Bio-syncretic robots consisting of both living biological materials and non-living systems possess desirable attributes such as high energy efficiency, intrinsic safety, high sensitivity, and self-repairing capabilities. Compared with living biological materials or non-living traditional robots based on electromechanical systems, the combined system of a bio-syncretic robot holds many advantages. Therefore, developing bio-syncretic robots has been a topic of great interest, and significant progress has been achieved in this area over the past decade. This review systematically summarizes the development of bio-syncretic robots. First, potential trends in the development of bio-syncretic robots are discussed. Next, the current performance of bio-syncretic robots, including simple movement and controllability of velocity and direction, is reviewed. The living biological materials and non-living materials that are used in bio-syncretic robots, and the corresponding fabrication methods, are then discussed. In addition, recently developed control methods for bio-syncretic robots, including physical and chemical control methods, are described. Finally, challenges in the development of bio-syncretic robots are discussed from multiple viewpoints, including sensing and intelligence, living and non-living materials, control approaches, and information technology.
The type, model, quantity, and location of sensors installed on the intelligent vehicle test platform are different, resulting in different sensor information processing modules. The driving map used in intelligent vehicle test platform has no uniform standard, which leads to different granularity of driving map information. The sensor information processing module is directly associated with the driving map information and decision-making module, which leads to the interface of intelligent driving system software module has no uniform standard. Based on the software and hardware architecture of intelligent vehicle, the sensor information and driving map information are processed by using the formal language of driving cognition to form a driving situation graph cluster and output to a decision-making module, and the output result of the decision-making module is shown as a cognitive arrow cluster, so that the whole process of intelligent driving from perception to decision-making is completed. The formalization of driving cognition reduces the influence of sensor type, model, quantity, and location on the whole software architecture, which makes the software architecture portable on different intelligent driving hardware platforms.
Cycling is an eco-friendly method of transport and recreation. With the intent of reducing the energy cost of cycling without providing an additional energy source, we have proposed the use of a torsion spring for knee-extension support. We developed an exoskeleton prototype using a crossing four-bar mechanism as a knee joint with an embedded torsion spring. This study evaluates the passive knee exoskeleton using constant-power cycling tests performed by eight healthy male participants. We recorded the surface electromyography over the rectus femoris muscles of both legs, while the participants cycled at 200 and 225 W on a trainer with the developed wheel-accelerating system. We then analyzed these data in time–frequency via a continuous wavelet transform. At the same cycling speed and leg cadence, the median power spectral frequency of the electromyography increases with cycling load. At the same cycling load, the median power spectral frequency decreases when cycling with the exoskeleton. Quadriceps activity can be relieved despite the exoskeleton consuming no electrical energy and not delivering net-positive mechanical work. This fundamental can be applied to the further development of wearable devices for cycling assistance.
The randomness and complexity of urban traffic scenes make it a difficult task for self-driving cars to detect drivable areas. Inspired by human driving behaviors, we propose a novel method of drivable area detection for self-driving cars based on fusing pixel information from a monocular camera with spatial information from a light detection and ranging (LIDAR) scanner. Similar to the bijection of collineation, a new concept called co-point mapping, which is a bijection that maps points from the LIDAR scanner to points on the edge of the image segmentation, is introduced in the proposed method. Our method positions candidate drivable areas through self-learning models based on the initial drivable areas that are obtained by fusing obstacle information with superpixels. In addition, a fusion of four features is applied in order to achieve a more robust performance. In particular, a feature called drivable degree (DD) is proposed to characterize the drivable degree of the LIDAR points. After the initial drivable area is characterized by the features obtained through self-learning, a Bayesian framework is utilized to calculate the final probability map of the drivable area. Our approach introduces no common hypothesis and requires no training steps; yet it yields a state-of-art performance when tested on the ROAD-KITTI benchmark. Experimental results demonstrate that the proposed method is a general and efficient approach for detecting drivable area.
Finding an optimal trajectory from an initial point to a final point through closely packed obstacles, and controlling a Hilare robot through this trajectory, are challenging tasks. To serve this purpose, path planners and trajectory-tracking controllers are usually included in a control loop. This paper highlights the implementation of a trajectory-tracking controller on a stepper motor-driven Hilare robot, with a trajectory that is described as a set of waypoints. The controller was designed to handle discrete waypoints with directional discontinuity and to consider different constraints on the actuator velocity. The control parameters were tuned with the help of multi-objective particle swarm optimization to minimize the average cross-track error and average linear velocity error of the mobile robot when tracking a predefined trajectory. Experiments were conducted to control the mobile robot from a start position to a destination position along a trajectory described by the waypoints. Experimental results for tracking the trajectory generated by a path planner and the trajectory specified by a user are also demonstrated. Experiments conducted on the mobile robot validate the effectiveness of the proposed strategy for tracking different types of trajectories.
Powdery mildew, which is caused by Blumeria graminis f. sp. tritici (Bgt), is an important leaf disease that affects wheat yield. Powdery mildew-resistance (Pm) gene Pm21 was first transferred into wheat in the 1980s, by translocating the Heuchera villosa chromosome arm 6VS to the wheat chromosome arm 6AL (6VS·6AL). Recently, new Bgt isolates that are virulent to Pm21 have been identified in some wheat fields, indicating that wheat breeders should be aware of the risk of deploying Pm21, although pathological details regarding these virulent isolates still remain to be discovered. Pm40 was identified and mapped on the wheat chromosome arm 7BS from several wheat lines developed from the progenies of a wild cross between wheat and Thinopyrum intermedium. Pm40 offers a broad spectrum of resistance to Bgt, which suggests that it is likely to provide potentially durable resistance. Cytological methods did not detect any large alien chromosomal segment in the wheat lines carrying Pm40. Lines with Pm40 and promising agronomical traits have been released by several wheat-breeding programs in the past several years. Therefore, we believe that Pm40 will play a role in powdery mildew-resistance wheat breeding after Pm21 resistance is overcome by Bgt isolates. In addition, both Pm21 and Pm40 were derived from alien species, suggesting that the resistance genes derived from alien species are potentially more durable or effective than those identified from wheat.
Wheatgrasses (Thinopyrum spp.), which are relatives of wheat (Triticum aestivum L.), have a perennial growth habit and offer resistance to a diversity of biotic and abiotic stresses, making them useful in wheat improvement. Many of these desirable traits from Thinopyrum spp. have been used to develop wheat cultivars by introgression breeding. The perennial growth habit of wheatgrasses inherits as a complex quantitative trait that is controlled by many unknown genes. Previous studies have indicated that Thinopyrum spp. are able to hybridize with wheat and produce viable/stable amphiploids or partial amphiploids. Meanwhile, efforts have been made to develop perennial wheat by domestication of Thinopyrum spp. The most promising perennial wheat–Thinopyrum lines can be used as grain and/or forage crops, which combine the desirable traits of both parents. The wheat–Thinopyrum lines can adapt to diverse agricultural systems. This paper summarizes the development of perennial wheat based on Thinopyrum, and the genetic aspects, breeding methods, and perspectives of wheat–Thinopyrum hybrids.
Wheat grown under rain-fed conditions is often affected by drought worldwide. Future projections from a climate simulation model predict that the combined effects of increasing temperature and changing rainfall patterns will aggravate this drought scenario and may significantly reduce wheat yields unless appropriate varieties are adopted. Wheat is adapted to a wide range of environments due to the diversity in its phenology genes. Wheat phenology offers the opportunity to fight against drought by modifying crop developmental phases according to water availability in target environments. This review summarizes recent advances in wheat phenology research, including vernalization (Vrn), photoperiod (Ppd), and also dwarfing (Rht) genes. The alleles, haplotypes, and copy number variation identified for Vrn and Ppd genes respond differently in different climatic conditions, and thus could alter not only the development phases but also the yield. Compared with the model plant Arabidopsis, more phenology genes have not yet been identified in wheat; quantifying their effects in target environments would benefit the breeding of wheat for improved drought tolerance. Hence, there is scope to maximize yields in water-limited environments by deploying appropriate phenology gene combinations along with Rht genes and other important physiological traits that are associated with drought resistance.
Global demand for vegetable oil is anticipated to double by 2030. The current vegetable oil production platforms, including oil palm and temperate oilseeds, are unlikely to produce such an expansion. Therefore, the exploration of novel vegetable oil sources has become increasingly important in order to make up this future vegetable oil shortfall. Triacylglycerol (TAG), as the dominant form of vegetable oil, has recently attracted immense interest in terms of being produced in plant vegetative tissues via genetic engineering technologies. Multidiscipline-based “-omics” studies are increasingly enhancing our understanding of plant lipid biochemistry and metabolism. As a result, the identification of biochemical pathways and the annotation of key genes contributing to fatty acid biosynthesis and to lipid assembly and turnover have been effectively updated. In recent years, there has been a rapid development in the genetic enhancement of TAG accumulation in high-biomass plant vegetative tissues and oilseeds through the genetic manipulation of the key genes and regulators involved in TAG biosynthesis. In this review, current genetic engineering strategies ranging from single-gene manipulation to multigene stacking aimed at increasing plant biomass TAG accumulation are summarized. New directions and suggestions for plant oil production that may help to further alleviate the potential shortage of edible oil and biodiesel are discussed.
Heterodera glycines (i.e., soybean cyst nematode, SCN) is the most damaging nematode pest affecting soybean crop worldwide. This nematode is managed by means of crop rotation with selected resistant sources. With increasing reports of virulent SCN populations that are able to break the resistance within commonly used sources, there is an increasing need to find new sources of resistance or to broaden the resistance background. This review summarizes recent findings about the genes controlling SCN resistance in soybean, and about how these genes interact to confer resistance against SCN in soybean. It also provides an update on molecular mapping and molecular markers that can be used for the mass selection and differentiation of different resistance lines and cultivars in order to expedite conventional breeding programs. In-depth knowledge of SCN parasitism proteins and soybean resistance responses to the pathogen is critical for the diversification of resistant sources through gene modification, gene stacking, or incorporation of novel sources of resistance through backcrossing or genetic engineering.
Field pea (Pisum sativum var. arvense L.) is an important legume crop around the world. It produces grains with high protein content and can improve the amount of available nitrogen in the soil. Aphanomyces root rot (ARR), caused by the soil-borne oomycete Aphanomyces euteiches Drechs. (A. euteiches), is a major threat to pea production in many pea-growing regions including Canada; it can cause severe root damage, wilting, and considerable yield losses under wet soil conditions. Traditional disease management strategies, such as crop rotations and seed treatments, cannot fully prevent ARR under conditions conducive for the disease, due to the longevity of the pathogen oospores, which can infect field pea plants at any growth stage. The development of pea cultivars with partial resistance or tolerance to ARR may be a promising approach to analyze the variability and physiologic specialization of A. euteiches in field pea and to improve the management of this disease. As such, the detection of quantitative trait loci (QTL) for resistance is essential to field pea-breeding programs. In this paper, the pathogenic characteristics of A. euteiches are reviewed along with various ARR management strategies and the QTL associated with partial resistance to ARR.
In recent years, wheat yield per hectare appears to have reached a plateau, leading to concerns for future food security with an increasing world population. Since its invention, synthetic hexaploid wheat (SHW) has been shown to be an effective genetic resource for transferring agronomically important genes from wild relatives to common wheat. It provides new sources for yield potential, drought tolerance, disease resistance, and nutrient-use efficiency when bred conventionally with modern wheat varieties. SHW is becoming more and more important for modern wheat breeding. Here, we review the current status of SHW generation, study, and application, with a particular focus on its contribution to wheat breeding. We also briefly introduce the most recent progress in our understanding of the molecular mechanisms for growth vigor in SHW. Advances in new technologies have made the complete wheat reference genome available, which offers a promising future for the study and applications of SHW in wheat improvement that are essential to meet global food demand.
Assessing the adsorption properties of nanoporous materials and determining their structural characterization is critical for progressing the use of such materials for many applications, including gas storage. Gas adsorption can be used for this characterization because it assesses a broad range of pore sizes, from micropore to mesopore. In the past 20 years, key developments have been achieved both in the knowledge of the adsorption and phase behavior of fluids in ordered nanoporous materials and in the creation and advancement of state-of-the-art approaches based on statistical mechanics, such as molecular simulation and density functional theory. Together with high-resolution experimental procedures for the adsorption of subcritical and supercritical fluids, this has led to significant advances in physical adsorption textural characterization. In this short, selective review paper, we discuss a few important and central features of the underlying adsorption mechanisms of fluids in a variety of nanoporous materials with well-defined pore structure. The significance of these features for advancing physical adsorption characterization and gas storage applications is also discussed.
Traditional optimization models often lack a systems-level perspective at conception, which limits their effectiveness. Expanding system boundaries allow scientists and engineers to model complex interactions more accurately, leading to higher efficiency and profitability in industrial systems. Ecological systems have evolved for billions of years under conditions of material and energy shortage, and ecologists have defined analysis tools and metrics for identifying important principles. These principles may provide the framework to circumvent the limitations of traditional optimization techniques. More specifically, by recruiting functional roles that are often found in ecological systems, but are absent in industrial systems, industries can better mimic how natural systems organize themselves. The objective of this analysis is to traditionally optimize a manufacturing process by comparing the model with ecological and resource-based performance metrics in order to redesign the model with the addition of important functional roles that are found throughout nature. Industry partners provided data for this analysis, which involved building a water network for an existing steel manufacturing facility in China. The results of the traditional optimization model indicate a 23%, 29%, and 20% decline in freshwater consumption, wastewater discharge, and total annual cost, respectively. However, our ecologically inspired optimization model provides an additional 21% and 25% decline in freshwater consumption and total annual cost, respectively. Furthermore, no water is discharged. These results suggest that this unconventional approach to optimization could provide an effective technique not used by existing algorithms to solve the challenging problem of pursuing more sustainable industrial systems.
Organic solid and liquid wastes contain large amounts of energy, nutrients, and water, and should not be perceived as merely waste. Recycling, composting, and combustion of non-recyclables have been practiced for decades to capture the energy and values from municipal solid wastes. Treatment and disposal have been the primary management strategy for wastewater. As new technologies are emerging, alternative options for the utilization of both solid wastes and wastewater have become available. Considering the complexity of the chemical, physical, and biological properties of these wastes, multiple technologies may be required to maximize the energy and value recovery from the wastes. For this purpose, biorefining tends to be an appropriate approach to completely utilize the energy and value available in wastes. Research has demonstrated that non-recyclable waste materials and bio-solids can be converted into usable heat, electricity, fuel, and chemicals through a variety of processes, and the liquid waste streams have the potential to support crop and algae growth and provide other energy recovery and food production options. In this paper, we propose new biorefining schemes aimed at organic solid and liquid wastes from municipal sources, food and biological processing plants, and animal production facilities. Four new breakthrough technologies—namely, vacuum-assisted thermophilic anaerobic digestion, extended aquaponics, oily wastes to biodiesel via glycerolysis, and microwave-assisted thermochemical conversion—can be incorporated into the biorefining schemes, thereby enabling the complete utilization of those wastes for the production of chemicals, fertilizer, energy (biogas, syngas, biodiesel, and bio-oil), foods, and feeds, and resulting in clean water and a significant reduction in pollutant emissions.