BigFCM: Fast, Precise and Scalable FCM on Hadoop

Clustering plays an important role in mining big data both as a modeling technique and a preprocessing step in many data mining process implementations. Fuzzy clustering provides more flexibility than non-fuzzy methods by allowing each data record to belong to more than one cluster to some degree. However, a serious challenge in fuzzy clustering is the lack of scalability. Massive datasets in emerging fields such as geosciences, biology and networking do require parallel and distributed computations with high performance to solve real-world problems. Although some clustering methods are already improved to execute on big data platforms, but their execution time is highly increased for large datasets. In this paper, a scalable Fuzzy C-Means (FCM) clustering named BigFCM is proposed and designed for the Hadoop distributed data platform. Based on the map-reduce programming model, it exploits several mechanisms including an efficient caching design to achieve several orders of magnitude reduction in execution time. Extensive evaluation over multi-gigabyte datasets shows that BigFCM is scalable while it preserves the quality of clustering

Importance-performance analysis of port’s services quality form perspective of containerized liner shipping

One of the important issues in port services is to evaluate the performance of the services. Without evaluating port services and their related components, these services cannot be considered desirable and its quality cannot be enhanced. However, the evaluation and the quality assurance of port services should be accomplished based on a scientific framework and a coherent framework to have desirable results. The importance-performance analysis model is an appropriate framework where each component is evaluated in terms of two dimensions of importance and performance. This study performs performance-importance analysis of ports’ services quality form perspective of containerized liner shipping in the Imam Khomeini port. In this exploratory study, 150 shipping lines experts are chosen, randomly in 2012. The study identifies 28 components of quality in port services and shipping lines’ experts are requested to evaluate these components in terms of two dimensions of importance and performance. Results reveal that there is a gap between the importance and performance of all port services components except three components of 6, 19 and 24. In addition, the results indicate that Importance-Performance Analysis (IPA) model is capable of evaluating and assuring quality in port services and can precisely identify the strengths and weaknesses of the seaport system and provide guidance for strategy formulation for quality improvement

IEC 61850-based WLAN peer-to-peer feeder protection improvement in smart grid substation automation system

The development of a computational platform of a communication network for an automation system using the precepts of IEC 61850 has become a great trend in substation automation systems (SASs) research. Recent developments in wireless communication technologies, especially the IEEE802.11 have enabled cost-effective remote control systems with a capability of monitoring, control and protection in the real-time operating conditions of substations. The message delay and throughput in substation automation while using the WLAN and smart grid must satisfy the standard requirements specified in the IEC 61850-5. In this paper, the end-to-end (ETE) delay of IEC 61850-based messages and operating time of the over current protection using the WLAN-based communication network has been evaluated using Opnet Modeler Edition 18.0

Second generation IEEE802.11N performance for IEC61850-based substation automation system

As the initial scope of supporting communications inside substation automation systems, IEC 61850 was defined to be mapped on Ethernet as the layer 2 communication network technology. It is because the Ethernet is the dominant technology for the Local area network – LAN and it brings significant advantages of high bandwidth and low latency especially with the use of optics fibers at the physical layer. Consequently, Ethernet is very appropriate for the substation indoor LAN with applications that require high bandwidth e.g. measurements and time-critical functions such as protection services. However, when IEC 61850 is extended to support large-scale communication networks between substations, control the centers to substations and DERs; Ethernet is no longer a good solution. The rapid emergence of DERs, the increasing amount of electric vehicles and smart meters reveal the shortcomings of Ethernet which rest in its high set-up cost and scalability problem following the use of cable for all connections. In this paper we focus on the use of the Wireless Local Area Network (WLAN) technology to support distribution substation applications. The advantages of this solution lie in the low cost of installation, enough data rates, and hassle-free deployment. However, it is not suitable for remote control communications in the distribution grid due to the limited coverage of the Wi-Fi radio. Some examples of using WLAN Compliance with IEC 61850 models to construct the SAS network as well as the network performance simulation results are also included in this paper based on the Opnet software

Opportunity for using WLAN with IEC 61850 and the future of this protocol

The unified protocols are unified in application interface, models, and seamless. They generate one standard protocol, one world called IEC 61850. IEC 61850 integrate the security, interoperability, modelling, mapping to a substation, and reliability. Presently, the more expensive fiber based Ethernet LAN is the most prevalent technology for medium and low voltage distribution substations. To circumvent this problem Wireless Local Area Network (WLAN) has been investigated for its suitability for applications that are compliant to IEC 61850: automation and metering; control and monitoring; and over-current protection. In this paper the IEEE 802.11n WLAN is studied when used in various IEC 61850 supported applications for substation automation. It also discusses the benefits of using GOOSE message to protect and control applications and the use of IEC 61850

Serum leptin and its relation to anthropometric measures of obesity in pre-diabetic Saudis

Background: Little information is available on leptin concentrations in individuals with IGT. This study aims to determine and correlate leptin levels to anthropometric measures of obesity in prediabetic, (IFG and IGT), type 2 diabetic and normoglycaemic Saudis. Methods: 308 adult Saudis (healthy controls n = 80; pre-diabetes n = 86; Type 2 diabetes n = 142) participated. Anthropometric parameters were measured and fasting blood samples taken. Serum insulin was analysed, using a solid phase enzyme amplified sensitivity immunoassay and also leptin concentrations, using radio-immunoassay. The remaining blood parameters were determined using standard laboratory procedures. Results: Leptin levels of diabetic and pre-diabetic men were higher than in normoglycaemic men (12.4 [3.2–72] vs 3.9 [0.8–20.0] ng/mL, (median [interquartile range], p = 0.0001). In females, leptin levels were significantly higher in pre-diabetic subjects (14.09 [2.8–44.4] ng/mL) than in normoglycaemic subjects (10.2 [0.25–34.8] ng/mL) (p = 0.046). After adjustment for BMI and gender, hip circumference was associated with log leptin (p = 0.006 with R2 = 0.086) among all subjects. Conclusion: Leptin is associated with measures of adiposity, hip circumference in particular, in the non-diabetic state among Saudi subjects. The higher leptin level among diabetics and pre-diabetics is not related to differences in anthropometric measures of obesity

Abnormalities of Motor Imagery and Relationship With Depressive Symptoms in Mildly Disabling Relapsing-Remitting Multiple Sclerosis

The effectiveness of motor imagery (MI) as an adjunct to physical rehabilitation has previously been shown. Motor imagery ability can be affected by neurologic disorders that affect motor and cognitive function. This study was designed to assess MI ability in persons with mildly disabling relapsing-remitting multiple sclerosis (RRMS) based on the functional and cognitive dysfunctions. Methods: Twenty-two participants with RRMS and 23 age-, gender-, and education-matched comparison subjects were evaluated by a battery of MI tasks, including a kinesthetic and visual imagery questionnaire, a mental hand rotation task, and a visual guided pointing task. Results: There was no significant difference in MI vividness between the participants with MS and the comparison group, but the accuracy and temporal correspondence of MI in the participants with MS differed significantly from those in the comparison group. Depression scores were significantly higher in participants with MS (P < 0.001), and depression was significantly correlated with disability (r = 0.4; P < 0.05). The correlation between accuracy of MI in the participants with MS and their cognitive ability was significant (r = 0.57; P < 0.05). The MI duration of participants with MS was significantly correlated with their disability (r = 0.59; P < 0.05) and their cognitive ability (r = −0.38; P = 0.009). Discussion: The preservation of MI ability was observed in participants with RRMS; however, abnormalities in accuracy and temporal aspects of MI were observed even in the participants with mild disease. Abnormalities in temporal aspects and accuracy of MI were related to disability and cognitive ability, respectively. In participants with MS, depression should be considered as a confounding factor for the MI task results. Conclusions: Our finding could be considered in the application of MI during the rehabilitation of persons with MS

Segmentation of Exercise Repetitions Enabling Real-Time Patient Analysis and Feedback Using a Single Exemplar

We present a segmentation algorithm capable of segmenting exercise repetitions in real-time. This approach uses subsequence dynamic time warping and requires only a single exemplar repetition of an exercise to correctly segment repetitions from other subjects, including those with limited mobility. This approach is invariant to low range of motion, instability in movements and sensor noise while remaining selective to different exercises. This algorithm enables responsive feedback for technology-assisted physical rehabilitation systems. We evaluated the algorithm against a publicly available dataset (CMU) and against a healthy population and stroke patient population performing rehabilitation exercises captured on a consumer-level depth sensor. We show the algorithm can consistently achieve correct segmentation in real-time

Laparoscopic adrenalectomy: 10-year experience, 67 procedures

Introduction: The purpose of this study was to evaluate the short-term and long-term results of laparoscopic adrenalectomies carried out in our center. Materials and Methods: A total of 67 laparoscopic adrenalectomies were performed during the 10 years between 1995 and 2005 at Shahid Labbafinejad Medical Center. A transperitoneal lateral approach was used in 65 (97.0%) of the patients, and retroperitoneal approach was used in 2 (3.0%). The clinical characteristics and the outcomes were reviewed in a retrospective study. Results: Indications for laparoscopic adrenalectomy in our patients were as follows: pheochromocytoma in 28 patients (41.8%), aldosteroneproducing adenoma in 15 (22.4%), pseudocyst in 6 (9.0%), Cushing syndrome (macronodular adrenocortical hyperplasia) in 5 (7.5%), nonfunctioning adenoma (incidentaloma) in 5 (7.5%), myelolipoma in 2 (3.0%), almost normal adrenal tissue in 2 (3.0%), adrenal cyst in 2 (3.0%), adenocarcinoma in 1 (1.4%), and schwannoma in 1 (1.4%). The mean operative time for unilateral cases was 149.0 ± 36.1 minutes. The mean intraoperative blood loss was 126 ± 36 mL. Conversion rate to open surgery was 7.5%. Reoperation due to hemorrhage was performed in 1 patient. Conclusion: Laparoscopic adrenalectomy is a safe procedure in some adrenal tumors and a reasonable option for selected large adrenal tumors when complete resection is technically feasible and there is no evidence of local invasion

Psychophysiological analysis of a pedagogical agent and robotic peer for individuals with autism spectrum disorders.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by ongoing problems in social interaction and communication, and engagement in repetitive behaviors. According to Centers for Disease Control and Prevention, an estimated 1 in 68 children in the United States has ASD. Mounting evidence shows that many of these individuals display an interest in social interaction with computers and robots and, in general, feel comfortable spending time in such environments. It is known that the subtlety and unpredictability of people’s social behavior are intimidating and confusing for many individuals with ASD. Computerized learning environments and robots, however, prepare a predictable, dependable, and less complicated environment, where the interaction complexity can be adjusted so as to account for these individuals’ needs. The first phase of this dissertation presents an artificial-intelligence-based tutoring system which uses an interactive computer character as a pedagogical agent (PA) that simulates a human tutor teaching sight word reading to individuals with ASD. This phase examines the efficacy of an instructional package comprised of an autonomous pedagogical agent, automatic speech recognition, and an evidence-based instructional procedure referred to as constant time delay (CTD). A concurrent multiple-baseline across-participants design is used to evaluate the efficacy of intervention. Additionally, post-treatment probes are conducted to assess maintenance and generalization. The results suggest that all three participants acquired and maintained new sight words and demonstrated generalized responding. The second phase of this dissertation describes the augmentation of the tutoring system developed in the first phase with an autonomous humanoid robot which serves the instructional role of a peer for the student. In this tutoring paradigm, the robot adopts a peer metaphor, where its function is to act as a peer. With the introduction of the robotic peer (RP), the traditional dyadic interaction in tutoring systems is augmented to a novel triadic interaction in order to enhance the social richness of the tutoring system, and to facilitate learning through peer observation. This phase evaluates the feasibility and effects of using PA-delivered sight word instruction, based on a CTD procedure, within a small-group arrangement including a student with ASD and the robotic peer. A multiple-probe design across word sets, replicated across three participants, is used to evaluate the efficacy of intervention. The findings illustrate that all three participants acquired, maintained, and generalized all the words targeted for instruction. Furthermore, they learned a high percentage (94.44% on average) of the non-target words exclusively instructed to the RP. The data show that not only did the participants learn nontargeted words by observing the instruction to the RP but they also acquired their target words more efficiently and with less errors by the addition of an observational component to the direct instruction. The third and fourth phases of this dissertation focus on physiology-based modeling of the participants’ affective experiences during naturalistic interaction with the developed tutoring system. While computers and robots have begun to co-exist with humans and cooperatively share various tasks; they are still deficient in interpreting and responding to humans as emotional beings. Wearable biosensors that can be used for computerized emotion recognition offer great potential for addressing this issue. The third phase presents a Bluetooth-enabled eyewear – EmotiGO – for unobtrusive acquisition of a set of physiological signals, i.e., skin conductivity, photoplethysmography, and skin temperature, which can be used as autonomic readouts of emotions. EmotiGO is unobtrusive and sufficiently lightweight to be worn comfortably without interfering with the users’ usual activities. This phase presents the architecture of the device and results from testing that verify its effectiveness against an FDA-approved system for physiological measurement. The fourth and final phase attempts to model the students’ engagement levels using their physiological signals collected with EmotiGO during naturalistic interaction with the tutoring system developed in the second phase. Several physiological indices are extracted from each of the signals. The students’ engagement levels during the interaction with the tutoring system are rated by two trained coders using the video recordings of the instructional sessions. Supervised pattern recognition algorithms are subsequently used to map the physiological indices to the engagement scores. The results indicate that the trained models are successful at classifying participants’ engagement levels with the mean classification accuracy of 86.50%. These models are an important step toward an intelligent tutoring system that can dynamically adapt its pedagogical strategies to the affective needs of learners with ASD