Dynamic Partnership in Online Logistics Community

Agents of various capabilities in the logistics community individually or together collaboratively serve very different shipment requests offline. With the challenges of global e-business, the capabilities of collaborative partnering and planning online increase agents’ competitiveness and enhance logistics process performance. In this paper, we define dynamic partnership – a conceptual underpinning to maximize the four e-rights for the online logistics community. Three core factors, namely credibility, reliability and viability are introduced to guide successful partnership online. A survey of logistics service providers in Hong Kong confirms the relevancy of the four e-rights and three core factors in collaboration efforts. A conceptual analysis with respect to partnership flexibility, collaboration and performance of dynamic partnership is given. To realize such partnership in the logistics community, the electronic platform requirements are identified

An Architectural Framework of a Decision Support Platform for e-Business

The rapid development of e-Business urges the need for online integrative partnership and collaborative business process management. These trends call for a new dimension of decision support: online multiplicitive decision interoperability. We define it as a decision support platform for e-Business, which facilitates inter-organizational decision making process allowing dynamic partnership. As it is not the common decision support system we familiar with, we propose a new architectural framework of this decision support platform. This framework carries a set of unique characteristics that should be incorporated in the design and development of such platform

Web Usage Mining to Extract Knowledge for Modelling Users of Taiwan Travel Recommendation Mobile APP

This work presents the design of a web mining system to understand the navigational behavior of passengers in developed Taiwan travel recommendation mobile app that provides four main functions including recommend by location , hot topic , nearby scenic spots information , my favorite and 2650 scenic spots. To understand passenger navigational patterns, log data from actual cases of app were collected and analysed by web mining system. This system analysed 58981 sessions of 1326 users for the month of June, 2014. Sequential profiles for passenger navigational patterns were captured by applying sequence-based representation schemes in association with Markov models and enhanced K-mean clustering algorithms for sequence behavior mining cluster patterns. The navigational cycle, time, function numbers, and the depth and extent (range) of app were statistically analysed. The analysis results can be used improved the passengers\u27 acceptance of app and help generate potential personalization recommendations for achieving an intelligent travel recommendation service

Modeling of Location Estimation for Object Tracking in WSN

Location estimation for object tracking is one of the important topics in the research of wireless sensor networks (WSNs). Recently, many location estimation or position schemes in WSN have been proposed. In this paper, we will propose the procedure and modeling of location estimation for object tracking in WSN. The designed modeling is a simple scheme without complex processing. We will use Matlab to conduct the simulation and numerical analyses to find the optimal modeling variables. The analyses with different variables will include object moving model, sensing radius, model weighting value α, and power-level increasing ratio k of neighboring sensor nodes. For practical consideration, we will also carry out the shadowing model for analysis

Application-Based Online Traffic Classification with Deep Learning Models on SDN Networks

The traffic classification based on the network applications is one important issue for network management. In this paper, we propose an application-based online and offline traffic classification, based on deep learning mechanisms, over software-defined network (SDN) testbed. The designed deep learning model, resigned in the SDN controller, consists of multilayer perceptron (MLP), convolutional neural network (CNN), and Stacked Auto-Encoder (SAE), in the SDN testbed. We employ an open network traffic dataset with seven most popular applications as the deep learning training and testing datasets. By using the TCPreplay tool, the dataset traffic samples are re-produced and analyzed in our SDN testbed to emulate the online traffic service. The performance analyses, in terms of accuracy, precision, recall, and F1 indicators, are conducted and compared with three deep learning models

Arresting rampant dental caries with silver diamine fluoride in a young teenager suffering from chronic oral graft versus host disease post-bone marrow transplantation: a case report

BACKGROUND: Rampant caries is an advanced and severe dental disease that affects multiple teeth. This case describes the management of rampant caries in a young teenager suffering from chronic oral graft versus host disease after allogeneic bone marrow transplantation. CASE PRESENTATION: A 14-year-old Chinese boy suffering from β–thalassemia major was referred to the dental clinic for the management of rampant dental caries. An oral examination revealed pale conjunctiva, bruising of lips, and depapillation of tongue indicating an underlying condition of anemia. The poor oral condition due to topical and systemic immunosuppressants was seriously aggravated, and rampant caries developed rapidly, affecting all newly erupted, permanent teeth. The teeth were hypersensitive and halitosis was apparent. Strategies for oral health education and diet modification were given to the patient. Xylitol chewing gum was used to stimulate saliva flow to promote remineralization of teeth. Silver diamine fluoride was topically applied to arrest rampant caries and to relieve pain from hypersensitivity. Carious teeth with pulpal involvement were endodontically treated. Stainless steel crowns were provided on molars to restore chewing function, and polycarbonate crowns were placed on premolars, upper canines and incisors. CONCLUSION: This case report demonstrates success in treating a young teenager with severe rampant dental decay by contemporary caries control and preventive strategy

Lead Exposure Is Associated with Decreased Serum Paraoxonase 1 (PON1) Activity and Genotypes

Lead exposure causes cardiac and vascular damage in experimental animals. However, there is considerable debate regarding the causal relationship between lead exposure and cardiovascular dysfunction in humans. Paraoxonase 1 (PON1), a high-density lipoprotein-associated antioxidant enzyme, is capable of hydrolyzing oxidized lipids and thus protects against atherosclerosis. Previous studies have shown that lead and several other metal ions are able to inhibit PON1 activity in vitro. To investigate whether lead exposure has influence on serum PON1 activity, we conducted a cross-sectional study of workers from a lead battery manufactory and lead recycling plant. Blood samples were analyzed for whole-blood lead levels, serum PON1 activity, and three common PON1 polymorphisms (Q192R, L55M, −108C/T). The mean blood lead level (± SD) of this cohort was 27.1 ± 15 μg/dL. Multiple linear regression analysis showed that blood lead levels were significantly associated with decreased serum PON1 activity (p < 0.001) in lead workers. This negative correlation was more evident for workers who carry the R192 allele, which has been suggested to be a risk factor for coronary heart disease. Taken together, our results suggest that the decrease in serum PON1 activity due to lead exposure may render individuals more susceptible to atherosclerosis, particularly subjects who are homozygous for the R192 allele

SIMD Everywhere Optimization from ARM NEON to RISC-V Vector Extensions

Many libraries, such as OpenCV, FFmpeg, XNNPACK, and Eigen, utilize Arm or x86 SIMD Intrinsics to optimize programs for performance. With the emergence of RISC-V Vector Extensions (RVV), there is a need to migrate these performance legacy codes for RVV. Currently, the migration of NEON code to RVV code requires manual rewriting, which is a time-consuming and error-prone process. In this work, we use the open source tool, "SIMD Everywhere" (SIMDe), to automate the migration. Our primary task is to enhance SIMDe to enable the conversion of ARM NEON Intrinsics types and functions to their corresponding RVV Intrinsics types and functions. For type conversion, we devise strategies to convert Neon Intrinsics types to RVV Intrinsics by considering the vector length agnostic (vla) architectures. With function conversions, we analyze commonly used conversion methods in SIMDe and develop customized conversions for each function based on the results of RVV code generations. In our experiments with Google XNNPACK library, our enhanced SIMDe achieves speedup ranging from 1.51x to 5.13x compared to the original SIMDe, which does not utilize customized RVV implementations for the conversions