The expansion of textile and clothing firms of China to Asian Least Developed Countries: The Case of Cambodia

Since the 1990s, the rapid expansion of China’s textiles and clothing enterprises to Cambodia has been closely linked to the phenomenon of industrial clustering of textiles and clothing firms at the Yangtze River Delta, Pearl River Delta and Bohai Rim. The report adopts the case study approach to examine the pattern and features of overseas foreign direct investment (OFDI) of textile and clothing firms in Zhejiang province and Jiangsu province of the Yangtze River Delta to the least developed countries (LDCs) in the Asian and Pacific region, particularly Cambodia, and make the corresponding policy suggestions on the sustainability of South-South investment and cooperation. The fieldwork in Zhejiang province for this study showed that the subsidiaries of Chinese textile and clothing firms in Cambodia had been gradually integrating into the vertically-integrated value chain of textile and clothing firms in China, thereby becoming an important node in global textile and clothing value chain. Interviews (see annex 1) by the authors in the Yiwu specialized wholesale market indicated that business linkages between the specialized wholesale market and the Asia-Pacific LDCs have been developing fast in the past decade, although the ratio of businessmen from the Asia-Pacific LDCs is relatively limited compared with those from the LDCs in Africa. The internationalization of specialized wholesale markets has promoted commercial activities between China and LDCs in the Asia-Pacific region and led to an increase of OFDI from Chinese textile and clothing firms to LDCs. The fieldwork in Jiangsu province has demonstrated that Chinese textile and clothing firms have started to change their investment behaviour from voluntary overseas expansion by individual firms to the establishment of overseas economic and trade cooperation zones, such as Sihanoukville Special Economic Zone (SSEZ) (see annex 2) in Cambodia, which facilitates the collective expansion of Chinese textile and clothing firms and improves the textile and clothing manufacturing capability in Cambodia. The OFDI from China to LDCs has not had a great impact on local employment. However, the global financial crisis has led to a rising number of unemployed textile and clothing workers in China. The factors constraining sustainable OFDI from China in Cambodia include poor infrastructure, relatively high labour costs compared with other LDCs, low efficiency of government assistance and inadequate financial services. The policy suggestions on facilitating sustainable investment from China to the LDCs from the perspective of Cambodia are to: (a) encourage OFDI by Chinese textile and clothing firms in overseas economic and trade cooperation zones in the Asia-Pacific LDCs; (b) forge the regional production network between China and the LDCs; (c) upgrade the financial package to support Chinese textile and clothing firms’ FDI; and (d) improve the infrastructure facilities and government efficiency in the LDCs.Textile and clothing, China, LDCs, Cambodia

Generalized Bilinear Differential Operators Application in a (3+1)-Dimensional Generalized Shallow Water Equation

The relations between Dp-operators and multidimensional binary Bell polynomials are explored and applied to construct the bilinear forms with Dp-operators of nonlinear equations directly and quickly. Exact periodic wave solution of a (3+1)-dimensional generalized shallow water equation is obtained with the help of the Dp-operators and a general Riemann theta function in terms of the Hirota method, which illustrate that bilinear Dp-operators can provide a method for seeking exact periodic solutions of nonlinear integrable equations. Furthermore, the asymptotic properties of the periodic wave solutions indicate that the soliton solutions can be derived from the periodic wave solutions

Nonlinear Super Integrable Couplings of Super Classical-Boussinesq Hierarchy

Nonlinear integrable couplings of super classical-Boussinesq hierarchy based upon an enlarged matrix Lie super algebra were constructed. Then, its super Hamiltonian structures were established by using super trace identity. As its reduction, nonlinear integrable couplings of the classical integrable hierarchy were obtained

Petrographic characterization to build an accurate rock model using micro-CT: Case study on low-permeable to tight turbidite sandstone from Eocene Shahejie Formation

Pore scale flow simulations heavily depend on petrographic characterizing and modeling of reservoir rocks. Mineral phase segmentation and pore network modeling are crucial stages in micro-CT based rock modeling. The success of the pore network model (PNM) to predict petrophysical properties relies on image segmentation, image resolution and most importantly nature of rock (homogenous, complex or microporous). The pore network modeling has experienced extensive research and development during last decade, however the application of these models to a variety of naturally heterogenous reservoir rock is still a challenge. In this paper, four samples from a low permeable to tight sandstone reservoir were used to characterize their petrographic and petrophysical properties using high-resolution micro-CT imaging. The phase segmentation analysis from micro-CT images shows that 5-6% microporous regions are present in kaolinite rich sandstone (E3 and E4), while 1.7-1.8% are present in illite rich sandstone (El and E2). The pore system percolates without micropores in El and E2 while it does not percolate without micropores in E3 and E4. In El and E2, total MICP porosity is equal to the volume percent of macrospores determined from micro-CT images, which indicate that the macropores are well connected and microspores do not play any role in non-wetting fluid (mercury) displacement process. Whereas in E3 and E4 sandstones, the volume percent of micropores is far less (almost 50%) than the total MICP porosity which means that almost half of the pore space was not detected by the micro-CT scan. PNM behaved well in El and E2 where better agreement exists in PNM and MICP measurements. While E3 and E4 exhibit multiscale pore space which cannot be addressed with single scale PNM method, a multiscale approach is needed to characterize such complex rocks. This study provides helpful insights towards the application of existing micro-CT based petrographic characterization methodology to naturally complex petroleum reservoir rocks

Bim-Based Risk Identification System in tunnel construction

This paper presents an innovative approach of integrating Building Information Modeling (BIM) and expert systems to address deficiencies in traditional safety risk identification process in tunnel construction. A BIM-based Risk Identification Expert System (B-RIES) composed of three main built-in subsystems: BIM extraction, knowledge base management, and risk identification subsystems, is proposed. The engineering parameter information related to risk fac­tors is first extracted from BIM of a specific project where the Industry Foundation Classes (IFC) standard plays a bridge role between the BIM data and tunnel construction safety risks. An integrated knowledge base, consisting of fact base, rule base and case base, is then established to systematize the fragmented explicit and tacit knowledge. Finally, a hybrid inference approach, with case-based reasoning and rule-based reasoning combined, is developed to improve the flexibil­ity and comprehensiveness of the system reasoning capacity. B-RIES is used to overcome low-efficiency in traditional information extraction, reduce the dependence on domain experts, and facilitate knowledge sharing and communication among dispersed clients and domain experts. The identification of a safety hazard regarding the water gushing in one metro station of China is presented in a case study. The results demonstrate the feasibility of B-RIES and its application effectiveness

Spatiotemporal variation of marsh vegetation productivity and climatic effects in Inner Mongolia, China

Net primary productivity (NPP) is a vital ecological index that reflects the ecological function and carbon sequestration of marsh ecosystem. Inner Mongolia has a large area of marshes, which play a crucial role in the East Asian carbon cycle. Under the influence of climate change, the NPP of Inner Mongolian marsh has changed significantly in the past few decades, but the spatiotemporal variation in marsh vegetation NPP and how climate change affects marsh NPP remain unclear. This study explores, for the first time, the spatiotemporal variation of marsh NPP and its response to climatic change in Inner Mongolia based on the MODIS-NPP and climate datasets. We find that the long-term average annual NPP of marsh is 339.85 g⋅C/m2 and the marsh NPP shows a significantly increasing trend (4.44 g⋅C/m2/a; p < 0.01) over Inner Mongolia during 2000–2020. Spatially, the most prominent increase trend of NPP is mainly distributed in the northeast of the region (Greater Khingan Mountains). The partial correlation results show that increasing autumn and summer precipitation can increase the NPP of marsh vegetation over Inner Mongolia. Regarding the temperature effects, we observe a strong asymmetric effect of maximum (Tmax) and minimum (Tmin) temperature on annual NPP. A high spring Tmax can markedly increase marsh NPP in Inner Mongolia, whereas a high Tmin can significantly reduce it. In contrast to spring temperature effects on NPP, a high summer Tmax can decrease NPP, whereas a high Tmin can increase it. Our results suggest different effects of seasonal climate conditions on marsh vegetation productivity and highlight the influences of day-time and night-time temperatures. This should be considered in simulating and predicting marsh carbon sequestration in global arid and semi-arid regions

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Improvement in Trapezoidal Pulse Shaping Pile-Up in Nuclear Signal Processing

In digital nuclear spectroscopy, trapezoidal shaping is widely used. Compared with traditional CR-RC4 semi-Gaussian shaping, it has a better energy resolution and higher counting rates, but does not void the pulse pile-up in the case of extreme counting rates. In this paper, a new recursive algorithm is proposed that can improve the anti-pile-up ability, and is easy to implement in any DSP-based processor that is used in any digital pulse shaping filter section. The complete deduction and simulation are presented in this paper

Cluster-based information fusion for probabilistic risk analysis in complex projects under uncertainty

This paper proposes a hybrid soft computing approach that integrates the Dempster–Shafer (D–S) evidence theory and cluster analysis for probabilistic risk analysis in complex projects under uncertainty. The fusion model tends to solve multi-criteria decision-making problems with a focus on the information content reflected from evidence. Risk factors are quantified into a continuous numeric scale for risk level classification and each factor value is turned into a basic probability assignment (BPA). A sorting operator is used to aggregate the evidence into risk level based clusters. The D–S evidence theory is first used to fuse similar evidence within each cluster, and then the weighted ratio method is used to fuse conflict evidence between clusters. The fused result is defuzzied into a crisp value to give a conveniently referred value for decision-making. Global sensitivity analysis is conducted to depict the effect of each risk factor on the overall estimated risk level. The developed approach is used to assess the water leakage condition of Line 2 of the Wuhan metro system in China to demo its feasibility. The tunnel is assessed to lie in a good condition with a tolerance of 5% measurement error. The proposed two-step fusion process is capable to reserve more details through computation and enhances the confidence in risk classification results compared to that based on a separate piece of evidence. This research contributes to (a) a systematic classification and fusion-based quantitative risk analysis method; (b) practical risk assessment of water leakage in operational tunnels.Ministry of Education (MOE)Nanyang Technological UniversityThe Ministry of Education Tier 1 Grants, Singapore (No. 04MNP002126C120; No. 04MNP000279C120) and the StartUp Grant at Nanyang Technological University, Singapore (No. 04INS000423C120) are acknowledged for their financial support of this research