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The studies of Unmanned Air/Ground Vehicle (UAV/UGV) cooperative detection systems have received much attention due to their wide applications in the disaster rescue, target tracking, intelligent surveillance, and automatical package delivery missions. UAVs provide a broad view and have a fast speed in the air, while UGVs have sufficient load capacity and can serve as repeater stations on the ground. The path planning of a UAV/UGV cooperative system is an important but difficult issue, which aims to plan paths for both the UAVs and the UGVs in the system to cooperatively complete a mission. In this article, we consider the path planning problem of the UAV/UGV cooperative system for illegal urban building detection, by taking the limits of UGV speed, UAV load power, and UAV/UGV communication restriction into consideration. To solve this problem, we first model the path planning problem as a constraint optimization problem which tries to minimize an overall execution time for completing the illegal urban building detection tasks, and then propose a two-level memetic algorithm (called Two-MA) to solve the path planning problems of both the UAV and the UGV. Experiments on both synthetic and real-world data sets show the superiority of the proposed Two-MA over several states-of-the-art algorithms in solving the path planning problems of the UAV and UGV for illegal urban building detection tasks.

This study tested the concurrent and construct validity of smartphone accelerometry measurement of sit-to-stand (STS) performance in individuals aged 65–89 years. Normal and fast STS times were recorded by smartphone accelerometer, force plate, and video motion systems concurrently, and isokinetic knee extension power and STS whole-body power were obtained. Normal and fast speed STS times from a smartphone accelerometer agreed closely with force plate and motion system methods (mean difference = 0.04 s). Normal and fast STS times were inversely related to isokinetic knee extension power (r = −.93, p r = −.82, p r = −.76, p r = −.70, p

Great advances have been made in mass spectrometric data interpretation for intact glycopeptide analysis. However, accurate identification of intact glycopeptides and modified saccharide units at the site-specific level and with fast speed remains challenging. Here, we present a glycan-first glycopeptide search engine, pGlyco3, to comprehensively analyze intact N- and O-glycopeptides, including glycopeptides with modified saccharide units. A glycan ion-indexing algorithm developed for glycan-first search makes pGlyco3 5–40 times faster than other glycoproteomic search engines without decreasing accuracy or sensitivity. By combining electron-based dissociation spectra, pGlyco3 integrates a dynamic programming-based algorithm termed pGlycoSite for site-specific glycan localization. Our evaluation shows that the site-specific glycan localization probabilities estimated by pGlycoSite are suitable to localize site-specific glycans. With pGlyco3, we confidently identified N-glycopeptides and O-mannose glycopeptides that were extensively modified by ammonia adducts in yeast samples. The freely available pGlyco3 is an accurate and flexible tool that can be used to identify glycopeptides and modified saccharide units.

Due to the influence of environmental interference and too fast speed, there are some problems in ski motion capture, such as inaccurate motion capture, motion delay, and motion loss, resulting in the inconsistency between the actual motion of later athletes and the motion of virtual characters. To solve the above problems, a real-time skiing motion capture method of snowboarders based on a 3D vision sensor is proposed. This method combines the Time of Fight (TOF) camera and high-speed vision sensor to form a motion acquisition system. The collected motion images are fused to form a complete motion image, and the pose is solved. The pose data is bound with the constructed virtual character model to drive the virtual model to synchronously complete the snowboarding motion and realize the real-time capture of skiing motion. The results show that the motion accuracy of the system is as high as 98.6%, which improves the capture effect, and the motion matching proportion is better and more practical. It is also excellent in the investigation of motion delay and motion loss.

The evidence of the responsiveness of dopaminergic medication on gait in patients with Parkinson’s disease is contradicting. This could be due to differences in complexity of the context gait was in performed. This study analysed the effect of dopaminergic medication on arm swing, an important movement during walking, in different contexts. Forty-five patients with Parkinson’s disease were measured when walking at preferred speed, fast speed, and dual-tasking conditions in both OFF and ON medication states. At preferred, and even more at fast speed, arm swing improved with medication. However, during dual-tasking, there were only small or even negative effects of medication on arm swing. Assuming that dual-task walking most closely reflects real-life situations, the results suggest that the effect of dopaminergic medication on mobility-relevant movements, such as arm swing, might be small in everyday conditions. This should motivate further studies to look at medication effects on mobility in Parkinson’s disease, as it could have highly relevant implications for Parkinson’s disease treatment and counselling.

When reaching to targets within arm’s reach, intentional trunk motion must be neutralized by compensatory motion of the upper limb (UL). Advanced age has been associated with deterioration in the coordination of multi-joint UL movements. In the current study, we looked to determine if older adults also have difficulties modifying their UL movements (i.e., coordination between the shoulder and elbow joints), during a complex reaching task when trunk motion is manipulated. Two groups of healthy participants were recruited: 18 young (mean age = 24.28 ± 2.89 years old) and 18 older (mean age = 72.11 ± 2.39 years old) adults. Participants reached to a target with their eyes closed, while simultaneously moving the trunk forward. In 40% of trials, the trunk motion was unexpectedly blocked. Participants performed the task with both their dominant and non-dominant arms, and at a preferred and fast speed. All participants were able to coordinate motion at the elbow and shoulder joints in a similar manner and modify this coordination in accordance with motion at the trunk, regardless of the hand used or speed of movement. Specifically, in reaches that involved forward trunk motion (free-trunk trials), all participants demonstrated increased elbow flexion (i.e., less elbow extension) compared to blocked-trunk trials. In contrast, when trunk motion was blocked (blocked-trunk trials), all reaching movements were accompanied by increased shoulder horizontal adduction. While coordination of UL joints was similar across older and young adults, the extent of changes at the elbow and shoulder was smaller and less consistent in older adults compared to young participants, especially when trunk motion was involved. These results suggest that older adults can coordinate their UL movements based on task requirements, but that their performance is not as consistent as young adults.

Gait speed estimation using wearable inertial sensors during daily activities suffers from high complexity and inaccuracies in distance estimation when integrating acceleration signals. The aim of the study was to investigate the agreement between the methods of gait speed estimation using the persons’ walk ratio (step-length/step-frequency relation) or step-frequency (number of steps per minute) and a “gold standard”. For this cross-sectional validation study, 20 healthy community-dwelling older persons (mean age 72.1 years; 70% women) walked at slow, normal, and fast speed over an instrumented walkway (reference measure). Gait speed was calculated using the person’s pre-assessed walk ratio. Furthermore, the duration of walking and number of steps were used for calculation. The agreement between gait speed calculation using the walk ratio or step-frequency (adjusted to body height) and reference was r = 0.98 and r = 0.93, respectively. Absolute and relative mean errors of calculated gait speed using pre-assessed walk ratio ranged between 0.03–0.07 m/s and 1.97–4.17%, respectively. After confirmation in larger cohorts of healthy community-dwelling older adults, the mean gait speed of single walking bouts during activity monitoring can be estimated using the person’s pre-assessed walk ratio. Furthermore, the mean gait speed can be calculated using the step-frequency and body height and can be an additional parameter in stand-alone activity monitoring.

High-performance solar-blind UV detector with high response and fast speed is needed in multiple types of areas, which is hard to achieve in one device with a simple structure and device fabrication process. Here, the effects of Ag nanoparticles (NPs) with different sizes on UV response characteristics of the device are studied, the Ag NPs with different sizes that are made from a simple vacuum anneal method. Ag NPs with different sizes could modulate the peak response position of the mixed-phase MgZnO detector from near UV range (350 nm) to deep UV range (235 nm), and the enhancement effect of the Ag NPs on the UV response differs much with the crystal structure and the basic UV response of the MgZnO thin film. When high density 20-40 nm Ag NPs is induced, the deep UV (235 nm) response of the mixed-phase MgZnO detector is increased by 226 times, the I uv/I dark ratio of the modified device is increased by 17.5 times. The slight enhancement in UV light intensity from 20 to 40 nm Ag NPs induces multiple tunnel breakdown phenomena within the mixed-phase MgZnO thin film, which is the main reason for the abnormal great enhancement effect on deep UV response of the device, so the recovery speed of the modified device is not influenced. Therefore, Ag NPs with different sizes could effectively modulate the UV response peak position of mixed-phase MgZnO thin films, and the introduction of Ag NPs with high density and small size is a simple way to greatly increase the sensitivity of the mixed-phase MgZnO detector at deep UV light without decreasing the device speed. Published version This work was supported by the National Natural Science Foundation of China under grant nos. (51872187, 11774241, 51371120, 51302174, 61704111, 61504083, and 61574051), the Natural Science Foundation of Guangdong Province (2016A030313060 and 2017A030310524), the Public Welfare Capacity Building in Guangdong Province (2015A010103016), the Project of Department of Education of Guangdong Province (2014KTSCX110), the Science and Technology Research Items of Shenzhen (JCYJ20170818144255777, JCYJ20170818144212483, JCYJ20180507182248925, JCYJ201602261920, JCYJ20170818143417082, and JCYJ20160226192033020), the Science and Technology Foundation of Shenzhen, and the National Key Research and Development Program of China (2017YFB0404100 and 2017YFB0403000).

The outbreak of the new type of coronavirus pneumonia (COVID-19) has caused a huge impact on the world. In this case, only by adhering to the prevention and control methods of early diagnosis, early isolation, and early treatment, can the spread of the virus be prevented to the greatest extent. This article uses artificial intelligence–assisted medical imaging diagnosis as the research object, combines artificial intelligence and CT medical imaging diagnosis, introduces an intelligent COVID-19 detection system, and uses it to achieve COVID-19 disease screening and lesion evaluation. CT examination has the advantages of fast speed and high accuracy, which can provide a favorable basis for clinical diagnosis. This article collected 32 lung CT scan images of patients with confirmed COVID-19. Two professional radiologists analyzed the CT images using traditional imaging diagnostic methods and artificial intelligence–assisted imaging diagnostic methods, and the comparison showed the gap between the two methods. According to experiments, CT imaging diagnosis assisted by artificial intelligence only takes 0.744 min on average, which can save a lot of time and cost compared with the average time of 3.623 min for conventional diagnosis. In terms of comprehensive test accuracy, it can be concluded that the combination of artificial intelligence and imaging diagnosis has extremely high application value in COVID-19 diagnosis.

We first report two-dimensional (2D) perovskite Ca2Nb3O10 ultraviolet photodetectors (UV PDs), which are prepared via a facile calcination-exfoliation method. The 2D Ca2Nb3O10 PDs demonstrate high performance at 3 V at 280 nm, high responsivity (14.94 A W-1), high detectivity (8.7 × 1013 Jones), high spectral selectivity (R280/R400 = 8.84 × 103), fast speed (0.08/5.6 ms), and long-term stability, exceeding those of most reported UV PDs. Furthermore, the Ca2Nb3O10 PDs integrated with poly(ethylene terephthalate) (PET) show excellent flexibility and have high linear dynamic range (96 dB). Our work provides a general strategy for searching new UV PDs based on numerous layered niobates. The Ca2Nb3O10 nanosheets may be one of the optimum semiconductor materials for next-generation high-performance UV PDs.

High performance p-GaN/oxide layer/n-GaN ultraviolet (UV) photodetector was fabricated in this paper. The UV detector composed of n-GaN and p-GaN film with oxide layer which was constructed by directly contacting way. The detector based on GaN p-GaN/oxide layer/n-GaN structure showed high UV response with fast speed. The results indicated that the fabrication of large-scale GaNbased materials was greatly facilitated with relatively low cost contacting method. Furthermore, it offered a new method to obtain UV detector for GaN-based materials with high performance.

In recent years, target detection framework based on deep learning has made brilliant achievements. However, real-life traffic sign detection remains a great challenge for most of the state-of-the-art object detection methods. The existing deep learning models are inadequate to effectively extract the features of small traffic signs from large images in real-world conditions. In this paper, we address the small traffic sign detection challenge by proposing a novel small traffic sign detection method based on a highly efficient end-to-end deep network model. The proposed method features fast speed and high precision as it attaches three key insights to the established You Only Look Once (YOLOv3) architecture and other correlated algorithms. Besides, network pruning is appropriately introduced to minimize network redundancy and model size while keeping a competitive detection accuracy. Furtherly, four scale prediction branches are also adopted to significantly enrich the feature maps of multi-scales prediction. In our method, we adjust the loss function to balance the contribution of error source to the total loss. The effectiveness, and robustness of the network is further proved with experiments on Tsinghua-Tencent 100 K traffic sign dataset. The experimental results indicate that our proposed method has achieved better accuracy than that of the original YOLOv3 model. Compared with the schemes in relevant literatures our proposed method not only emerges performance superiors in detection recall and accuracy, but also achieves 1.9–2.7x improvement in detection speed.

Image segmentation of moving target in complex background has a good application prospect. However, many factors cause the gray level and content of the image to change between adjacent two frames. Based on the research and analysis of various moving target segmentation algorithms, this paper proposes a new dynamic segmentation algorithm based on dynamic contours based on the principle of simple calculation, fast speed and accurate extraction of moving targets. Analysis and experiments prove that the algorithm needs to adjust less parameters and has strong anti-interference ability, which can effectively eliminate the occlusion of moving targets between multiple frames. Moreover, for complex backgrounds and non-overlapping multi-target motion situations, the outer contours of individual moving targets can be accurately located. In addition, the algorithm has a potential parallel mechanism and is easy to implement real-time moving image segmentation.

Photovoltaic microgrids have discontinuity and intermittency issues that affect the load current amplitude and frequency, and this leads to disturbances. The unified power quality conditioner (UPQC) combines a series active power filter (SAPF) and a parallel active power filter (PAPF), and therefore, it has the advantages of SAPF and PAPF to treat power quality problems. Considering detection precision and response speed, and to meet the fast speed and accuracy of PAPF harmonic detection, the algorithm principle of adaptation and least-squares lattice (LSL) is analyzed. A new harmonic detection method of three-phase circuit based on LSL adaptive algorithm is proposed. The two other harmonic detection methods between least mean square and instantaneous power theory based are compared using the Simulink simulation platform. The compensation effect of LSL is more stable and better than the two other methods. The experimental results also indicate that the new method based on LSL can accurately detect and compensate the harmonic component in photovoltaic microgrids in real time. Therefore, this harmonic detection method based on LSL can be used in the practical engineering applications, and it provides a new method for harmonic detection of UPQC PAPF.

Rumor, with the fast speed of transmission, may bring us panic, even economic loss. Thus, it is significant for us to take effective steps to control the rumor spreading. Unfortunately, most of the existing works ignore that the spreading probability is not a constant, but depends on the number of spreaders currently. That is to say, the more spreaders, the larger spreading probability. In order to overcome this shortcoming, in this paper, we propose a novel susceptible–infected–removed (SIR) rumor spreading model with the influence mechanism, called SIR-IM, which first incorporates the number of current spreaders into the spreading probability. Then, it employs time function to describe the rate of people from spreader to stifler as time goes on. Moreover, we not only derive mean-field equations to describe the dynamics of our SIR model, but also give theoretical analysis. Numerical simulations are conducted on social networks, which show that the influence mechanism can accelerate the rumor spreading.

The next generation of manufacturing system calls for feasible solutions with high efficiency and flexibility. Thus in recent years, Human-Robot Collaboration (HRC) research attracts many attentions worldwide since it unites the repeatability and accuracy of robots and the adaptivity and intelligence of human operators. In this paper, the system architecture of an HRC solution is presented, together with the novel method of vision sensing and controlling towards a smart solution with high accuracy and fast speed. The proposed method is demonstrated through implementations in the real robot cell, and evaluated by quantifiable measurements. Future research outlooks are addressed at the end of the paper.

Purpose With the continuous upgrading of the production mode of the manufacturing system, the characteristics of multi-variety, small batch and mixed fluidization are presented, and the production environment becomes more and more complex. To improve the efficiency of solving multi-objective flexible job shop scheduling problem (FJSP), an improved hybrid particle swarm optimization algorithm (IH-PSO) is proposed. Design/methodology/approach After reviewing literatures on FJSP, an IH-PSO algorithm for solving FJSP is developed. First, IH-PSO algorithm draws on the crossover and mutation operations of genetic algorithm (GA) algorithm and proposes a new method for updating particles, which makes the offspring particles inherit the superior characteristics of the parent particles. Second, based on the improved simulated annealing (SA) algorithm, the method of updating the individual best particles expands the search scope of the domain and solves the problem of being easily trapped in local optimum. Finally, analytic hierarchy process (AHP) is used in this paper to solve the optimal solution satisfying multi-objective optimization. Findings Through the benchmark experiment and the production example experiment, it is verified that the proposed algorithm has the advantages of high quality of solution and fast speed of convergence. Research limitations/implications This method does not consider the unforeseen events that occur during the process of scheduling and cause the disruption of normal production scheduling activities, such as machine breakdown. Practical implications IH-PSO algorithm combines PSO algorithm with GA and SA algorithms. This algorithm retains the advantage of fast convergence speed of traditional PSO algorithm and has the characteristic of inheriting excellent genes. In addition, the improved SA algorithm is used to solve the problem of falling into local optimum. Social implications This research provides an efficient scheduling method for solving the FJSP problem. Originality/value This research proposes an IH-PSO algorithm to solve the FJSP more efficiently and meet the needs of multi-objective optimization.

This paper simulates an ideal COVID-19 vaccine that confers immediate sterilizing immunity against all SARS-CoV-2 variants. The purpose was to explore how well this ideal vaccine could protect a population against common conditions (such as vaccine hesitancy) that might impair vaccine effectiveness. Simulations were done with an SEIRS spreadsheet model that ran two parallel subpopulations: one that accepted vaccination, and another that refused it. The two subpopulations could transmit infections to one another. Success was judged by the rate of new cases in the period from 1-5 years after the introduction of the vaccine.Under good conditions, including a small subpopulation that refused vaccination, rapid distribution of the vaccine, duration of vaccinal immunity longer than 12 months, good retention of interest in getting vaccinated after the first year, strict maintenance of nonpharmaceutical interventions (NPIs) such as masking, and new variants with R0s less than 4.0, the vaccine was able to end the epidemic. With violation of these conditions, the post-vaccine era futures ranged from endemic COVID at a low or medium level to rates of COVID cases worse than anything seen in the US up to late 2021. The most important conditions for keeping case rates low were a fast speed of vaccine distribution, a low percentage of the population that refuses vaccination, a long duration of vaccinal immunity, and continuing maintenance of NPIs after vaccination began. On the other hand, a short duration of vaccinal immunity, abandonment of NPIs, and new variants with a high R0 were powerful barriers to disease control. New variants with high R0s were particularly damaging, producing high case rates except when vaccination speed was unrealistically rapid. A recurring finding was that most disease afflicting the vaccinated population in these simulations originated in the unvaccinated population, and cutting off interaction with the unvaccinated population caused a sharp drop in the case rate of the vaccinated population.In conclusion, multiple common conditions can compromise the effectiveness of even an “ideal” vaccine.

In the elderly, walking while simultaneously engaging in other activities becomes more difficult. This study aimed to examine the changes in cortical activity during walking with aging. We try to reveal the effects of an additional task and increased walking speed on cortical activation in the young-old and the old-old elderly. Twenty-seven young-old (70.2 ± 3.0 years) and 23 old-old (78.0 ± 2.3 years) participated in this study. Each subject completed four walking tasks on the treadmill, a 2 × 2 design; two single-task (ST) walking conditions with self-selected walking speed (SSWS) and fast walking speed (FWS), and two dual-task (DT) walking conditions with SSWS and FWS. Functional near-infrared spectroscopy was applied for measurement of cerebral oxyhemoglobin (oxyHb) concentration during walking. Cortical activities were increased during DT conditions compared with ST conditions but decreased during the FWS compared with the SSWS on the primary leg motor cortex, supplementary motor area, and dorsolateral prefrontal cortex in both the young-old and the old-old. These oxyHb concentration changes were significantly less prominent in the old-old than in the young-old. This study demonstrated that changes in cortical activity during dual-task walking are lower in the old-old than in the young-old, reflecting the reduced adaptive plasticity with severe aging.

A continual change in resistance plays an important role in simulating the biological synapses and an abrupt switching mode helps to store information with fast speed and low-power operation. The manipulation of dual-mode switching and comprehension of switching mechanisms, which are the key to developing multifunctional resistance random access memory devices, has made little progress due to the complexity of the influencing factors. We observe that both gradual and abrupt reset behaviors exist in the $\mathrm{Ta}/{\mathrm{Ta}}_{2}{\mathrm{O}}_{5}/\mathrm{In}\text{\ensuremath{-}}\mathrm{Sn}\text{\ensuremath{-}}\mathrm{O}$ (ITO) structure, and the utilization of various compliance currents (${I}_{\mathrm{CC}}$) can control the reset processes of the device. The difference in switching behavior is a result of the different compositions of the conductive filaments (CFs). $\mathrm{Ta}$ and oxygen-vacancy dual filaments contribute to resistive switching at low ${I}_{\mathrm{CC}}$, whereas the change in valence states of ${\mathrm{Ta}\mathrm{O}}_{x}$ dominates the formation and rupture of CFs at high ${I}_{\mathrm{CC}}$. The introduction of a thin ${\mathrm{Mo}\mathrm{S}}_{2}$ intermediate layer also leads to a transition between abrupt- and gradual-switching modes. The $\mathrm{Ta}/{\mathrm{Mo}\mathrm{S}}_{2}/{\mathrm{Ta}}_{2}{\mathrm{O}}_{5}/\mathrm{ITO}$ device exhibits digital switching behavior with ultralow power consumption. Moreover, the $\mathrm{Ta}/{\mathrm{Ta}}_{2}{\mathrm{O}}_{5}/{\mathrm{Mo}\mathrm{S}}_{2}/\mathrm{ITO}$ device with an analog reset process can be applied to realize synaptic functions.

One of the suitable control schemes for the QUAV system is nonsingular fast terminal SMC (NFTSMC). Combined NFTSMC and FO, robustness against the disturbances, fast speed, singularity avoidance, fast convergence due to the existence of the fractional integral and derivative in the sliding manifolds, can be obtained. In addition, the use of the adaptive law with FONFTSMC, the upper bounds of the disturbances are coped.

Soft actuators that exhibit large deformation and can move at a fast speed in response to external stimuli have been in high demand for biomimetic applications. In this paper, we propose a convenient approach to fabricate a reversible and thermal-responsive composite hydrogel. Under the irradiation of visible light, the striped hydrogel can bend at a speed of up to 65.72°/s with carbon nanotubes loaded at a concentration of 3 mg/mL. A jellyfish-like miniature soft robot is made using this hydrogel. When driven by visible light, the robot can move at a maximum speed of 3.37 mm/s. Besides swimming, other motion modes, including walking and jumping, are also achieved by the robot. In addition, the robot can perform directional transportation of tiny objects. As a new actuation approach for the research of jellyfish-like miniature soft robots, this work is of great significance to the development of flexible bionic robots. Moreover, this work also offers some important insights into the research of biomimetic robots driven by visible light.

At present, many industrial applications urgently need a real-time high-accuracy automatic counting algorithm to realize the counting function of various objects. Some scholars are studying the use of deep learning algorithms to count objects in industrial applications. However, facing closely arranged and different objects, deep learning algorithms cannot achieve this function. Aiming at the application background of regular cuboids counting with a wide range of requirements, this paper provides a real-time high-accuracy automatic counting algorithm based on a deep understanding of the scene and a comprehensive use of multiple computer vision technologies. The algorithm can meet the stringent requirements of industrial applications and realize real-time high-accuracy automatic counting of closely arranged cuboids. And there is no requirement for whether the surfaces of the cuboids are the same, and there is no need to prepare the template pictures of the cuboids in advance. The algorithm mainly uses the characteristics of gaps between cuboids, and adopts improved Hough line detection to identify gaps, and finally realizes the counting function. The algorithm proposed in this paper has been verified on the counting of pillboxes in the industrial application of smart pharmacies. Compared with the deep learning algorithm, it has the characteristics of good interpretability, good generalization, fast speed and high accuracy.