CHINA´S WTO ACCESSION: ITS IMPACT ON CHINESE EMPLOYMENT

It is often claimed that the WTO membership will benefit China by increasing exports and employment and forcing domestic firms to improve efficiency through competition. Benefits are expected to accrue through improved resource allocation and greater economic efficiency resulting from trade liberalization and greater global competition. In the paper we argue that although some sectors will benefit from competition others will suffer a great deal especially in the short and medium terms. The net overall benefits are likely to accrue only in the long run. During the transition period China will face enormous problems of restructuring of state-owned enterprises (SOEs), and banking, insurance and financial services, entailing significant loss of employment. The employment impact of the accession with special reference to SOEs is considered since they are generally less competitive than the non-state enterprises. Reduction in SOE employment may not be compensated by an increase in employment in the non-state sector. The experience of three specific industries is discussed: textiles and clothing, automobiles and household appliances. Besides the unemployment impact of the accession, the paper examines the possibility of a ‘flying geese model’ of trade and development working within China to maintain its global competitiveness on account of low labour costs in the hinterland. It also discusses China’s possible response to global competition to protect employment, for example. Three types of response are considered: non-compliance of the WTO accord, devaluation, and a production shift from tradeables to non-tradeables.

Energy Eigenvalues For Supersymmetric Potentials via Quantum Hamilton-Jacobi Formalism

Using quantum Hamilton-Jacobi formalism of Leacock and Padgett, we show how to obtain the exact eigenvalues for supersymmetric (SUSY) potentials.Comment: 15 pages Latex Compile twice to get cross references correct. 2 Figures not included. Requests for figures should be sent to [email protected]

Can imaging suggest the aetiology in skull base osteomyelitis? : a systematic literature review

Purpose: To assess differentiating features between bacterial, Aspergillus, and Mucor skull base osteomyelitis (SBO) with regard to clinical presentation and imaging appearances. Material and methods: A literature search was performed in April 2020 for studies on SBO with a minimum sample size of 10 patients. Studies that reported presenting symptoms, cross-sectional imaging findings, complications, and mortality were included in the analysis. The quality of included articles was tested using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. A data extraction form was used to retrieve relevant parameters from each of the articles. Results: Thirteen articles were included in the final analysis. Diabetes mellitus was the most common predisposing factor (12.5-91.0%). Presenting complaints in all bacterial SBO studies were otogenic, while fungal SBO patients had nasal/ocular complaints. Rates of mortality and surgical intervention in the fungal group were 50-100% and 50%, respectively, as compared to the bacterial group - 7-87% and 10%, respectively. On imaging, the site of initial infection in bacterial SBO was the external auditory canal, while in fungal SBO it was the paranasal sinus. The incidenceof orbital extension was < 5% in bacterial and 44-70% in fungal SBO, among which Mucor had rates of 65-70%. Bone erosion was less extensive in bacterial SBO, and the patterns differed. The highest incidence of vascular involvement and non-enhancing lesions (23-36%) was seen in Mucor. Aspergillus showed highest sino-cranial extension (52-55%) and homogenous bright enhancement. Conclusions: Systematic analysis of the clinico-radiological parameters in each of the studies revealed differences in presentation, clinical course, extension, bone erosion, and enhancement

Temporal computation by synaptic signaling pathways

Synaptic signaling comprises a complex molecular network. Such networks carry out diverse operations such as molecular logic, signal amplification, memory and other aspects of cellular decision-making ([Bray, 1995]). The synapse in particular encounters complex input patterns that have different temporal sequences. Different input patterns to the synapse are known to give rise to a range of synaptic responses, including facilitation, depression and various forms of short and long-term potentiation. In many cases the stimuli that generate these disparate responses are tens of seconds or more in length, much greater than the typical time-courses of calcium dynamics. In this paper I propose that the synaptic signaling network can perform temporal computation operations such as tuning for stimulus duration or interval. Using simulation methods I show that the simple time-courses of individual signaling pathways combine in the network to give rise to different temporally selective responses. Downstream pathways that exhibit temporal integration or amplitude thresholding select different input patterns and thus perform temporal computation

Managing models of signaling networks

Signaling pathways participate in complex information processing networks. These networks handle housekeeping functions of the cell as well as specialized functions such as synaptic plasticity. I report two developments in managing such networks: a compilation of mass-action kinetic models of signaling pathways, and shared motifs in the chemistry of interactions between signaling pathways. These motifs may prove useful in abstracting signaling networks, without compromising chemical reaction details. The combination of a library of signaling pathway models, and high-level rules to connect these pathways, may simplify development of complex signaling network models

Models of cell signaling pathways

Cellular signaling circuits handle an enormous range of computations. Beyond the housekeeping, replicating and other functions of individual cells, signaling circuits must implement the immensely complex logic of development and function of multicellular organisms. Computer models are useful tools to understand this complexity. Recent studies have extended such models to include electrical, mechanical and spatial details of signaling, and to address the stochastic effects that arise when small numbers of molecules interact. Increasing numbers of models have been developed in close conjunction with experiments, and this interplay gives a deeper and more reliable insight into signaling function

Understanding complex signaling networks through models and metaphors

Signaling networks are complex both in terms of the chemical and biophysical events that underlie them, and in the sheer number of interactions. Computer models are powerful tools to deal with both aspects of complexity, but their utility goes beyond simply replicating signaling events in silicon. Their great advantage is as a tool to understanding. The completeness of the description demanded by computer models highlights gaps in knowledge. The quantitative description in models facilitates a mapping between different kinds of analysis methods for complex systems. Systems analysis methods can highlight stable states of signaling networks and describe the transitions between them. Modeling also reveals functional similarities between signaling network properties and other well-understood systems such as electronic devices and neural networks. These suggest various metaphors as a tool to understanding. Based on such descriptions, it is possible to regard signaling networks as systems that decode complex inputs in time, space and chemistry into combinatorial output patterns of signaling activity. This would provide a natural interface to the combinatorial input patterns required by genetic circuits. Thus, a combination of computer modeling methods to capture the complexity and details, and useful abstractions revealed by these models, is necessary to achieve both rigorous description as well as human understanding