Toward Efficient Urban Form in China

Land efficiency in urban China is examined, using Tianjin as a case study, from the perspective of agricultural land conservation; reduction in energy use, conventional pollution, and greenhouse gas emissions; and human time savings. Issues addressed include increased scatter on the periphery, over-consumption of industrial land, over fiscal dependence on land sales, and loss of valuable agricultural and environmental services land. Policy implications discussed include the need for greater variation in urban densities (leveraging already high densities in urban China – one-third the global median), less broad-brush agricultural land conservation policies, higher floor area ratios near rapid transit stations, etc.China, land conversion, land efficiency, land use policy, urban density

Investigating operations of industrial parks in Beijing: efficiency at different stages

Industrial parks enjoy significant importance in many countries and regions. This study presents a multi-stage operational process to evaluate the efficiency of parks at each stage using an empirical study of Beijing. The study finds that only three of 22 parks were efficient overall during 2006–2008 and two of 22 were efficient during 2009–2012. The promotion of business, facilitation of production, and rewards of economic returns are highly correlated stages for efficiency performance. The results suggest that Beijing’s government should expend more effort developing the potential to generate outputs given current land and investment inputs. In addition, it provides a tool to strengthen the organisational capacity development of industrial parks by emphasising their multi-dimensions in inputs and outputs, selecting the right competitors at the right organisational stage, locating sources of efficiency and inefficiency, and understanding progression and balance of internal stages during operation

Evaluation of environmental purification service for Urban Green Space in Nanjing

Urban environmental pollution intensifies with the acceleration of industrialization and urbanization. Urban green space plays an important role in improving the quality of urban environment. Statistical reports from 2002 to 2013 were analysed to estimate the environmental purification value of urban green space in Nanjing by using the production cost method and substituted expenses method. Results showed that the environmental purification value of urban green space from 2002 to 2013 increased from 0.212 billion to 0.354 billion RMB, showing an increase of 0.142 billion RMB and an annual average growth rate of 14% in the past 12 years. Carbon fixation and oxygen release of urban green space ecosystems are transferable in regional space; hence, these services can be performed by the natural ecosystems beyond the city. However, harmful gas absorption, dust detention and noise reduction of urban green space is not transferable in space and thus must be performed by the urban ecosystem. Therefore, aside from innovating technologies for pollution-reducing and pollution-controlling, increasing green space coverage, optimizing green plant distribution structure, and enhancing urban green space management must be executed to improve the urban ecological environment

Editorial: Neuromorphic engineering for robotics

Neuromorphic engineering aims to apply insights from neurobiology to develop next-generation artificial intelligence for computation, sensing, and the control of robotic systems. There has been a rapid expansion of neuromorphic engineering technologies for robotics due to several developments. First, the success and limitation of deep neural networks has greatly increased the belief that biological intelligence can further boost the computing performance of artificial intelligence in terms of data, power, and computing efficiency. Second, the emergence of novel neuromorphic hardware and sensors has shown greater application-level performance compared with conventional CPUs and GPUs. Third, the pace of progress in neuroscience has accelerated dramatically in recent years, providing a wealth of new understanding and insights regarding the functioning of brains at the neuron level. Therefore, neuromorphic engineering can represent a fundamental revolution for robotics in many ways. We have published this Research Topic to collect theoretical and experimental results regarding neuromorphic engineering technologies for the design, control, and real-world applications of robotic systems. After carefully and professionally reviewing all submissions, four high-quality manuscripts were accepted. These articles are reviewed below.Feldotto et al. propose a novel framework to examine the control of biomechanics using physics simulations informed by electromyography (EMG) data. These signals drive a virtual musculoskeletal model in the Neurorobotics Platform (NRP), which is then used to evaluate resulting joint torques. They use their framework to analyze raw EMG data collected during an isometric knee extension study to identify synergies that drive a musculoskeletal lower limb model. The NRP forms a highly modular integrated simulation platform that allows these in silico experiments. Their framework allows research of the neurobiomechanical control of muscles during tasks, which would otherwise not be possible. Gu et al. propose a novel American sign language (ASL) translation method based on wearable sensors. By leveraging the initial sensors to capture signs and surface electromyography (EMG) sensors to detect facial expressions, they can extract features from input signals. The encouraging results indicate that the proposed models are suitable for highly accurate sign language translation. With complete motion capture sensors and facial expression recognition methods, the sign language translation system has the potential to recognize more sentences. Ehrlich et al. demonstrate a neuromorphic adaptive control of a wheelchair-mounted robotic arm deployed on Intel's Loihi chip. The proposed controller provides the robotic arm with adaptive signals, guiding its motion while accounting for kinematic changes in real time. They further demonstrate the capacity of the controller to compensate for unexpected inertia-generating payloads using online learning. Akl et al. show how SNNs can be applied to different DRL algorithms, such as the deep Q-network (DQN) and the twin-delayed deep deterministic policy gradient (TD3), for discrete and continuous action space environments, respectively. They show that randomizing the membrane parameters, instead of selecting uniform values for all neurons, has stabilizing effects on the training. They conclude that SNNs can be used for learning complex continuous control problems with state-of-the-art DRL algorithms.Overall, we hope that this Research Topic can provide some references and novel ideas for the study of neuromorphic robotics

Industry-scale production of a perovskite oxide as oxygen carrier material in chemical looping

How to upscale the production of oxygen carrier particles from laboratory level to industrial level is still challenging in the field of chemical looping. The upscaled oxygen carrier must maintain its physical and chemical properties. In the present contribution, a spray drying granulation protocol was developed to produce a perovskite oxygen carrier (CaMn0.5Ti0.375Fe0.125O3-δ) at an industrial scale. The micro-fluidized bed thermogravimetric (MFB-TGA) experiments were performed to measure the oxygen uncoupling and redox reaction kinetics under the fluidization state with enhanced heat and mass transfer, and the obtained experimental data at different temperatures were fitted by a fluidized-bed reactor coupled with a semi-empirical kinetic model. The physical and chemical properties of granulates were compared with those of the same perovskite composition prepared at the laboratory level. The results show the volume fraction of particle size at 75–500 μm is greater than 90% for the upscaled granulats, and the particles show no degradation in reactivity and no agglomeration for more than 20 redox cycles at high temperatures. The heterogeneous reaction rates are high, especially for the oxidation, e.g. it only spent ∼ 5 s to achieve full oxidation. Low attrition index of 3.74 wt% was found after the five-hour attrition test. The industrial-scale particles possess similar chemical and physical properties as the laboratory-scale particles with regards to the reaction kinetics, attrition index, crystalline phase, etc. The required bed inventories and fan energy consumption were finally estimated and found to be lower than other oxygen carriers reported in the literature.acceptedVersio

Language-Conditioned Imitation Learning with Base Skill Priors under Unstructured Data

The growing interest in language-conditioned robot manipulation aims to develop robots capable of understanding and executing complex tasks, with the objective of enabling robots to interpret language commands and manipulate objects accordingly. While language-conditioned approaches demonstrate impressive capabilities for addressing tasks in familiar environments, they encounter limitations in adapting to unfamiliar environment settings. In this study, we propose a general-purpose, language-conditioned approach that combines base skill priors and imitation learning under unstructured data to enhance the algorithm's generalization in adapting to unfamiliar environments. We assess our model's performance in both simulated and real-world environments using a zero-shot setting. In the simulated environment, the proposed approach surpasses previously reported scores for CALVIN benchmark, especially in the challenging Zero-Shot Multi-Environment setting. The average completed task length, indicating the average number of tasks the agent can continuously complete, improves more than 2.5 times compared to the state-of-the-art method HULC. In addition, we conduct a zero-shot evaluation of our policy in a real-world setting, following training exclusively in simulated environments without additional specific adaptations. In this evaluation, we set up ten tasks and achieved an average 30% improvement in our approach compared to the current state-of-the-art approach, demonstrating a high generalization capability in both simulated environments and the real world. For further details, including access to our code and videos, please refer to our supplementary materials

Rethinking of the relationship between agriculture and the “urban” economy in Beijing: an input-output approach

Despite the irresistible drive of urban growth, the questions as to whether and how agriculture is restructured and repositioned in relation to urban development have received little attention. Based on the method of hypothetical extraction from input-output tables, and on the Beijing case, this paper puts forward an approach to assess the dependence of the agro-economy on urban sectors. The research shows that in Beijing in the period from 1982 to 2007 the relationship between agriculture and the urban economy has gone through three phases. At the start of the economic reform, the relationship was weak, characterised by intensive inputs of agricultural productive materials and policy orders. What followed was a phase of disconnection characterised by fast urban growth and low competitive status of agriculture. The third was a period of increasingly integrated development with technological inputs and upgrading of the management of agriculture. The current strong relationship indicates that agriculture can be tuned to meet the preferences of urban consumers. Key associated urban sectors are screened out to verify this relationship. The approach is valuable for quantifying the structural relationship between agriculture and urban sectors, for further analysing rural-urban economic relationships to support development policy design and programming. First published online: 28 Jan 201

OpenNDD: Open Set Recognition for Neurodevelopmental Disorders Detection

Neurodevelopmental disorders (NDDs) are a highly prevalent group of disorders and represent strong clinical behavioral similarities, and that make it very challenging for accurate identification of different NDDs such as autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). Moreover, there is no reliable physiological markers for NDDs diagnosis and it solely relies on psychological evaluation criteria. However, it is crucial to prevent misdiagnosis and underdiagnosis by intelligent assisted diagnosis, which is closely related to the follow-up corresponding treatment. In order to relieve these issues, we propose a novel open set recognition framework for NDDs screening and detection, which is the first application of open set recognition in this field. It combines auto encoder and adversarial reciprocal points open set recognition to accurately identify known classes as well as recognize classes never encountered. And considering the strong similarities between different subjects, we present a joint scaling method called MMS to distinguish unknown disorders. To validate the feasibility of our presented method, we design a reciprocal opposition experiment protocol on the hybrid datasets from Autism Brain Imaging Data Exchange I (ABIDE I) and THE ADHD-200 SAMPLE (ADHD-200) with 791 samples from four sites and the results demonstrate the superiority on various metrics. Our OpenNDD has achieved promising performance, where the accuracy is 77.38%, AUROC is 75.53% and the open set classification rate is as high as 59.43%.Comment: 10 pages, 2 figure