechanism from natural convection to heat conduction mainly contributes to the evolution of the thermal performance of waxy crude oil during its static cooling.

Neoadjuvant chemotherapy (NAC) has become the main treatment option for breast cancer. Its adverse drug reactions (ADRs) make NAC painful both physiologically and psychologically. The factor pathological complete remission (pCR) describes how well a series of six or more chemotherapeutic treatments works on a patient. This study investigated the possibility of predicting pCR using only the nodal sizes of the first three treatments. A best feature combination for each breast cancer subtype was screened from the real nodal sizes of the first three treatments and the nodal sizes` of the next three treatments predicted from those of the first three ones. The prediction was evaluated by the metrics Avc = (sensitivity + specificity)/2. A triple-negative breast cancer (TN) patient may have an estimation of pCR Avc = 0.8696 after taking just three treatments. At least Avc = 0.7594 was achieved for all the four breast cancer subtypes investigated in this study.

We show that quasi-single-mode transmission in few-mode fibers (FMFs) can reduce the number of spans for a fixed transmission distance and, consequently, the cost per bit for terrestrial transmission systems by minimizing both the Capex and Opex. The Gaussian-noise model is employed to estimate the nonlinear noise power spectral density both, which depends on the effective area of the FMF and span length, for Er-doped fiber amplifier (EDFA) and hybrid Raman/EDFA systems. Together with amplified spontaneous emission noise, an optical signal-to-noise ratio (OSNR) for a fixed transmission distance as a function of the effective area of the FMF and span length can be obtained. Given a target OSNR for a particular modulation format, we determine the maximum span length or the minimum number of spans as the effective area of the FMF varies both analytically and through numerical simulations. The effect of multipath interference in the FMF on the minimum number of span has also been investigated.

This paper introduces a new multicarrier system, named sparse orthogonal circulant transform multiplexing (S-OCTM), for optical fiber communication. This technique uses an inverse sparse orthogonal circulant transform (S-OCT) matrix, which is simple and contains only two nonzero elements in each column, to multiplex information of different subcarriers. We compared the proposed scheme with conventional orthogonal frequency division multiplexing (OFDM), orthogonal chirp division multiplexing (OCDM), and discrete-Fourier-transform spreading OFDM (DFT-S-OFDM) in a coherent optical communication system. It is shown that S-OCTM, while exhibiting the complexity among the least, avoids the performance disadvantages of all investigated conventional schemes. It is theoretically proved that the S-OCT matrix equalizes the bandwidth limitation effect that degrades the performance of conventional OFDM. It also shows a greatly reduced peak-to-average power ratio and higher tolerance to fiber nonlinearity than OFDM and OCDM. On the other hand, compared to DFT-S-OFDM, S-OCTM shows a better dispersion tolerance under insufficient length of cyclic prefix and is more tolerable to strong optical filtering. The performance advantages and low complexity enable the proposed scheme to be a promising multicarrier solution for optical communications.

The unit commitment problem is a large-scale, nonlinear, and mixed-integer optimization problem in an electric power system. Numerous researchers concentrate on minimizing its total generation cost. Cuckoo search is an efficient metaheuristic swarm-based approach that balances between local and global search strategy. Owing to its easy implementation and rapid convergence, it has been successfully used to solve a wide variety of optimization problems. This paper proposes an improved binary cuckoo search algorithm (IBCS) for solving the unit commitment problem. A new binary updating mechanism is introduced to help the IBCS choose a right search direction, and a heuristic search method based on a novel priority list can prevent it from being trapped into local optima. A 4-unit system is used as an example to validate the effectiveness of the proposed method.