Search results for: symptom cluster

Authors: Wided Abidi, Tahar Ezzedine

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Low-Energy Adaptive Clustering Hierarchy (LEACH) has been considered as one of the effective hierarchical routing algorithms that optimize energy and prolong the lifetime of network. Since the selection of Cluster Head (CH) in LEACH is carried out randomly, in this paper, we propose an approach of electing CH based on LEACH protocol. In other words, we present a formula for calculating the threshold responsible for CH election. In fact, we adopt three principle criteria: the remaining energy of node, the number of neighbors within cluster range and the distance between node and CH. Simulation results show that our proposed approach beats LEACH protocol in regards of prolonging the lifetime of network and saving residual energy.

Keywords: wireless sensors networks, LEACH protocol, cluster head election, energy efficiency

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Authors: Ameen Jameel Alawneh

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A Mobile ad-hoc network (MANET) is a collection of wireless mobile hosts that dynamically form a temporary network without the aid of a system administrator. It has neither fixed infrastructure nor wireless ad hoc sessions. It inherently reaches several nodes with a single transmission, and each node functions as both a host and a router. The network maybe represented as a set of clusters each managed by clusterhead. The cluster size is not fixed and it depends on the movement of nodes. We proposed a clusterhead size algorithm (CHSize). This clustering algorithm can be used by several routing algorithms for ad hoc networks. An elected clusterhead is assigned for communication with all other clusters. Analysis and simulation of the algorithm has been implemented using GloMoSim networks simulator, MATLAB and MAPL11 proved that the proposed algorithm achieves the goals.

Keywords: simulation, MANET, Ad-hoc, cluster head size, linked cluster algorithm, loss and dropped packets

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Authors: John Acord, Ankita Batla, Kiran Khepar, Maude Schmidt, Charlotte Allen, Russ Bradford

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Objectives: Despite the availability oftreatment options, Parkinson’s disease (PD) continues to impact heavily on a patient’s quality of life (QoL), as many symptoms that bother the patient remain unexplored and untreated in clinical settings. The aims of this research were to understand the burden of PDsymptoms from a patient perspective, particularly those which are the most persistent and debilitating, and to determine if current treatments and treatment algorithms adequately focus on their resolution. Methods: A13-question, online, patient-reported survey was created based on the MDS-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS)and symptoms listed on Parkinson’s Disease Patient Advocacy Groups websites, and then validated by 10 Parkinson’s patients. In the survey, patients were asked to choose both their most common and their most bothersome symptoms, whether they had received treatment for those and, if so, had it been effective in resolving those symptoms. Results: The most bothersome symptoms reported by the 111 participants who completed the survey were sleep problems (61%), feeling tired (56%), slowness of movements (54%), and pain in some parts of the body (49%). However, while 86% of patients reported receiving dopamine or dopamine like drugs to treat their PD, far fewer reported receiving targeted therapies for additional symptoms. For example, of the patients who reported having sleep problems, only 33% received some form of treatment for this symptom. This was also true for feeling tired (30% received treatment for this symptom), slowness of movements (62% received treatment for this symptom), and pain in some parts of the body (61% received treatment for this symptom). Additionally, 65% of patients reported that the symptoms they experienced were not adequately controlled by the treatments they received, and 9% reported that their current treatments had no effect on their symptoms whatsoever. Conclusion: The survey outcomes highlight that the majority of patients involved in the study received treatment focused on their disease, however, symptom-based treatments were less well represented. Consequently, patient-reported symptoms such as sleep problems and feeling tired tended to receive more fragmented intervention than ‘classical’ PD symptoms, such as slowness of movement, even though they were reported as being amongst the most bothersome symptoms for patients. This research highlights the need to explore symptom burden from the patient’s perspective and offer Customised treatment/support for both motor and non-motor symptoms maximize patients’ quality of life.

Keywords: survey, patient reported symptom burden, unmet needs, parkinson's disease

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Authors: Eva Tolmeijer, Emmanuelle Peters, Veena Kumari, Liam Mason

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Cognitive behavioral therapy for psychosis (CBTp) is effective in many but not all patients, making it important to better understand the factors that determine treatment outcomes. To date, no studies have examined whether neuroimaging can make clinically useful predictions about who will respond to CBTp. To this end, we used machine learning methods that make predictions about symptom improvement at the individual patient level. Prior to receiving CBTp, 22 patients with a diagnosis of schizophrenia completed a social-affective processing task during functional MRI. Multivariate pattern analysis assessed whether treatment response could be predicted by brain activation responses to facial affect that was either socially threatening or prosocial. The resulting models did significantly predict symptom improvement, with distinct multivariate signatures predicting psychotic (r=0.54, p=0.01) and affective (r=0.32, p=0.05) symptoms. Psychotic symptom improvement was accurately predicted from relatively focal threat-related activation across hippocampal, occipital, and temporal regions; affective symptom improvement was predicted by a more dispersed profile of responses to prosocial affect. These findings enrich our understanding of the neurobiological underpinning of treatment response. This study provides a foundation that will hopefully lead to greater precision and tailoring of the interventions offered to patients.

Keywords: cognitive behavioral therapy, machine learning, psychosis, schizophrenia

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Authors: Ibragim Issabekov, Byron Crape, Lyazzat Toleubekova

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Background: Palliative care substantially improves the quality of life of terminally-ill patients. Symptom control is one of the keystones in the management of patients in palliative care settings, lowering distress as well as improving the quality of life of patients with end-stage diseases. The most common symptoms causing significant distress for patients are pain, nausea and vomiting, increased respiratory secretions and mental health issues like depression. Aims are: 1. to identify best practices in symptom management in palliative patients in accordance with internationally approved guidelines and compare aforementioned with actual practices in Kazakhstan; to evaluate the criteria for assessing symptoms in terminally-ill patients, 2. to review the availability and utilization of pharmaceutical agents for pain control, management of excessive respiratory secretions, nausea, and vomiting, and delirium and 3. to develop recommendations for the systematic approach to end-of-life symptom management in Kazakhstan. Methods: The use of qualitative research methods together with systematic literature review have been employed to provide a rigorous research process to evaluate current approaches for symptom management of palliative patients in Kazakhstan. Qualitative methods include in-depth semi-structured interviews of the healthcare professionals involved in palliative care provision. Results: Obstacles were found in appropriate provision of palliative care. Inadequate education and training to manage severe symptoms, poorly defined laws and regulations for palliative care provision, and a lack of algorithms and guidelines for care were major barriers in the effective provision of palliative care. Conclusion: Assessment of palliative care in this medium-income transitional-economy country is one of the first steps in the initiation of integration of palliative care into the existing health system. Achieving this requires identifying obstacles and resolving these issues.

Keywords: end-of-life care, middle income country, palliative care, symptom control

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Authors: Hassan S. Hussein, Rania A. Abul Seoud, Amr M. Refaat

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Scientific researchers face in the analysis of very large data sets that is increasing noticeable rate in today’s and tomorrow’s technologies. Hadoop and Spark are types of software that developed frameworks. Hadoop framework is suitable for many Different hardware platforms. In this research, a scientific Linux cluster for Big Data analysis (SLBD) is presented. SLBD runs open source software with large computational capacity and high performance cluster infrastructure. SLBD composed of one cluster contains identical, commodity-grade computers interconnected via a small LAN. SLBD consists of a fast switch and Gigabit-Ethernet card which connect four (nodes). Cloudera Manager is used to configure and manage an Apache Hadoop stack. Hadoop is a framework allows storing and processing big data across the cluster by using MapReduce algorithm. MapReduce algorithm divides the task into smaller tasks which to be assigned to the network nodes. Algorithm then collects the results and form the final result dataset. SLBD clustering system allows fast and efficient processing of large amount of data resulting from different applications. SLBD also provides high performance, high throughput, high availability, expandability and cluster scalability.

Keywords: big data platforms, cloudera manager, Hadoop, MapReduce

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Authors: Te-Jen Chang, Ping-Sheng Huang, Shan-Ten Cheng, Chih-Lin Lin, I-Hui Pan, Tsung- Hsien Lin

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RSA system is a great contribution for the encryption and the decryption. It is based on the modular exponentiation. We call this system as “a large of numbers for calculation”. The operation of a large of numbers is a very heavy burden for CPU. For increasing the computational speed, in addition to improve these algorithms, such as the binary method, the sliding window method, the addition chain method, and so on, the cluster computer can be used to advance computational speed. The cluster system is composed of the computers which are installed the MPICH2 in laboratory. The parallel procedures of the modular exponentiation can be processed by combining the sliding window method with the addition chain method. It will significantly reduce the computational time of the modular exponentiation whose digits are more than 512 bits and even more than 1024 bits.

Keywords: cluster system, modular exponentiation, sliding window, addition chain

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Authors: Karish Thavabalan, Alistair Ovenell, Aman Sutaria, Annabelle Parkhouse, Numan Baydemir, Theodore Lally

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Background: Open discussions surrounding menopause are often associated with stigma, with many women feeling uncomfortable to engage in them with friends, colleagues, and healthcare professionals. Though the barriers regarding safe discussions of symptom management experienced by menopausal women are well documented, existing research offers little insight into whether these were shared by pre-menopausal women. This study aimed to explore the barriers regarding safe discussions about menopausal symptom management as perceived or experienced by pre-menopausal and menopausal women. Methods: This qualitative study was conducted over a 2-month period (March 2022 - April 2022) under the auspices of Imperial College Business School, London, UK. Snowball sampling was used to recruit both menopausal (age 45-70) and pre-menopausal participants (age <45), and sampling continued until data saturation was achieved. 16 semi-structured online interviews were conducted, and transcripts were thematically analyzed following Braun and Clarke’s six-step methodology. Results: A total of 7 higher themes regarding safe discussion of menopausal symptom management were identified by both pre-menopausal and menopausal women, including: “ineffective coping mechanisms”, “perceived onus to self-endure”, “lack of workplace support”, “poor knowledge of management approaches”, “poor healthcare infrastructure”, “poor support from friends and family”, “lack of knowledge and interest from a young age”. Conclusion: Identifying the barriers regarding safe discussion helped to highlight which areas require most significant intervention. Alongside tackling the barriers, menopausal women face, ultimately, there is a pertinent need to and address the lack of insight into menopause from a younger age and to encourage earlier discussions so as to not propagate the cycle of stigma.

Keywords: menopause, stigma, safe discussions, symptom management

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Authors: Karish Thavabalan, Aman Sutaria, Alistair Ovenell, Annabelle Parkhouse, Numan Baydemir, Theodore Lally

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Background: Open discussions surrounding menopause are often associated with stigma, with many women feeling uncomfortable engaging in them with friends, colleagues, and healthcare professionals. Though the barriers regarding safe discussions of symptom management experienced by menopausal women are well documented, existing research offers little insight into whether these were shared by pre-menopausal women. This study aimed to explore the barriers regarding safe discussions about menopausal symptom management as perceived or experienced by pre-menopausal and menopausal women. Methods: This qualitative study was conducted over a 2-month period (March 2022 - April 2022) under the auspices of Imperial College Business School, London, UK. Snowball sampling was used to recruit both menopausal (age 45-70) and pre-menopausal participants (age <45) and sampling continued until data saturation was achieved. Sixteen semi-structured online interviews were conducted, and transcripts were thematically analyzed following Braun and Clarke’s six-step methodology. Results: A total of 7 higher themes regarding safe discussion of menopausal symptom management were identified by both pre-menopausal and menopausal women, including: “ineffective coping mechanisms,” “perceived onus to self-endure,” “lack of workplace support,” “poor knowledge of management approaches,” “poor healthcare infrastructure,” “poor support from friends and family,” “lack of knowledge and interest from a young age.” Conclusion: Identifying the barriers regarding safe discussion helped to highlight which areas require the most significant intervention. Alongside tackling the barriers, menopausal women face, ultimately, there is a pertinent need to address the lack of insight into menopause from a younger age and to encourage earlier discussions so as to not propagate the cycle of stigma.

Keywords: menopause, safe discussion, symptom management, stigma

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Authors: Asma Afshari, Abdollah Jamshidi, Jamshid Razmyar, Mehrnaz Rad

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Clostridium perfringens is a serious pathogen which causes enteric diseases in domestic animals and food poisoning in humans. Spores can survive cooking processes and play an important role in the possible onset of disease. In this study RAPD-PCR and REP-PCR were used to examine the genetic diversity of 49isolates ofC. Perfringens type A from 3 different sources. The results of RAPD-PCR revealed the most genetic diversity among poultry isolates, while human isolates showed the least genetic diversity. Cluster analysis obtained from RAPD_PCR and based on the genetic distances split the 49 strains into five distinct major clusters (A, B, C, D, and E). Cluster A and C were composed of isolates from poultry meat, cluster B was composed of isolates from human feces, cluster D was composed of isolates from minced meat, poultry meat and human feces and cluster E was composed of isolates from minced meat. Further characterization of these strains by using (GTG) 5 fingerprint repetitive sequence-based PCR analysis did not show further differentiation between various types of strains. To our knowledge, this is the first study in which the genetic diversity of C. perfringens isolates from different types of meats and human feces has been investigated.

Keywords: C. perfringens, genetic diversity, RAPD-PCR, REP-PCR

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Authors: Ashok V. Sutagundar, Sunilkumar S. Manvi

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Energy, delay and bandwidth are the prime issues of wireless sensor network (WSN). Energy usage optimization and efficient bandwidth utilization are important issues in WSN. Event triggered data aggregation facilitates such optimal tasks for event affected area in WSN. Reliable delivery of the critical information to sink node is also a major challenge of WSN. To tackle these issues, we propose an event driven dynamic clustering and data aggregation scheme for WSN that enhances the life time of the network by minimizing redundant data transmission. The proposed scheme operates as follows: (1) Whenever the event is triggered, event triggered node selects the cluster head. (2) Cluster head gathers data from sensor nodes within the cluster. (3) Cluster head node identifies and classifies the events out of the collected data using Bayesian classifier. (4) Aggregation of data is done using statistical method. (5) Cluster head discovers the paths to the sink node using residual energy, path distance and bandwidth. (6) If the aggregated data is critical, cluster head sends the aggregated data over the multipath for reliable data communication. (7) Otherwise aggregated data is transmitted towards sink node over the single path which is having the more bandwidth and residual energy. The performance of the scheme is validated for various WSN scenarios to evaluate the effectiveness of the proposed approach in terms of aggregation time, cluster formation time and energy consumed for aggregation.

Keywords: wireless sensor network, dynamic clustering, data aggregation, wireless communication

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Authors: Daniel Gonzales, Zev Winkelman, Trung Tran, Ricardo Sanchez, Dulani Woods, John Hollywood

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We have developed a distributed computing capability, Digital Forensics Compute Cluster (DFORC2) to speed up the ingestion and processing of digital evidence that is resident on computer hard drives. DFORC2 parallelizes evidence ingestion and file processing steps. It can be run on a standalone computer cluster or in the Amazon Web Services (AWS) cloud. When running in a virtualized computing environment, its cluster resources can be dynamically scaled up or down using Kubernetes. DFORC2 is an open source project that uses Autopsy, Apache Spark and Kafka, and other open source software packages. It extends the proven open source digital forensics capabilities of Autopsy to compute clusters and cloud architectures, so digital forensics tasks can be accomplished efficiently by a scalable array of cluster compute nodes. In this paper, we describe DFORC2 and compare it with a standalone version of Autopsy when both are used to process evidence from hard drives of different sizes.

Keywords: digital forensics, cloud computing, cyber security, spark, Kubernetes, Kafka

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Authors: Ladda Her, Juntip Kanjanasilp, Ratree Sawangjit, Nathorn Chaiyakunapruk

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Cough is the common symptom of the respiratory tract infections or non-infections; the duration of cough indicates a classification and severity of disease. Herbal medicines can be used as the alternative to drugs for relief of cough symptoms from acute and chronic disease. Eucalyptus was used for reducing cough with evidences suggesting it has an active role in reduction of airway inflammation. The present study aims to evaluate efficacy and safety of eucalyptus for relief of cough symptom in respiratory disease. Method: The Cochrane Library, MEDLINE (PubMed), Scopus, CINAHL, Springer, Science direct, ProQuest, and THAILIS databases. From its inception until 01/02/2019 for randomized control trials. We follow for the efficacy and safety of eucalyptus for reducing cough. Methodological quality was evaluated by using the Cochrane risk of bias tool; two reviewers in our team screened eligibility and extracted data. Result: Six studies were included for the review and five studies were included in the meta-analysis, there were 1.911 persons including children (n: 1) and adult (n: 5) studies; for study in children and adult were between 1 and 80 years old, respectively. Eucalyptus was used as mono herb (n: 2) and in combination with other herbs form (n: 4). All of the studies with eucalyptus were compared for efficacy and safety with placebo or standard treatment, Eucalyptus dosage form in studies included capsules, spray, and syrup. Heterogeneity was 32.44 used random effect model (I² = 1.2%, χ² = 1.01; P-value = 0.314). The efficacy of eucalyptus was showed a reduced cough symptom statistically significant (n = 402, RR: 1.40, 95%CI [1.19, 1.65], P-value < 0.0001) when compared with placebo. Adverse events (AEs) were reported mild to moderate intensity with mostly gastrointestinal symptom. The methodological quality of the included trials was overall poor. Conclusion: Eucalyptus appears to be beneficial and safe for relieving in respiratory diseases focus on cough frequency. The evidence was inconclusive due to limited quality trial. Well-designed trials for evaluating the effectiveness in humans, the effectiveness for reducing cough symptom in human is needed. Eucalyptus had safety as monotherapy or in combination with other herbs.

Keywords: cough, eucalyptus, cineole, herbal medicine, systematic review, meta-analysis

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Authors: Jianfeng Hu

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Personal authentication based on electroencephalography (EEG) signals is one of the important field for the biometric technology. More and more researchers have used EEG signals as data source for biometric. However, there are some disadvantages for biometrics based on EEG signals. The proposed method employs entropy measures for feature extraction from EEG signals. Four type of entropies measures, sample entropy (SE), fuzzy entropy (FE), approximate entropy (AE) and spectral entropy (PE), were deployed as feature set. In a silhouettes calculation, the distance from each data point in a cluster to all another point within the same cluster and to all other data points in the closest cluster are determined. Thus silhouettes provide a measure of how well a data point was classified when it was assigned to a cluster and the separation between them. This feature renders silhouettes potentially well suited for assessing cluster quality in personal authentication methods. In this study, “silhouettes scores” was used for assessing the cluster quality of k-means clustering algorithm is well suited for comparing the performance of each EEG dataset. The main goals of this study are: (1) to represent each target as a tuple of multiple feature sets, (2) to assign a suitable measure to each feature set, (3) to combine different feature sets, (4) to determine the optimal feature weighting. Using precision/recall evaluations, the effectiveness of feature weighting in clustering was analyzed. EEG data from 22 subjects were collected. Results showed that: (1) It is possible to use fewer electrodes (3-4) for personal authentication. (2) There was the difference between each electrode for personal authentication (p<0.01). (3) There is no significant difference for authentication performance among feature sets (except feature PE). Conclusion: The combination of k-means clustering algorithm and silhouette approach proved to be an accurate method for personal authentication based on EEG signals.

Keywords: personal authentication, K-mean clustering, electroencephalogram, EEG, silhouettes

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Authors: R. Berglund, T. Backström, M. Bellgran

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This study explores how management addresses psychosocial risks in seven teams of engineers and technicians in the midst of the fourth industrial revolution. The sample is from an ongoing quasi-experiment about psychosocial risk management in a manufacturing company in Sweden. Each of the seven teams belongs to one of two clusters: a positive cluster or a negative cluster. The positive cluster reports a significantly positive change in psychosocial risk levels between two time-points and the negative cluster reports a significantly negative change. The data are collected using semi-structured interviews. The results of the computer aided thematic analysis show that there are more differences than similarities when comparing the risk treatment actions taken between the two clusters. Findings show that the managers in the positive cluster use more enabling actions that foster and support formal and informal relationship building. In contrast, managers that use less enabling actions hinder the development of positive group processes and contribute negative changes in psychosocial risk levels. This exploratory study sheds some light on how management can influence significant positive and negative changes in psychosocial risk levels during a risk management process.

Keywords: group process model, risk treatment, risk management, psychosocial

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Authors: K. Gomathi

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Mobile Ad hoc Networks (MANET) is defined as collection of routable wireless mobile nodes with no centralized administration and communicate each other using radio signals. Especially MANETs deployed in hostile environments where hackers will try to disturb the secure data transfer and drain the valuable network resources. Since MANET is battery operated network, preserving the network resource is essential one. For resource constrained computation, efficient routing and to increase the network stability, the network is divided into smaller groups called clusters. The clustering architecture consists of Cluster Head(CH), ordinary node and gateway. The CH is responsible for inter and intra cluster routing. CH election is a prominent research area and many more algorithms are developed using many different metrics. The CH with longer life sustains network lifetime, for this purpose Secondary Cluster Head(SCH) also elected and it is more economical. To nominate efficient CH, a Enhanced Distributed Weighted Clustering Algorithm (EDWCA) has been proposed. This approach considers metrics like battery power, degree difference and speed of the node for CH election. The proficiency of proposed one is evaluated and compared with existing algorithm using Network Simulator(NS-2).

Keywords: MANET, EDWCA, clustering, cluster head

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Authors: Danuvasin Charoen

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This research investigates cluster development in the area called the Greater Mekong Subregion (GMS), which consists of Thailand, the People’s Republic of China (PRC), the Yunnan Province and Guangxi Zhuang Autonomous Region, Myanmar, the Lao People’s Democratic Republic (Lao PDR), Cambodia, and Vietnam. The study utilized Porter’s competitiveness theory and the cluster mapping approach to analyze the competitiveness of the region. The data collection consists of interviews, focus groups, and the analysis of secondary data. The findings identify some evidence of cluster development in the GMS; however, there is no clear indication of collaboration among the components in the clusters. GMS clusters tend to be stand-alone. The clusters in Vietnam, Lao PDR, Myanmar, and Cambodia tend to be labor intensive, whereas the clusters in Thailand and the PRC (Yunnan) have the potential to successfully develop into innovative clusters. The collaboration and integration among the clusters in the GMS area are promising, though it could take a long time. The most likely relationship between the GMS countries could be, for example, suppliers of the low-end, labor-intensive products will be located in the low income countries such as Myanmar, Lao PDR, and Cambodia, and these countries will be providing input materials for innovative clusters in the middle income countries such as Thailand and the PRC.

Keywords: cluster, GMS, competitiveness, development

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Authors: Pei-Fan Mu

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Background: Lung cancer is the most common cancer with the highest mortality rate. Patients with lung cancer under chemotherapy treatment experience life-threatening uncertainty. This study was based on the broaden-and-build theory using intentionality reflection of the body and internalization of positive prioritization strategies to enhance positive emotions of patients with lung cancer. Purpose: The purpose of this study was to use a quasi-experimental research design to examine the effectiveness of the enhancing positive emotion program. Method: Data were collected from a medical center in Taiwan. Fifty-four participants with lung cancer were recruited. Thirty participants were in the experiential group receiving the two weeks program. The content of the program includes awareness and understanding of the symptom experience, co-existing with illness and establishing self-identity, cognitive-emotion adjustment and establishing a new body schema, and symptom management to reach spiritual well-being. Twenty-four participants were in the control group receiving regular nursing care. Baseline, one month later and two months later, programmed measurements of symptoms of distress, positive emotion, and psychological well-being. Results: These two weeks of enhancing the positive emotion program resulted in a significantly improved positive emotion score for the experimental group compared to the control group. The findings of this study indicated that the positive emotion had significant differences between the two groups. There were no differences in symptom distress between the two groups. Discussion: The findings indicated that the enhancing positive emotion program could help patients enhance their life-threatening facing conditions.

Keywords: positive emotion, lung cancer, experimental design, symptom distress

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Authors: Martin Juhas, Bohuslava Juhasova, Igor Halenar, Andrej Elias

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This article deals with the possibility of increasing efficiency, reliability and safety of the system for teledosimetric data collection management and their evaluation as a part of complex study for activity “Research of data collection, their measurement and evaluation with mobile and autonomous units” within project “Research of monitoring and evaluation of non-standard conditions in the area of nuclear power plants”. Possible weaknesses in existing system are identified. A study of available cluster solutions with possibility of their deploying to analysed system is presented.

Keywords: teledosimetric data, efficiency, reliability, safety, cluster solution

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Authors: Ahmed H. Abdullah, Pavel Kroupa

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Globular clusters (GC) are important objects for tracing the early evolution of a galaxy. We study the correlation between the cluster population and the global properties of the host galaxy. We found that the correlation between cluster population (NGC) and the baryonic mass (Mb) of the host galaxy are best described as 10 −5.6038Mb. In order to understand the origin of the U -shape relation between the GC specific frequency (SN) and Mb (caused by the high value of SN for dwarfs galaxies and giant ellipticals and a minimum SN for intermediate mass galaxies≈ 1010M), we derive a theoretical model for the specific frequency (SNth). The theoretical model for SNth is based on the slope of the power-law embedded cluster mass function (β) and different time scale (Δt) of the forming galaxy. Our results show a good agreement between the observation and the model at a certain β and Δt. The model seems able to reproduce higher value of SNth of β = 1.5 at the midst formation time scale.

Keywords: galaxies: dwarf, globular cluster: specific frequency, number of globular clusters, formation time scale

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Authors: Nathakhun Wiroonsri

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There are various cluster validity measures used for evaluating clustering results. One of the main objectives of using these measures is to seek the optimal unknown number of clusters. Some measures work well for clusters with different densities, sizes and shapes. Yet, one of the weaknesses that those validity measures share is that they sometimes provide only one clear optimal number of clusters. That number is actually unknown and there might be more than one potential sub-optimal option that a user may wish to choose based on different applications. We develop two new cluster validity indices based on a correlation between an actual distance between a pair of data points and a centroid distance of clusters that the two points are located in. Our proposed indices constantly yield several peaks at different numbers of clusters which overcome the weakness previously stated. Furthermore, the introduced correlation can also be used for evaluating the quality of a selected clustering result. Several experiments in different scenarios, including the well-known iris data set and a real-world marketing application, have been conducted to compare the proposed validity indices with several well-known ones.

Keywords: clustering algorithm, cluster validity measure, correlation, data partitions, iris data set, marketing, pattern recognition

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Authors: Adji Achmad Rinaldo Fernandes

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Path analysis is an analytical technique to study the causal relationship between independent and dependent variables. In this study, the integration of Clusters in the Ward Linkage method was used in a variety of clusters with path analysis. The variables used are character (x₁), capacity (x₂), capital (x₃), collateral (x₄), and condition of economy (x₄) to on time pay (y₂) through the variable willingness to pay (y₁). The purpose of this study was to compare the Ward Linkage method cluster integration in various clusters with path analysis to classify willingness to pay (y₁). The data used are primary data from questionnaires filled out by customers of Bank X, using purposive sampling. The measurement method used is the average score method. The results showed that the Ward linkage method cluster integration with path analysis on 2 clusters is the best method, by comparing the coefficient of determination. Variable character (x₁), capacity (x₂), capital (x₃), collateral (x₄), and condition of economy (x₅) to on time pay (y₂) through willingness to pay (y₁) can be explained by 58.3%, while the remaining 41.7% is explained by variables outside the model.

Keywords: cluster integration, linkage, path analysis, compliant paying behavior

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Authors: Abdelaziz Fellah, Allaoua Maamir

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We propose a domain-independent merging-cluster filter approach complemented with a set of algorithms for identifying approximate duplicate entities efficiently and accurately within a single and across multiple data sources. The near-optimal merging-cluster filter (MCF) approach is based on the Monge-Elkan well-tuned algorithm and extended with an affine variant of the Smith-Waterman similarity measure. Then we present constant, variable, and function threshold algorithms that work conceptually in a divide-merge filtering fashion for detecting near duplicates as hierarchical clusters along with their corresponding representatives. The algorithms take recursive refinement approaches in the spirit of filtering, merging, and updating, cluster representatives to detect approximate duplicates at each level of the cluster tree. Experiments show a high effectiveness and accuracy of the MCF approach in detecting approximate duplicates by outperforming the seminal Monge-Elkan’s algorithm on several real-world benchmarks and generated datasets.

Keywords: data mining, data cleaning, approximate duplicates, near-duplicates detection, data mining applications and discovery

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Authors: F. Berna Benli, Özgür Keskin

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Lately, many mathematicians have been studied the statistical convergence of a sequence of fuzzy numbers. We know that Lambda-statistically convergence is a kind of convergence between ordinary convergence and statistical convergence. In this paper, we will introduce the new kind of convergence such as λ-levelwise statistical convergence. Then, we will define the concept of the λ-levelwise statistical cluster and limit points of a sequence of fuzzy numbers. Also, we will discuss the relations between the sets of λ-levelwise statistical cluster points and λ-levelwise statistical limit points of sequences of fuzzy numbers. This work has been extended in this paper, where some relations have been considered such that when lambda-statistical limit inferior and lambda-statistical limit superior for lambda-statistically convergent sequences of fuzzy numbers are equal. Furthermore, lambda-statistical boundedness condition for different sequences of fuzzy numbers has been studied.

Keywords: fuzzy number, λ-levelwise statistical cluster points, λ-levelwise statistical convergence, λ-levelwise statistical limit points, λ-statistical cluster points, λ-statistical convergence, λ-statistical limit points

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Authors: Jularat Chumnaul

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In forensic science, corpses from various homicides are different; there are both complete and incomplete, depending on causes of death or forms of homicide. For example, some corpses are cut into pieces, some are camouflaged by dumping into the river, some are buried, some are burned to destroy the evidence, and others. If the corpses are incomplete, it can lead to the difficulty of personally identifying because some tissues and bones are destroyed. To specify gender of the corpses from skeletal remains, the most precise method is DNA identification. However, this method is costly and takes longer so that other identification techniques are used instead. The first technique that is widely used is considering the features of bones. In general, an evidence from the corpses such as some pieces of bones, especially the skull and pelvis can be used to identify their gender. To use this technique, forensic scientists are required observation skills in order to classify the difference between male and female bones. Although this technique is uncomplicated, saving time and cost, and the forensic scientists can fairly accurately determine gender by using this technique (apparently an accuracy rate of 90% or more), the crucial disadvantage is there are only some positions of skeleton that can be used to specify gender such as supraorbital ridge, nuchal crest, temporal lobe, mandible, and chin. Therefore, the skeletal remains that will be used have to be complete. The other technique that is widely used for gender specifying in forensic science and archeology is skeletal measurements. The advantage of this method is it can be used in several positions in one piece of bones, and it can be used even if the bones are not complete. In this study, the classification and cluster analysis are applied to this technique, including the Kth Nearest Neighbor Classification, Classification Tree, Ward Linkage Cluster, K-mean Cluster, and Two Step Cluster. The data contains 507 particular individuals and 9 skeletal measurements (diameter measurements), and the performance of five methods are investigated by considering the apparent error rate (APER). The results from this study indicate that the Two Step Cluster and Kth Nearest Neighbor method seem to be suitable to specify gender from human skeletal remains because both yield small apparent error rate of 0.20% and 4.14%, respectively. On the other hand, the Classification Tree, Ward Linkage Cluster, and K-mean Cluster method are not appropriate since they yield large apparent error rate of 10.65%, 10.65%, and 16.37%, respectively. However, there are other ways to evaluate the performance of classification such as an estimate of the error rate using the holdout procedure or misclassification costs, and the difference methods can make the different conclusions.

Keywords: skeletal measurements, classification, cluster, apparent error rate

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Authors: Jarunee Wonglimpiyarat

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This paper explores the innovation management strategy of Thailand in moving towards the Detroit of Asia. The study analyses Thailand’s automotive cluster based on Porter’s Diamond Model and national innovation system (NIS) framework. A qualitative methodology was carried out, using semi-structured interviews with the players in the Thai automotive industry. Thailand took a different NIS approach by pursuing an Original Equipment Manufacture (OEM) strategy to attract foreign investments in building its automotive cluster, a different path from other Asian countries that competed with Own Brand Manufacture (OBM) strategies. The findings provide useful lessons for other newly industrialized countries (NICs) in adopting the cluster policies to move up the technological ladders.

Keywords: innovation management strategy, national innovation system (NIS), Detroit of Asia, original equipment manufacturer (OEM)

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Authors: Olusola Joseph Adesina, Abiodun Ezekiel Adesina

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Efforts at improving the falling standard of education in the country call for the need-based assessment of the primary tier of education in Nigeria. Teachers’ cluster meeting intervention is a step towards enhancing the teachers’ professional competency, efficient and effective pupils’ academic achievement and attitudinal development. The study thus determined the impact of the intervention on pupils’ achievement in Oyo State, Nigeria. Three research questions and four hypotheses guided the study. Pre-test, post-test control group, quasi-experimental design was adopted for the study. Eight intact classes from eight different schools were randomly selected into treatment and control groups. Two response instruments, pupils academic achievement test (PAAT; r = 0.87) and pupils attitude to lesson scale (PALS; r = 0.80) were used for data collection. Mean, standard deviation and analysis of covariance (ANCOVA) were used to analyse the collected data. The results showed that the teachers’ cluster meeting have significant impact on pupils academic achievement (F (1,327) =41.79; p<0.05) and attitudinal development (F (1,327) =26.01; p<0.05) in the core subjects of primary schools in Oyo State, Nigeria. The study therefore recommended among others that teachers’ cluster meeting should be sustained for teachers’ professional development and pupils’ upgradement in the State.

Keywords: teachers’ cluster meeting, pupils’ academic achievement, pupils’ attitudinal development, academic achievement

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Authors: Ammar Asma, Bouafia Nabiha, Dhahri Meriem, Ben Cheikh Asma, Ezzi Olfa, Chafai Rim, Njah Mansour

Abstract:

Despite recent advances in treatment of the lung cancer patients, the prognosis remains poor. Information is limited regarding health related quality of life (QOL) status of advanced lung cancer patients. The purposes of this study were: to assess patient reported symptom burden, to measure their QOL, and to identify determinant factors associated with QOL. Materials/Methods: A cross sectional study of 60 patients was carried out from over the period of 03 months from February 1st to 30 April 2016. Patients were recruited in two department of health care: Pneumology department in a university hospital in Sousse and an oncology unit in a University Hospital in Kairouan. Patients with advanced stage (III and IV) of lung cancer who were hospitalized or admitted in the day hospital were recruited by convenience sampling. We used a questionnaire administrated and completed by a trained interviewer. This questionnaire is composed of three parts: demographic, clinical and therapeutic information’s, QOL measurements: based on the SF-36 questionnaire, Symptom’s burden measurement using the Lung Cancer Symptom Scale (LCSS). To assess Correlation between symptoms burden and QOL, we compared the scores of two scales two by two using the Pearson correlation. To identify factors influencing QOL in Lung cancer, a univariate statistical analysis then, a stepwise backward approach, wherein the variables with p< 0.2, were carried out to determine the association between SF-36 scores and different variables. Results: During the study period, 60 patients consented to complete symptom and quality of life questionnaires at a single point time (72% were recruited from day hospital). The majority of patients were male (88%), age ranged from 21 to 79 years with a mean of 60.5 years. Among patients, 48 (80%) were diagnosed as having non-small cell lung carcinoma (NSCLC). Approximately, 60 % (n=36) of patients were in stage IV, 25 % in stage IIIa and 15 % in stage IIIb. For symptom burden, the symptom burden index was 43.07 (Standard Deviation, 21.45). Loss of appetite and fatigue were rated as the most severe symptoms with mean scores (SD): 49.6 (25.7) and 58.2 (15.5). The average overall score of SF36 was 39.3 (SD, 15.4). The physical and emotional limitations had the lowest scores. Univariate analysis showed that factors which influence negatively QOL were: married status (p<0.03), smoking cessation after diagnosis (p<0.024), LCSS total score (p<0.001), LCSS symptom burden index (p<0.001), fatigue (p<0.001), loss of appetite (p<0.001), dyspnea (p<0.001), pain (p<0.002), and metastatic stage (p<0.01). In multivariate analysis, unemployment (p<0.014), smoking cessation after diagnosis (p<0.013), consumption of analgesic (p<0.002) and the indication of an analgesic radiotherapy (p<0.001) are revealed as independent determinants of QOL. The result of the correlation analyses between total LCSS scores and the total and individual domain SF36 scores was significant (p<0.001); the higher total LCSS score is, the poorer QOL is. Conclusion: A built in support of lung cancer patients would better control the symptoms and promote the QOL of these patients.

Keywords: quality of life, lung cancer, metastasis, symptoms burden

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Authors: Ming Liu, Chong Wu, Bingquan Liu, Lei Chen

Abstract:

Internet users have to face the massive amount of textual data every day. Organizing texts into categories can help users dig the useful information from large-scale text collection. Clustering, in fact, is one of the most promising tools for categorizing texts due to its unsupervised characteristic. Unfortunately, most of traditional clustering algorithms lose their high qualities on large-scale text collection. This situation mainly attributes to the high- dimensional vectors generated from texts. To effectively and efficiently cluster large-scale text collection, this paper proposes a vector reconstruction based clustering algorithm. Only the features that can represent the cluster are preserved in cluster’s representative vector. This algorithm alternately repeats two sub-processes until it converges. One process is partial tuning sub-process, where feature’s weight is fine-tuned by iterative process. To accelerate clustering velocity, an intersection based similarity measurement and its corresponding neuron adjustment function are proposed and implemented in this sub-process. The other process is overall tuning sub-process, where the features are reallocated among different clusters. In this sub-process, the features useless to represent the cluster are removed from cluster’s representative vector. Experimental results on the three text collections (including two small-scale and one large-scale text collections) demonstrate that our algorithm obtains high quality on both small-scale and large-scale text collections.

Keywords: vector reconstruction, large-scale text clustering, partial tuning sub-process, overall tuning sub-process

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Authors: Natthawut Arin

Abstract:

In forensic contexts, competency to stand trial assessments are the most common referrals. The defendants may attempt to endorse psychopathology symptoms and feign incompetent. Coaching, which can be teaching them test-taking strategies to avoid detection of psychopathological symptoms feigning. Recently, the Symptom Validity Testings (SVTs) were created to detect feigning. Moreover, the works of the literature showed that the effects of coaching on SVTs may be more robust to the effects of coaching. Thai Symptom Validity Test (SVT-Th) was designed as SVTs which demonstrated adequate psychometric properties and ability to classify between feigners and honest responders. Thus, the current study to examine the utility as the robustness of SVT-Th in the detection of feigned psychopathology. Participants consisted of 120 were recruited from undergraduate courses in psychology, randomly assigned to one of three groups. The SVT-Th was administered to those three scenario-experimental groups: (a) Uncoached group were asked to respond honestly (n=40), (b) Symptom-coached without warning group were asked to feign psychiatric symptoms to gain incompetency to stand trial (n=40), while (c) Test-coached with warning group were asked to feign psychiatric symptoms to avoid test detection but being incompetency to stand trial (n=40). Group differences were analyzed using one-way ANOVAs. The result revealed an uncoached group (M = 4.23, SD.= 5.20) had significantly lower SVT-Th mean scores than those both coached groups (M =185.00, SD.= 72.88 and M = 132.10, SD.= 54.06, respectively). Classification rates were calculated to determine the classification accuracy. Result indicated that SVT-Th had overall classification accuracy rates of 96.67% with acceptable of 95% sensitivity and 100% specificity rates. Overall, the results of the present study indicate that the SVT-Th yielded high adequate indices of accuracy and these findings suggest that the SVT-Th is robustness against coaching.

Keywords: incompetency to stand trial, coaching, robustness, classification accuracy

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