Search results for: case pile wave analysis program (CAPWAP)

Authors: Surbhi Kaura, Sachin Kandhari, Shahiduz Zafar

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Chronic stroke, characterized by persistent motor deficits, often necessitates comprehensive rehabilitation interventions to improve functional outcomes and mitigate long-term dependency. Luna neurorobotic devices, integrated with EMG feedback systems, provide an innovative platform for facilitating neuroplasticity and functional improvement in stroke survivors. This retrospective study aims to investigate the impact of EMG-based Luna neurorobotic interventions on the strengthening of the affected side in chronic stroke patients. In rehabilitation, active patient participation significantly activates the sensorimotor network during motor control, unlike passive movement. Stroke is a debilitating condition that, when not effectively treated, can result in significant deficits and lifelong dependency. Common issues like neglecting the use of limbs can lead to weakness in chronic stroke cases. In rehabilitation, active patient participation significantly activates the sensorimotor network during motor control, unlike passive movement. This study aims to assess how electromyographic triggering (EMG-triggered) robotic treatments affect walking, ankle muscle force after an ischemic stroke, and the coactivation of agonist and antagonist muscles, which contributes to neuroplasticity with the assistance of biofeedback using robotics. Methods: The study utilized robotic techniques based on electromyography (EMG) for daily rehabilitation in long-term stroke patients, offering feedback and monitoring progress. Each patient received one session per day for two weeks, with the intervention group undergoing 45 minutes of robot-assisted training and exercise at the hospital, while the control group performed exercises at home. Eight participants with impaired motor function and gait after stroke were involved in the study. EMG-based biofeedback exercises were administered through the LUNA neuro-robotic machine, progressing from trigger and release mode to trigger and hold, and later transitioning to dynamic mode. Assessments were conducted at baseline and after two weeks, including the Timed Up and Go (TUG) test, a 10-meter walk test (10m), Berg Balance Scale (BBG), and gait parameters like cadence, step length, upper limb strength measured by EMG threshold in microvolts, and force in Newton meters. Results: The study utilized a scale to assess motor strength and balance, illustrating the benefits of EMG-biofeedback following LUNA robotic therapy. In the analysis of the left hemiparetic group, an increase in strength post-rehabilitation was observed. The pre-TUG mean value was 72.4, which decreased to 42.4 ± 0.03880133 seconds post-rehabilitation, with a significant difference indicated by a p-value below 0.05, reflecting a reduced task completion time. Similarly, in the force-based task, the pre-knee dynamic force in Newton meters was 18.2NM, which increased to 31.26NM during knee extension post-rehabilitation. The post-student t-test showed a p-value of 0.026, signifying a significant difference. This indicated an increase in the strength of knee extensor muscles after LUNA robotic rehabilitation. Lastly, at baseline, the EMG value for ankle dorsiflexion was 5.11 (µV), which increased to 43.4 ± 0.06 µV post-rehabilitation, signifying an increase in the threshold and the patient's ability to generate more motor units during left ankle dorsiflexion. Conclusion: This study aimed to evaluate the impact of EMG and dynamic force-based rehabilitation devices on walking and strength of the affected side in chronic stroke patients without nominal data comparisons among stroke patients. Additionally, it provides insights into the inclusion of EMG-triggered neurorehabilitation robots in the daily rehabilitation of patients.

Keywords: neurorehabilitation, robotic therapy, stroke, strength, paralysis

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Authors: H. M. El-Askary, S. A. Abd El-Mawla, M. Allali, M. M. El-Hattab, M. El-Raey, A. M. Farahat, M. Kafatos, S. Nickovic, S. K. Park, A. K. Prasad, C. Rakovski, W. Sprigg, D. Struppa, A. Vukovic

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A clear distinction between weather and climate is a necessity because while they are closely related, there are still important differences. Climate change is identified when we compute the statistics of the observed changes in weather over space and time. In this work we will show how the changing climate contribute to the frequency, magnitude and extent of different extreme events using a multi sensor approach with some synergistic modeling activities. We are exploring satellite observations of dust over North Africa, Gulf Region and the Indo Gangetic basin as well as dust versus anthropogenic pollution events over the Delta region in Egypt and Seoul through remote sensing and utilize the behavior of the dust and haze on the aerosol optical properties. Dust impact on the retreat of the glaciers in the Himalayas is also presented. In this study we also focus on the identification and monitoring of a massive dust plume that blew off the western coast of Africa towards the Atlantic on October 8th, 2012 right before the development of Hurricane Sandy. There is evidence that dust aerosols played a non-trivial role in the cyclogenesis process of Sandy. Moreover, a special dust event "An American Haboob" in Arizona is discussed as it was predicted hours in advance because of the great improvement we have in numerical, land–atmosphere modeling, computing power and remote sensing of dust events. Therefore we performed a full numerical simulation to that event using the coupled atmospheric-dust model NMME–DREAM after generating a mask of the potentially dust productive regions using land cover and vegetation data obtained from satellites. Climate change also contributes to the deterioration of different marine habitats. In that regard we are also presenting some work dealing with change detection analysis of Marine Habitats over the city of Hurghada, Red Sea, Egypt. The motivation for this work came from the fact that coral reefs at Hurghada have undergone significant decline. They are damaged, displaced, polluted, stepped on, and blasted off, in addition to the effects of climate change on the reefs. One of the most pressing issues affecting reef health is mass coral bleaching that result from an interaction between human activities and climatic changes. Over another location, namely California, we have observed that it exhibits highly-variable amounts of precipitation across many timescales, from the hourly to the climate timescale. Frequently, heavy precipitation occurs, causing damage to property and life (floods, landslides, etc.). These extreme events, variability, and the lack of good, medium to long-range predictability of precipitation are already a challenge to those who manage wetlands, coastal infrastructure, agriculture and fresh water supply. Adding on to the current challenges for long-range planning is climate change issue. It is known that La Niña and El Niño affect precipitation patterns, which in turn are entwined with global climate patterns. We have studied ENSO impact on precipitation variability over different climate divisions in California. On the other hand the Nile Delta has experienced lately an increase in the underground water table as well as water logging, bogging and soil salinization. Those impacts would pose a major threat to the Delta region inheritance and existing communities. There has been an undergoing effort to address those vulnerabilities by looking into many adaptation strategies.

Keywords: remote sensing, modeling, long range transport, dust storms, North Africa, Gulf Region, India, California, climate extremes, sea level rise, coral reefs

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Authors: Chiu-Yin Leung

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This research investigates a complex assemblage among indigenous identities, socio-political organization and national apparatus in the border landscape of post-colonial Hong Kong. This former British colony had designated a transient mode of governance in its New Territories and particularly the northernmost borderland in 1951-2012. With a discriminated system of land provisions for the indigenous villagers, the place has been inherited with distinctive village-based culture, historic monuments and agrarian practices until its sovereignty return into the People’s Republic of China. In its latest development imperatives by the national strategic planning, the frontier area of Hong Kong has been identified as a strategy site for regional economic integration in South China, with cross-border projects of innovation and technology zones, mega-transport infrastructure and inter-jurisdictional arrangement. Contemporary literature theorizes borders as the material and discursive production of territoriality, which manifest in state apparatus and the daily lives of its citizens and condense in the contested articulations of power, security and citizenship. Drawing on the concept of assemblage, this paper attempts to tract how the border regime and infrastructure in Hong Kong as a city are deeply ingrained in the everyday lived spaces of the local communities but also the changing urban and regional strategies across different longitudinal moments. Through an intensive ethnographic fieldwork among the borderland villages since 2008 and the extensive analysis of colonial archives, new development plans and spatial planning frameworks, the author navigates the genealogy of the border landscape in Ta Kwu Ling frontier area and its implications as the milieu for new state space, covering heterogeneous fields particularly in indigenous rights, heritage preservation, rural sustainability and regional economy. Empirical evidence suggests an apparent bias towards indigenous power and colonial representation in classifying landscape values and conserving historical monuments. Squatter and farm tenants are often deprived of property rights, statutory participation and livelihood option in the planning process. The postcolonial bureaucracies have great difficulties in mobilizing resources to catch up with the swift, political-first approach of the mainland counterparts. Meanwhile, the cultural heritage, lineage network and memory landscape are not protected altogether with any holistic view or collaborative effort across the border. The enactment of land resumption and compensation scheme is furthermore disturbed by lineage-based customary law, technocratic bureaucracy, intra-community conflicts and multi-scalar political mobilization. As many traces of colonial misfortune and tyranny have been whitewashed without proper management, the author argues that postcolonial justice is yet reconciled in this fragmented border landscape. The assemblage of border in mainstream representation has tended to oversimplify local struggles as a collective mist and setup a wider production of schizophrenia experiences in the discussion of further economic integration among Hong Kong and other mainland cities in the Pearl River Delta Region. The research is expected to shed new light on the theorizing of border regions and postcolonialism beyond Eurocentric perspectives. In reassembling the borderland experiences with other arrays in state governance, village organization and indigenous identities, the author also suggests an alternative epistemology in reconciling socio-spatial differences and opening up imaginaries for positive interventions.

Keywords: heritage conservation, indigenous communities, post-colonial borderland, regional development, rural sustainability

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Authors: Lauren B. Birney

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The envisioned long-term outcome of this three-year research, and implementation plan is for 1) teachers and students to design and build their own computational models of real-world environmental-human health phenomena occurring within the context of the “Human Harbor” and 2) project researchers to evaluate the degree to which these integrated Computer Science (CS) education experiences in New York City (NYC) public school classrooms (PreK-12) impact students’ computational-technical skill development, job readiness, career motivations, and measurable abilities to understand, articulate, and solve the underlying phenomena at the center of their models. This effort builds on the partnership’s successes over the past eight years in developing a benchmark Model of restoration-based Science, Technology, Engineering, and Math (STEM) education for urban public schools and achieving relatively broad-based implementation in the nation’s largest public school system. The Billion Oyster Project Curriculum and Community Enterprise for Restoration Science (BOP-CCERS STEM + Computing) curriculum, teacher professional developments, and community engagement programs have reached more than 200 educators and 11,000 students at 124 schools, with 84 waterfront locations and Out of School of Time (OST) programs. The BOP-CCERS Partnership is poised to develop a more refined focus on integrating computer science across the STEM domains; teaching industry-aligned computational methods and tools; and explicitly preparing students from the city’s most under-resourced and underrepresented communities for upwardly mobile careers in NYC’s ever-expanding “digital economy,” in which jobs require computational thinking and an increasing percentage require discreet computer science technical skills. Project Objectives include the following: 1. Computational Thinking (CT) Integration: Integrate computational thinking core practices across existing middle/high school BOP-CCERS STEM curriculum as a means of scaffolding toward long term computer science and computational modeling outcomes. 2. Data Science and Data Analytics: Enabling Researchers to perform interviews with Teachers, students, community members, partners, stakeholders, and Science, Technology, Engineering, and Mathematics (STEM) industry Professionals. Collaborative analysis and data collection were also performed. As a centerpiece, the BOP-CCERS partnership will expand to include a dedicated computer science education partner. New York City Department of Education (NYCDOE), Computer Science for All (CS4ALL) NYC will serve as the dedicated Computer Science (CS) lead, advising the consortium on integration and curriculum development, working in tandem. The BOP-CCERS Model™ also validates that with appropriate application of technical infrastructure, intensive teacher professional developments, and curricular scaffolding, socially connected science learning can be mainstreamed in the nation’s largest urban public school system. This is evidenced and substantiated in the initial phases of BOP-CCERS™. The BOP-CCERS™ student curriculum and teacher professional development have been implemented in approximately 24% of NYC public middle schools, reaching more than 250 educators and 11,000 students directly. BOP-CCERS™ is a fully scalable and transferable educational model, adaptable to all American school districts. In all settings of the proposed Phase IV initiative, the primary beneficiary group will be underrepresented NYC public school students who live in high-poverty neighborhoods and are traditionally underrepresented in the STEM fields, including African Americans, Latinos, English language learners, and children from economically disadvantaged households. In particular, BOP-CCERS Phase IV will explicitly prepare underrepresented students for skilled positions within New York City’s expanding digital economy, computer science, computational information systems, and innovative technology sectors.

Keywords: computer science, data science, equity, diversity and inclusion, STEM education

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Authors: Murat Arıbaş, Uğur Özcan

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Due to the increasing number of universities and academics, the fund of the universities for research activities and grants/supports given by government institutions have increased number and quality of academic research projects. Although every academic research project has a specific purpose and importance, limited resources (money, time, manpower etc.) require choosing the best ones from all (Amiri, 2010). It is a pretty hard process to compare and determine which project is better such that the projects serve different purposes. In addition, the evaluation process has become complicated since there are more than one evaluator and multiple criteria for the evaluation (Dodangeh, Mojahed and Yusuff, 2009). Mehrez and Sinuany-Stern (1983) determined project selection problem as a Multi Criteria Decision Making (MCDM) problem. If a decision problem involves multiple criteria and objectives, it is called as a Multi Attribute Decision Making problem (Ömürbek & Kınay, 2013). There are many MCDM methods in the literature for the solution of such problems. These methods are AHP (Analytic Hierarchy Process), ANP (Analytic Network Process), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation), UTADIS (Utilities Additives Discriminantes), ELECTRE (Elimination et Choix Traduisant la Realite), MAUT (Multiattribute Utility Theory), GRA (Grey Relational Analysis) etc. Teach method has some advantages compared with others (Ömürbek, Blacksmith & Akalın, 2013). Hence, to decide which MCDM method will be used for solution of the problem, factors like the nature of the problem, types of choices, measurement scales, type of uncertainty, dependency among the attributes, expectations of decision maker, and quantity and quality of the data should be considered (Tavana & Hatami-Marbini, 2011). By this study, it is aimed to develop a systematic decision process for the grant support applications that are expected to be evaluated according to their scientific adequacy by multiple evaluators under certain criteria. In this context, project evaluation process applied by The Scientific and Technological Research Council of Turkey (TÜBİTAK) the leading institutions in our country, was investigated. Firstly in the study, criteria that will be used on the project evaluation were decided. The main criteria were selected among TÜBİTAK evaluation criteria. These criteria were originality of project, methodology, project management/team and research opportunities and extensive impact of project. Moreover, for each main criteria, 2-4 sub criteria were defined, hence it was decided to evaluate projects over 13 sub-criterion in total. Due to superiority of determination criteria weights AHP method and provided opportunity ranking great number of alternatives TOPSIS method, they are used together. AHP method, developed by Saaty (1977), is based on selection by pairwise comparisons. Because of its simple structure and being easy to understand, AHP is the very popular method in the literature for determining criteria weights in MCDM problems. Besides, the TOPSIS method developed by Hwang and Yoon (1981) as a MCDM technique is an alternative to ELECTRE method and it is used in many areas. In the method, distance from each decision point to ideal and to negative ideal solution point was calculated by using Euclidian Distance Approach. In the study, main criteria and sub-criteria were compared on their own merits by using questionnaires that were developed based on an importance scale by four relative groups of people (i.e. TUBITAK specialists, TUBITAK managers, academics and individuals from business world ) After these pairwise comparisons, weight of the each main criteria and sub-criteria were calculated by using AHP method. Then these calculated criteria’ weights used as an input in TOPSİS method, a sample consisting 200 projects were ranked on their own merits. This new system supported to opportunity to get views of the people that take part of project process including preparation, evaluation and implementation on the evaluation of academic research projects. Moreover, instead of using four main criteria in equal weight to evaluate projects, by using weighted 13 sub-criteria and decision point’s distance from the ideal solution, systematic decision making process was developed. By this evaluation process, new approach was created to determine importance of academic research projects.

Keywords: Academic projects, Ahp method, Research projects evaluation, Topsis method.

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Authors: Reza Nematirad, Anil Pahwa, Balasubramaniam Natarajan

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Time series data consist of successive data points collected over a period of time. Accurate prediction of future values is essential for informed decision-making in several real-world applications, including electricity load demand forecasting, lifetime estimation of industrial machinery, traffic planning, weather prediction, and the stock market. Due to their critical relevance and wide application, there has been considerable interest in time series forecasting in recent years. However, the proliferation of sensors and IoT devices, real-time monitoring systems, and high-frequency trading data introduce significant intricate temporal variations, rapid changes, noise, and non-linearities, making time series forecasting more challenging. Classical methods such as Autoregressive integrated moving average (ARIMA) and Exponential Smoothing aim to extract pre-defined temporal variations, such as trends and seasonality. While these methods are effective for capturing well-defined seasonal patterns and trends, they often struggle with more complex, non-linear patterns present in real-world time series data. In recent years, deep learning has made significant contributions to time series forecasting. Recurrent Neural Networks (RNNs) and their variants, such as Long short-term memory (LSTMs) and Gated Recurrent Units (GRUs), have been widely adopted for modeling sequential data. However, they often suffer from the locality, making it difficult to capture local trends and rapid fluctuations. Convolutional Neural Networks (CNNs), particularly Temporal Convolutional Networks (TCNs), leverage convolutional layers to capture temporal dependencies by applying convolutional filters along the temporal dimension. Despite their advantages, TCNs struggle with capturing relationships between distant time points due to the locality of one-dimensional convolution kernels. Transformers have revolutionized time series forecasting with their powerful attention mechanisms, effectively capturing long-term dependencies and relationships between distant time points. However, the attention mechanism may struggle to discern dependencies directly from scattered time points due to intricate temporal patterns. Lastly, Multi-Layer Perceptrons (MLPs) have also been employed, with models like N-BEATS and LightTS demonstrating success. Despite this, MLPs often face high volatility and computational complexity challenges in long-horizon forecasting. To address intricate temporal variations in time series data, this study introduces Times2D, a novel framework that parallelly integrates 2D spectrogram and derivative heatmap techniques. The spectrogram focuses on the frequency domain, capturing periodicity, while the derivative patterns emphasize the time domain, highlighting sharp fluctuations and turning points. This 2D transformation enables the utilization of powerful computer vision techniques to capture various intricate temporal variations. To evaluate the performance of Times2D, extensive experiments were conducted on standard time series datasets and compared with various state-of-the-art algorithms, including DLinear (2023), TimesNet (2023), Non-stationary Transformer (2022), PatchTST (2023), N-HiTS (2023), Crossformer (2023), MICN (2023), LightTS (2022), FEDformer (2022), FiLM (2022), SCINet (2022a), Autoformer (2021), and Informer (2021) under the same modeling conditions. The initial results demonstrated that Times2D achieves consistent state-of-the-art performance in both short-term and long-term forecasting tasks. Furthermore, the generality of the Times2D framework allows it to be applied to various tasks such as time series imputation, clustering, classification, and anomaly detection, offering potential benefits in any domain that involves sequential data analysis.

Keywords: derivative patterns, spectrogram, time series forecasting, times2D, 2D representation

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Authors: M. H. Pazandeh, M. Doudi, S. Barahimi, L. Rahimzadeh Torabi

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The use of antibiotics is essential in reducing the occurrence of adverse effects and inhibiting the emergence of antibiotic resistance in microbial populations. The necessity for a novel methodology concerning local administration of antibiotics has arisen, with particular focus on dealing with localized infections prompted by bacterial colonization of medical devices or implant materials. Bioactive glasses (BG) are extensively employed in the field of regenerative medicine, encompassing a diverse range of materials utilized for drug delivery systems. In the present investigation, various drug carriers for imipenem and tetracycline, namely single systems BG/SnO2, BG/NiO with varying proportions of metal oxide, and nanocomposite BG/SnO2/NiO, were synthesized through the sol-gel technique. The antibacterial efficacy of the synthesized samples was assessed through the utilization of the disk diffusion method with the aim of neutralizing Staphylococcus aureus as the bacterial model. The current study involved the examination of the bioactivity of two samples, namely BG10SnO2/10NiO and BG20SnO2, which were chosen based on their heightened bacterial inactivation properties. This evaluation entailed the employment of two techniques: the measurement of the pH of simulated body fluid (SBF) solution and the analysis of the sample tablets through X-ray diffraction (XRD), scanning electron microscopy (SEM), and Fourier transform infrared (FTIR) spectroscopy. The sample tablets were submerged in SBF for varying durations of 7, 14, and 28 days. The bioactivity of the composite bioactive glass sample was assessed through characterization of alterations in its surface morphology, structure, and chemical composition. This evaluation was performed using scanning electron microscopy (SEM), Fourier-transform infrared (FTIR) spectroscopy, and X-ray diffraction spectroscopy. Subsequently, the sample was immersed in simulated liquids to simulate its behavior in biological environments. The specific body fat percentage (SBF) was assessed over a 28-day period. The confirmation of the formation of a hydroxyapatite surface layer serves as a distinct indicator of bioactivity. The infusion of antibiotics into the composite bioactive glass specimen was done separately, and then the release kinetics of tetracycline and imipenem were tested in simulated body fluid (SBF). Antimicrobial effectiveness against various bacterial strains have been proven in numerous instances using both melt and sol-gel techniques to create multiple bioactive glass compositions. An elevated concentration of calcium ions within a solution has been observed to cause an increase in the pH level. In aqueous suspensions, bioactive glass particles manifest a significant antimicrobial impact. The composite bioactive glass specimen exhibits a gradual and uninterrupted release, which is highly desirable for a drug delivery system over a span of 72 hours. The reduction in absorption, which signals the loss of a portion of the antibiotic during the loading process from the initial phosphate-buffered saline solution, indicates the successful bonding of the two antibiotics to the surfaces of the bioactive glass samples. The sample denoted as BG/10SnO2/10NiO exhibits a higher loading of particles compared to the sample designated as BG/20SnO2 in the context of bioactive glass. The enriched sample demonstrates a heightened bactericidal impact on the bacteria under investigation while concurrently preserving its antibacterial characteristics. Tailored bioactive glass that incorporates hydroxyapatite, with a regulated and efficient release of drugs targeting bacterial infections, holds promise as a potential framework for bone implant scaffolds following rigorous clinical evaluation, thereby establishing potential future biomedical uses. During the modification process, the introduction of metal oxides into bioactive glass resulted in improved antibacterial characteristics, particularly in the composite bioactive glass sample that displayed the highest level of efficiency.

Keywords: antibacterial, bioactive glasses, implant infections, multi drug resistant

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Authors: David J. Foran, Nhan Do, Samuel Ajjarapu, Wenjin Chen, Tahsin Kurc, Joel H. Saltz

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The large-scale data and computational requirements of investigators throughout the clinical and research communities demand an informatics infrastructure that supports both existing and new investigative and translational projects in a robust, secure environment. In some subspecialties of medicine and research, the capacity to generate data has outpaced the methods and technology used to aggregate, organize, access, and reliably retrieve this information. Leading health care centers now recognize the utility of establishing an enterprise-wide, clinical data warehouse. The primary benefits that can be realized through such efforts include cost savings, efficient tracking of outcomes, advanced clinical decision support, improved prognostic accuracy, and more reliable clinical trials matching. The overarching objective of the work presented here is the development and implementation of a flexible Intelligent Retrieval and Interrogation System (IRIS) that exploits the combined use of computational imaging, genomics, and data-mining capabilities to facilitate clinical assessments and translational research in oncology. The proposed System includes a multi-modal, Clinical & Research Data Warehouse (CRDW) that is tightly integrated with a suite of computational and machine-learning tools to provide insight into the underlying tumor characteristics that are not be apparent by human inspection alone. A key distinguishing feature of the System is a configurable Extract, Transform and Load (ETL) interface that enables it to adapt to different clinical and research data environments. This project is motivated by the growing emphasis on establishing Learning Health Systems in which cyclical hypothesis generation and evidence evaluation become integral to improving the quality of patient care. To facilitate iterative prototyping and optimization of the algorithms and workflows for the System, the team has already implemented a fully functional Warehouse that can reliably aggregate information originating from multiple data sources including EHR’s, Clinical Trial Management Systems, Tumor Registries, Biospecimen Repositories, Radiology PAC systems, Digital Pathology archives, Unstructured Clinical Documents, and Next Generation Sequencing services. The System enables physicians to systematically mine and review the molecular, genomic, image-based, and correlated clinical information about patient tumors individually or as part of large cohorts to identify patterns that may influence treatment decisions and outcomes. The CRDW core system has facilitated peer-reviewed publications and funded projects, including an NIH-sponsored collaboration to enhance the cancer registries in Georgia, Kentucky, New Jersey, and New York, with machine-learning based classifications and quantitative pathomics, feature sets. The CRDW has also resulted in a collaboration with the Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) at the U.S. Department of Veterans Affairs to develop algorithms and workflows to automate the analysis of lung adenocarcinoma. Those studies showed that combining computational nuclear signatures with traditional WHO criteria through the use of deep convolutional neural networks (CNNs) led to improved discrimination among tumor growth patterns. The team has also leveraged the Warehouse to support studies to investigate the potential of utilizing a combination of genomic and computational imaging signatures to characterize prostate cancer. The results of those studies show that integrating image biomarkers with genomic pathway scores is more strongly correlated with disease recurrence than using standard clinical markers.

Keywords: clinical data warehouse, decision support, data-mining, intelligent databases, machine-learning.

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Authors: Lizinis Cassendra Frederick Dony, Jirom Jeremy Frederick Dony, Cyril Supain Christopher

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The purpose of this paper is to improve the devotion to leadership through HR practices implementation at the PETRONAS stations. This emphasize the importance of personal grooming and Customer Care hospitality training for their front line working individuals and teams’ at PETRONAS stations in Sabah. Based on Thomas Edison, International Leadership Journal, theory, research, education and development practice and application to all organizational phenomena may affect or be affected by leadership. FINDINGS – PETRONAS in short called Petroliam Nasional Berhad is a Malaysian oil and gas company that was founded on August 17, 1974. Wholly owned by the Government of Malaysia, the corporation is vested with the entire oil and gas resources in Malaysia and is entrusted with the responsibility of developing and adding value to these resources. Fortune ranks PETRONAS as the 68th largest company in the world in 2012. It also ranks PETRONAS as the 12th most profitable company in the world and the most profitable in Asia. As of the end of March 2005, the PETRONAS Group comprised 103 wholly owned subsidiaries, 19 partly owned outfits and 57 associated companies. The group is engaged in a wide spectrum of petroleum activities, including upstream exploration and production of oil and gas to downstream oil refining, marketing and distribution of petroleum products, trading, gas processing and liquefaction, gas transmission pipeline network operations, marketing of liquefied natural gas; petrochemical manufacturing and marketing; shipping; automotive engineering and property investment. PETRONAS has growing their marketing channel in a competitive market. They have combined their resources to pursue common goals. PETRONAS provides opportunity to carry out Industrial Training Job Placement to the University students in Malaysia for 6-8 months. The effects of the Industrial Training have exposed them to the real working environment experience acting representing on behalf of General Manager for almost one year. Thus, the management education and reward incentives schemes have aspire the working teams transformed to gain their good leadership. Furthermore, knowledge and experiences are very important in the human capital development transformation. SPSS extends the accurate analysis PETRONAS achievement through 280 questionnaires and 81 questionnaires through excel calculation distributed to interview face to face with the customers, PETRONAS dealers and front desk staffs stations in the 17 stations in Kota Kinabalu, Sabah. Hence, this research study will improve its service quality innovation and business sustainability performance optimization. ORIGINALITY / VALUE – The impact of Transformational Leadership practices have influenced the working team’s behaviour as a Brand Ambassadors of PETRONAS. Finally, the findings correlation indicated that PETRONAS stations needs more HR resources practices to deploy more customer care retention resources in mitigating the business challenges in oil and gas industry. Therefore, as the business established at stiff competition globally (Cooper, 2006; Marques and Simon, 2006), it is crucial for the team management should be capable to minimize noises risk, financial risk and mitigating any other risks as a whole at the optimum level. CONCLUSION- As to conclude this research found that both transformational and transactional contingent reward leadership4 were positively correlated with ratings of platoon potency and ratings of leadership for the platoon leader and sergeant were moderately inter correlated. Due to this identification, we recommended that PETRONAS management should offers quality team management in PETRONAS stations in a broader variety of leadership training specialization in the operation efficiency at the front desk Customer Care hospitality. By having the reliability and validity of job experiences, it leverages diversity teamwork and cross collaboration. Other than leveraging factor, PETRONAS also will strengthen the interpersonal front liners effectiveness and enhance quality of interaction through effective communication. Finally, through numerous CSR correlation studies regression PETRONAS performance on Corporate Social Performance and several control variables.1 CSR model activities can be mis-specified if it is not controllable under R & D which evident in various feedbacks collected from the local communities and younger generation is inclined to higher financial expectation from PETRONAS. But, however, it created a huge impact on the nation building as part of its social adaptability overreaching their business stakeholders’ satisfaction in Sabah.

Keywords: human resources practices implementation (hrpi), source of competitive advantage in people’s development (socaipd), corporate social responsibility (csr), service quality at front desk stations (sqafd), impacts of petronas leadership (iopl)

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