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International climate politics are gradually changing in terms of new and ground-breaking policies and decision-making spearheaded by national governments. The growing global demand to combat climate change reflects the current challenges the world is facing. India’s negotiations at United Nations Conference on Climate Change are based on “equity,” “historical responsibility” and the “polluter pays” agenda, until a shift in the voluntary reduction of carbon emissions takes place. The purpose of this study is to understand why India, a “deal breaker”, is seen as a “deal maker” in climate governance?

For a state like India, domestic preferences are equally important in introducing climate policies alongside its concerns over poverty reduction and economic development, which also stand with its sustainable development goals. This paper explains India’s decision-making using a two-level approach focusing on “domestic preferences.” This rationale is based on India’s historical background as well as new upcoming challenges.

This paper shows that India has both the domestic needs and long-term benefits of combating climate change to cut carbon emissions, which gives the responsibility primarily to domestic audiences and international societies.

This paper uses an international political lens to critically analyze India’s climate positions and politics from both domestic and international levels, demonstrating the importance of considering both short- and long-term goals. The outcome benefits not only the policymakers in India but also stakeholders in the Asia-Pacific and beyond.

Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.

This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.

In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.

The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.

Visual object tracking plays a significant role in intelligent robot systems. This study aims to focus on unlocking the tracking performance potential of the deep network and presenting a dynamic template update strategy for the Siamese trackers.

This paper presents a novel and efficient Siamese architecture for visual object tracking which introduces densely connected convolutional layers and a dynamic template update strategy into Siamese tracker.

The most advanced performance can be achieved by introducing densely connected convolutional neural networks that have not yet been applied to the tracking task into SiamRPN. By using the proposed architecture, the experimental results demonstrate that the performance of the proposed tracker is 5.8% (area under curve), 5.4% expected average overlap (EAO) and 3.5% (EAO) higher than the baseline on the OTB100, VOT2016 and VOT2018 data sets and achieves an excellent EAO score of 0.292 on the VOT2019 data set.

This study explores a deeper backbone network with each convolutional network layer densely connected. In response to tracking errors caused by templates that are not updated, this study proposes a dynamic template update strategy.

This paper aims to examine impacts of the poverty alleviation relocation (PAR) program on diet quality of low-income households in China. We explore the impact mechanism of relocation on diet quality and the heterogeneous effects of different relocation modes.

A fixed effects model is constructed using panel data of 1126 low-income households collected over three years in eight provinces of China. The PAR program provides a natural experiment which dramatically changes the living conditions surrounding farmers. We are able to identify the causal effects of relocation on diet quality free from selection bias.

The empirical results show that the PAR program improves diet quality of low-income households and that better market access and increasing incomes induced by relocation play an important role in this improvement. Improved market access significantly reduces the over-consumption of staple foods, whereas higher income significantly reduces the intake divergences of non-staple foods. The impacts of different relocation modes on diet quality are highly heterogeneous.

Our findings indicate that the PAR program benefits diet quality of low-income households through greater market access and increases in total household income. Market improvements and food subsidies are conducive to improving the diet quality of the low income.

Despite widespread evidences of healthy diets being associated with household environments and income, selection bias remains. This paper utilizes an exogenous program to explore the causal impacts of market access and family income on diet quality and to separate their different effects.

The purpose of this paper is to design a climbing robot connected by a connecting rod mechanism to achieve multi-functional tasks such as obstacles crossing and climbing of power transmission towers.

A connecting rod type gripper has been designed to achieve stable grasping of angle steel. Before grasping, use coordination between structures to achieve stable docking and grasping. By using the alternating movements of two claws and the middle climbing mechanism, the climbing and obstacle crossing of the angle steel were achieved.

Through a simple linkage mechanism, a climbing robot has been designed, greatly reducing the overall mass of the robot. It can also carry a load of 1 kg, and the climbing mechanism can perform stable climbing. The maximum step distance of the climbing robot is 543 mm, which can achieve the crossing of angle steel obstacles.

A transmission tower climbing mechanism was proposed by analyzing the working environment. Through the locking ability of the screw nut, stable clamping of the angle steel is achieved, and a pitch mechanism is designed to adjust the posture of the hand claw.

This study aims to investigate the effect of V doping on the microstructure, chemical stability, mechanical and vacuum tribological behavior of sputtered MoS₂ coatings.

The MoS₂-V coatings are fabricated via tuning V target current by magnetron sputtering technique. The structural characteristic and elemental content of the coatings are measured by field emission scanning electron microscopy, X-ray diffractometer, electron probe X-ray micro-analyzer, Raman, X-ray photoelectron spectroscopy, high resolution transmission electron microscope and energy dispersive spectrometer. The hardness of the deposited coatings are tested by a nanoindentation technique. The vacuum tribological properties of MoS₂-V coatings are studied by a ball-on-disc tribometer.

Introducing V into the MoS₂ coatings results in a more compact microstructure. The hardness of the coatings increases with the doping of V. The MoS₂-V coating deposited at a current of 0.2 A obtains the lowest friction coefficient (0.043) under vacuum. As the amount of V doping increases, the wear rate of the coating decreases first and then increases, among which the coating deposited at a current of 0.5 A has the lowest wear rate of 2.2 × 10–6 mm³/N·m.

This work elucidates the role of V doping on the lubrication mechanism of MoS₂ coatings in a vacuum environment, and the MoS₂-V coating is expected to be applied as a solid lubricant in space environment.

The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.

The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.

The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.

The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.

The purpose of this paper is to enhance control accuracy, energy efficiency and productivity of customized industrial robots by the proposed multi-objective trajectory optimization approach. To obtain accurate dynamic matching torques of the robot joints with optimal motion, an improved dynamic model built by a novel parameter identification method has been proposed.

This paper proposes a novel multi-objective optimal approach to minimize the time and energy consumption of robot trajectory. First, the authors develop a reliable dynamic parameters identification method to obtain joint torques for formulating the normalized energy optimization function and dynamic constraints. Then, optimal trajectory variables are solved by converting the objective function into relaxation constraints based on second-order cone programming and Runge–Kutta discrete method to reduce the solving complexity.

Extensive experiments via simulation and in real customized robots are conducted. The results of this paper illustrate that the accuracy of joint torque predicted by the proposed model increases by 28.79% to 79.05% over the simplified models used in existing optimization studies. Meanwhile, under the same solving efficiency, the proposed optimization trajectory consumes a shorter time and less energy compared with the existing optimization ones and the polynomial trajectory.

A novel time-energy consumption optimal trajectory planning method based on dynamic identification is proposed. Most existing optimization methods neglect the effect of dynamic model reliability on energy efficiency optimization. A novel parameter identification approach and a complete dynamic torque model are proposed. Experimental results of dynamic matching torques verify that the control accuracy of optimal robot motion can be significantly improved by the proposed model.

Purpose – To review the place of bicycle transportation within the Chinese national objective of sustainable development.

Methodology – The chapter provides an analysis of the evolution of bicycle transportation policies in China, and a discussion of the latest developments in the function and operation of public bicycle hire schemes.

Findings – Due to high population density, the prevailing mix of land use and a lack of affordability of cars and motor scooters, bicycle transportation has historically been very common in the urban areas of China. However, since the 1990s, many Chinese cities implemented restrictive policies on the development of bicycle transportation and the modal share of bicycles has reduced sharply.

Practical implications – The chapter suggests that China would need to create favourable conditions for bicycle transportation in urban areas through means such as policy support, land use planning, use of economic levers and through creating an acceptable social and cultural atmosphere for cycling. Finally, the maintenance of a relatively high proportion of bicycle traffic would need to be regarded as an index for sustainable urban development.

Research and development (R&D) projects are often pursued through a project portfolio (PP). R&D PPs involve many stakeholders, and without proactive management, their interactions may lead to conflict risks. These conflict risks change dynamically with different stages of the PP life cycle, increasing the challenge of PP risk management. Existing conflict risk research mainly focuses on source identification but lacks risk assessment work. To better manage the stakeholder conflict risks (SCRs) of R&D PPs, this study employs the dynamic Bayesian network (DBN) to construct its dynamic assessment model.

This study constructs a DBN model to assess the SCRs in R&D PP. First, an indicator system of SCRs is constructed from the life cycle perspective. Then, the risk relationships within each R&D PPs life cycle stage are identified via interpretative structural modeling (ISM). The prior and conditional probabilities of risks are obtained by expert judgment and Monte Carlo simulation (MCS). Finally, crucial SCRs at each stage are identified utilizing propagation analysis, and the corresponding risk responses are proposed.

The results of the study identify the crucial risks at each stage. Also, for the crucial risks, this study suggests appropriate risk response strategies to help managers better perform risk response activities.

This study dynamically assesses the stakeholder conflict risks in R&D PPs from a life-cycle perspective, extending the stakeholder risk management research. Meanwhile, the crucial risks are identified at each stage accordingly, providing managerial insights for R&D PPs.