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Porous additive manufactured (AM) cages were a new selection for posterior lumbar interbody fusion (PLIF) surgery due to their low rigidity. Past studies had investigated porous AM cages; however, their structural designs and performances were evaluated using oversimplified numerical models. Thus, the purpose of this research was to investigate the porous structure of the AM cages using a realistic spine model. Three-dimensional finite element models of T10-S1 multilevel spine were developed using ANSYS Workbench. Eleven types of the AM cages were analyzed and compared to understand their strengths and limitations. The results showed that the sparse density in microstructure geometry would increase the intersegmental rotation but the von Mises stress of cage becomes enlarge simultaneously. Adding a PEEK layer to the microstructure is not a good choice to prevent the cage stress getting higher value. The outcomes of this study can help surgeons to understand the biomechanics of PLIF surgery combined with the use of porous AM cages.

MAXM is a multi-channel MAC protocol for Ad Hoc Networks. The idea of MAXM is to maximize utilization of bandwidth by adopting a distributed self-stabilizing maximal matching-transmission algorithm. In this paper, we prove that given a fixed number, p, of packets, MAXM is able to guarantee that all packets are delivered within O(pN+pT) time in the network of N mobile hosts, where T is the time to transmit a packet on a non-conflicting channel.

Intention is an important factor to drive human behavior. With a certain intention, an action becomes more natural and reasonable. In this paper, an agent with intention, which not only possesses facial expressions, hand gestures, body motions, but also holds memory and personality, is presented. The agent exploits the social dialogue to show intention and its care for students in a learning system. The agent will purposefully guide students in the learning process, care about their needs during learning, provide empathy feedback in order to lessen their negative emotions, and offer appropriate reproach or encouragement to help them overcome the obstacles to English learning. A preliminary experiment will be done to show that this agent can interact with students more naturally and meanwhile strengthen their will to learn as well as make them more willing to accept the opinions from the agent.

Selecting transparently a proper network connection for voice communication will be a fundamental requirement in future multi-mode heterogeneous wireless network. This paper extends always best connected (ABC) to a fine-grain paradigm called always best network connection (ABNC) to address this issue. Instead of selecting a best access network as in conventional ABC, ABNC enable users to select a best network connection, which consists of source and destination access network pair, to satisfy quality constraint and users' preference. To support ABNC, we develop a user profile to specify network connection priority. Meanwhile we extend SIP and propose a network selection information service (NSIS) based on MIH (media independent handover) to collect information of both source and destination access networks for decision making. Finally, analytic hierarchy process (AHP) is used to recommend a network connection with assistance of user profile and NSIS. An example is illustrated to show that AHP can successfully select a good network connection that fulfill the requirement of users.

This study proposes the application of the MCSCL groupware for the hearing-impaired students to engage in learning the mathematics word problems. This groupware, which integrates the collaborative argumentation theory, the scaffolding tools, and the comic, is to be applied in the activities of the collaborative learning. We present the system architecture which describes the implementation based on integration of the PDA, the network technology, and the learning strategy. Furthermore, this study employed the groupware as a channel for the hearing-impaired students to improve the experience of learning the mathematics word problems.

The Internet can be used to promote collaborative learning among students. However, since the Web-based collaborative learning environment relies entirely on the availability of computers and the Internet, students do not always have access to the system, leading to doubt in the interaction among the students, and reducing the effectiveness of collaborative learning. For this reason, this study employed the activity monitoring and ubiquitous activity awareness through SMS messages in a GSM network to enhance the learning activity awareness. In the experimental stage, a Web-based TGT collaborative learning activity was conducted at an undergraduate university. The analytical results demonstrate that awareness of the collaborative learning context significantly increased student participation in learning activity and improving student performance.

This paper presents a new parallel distributed processing (PDP) approach to solve job-shop scheduling problem which is NP-complete. In this approach, a stochastic model and a controlled external energy is used to improve the scheduling solution iteratively. Different to the processing element (PE) of the Hopfield neural network model, each PE of our model represents an operation of a certain job. So, the functions of each PE are a little more complicated than that of a Hopfield PE. Under such model, each PE is designed to perform some stochastic, collective computations. From the experimental result, the solutions can be improved toward optimal ones much faster than other methods. Instead of the polynomial number of variables needed in neural network approach, the variables number needed to formulate a job-shop problem in our model is only a linear function of the operation number contained in the given job-shop problem.

The Universal Mobile Telecommunication System (UMTS) adopts WCDMA as the air interface that provides variable transmission rate services. Four traffic classes are identified in UMTS, given as controversial, streaming, interactive, and background classes. To efficiently utilize the radio bandwidth, the shared channel technology is proposed to deliver interactive and background traffics. This paper proposes a "shared-channel assignment and scheduling" (SCAS) algorithm to allocate shared channels to interactive and background connections. We formally prove that the SCAS algorithm can satisfy the transmission rate requirement of each interactive connection and can efficiently utilize the radio bandwidth.

Utilization of multiple channels in ad hoc networks provides the benefits of reducing the probability of collisions, decreasing the normalized propagation delay and support of QoS. However, problems like busy waiting or deadlock existence degrade the bandwidth utilization. We present the modelling of the transmission-request network as a directed graph in the multichannel environment. A distributed self-stabilizing algorithm which performs maximal matching is also presented for the new model. By using the algorithm, we propose a new multichannel MAC protocol, named MAXM (MAXimal Matching multichannel MAC protocol), for ad hoc networks. The idea of MAXM is to maximize utilization of bandwidth by adopting maximal matching. MAXM is deadlock-free, self-stabilizing and able to converge in finite time. Simulation results show that MAXM offers significant improvements over previous work in throughput and transmission delay.

Reliable multicast support at the MAC (medium access control) layer will be of great benefit to the routing function, multicast applications and cluster maintenance in adhoc networks. We propose a reliable MAC protocol with multicast support for multi-channel ad-hoc networks. This protocol, named SNMS (sequenced neighbor with multicast support), uses sender-based data transmission strategy in order to provide efficient multicast. SNMS uses one control channel to deliver the control packets and several data channels to delivery data packets. In SNMS, (i) no any handshake (such as CTS/RTS) or any carrier sensing technology is needed, (ii) no hidden terminal problem or exposed terminal problem will exist, and (iii) no form of clock synchronization is required. Compared with SNDR (sequenced-neighbor double-reservation), the proposed protocol achieves higher reliable multicast efficiency; namely, it delivers multicast packets to receivers reliably with a smaller number of transmissions. Simulation results show that, in the multicast scenario, SNMS delivers a significant improvement over to SNDR.

The H-lattice is a geometric model for describing the construction and occupied configuration of the hypercube multiprocessors. In addition, it can model the faulty hypercube in the presence of faulty processors and/or links. The H-lattice scheme, based on this model, can allocate fault free subcubes for various allocations, such as first/best/worst/next fit, and subcube inclusion/ exclusion allocations, in a more simple and efficient manner than ever. In this scheme, the viewpoint of the minimum destroyed degree is introduced to select the best-fit subcube, which is proved to be better than that of other schemes. Mathematical analyses and simulation are also derived to demonstrate the efficiency of the proposed scheme.

A stochastic, heuristic search algorithm called simulated annealing is considered for the problem of static task assignment scheduling in distributed computing systems. The purpose of task assignment scheduling is to assign modules of programs over a set of interconnected processors in order to reduce the job turnaround time as well as to obtain the best system performance. It is shown that the approach of simulated annealing, with a properly designed annealing schedule and a good move generation strategy can be used to solve this problem in an efficient way. >

The current search tools for retrieving information on the WWW are not suitable for building customized information repositories because these search tools are designed for general users with the result of only an unstructured collection of documents. We present a personal digital library capable of efficiently retrieving information on the World Wide Web. We adopt several new strategies to overcome the shortcomings of current tools. The first strategy, classification, merges and organizes the retrieved documents to put them in a structural hierarchical frame. The second strategy, user profile, saves time and bandwidth for the access of the documents and permits the users to build their own customized category structure. The third strategy, multi-search, capitalizes on the power of multiple search engines to broaden the domains of information sources and alleviate the overloading of a single search engine. Furthermore we derive in detail the techniques for speeding up the iterative process of clustering.

The explosion of the World Wide Web as a global information network brings with it a number of related challenges for information retrieval and automation. The link structure, which is the main feature of the hypermedia environment, can be a rich source of information for exploration. This paper is centered around the exploitation of hyperlinks in the subject of automatic discovery. The paper discusses the design of site traversal algorithms and identifies some important parameters for hyperlink-based information discovery on the Web. Meanwhile, it proposes a new evaluation method, the search depth, for comparing various hyperlink traversal algorithms.

A critical factor that affects the performance of neural network training algorithms and the generalization of trained networks is the training instances. The authors consider the handling of don't care attributes in training instances. Several approaches are discussed and their experimental results are presented. The following approaches are considered: (1) replace don't care attributes with a fixed value; (2) replace don't care attributes with their maximum or minimum encoded values; (3) replace don't care attributes with their maximum and minimum encoded values; and (4) replace don't care attributes with all their possible encoded values. >

Novel learning algorithms called terminal attractor backpropagation (TABP) and heuristic terminal attractor backpropagation (HTABP) for multilayer networks are proposed. The algorithms are based on the concepts of terminal attractors, which are fixed points in the dynamic system violating Lipschitz conditions. The key concept in the proposed algorithms is the introduction of time-varying gains in the weight update law. The proposed algorithms preserve the parallel and distributed features of neurocomputing, guarantee that the learning process can converge in finite time, and find the set of weights minimizing the error function in global, provided such a set of weights exists. Simulations are carried out to demonstrate the global optimization properties and the superiority of the proposed algorithms over the standard backpropagation algorithm. >