Personalized behavior management as a replacement for medications for pain control and mood regulation

A lack of personalized approaches in non-medication pain management has prevented these alternative forms of treatment from achieving the desired efficacy. One hundred and ten female patients with fibromyalgia syndrome (FMS) and 60 healthy women without chronic pain were assessed for severity of chronic or retrospective occasional pain, respectively, along with alexithymia, depression, anxiety, coping strategies, and personality traits. All analyses were conducted following a ‘resource matching’ hypothesis predicting that to be effective, a behavioral coping mechanism diverting or producing cognitive resources should correspond to particular mechanisms regulating pain severity in the patient. Moderated mediation analysis found that extraverts could effectively cope with chronic pain and avoid the use of medications for pain and mood management by lowering depressive symptoms through the use of distraction mechanism as a habitual (‘out-of-touch-with-reality’) behavior. However, introverts could effectively cope with chronic pain and avoid the use of medications by lowering catastrophizing through the use of distraction mechanism as a situational (‘in-touch-with-reality’) behavior. Thus, personalized behavior management techniques applied according to a mechanism of capturing or diverting the main individual ‘resource’ of the pain experience from its ‘feeding’ to supporting another activity may increase efficacy in the reduction of pain severity along with decreasing the need for pain relief and mood-stabilizing medications.

Adaptive Performance Modeling of Data-intensive Workloads for Resource Provisioning in Virtualized Environment

The processing of data-intensive workloads is a challenging and time-consuming task that often requires massive infrastructure to ensure fast data analysis. The cloud platform is the most popular and powerful scale-out infrastructure to perform big data analytics and eliminate the need to maintain expensive and high-end computing resources at the user side. The performance and the cost of such infrastructure depend on the overall server configuration, such as processor, memory, network, and storage configurations. In addition to the cost of owning or maintaining the hardware, the heterogeneity in the server configuration further expands the selection space, leading to non-convergence. The challenge is further exacerbated by the dependency of the application’s performance on the underlying hardware. Despite an increasing interest in resource provisioning, few works have been done to develop accurate and practical models to proactively predict the performance of data-intensive applications corresponding to the server configuration and provision a cost-optimal configuration online. In this work, through a comprehensive real-system empirical analysis of performance, we address these challenges by introducing ProMLB: a proactive machine-learning-based methodology for resource provisioning. We first characterize diverse types of data-intensive workloads across different types of server architectures. The characterization aids in accurately capture applications’ behavior and train a model for prediction of their performance. Then, ProMLB builds a set of cross-platform performance models for each application. Based on the developed predictive model, ProMLB uses an optimization technique to distinguish close-to-optimal configuration to minimize the product of execution time and cost. Compared to the oracle scheduler, ProMLB achieves 91% accuracy in terms of application-resource matching. On average, ProMLB improves the performance and resource utilization by 42.6% and 41.1%, respectively, compared to baseline scheduler. Moreover, ProMLB improves the performance per cost by 2.5× on average.

Energy-Spectrum Efficient Content Distribution in Fog-RAN Using Rate-Splitting, Common Message Decoding, and 3D-Resource Matching

<div>Multi-objective resource allocation is studied for edge-caching enabled fog-radio access network. Notably, joint maximization of the energy-efficiency (EE) and spectrum-efficiency (SE) and interference management are investigated for distributing contents from the cache-enabled fog access points (F-APs) and cloud base station (CBS) to the user devices (UDs). In our envisioned system, the UDs are grouped into multiple non-overlapping device-clusters based on their locations. A rate-splitting with common message decoding based transmission strategy is applied to enable UDs of each device-cluster to receive data from a suitably selected F-AP and CBS over the same radio resource blocks. To maximize system EE and SE jointly, a multi-objective optimization problem (MOOP) is formulated and it is solved in three stages. At first, by employing the $\epsilon$-constraint method, the MOOP is converted to an EE-SE trade-off optimization problem. Then, by leveraging iterative function evaluation based power control and generalized 3D-resource matching, the EE-SE trade-off optimization problem is solved and a novel resource allocation algorithm is proposed to obtain near-optimal Pareto-front for the proposed MOOP. To reduce the complexity of obtaining near-optimal Pareto-front, a sub-optimal resource allocation algorithm is proposed as well. Finally, a low-complexity algorithm is devised to select a suitable operating EE-SE pair from the obtained Pareto-front. The conducted simulations demonstrate that the proposed resource allocation schemes achieve substantial improvement of system EE and SE over the benchmark schemes. </div>

Study on the Personalized Learning Model of Learner-Learning Resource Matching

With the development of service integration technology, online learning platforms have gathered a large number of learning resources, causing learners to get lost in a variety of course information and it is difficult to obtain learning resources that match their own needs. The proposal of personalized learning gives the problem a direction to solve. However, current personalized learning resource recommendation services facing problems such as excessive candidate resources, sparse history and cold starts. In addition, the learning resources provided also show problems of "difficult or easy, uneven quality". For this article researches the personalized learning recommendation model of learner-learning resource matching. The main content includes three parts: First, build a demand model based on learner registration information, learning behavior and other data. Second, analyze the access behavior of learning resources and assess their quality. Third, calculate the matching degree between learners and learning resources based on the demand model and the quality information of the learning resources, and recommend them.

Knowledge management practice of medical cloud logistics industry: transportation resource semantic discovery based on ontology modelling

PurposeThe booming of the Internet of things (IoT) and artificial intelligence (AI) techniques contributes to knowledge adoption and management innovation for the healthcare industry. It is of great significance to transport the medical resources to required places in an efficient way. However, it is difficult to exactly discover matched transportation resources and deliver to its destination due to the heterogeneity. This paper studies the medical transportation resource discovery mechanism, leading to efficiency improvement and operational innovation.Design/methodology/approachTo solve the transportation resource semantic discovery problem under the novel cloud environment, the ontology modelling approach is used for both transportation resources and tasks information modes. Besides, medical transportation resource discovery mechanism is proposed, and resource matching rules are designed including three stages: filtering reasoning, QoS-based matching and user preferences-based rank to satisfy personalized demands of users. Furthermore, description logic rules are built to express the developed matching rules.FindingsAn organizational transportation case is taken as an example to describe the medical transportation logistics resource semantic discovery process under cloud medical service scenario. Results derived from the proposed semantic discovery mechanism could assist operators to find the most suitable resources.Research limitations/implicationsThe case study validates the effectiveness of the developed transportation resource semantic discovery mechanism, contributing to knowledge management innovation for the medical logistics industry.Originality/valueTo improve task-resource matching accuracy under cloud scenario, this study develops a transportation resource semantic discovery procedure from the viewpoint of knowledge management. The novel knowledge management practice contributes to operational management of the cloud medical logistics service by introducing ontology modelling and creative management.