535 research outputs found
From confrontation to co-production: how China's ENGOs facilitate residents' waste management systems
No full textWaste management has emerged as a critical challenge in multiple countries, where governance structures frequently exhibit insufficient robustness. Environmental non-governmental organizations (ENGOs), as pivotal stakeholders in this domain, must assume a more substantial role. However, their contributions have historically been perceived as limited. Whether and how ENGOs can play an important role deserves the attention of researchers. This paper investigates the evolving role of China's ENGOs through a longitudinal case study of nine organizations, examining their transition from adversarial to co-productive strategies, thereby fostering a tripartite collaboration system among government, enterprises, and residents. The findings reveal a three-phase evolution in organizational structure, issue focus, and tactical approaches, culminating in a three-cycle co-production framework for waste management. This study not only enriches co-production theory but also provides valuable insights into the critical role of ENGOs, offering practical guidance for sustainable waste governance
Untangling the critical success factors of the latest compulsory waste sorting initiative in Shanghai: the role of accountability governance
No full textMunicipal solid waste sorting is an essential element of urban sustainability as cities transition to a circular economy. As a mega-city, Shanghai has achieved remarkable milestones in its latest compulsory waste sorting program. This success has garnered widespread attention, and most studies have primarily focused on policy interventions from either a macro perspective or micro-analysis of individual behaviours. However, these studies have often overlooked the intricacies of multi-stakeholder coordination and the division of responsibilities, which frequently contributed to the failure of waste sorting initiatives. Furthermore, existing research lacks a systematic theoretical framework to elucidate multi-stakeholder accountability mechanisms. Therefore, this research adopts a case study approach to untangle the factors that led to Shanghai's success. Through the lens of accountability theory, this study systematically elaborates stakeholder accountability mechanisms and offers a distinctive multi-stakeholder perspective to explain Shanghai's success across vertical, horizontal, and felt accountability dimensions. This informative exemplar provides crucial empirical insights for other cities, especially those grappling with challenges in promoting and managing waste sorting initiatives
Zhida: blockchain potential in household waste recycling
No full textIn 2020, the chief executive officer of Zhida Environmental Technology, a waste management company based in Nanjing, China, was considering adopting blockchain technology into the company's work process. With the concept of Internet plus recycling, the company was committed to waste sorting and had introduced innovative household waste solutions. However, new challenges were emerging, including stagnant resident participation rates, low profit returns, competitor expansion, and limited support from the local government. Inherent blockchain technology functions such as digital token services, a transparent recycling chain, and collaborative governance mechanisms could potentially improve the company's current operations and provide a first mover position in the market. However,the chief executive officer had to thoroughly consider the decision of adopting blockchain technology: What true value could it offer and what potential challenges could arise
Fully-channel regional attention network for disease-location recognition with tongue images
No full textObjectiveUsing the deep learning model to realize tongue image-based disease location recognition and focus on solving two problems: 1. The ability of the general convolution network to model detailed regional tongue features is weak; 2. Ignoring the group relationship between convolution channels, which caused the high redundancy of the model.MethodsTo enhance the convolutional neural networks. In this paper, a stochastic region pooling method is proposed to gain detailed regional features. Also, an inner-imaging channel relationship modeling method is proposed to model multi-region relations on all channels. Moreover, we combine it with the spatial attention mechanism.ResultsThe tongue image dataset with the clinical disease-location label is established. Abundant experiments are carried out on it. The experimental results show that the proposed method can effectively model the regional details of tongue image and improve the performance of disease location recognition.ConclusionIn this paper, we construct the tongue image dataset with disease-location labels to mine the relationship between tongue images and disease locations. A novel fully-channel regional attention network is proposed to model the local detail tongue features and improve the modeling efficiency.SignificanceThe applications of deep learning in tongue image disease-location recognition and the proposed innovative models have guiding significance for other assistant diagnostic tasks. The proposed model provides an example of efficient modeling of detailed tongue features, which is of great guiding significance for other auxiliary diagnosis applications
San yu tang quan ji /
No full textBinder's title; also listed under title: Lu Chʻing-hsien kung chʻüan chi cf. Chiang-su sheng li kuo hsüeh tʻu shu kuan shu mu.Lu shi yu nian pu, Lu Chenzheng ji -- San yu tang wen ji, 12 juan -- Wai ji, 6 juan -- Chong si lu -- Sheng yan, 12 juan -- Ri ji, 10 juan -- Du li zhi yi.Mode of access: Internet
Fluorescent chemical probes for accurate tumor diagnosis and targeting therapy
No full textSurgical resection of solid tumors is currently the gold standard and preferred therapeutic strategy for cancer. Chemotherapy drugs also make a significant contribution by inhibiting the rapid growth of tumor cells and these two approaches are often combined to enhance treatment efficacy. However, surgery and chemotherapy inevitably lead to severe side effects and high systemic toxicity, which in turn results in poor prognosis. Precision medicine has promoted the development of treatment modalities that are developed to specifically target and kill tumor cells. Advances in in vivo medical imaging for visualizing tumor lesions can aid diagnosis, facilitate surgical resection, investigate therapeutic efficacy, and improve prognosis. In particular, the modality of fluorescence imaging has high specificity and sensitivity and has been utilized for medical imaging. Therefore, there are great opportunities for chemists and physicians to conceive, synthesize, and exploit new chemical probes that can image tumors and release chemotherapy drugs in vivo. This review focuses on small molecular ligand-targeted fluorescent imaging probes and fluorescent theranostics, including their design strategies and applications in clinical tumor treatment. The progress in chemical probes described here suggests that fluorescence imaging is a vital and rapidly developing field for interventional surgical imaging, as well as tumor diagnosis and therapy
Barriers to compulsory waste sorting for a circular economy in China
No full textHousehold waste source separation substantially reduces the amount of rubbish sent to landfills and incinerators. It enables value recovery from useful waste for transitioning to a more resource efficient and circular economy. Confronted by the severe waste management problems, China recently implemented its most strict compulsory waste sorting program in big cities to date. Despite the failures of waste sorting projects in China in the past, it is unclear what the implementation barriers are, how they interact, and how they can be overcome. This study addresses this knowledge gap through a systematic barrier study involving all the relevant stakeholders in Shanghai and Beijing. It uncovers the complex interrelationships between barriers using the fuzzy decision-making trial and evaluation laboratory (Fuzzy DEMATEL) method. “Hasty and inappropriate planning” and “lack of policy support at the grassroots level”, two new barriers that are not reported in the literature, are found to be the most influential barriers. Policy implications are discussed based on the study findings to inform the policy deliberations on the implementation of compulsory waste sorting.</p
Accelerated Discovery of Strong and Thermally Stable Nanostructured High-Entropy Alloys
No full textNanostructured metals and alloys typically exhibit higher strength and hardness compared to their bulk-state counterparts. However, grain coarsening in such nanostructured materials at elevated temperatures sacrifices their mechanical performance and limits their wide engineering applications. Thus, the discovery of strong and thermally stable nanostructured materials is needed. High-entropy alloys (HEAs) comprise multi-principal elements (typically more than four) and exhibit both enhanced thermal stability and mechanical strength. So far, only a few HEAs have been empirically identified from a vast compositional space. This thesis focuses on discovering strong and thermally stable nanostructured HEAs based on emerging combinatorial high-throughput techniques over a wide temperature range, as well as providing a fundamental understanding of phase formability and transition mechanisms.The materials studied in this thesis are a group of refractory high-entropy alloys (RHEAs) - TiZrHfNbTa system. This work performed the magnetron co-sputtering technique to deposit (TiZrHf)x(NbTa)1-x alloy libraries on silicon wafers. The synchrotron X-ray mapping method enabled the characterization of the composition and structure distributions, showing uniform composition gradient and phase transition from fully crystalline bcc to nanocomposite structures. The phase formability was discussed based on the critical cooling rate. The nanoindentation mapping method determined the distributions of elastic modulus and hardness. High-throughput annealing allowed for adding a temperature dimension into composition-structure-property relationships. Increasing the TiZrHf component enhanced thermal stability upon annealing, exhibiting a crystallization process different from conventional mechanisms. CALPHAD calculation also demonstrated the phase transition tendency over the composition space.
Similar high-throughput strategies were performed in TiZrNb, TiZrTa, and TiZrNbTa systems. TiZrNb underwent grain growth in a single bcc phase; TiZrNbTa showed no significant change; TiZrTa transformed from fcc to bcc phases as the TiZr content increased. Accordingly, the effects of individual elements in TiZrHfNbTa systems were evaluated. This thesis also explored the addition of Mo or W in the TiZrHfNbTa system (i.e., TiZrHfNbTaMo and TiZrHfNbTaW), leading to the formation of amorphous structure and the improvement of hardness and thermal stability.
The thesis demonstrates that high-throughput methodologies are effective ways for the accelerated discovery of nanostructured HEAs with improved thermal stability and hardness. The combinatorial methodology in this thesis exhibits versatility in the selection of composition and temperature spaces for other compositionally complex alloys.Ph.D
A study of optimization and optimal control computation : exact penalty function approach
No full textIn this thesis, We propose new computational algorithms and methods for solving four classes of constrained optimization and optimal control problems. In Chapter 1, we present a brief review on optimization and optimal control. In Chapter 2, we consider a class of continuous inequality constrained optimization problems. The continuous inequality constraints are first approximated by smooth function in integral form. Then, we construct a new exact penalty function, where the summation of all these approximate smooth functions in integral form, called the constraint violation, is appended to the objective function. In this way, we obtain a sequence of approximate unconstrained optimization problems. It is shown that if the value of the penalty parameter is sufficiently large, then any local minimizer of the corresponding unconstrained optimization problem is a local minimizer of the original problem. For illustration, three examples are solved using the proposed method.From the solutions obtained, we observe that the values of their objective functions are amongst the smallest when compared with those obtained by other existing methods available in the literature. More importantly, our method finds solutions which satisfy the continuous inequality constraints.In Chapter 3, we consider a general class of nonlinear mixed discrete programming problems. By introducing continuous variables to replace the discrete variables, the problem is first transformed into an equivalent nonlinear continuous optimization problem subject to original constraints and additional linear and quadratic constraints. However, the existing gradient-based optimization techniques have difficulty to solve this equivalent nonlinear optimization problem effectively due to the new quadratic inequality constraint. Thus, an exact penalty function is employed to construct a sequence of unconstrained optimization problems, each of which can be solved effectively by unconstrained optimization techniques, such as conjugate gradient or quasi-Newton types of methods.It is shown that any local optimal solution of the unconstrained optimization problem is a local optimal solution of the transformed nonlinear constrained continuous optimization problem when the penalty parameter is sufficiently large. Numerical experiments are carried out to test the efficiency of the proposed method.In Chapter 4, we investigate the optimal design of allpass variable fractional delay (VFD) filters with coefficients expressed as sums of signed powers-of-two terms, where the weighted integral squared error is minimized. A new optimization procedure is proposed to generate a reduced discrete search region. Then, a new exact penalty function method is developed to solve the optimal design of allpass variable fractional delay filter with signed powers-of-two coefficients. Design examples show that the proposed method is highly effective. Compared with the conventional quantization method, the solutions obtained by our method are of much higher accuracy. Furthermore, the computational complexity is low.In Chapter 5, we consider an optimal control problem in which the control takes values from a discrete set and the state and control are subject to continuous inequality constraints. By introducing auxiliary controls and applying a time-scaling transformation, we transform this optimal control problem into an equivalent optimal control problem subject to original constraints and additional linear and quadratic constraints, where the decision variables are taking values from a feasible region, which is the union of some continuous sets. However, due to the new quadratic constraints, standard optimization techniques do not perform well when they are applied to solve the transformed problem directly.We introduce a novel exact penalty function to penalize constraint violations, and then append this penalty function to the objective function, forming a penalized objective function. This leads to a sequence of approximate optimal control problems, each of which can be solved by using optimal control techniques, and consequently, many optimal control software packages, such as MISER 3.4, can be used. Convergence results how that when the penalty parameter is sufficiently large, any local solution of the approximate problem is also a local solution of the original problem. We conclude this chapter with some numerical results for two train control problems.In Chapter 6, some concluding remarks and suggestions for future research directions are made
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