Applying Association Rules and Co-location Techniques on Geospatial Web Services

Most contemporary GIS have only very basic spatial analysis and data mining functionality and many are confined to analysis that involves comparing maps and descriptive statistical displays like histograms or pie charts. Emerging Web standards promise a network of heterogeneous yet interoperable Web Services. Web Services would greatly simplify the development of many kinds of data integration and knowledge management applications. Geospatial data mining describes the combination of two key market intelligence software tools: Geographical Information Systems and Data Mining Systems. This research aims to develop a Spatial Data Mining web service it uses rule association techniques and correlation methods to explore results of huge amounts of data generated from crises management integrated applications developed. It integrates between traffic systems, medical services systems, civil defense and state of the art Geographic Information Systems and Data Mining Systems functionality in an open, highly extensible, internet-enabled plug-in architecture. The Interoperability of geospatial data previously focus just on data formats and standards. The recent popularity and adoption of the Internet and Web Services has provided a new means of interoperability for geospatial information not just for exchanging data but for analyzing these data during exchange. An integrated, user friendly Spatial Data Mining System available on the internet via a web service offers exciting new possibilities for spatial decision making and geographical research to a wide range of potential users.   Keywords: Spatial Data Mining, Rule Association, Co-location, Web Services, Geospatial Dat

In-Network Data Reduction Approach Based On Smart Sensing

The rapid advances in wireless communication and sensor technologies facilitate the development of viable mobile-Health applications that boost opportunity for ubiquitous real- time healthcare monitoring without constraining patients' activities. However, remote healthcare monitoring requires continuous sensing for different analog signals which results in generating large volumes of data that needs to be processed, recorded, and transmitted. Thus, developing efficient in-network data reduction techniques is substantial in such applications. In this paper, we propose an in-network approach for data reduction, which is based on fuzzy formal concept analysis. The goal is to reduce the amount of data that is transmitted, by keeping the minimal-representative data for each class of patients. Using such an approach, the sender can effectively reconfigure its transmission settings by varying the target precision level while maintaining the required application classification accuracy. Our results show the excellent performance of the proposed scheme in terms of data reduction gain and classification accuracy, and the advantages that it exhibits with respect to state-of-the-art techniques.Scopu

THE INFLUENCES OF URBAN FORMS ON RESIDENTIAL ENERGY CONSUMPTION: A DEMAND-SIDE FORECASTING METHOD FOR ENERGY SCENARIOS

Current trends in energy demand impose increasing stress on the socio-ecological state of developed countries like the U.S. A major challenge lies in how to efficiently manage energy resources in a sustainable way to protect the environment. Various forecasting approaches have been developed to predict energy demand trends. These approaches have not investigated the influence of urban form on household energy consumption. This research combines one of the forecasting methods with sustainable development practices to predict possible energy demand based on different spatial housing forms (compact and dense, mixed uses, and low density). The research has five objectives; the first is to develop a spatial Planning Support System (PSS) to forecast residential energy consumption. The PSS is integrated with an existing urban simulation model called the Charlotte Land Use and Economic Simulator (CLUES). The second objective is to develop a statistical operational model of household energy consumption that accounts for socio-economic, geometric, spatial, and macroeconomic condition determinants. Inserted in the PSS, this model serves to forecast consumption under a series of scenarios that account for various policies in urban development, environmental protection, and green technology applications at fine (household) through coarse (traffic analysis zone) resolutions, over short- and long-terms. The third and fourth objectives assess the contribution of the geometries factors and the condition and socio-economic variables, respectively, to various alternatives of residential energy consumption. The fifth objective is to assess the consequences of different scenarios on social equity and energy share per household across population groups. The research is conducted in Mecklenburg County over the 2008-2037 horizon. It determines the suitable system architecture of the developed PSS, and finds the drivers that have significant impacts on residential energy consumption. In addition, the study examines the magnitude of different sustainable policies on household energy consumption and population groups. The expected outcome is an enhanced understanding of the energy implications of various policy and planning strategies at the local, regional, and national scales, in the context of various possible future contexts

A Novel Deep Learning Strategy for Classifying Different Attack Patterns for Deep Brain Implants

Deep brain stimulators (DBSs), a widely used and comprehensively acknowledged restorative methodology, are a type of implantable medical device which uses electrical stimulation to treat neurological disorders. These devices are widely used to treat diseases such as Parkinson, movement disorder, epilepsy, and psychiatric disorders. Security in such devices plays a vital role since it can directly affect the mental, emotional, and physical state of human bodies. In worst-case situations, it can even lead to the patient's death. An adversary in such devices, for instance, can inhibit the normal functionality of the brain by introducing fake stimulation inside the human brain. Nonetheless, the adversary can impair the motor functions, alter impulse control, induce pain, or even modify the emotional pattern of the patient by giving fake stimulations through DBSs. This paper presents a deep learning methodology to predict different attack stimulations in DBSs. The proposed work uses long short-term memory, a type of recurrent network for forecasting and predicting rest tremor velocity. (A type of characteristic observed to evaluate the intensity of the neurological diseases) The prediction helps in diagnosing fake versus genuine stimulations. The effect of deep brain stimulation was tested on Parkinson tremor patients. The proposed methodology was able to detect different types of emulated attack patterns efficiently and thereby notifying the patient about the possible attack. - 2013 IEEE.This work was supported by the Qatar National Research Fund (a member of Qatar Foundation) through NPRP under Grant 8-408-2-172.Scopu

Cloud computing based unsupervised fault diagnosis system in the context of Industry 4.0

ABSTRACT: New online fault monitoring and alarm systems, with the aid of Cyber-Physical Systems (CPS) and Cloud Technology (CT), are examined in this article within the context of Industry 4.0. The data collected from machines is used to implement maintenance strategies based on the diagnosis and prognosis of the machines' performance. As such, the purpose of this paper is to propose a Cloud Computing Platform containing three layers of technologies forming a Cyber-Physical System which receives unlabelled data to generate an interpreted online decision for the local team, as well as collecting historical data to improve the analyzer. The proposed troubleshooter is tested using unlabelled experimental data sets of rolling element bearing. Finally, the current and future Fault Diagnosis Systems and Cloud Technologies applications in the maintenance field are discussed

Assessment of level of serum cardiac troponin T in neonates with respiratory distress syndrome

Background: One of the most prevalent reasons for admission to neonatal intensive care units (NICUs) is respiratory distress syndrome (RDs). When myocardial cells are damaged, cardiac troponin I (cTnT) is released as a biomarker of myocardial damage, which is very specific and sensitive.Objective: To determine the level of cTnT in preterm infants who have respiratory distress syndrome as a marker of cardiac dysfunction.Patients and Methods: This study was carried as a case-control trial on forty preterm infants, 20 patients of respiratory distress syndrome at neonatal intensive care unit as a group I, 20 apparently healthy newborns as a control group. Serum cardiac troponin T level sample was taken on the 3rd day of delivery.Results: A statistically significant difference in blood troponin was found between the groups tested, with a negative connection between serum troponin and gestational age, length, and APGAR scores at the first and fifth minutes of life. Respiratory rate and serum troponin were found to have a statistically significant connection. Any one of the echocardiographic measures had a statistically significant positive connection with serum troponin. Serum troponin was able to diagnose respiratory distress syndrome with cutoff ≥ 93.5 ng/mL with the area under the curve, Positive predictive value: 83.33% Positive predictive value: 83.33% Negative predictive value: 100 percent Accuracy: 90%.Conclusion: Cardiac troponin T can be used to detect cardiac dysfunction in ill newborns, especially in centers that do not have in-house echocardiography

Alleviating Air Pollutants Impact by Some Chemicals and Planting Distance off the Freeway and Their Effect on Pears Productivity and Fruit Quality

The goal of this study was to determine the effectiveness of exogenous application of some chemicals [Salicylic acid (SA; 200 and 400 mg/l), Ascorbic acid (AA; 1000 and 2000 mg/l), Proline (Pro; 100 and 200 mg/l), and Sodium hydrosulfide (NaHS; 250 and 500 M)] as well as planting distances (10 and 50 meters off the freeway) in alleviating air pollutants stress on "Le Conte" productivity and fruit quality of pears. During the 2019 & 2020 seasons, a study was accomplished at the 6th of October Agriculture Company. SA, AA, proline, and NaHS had a positive effect on reducing the fruit heavy metal content for the pear trees under stress. Also, SA (400 mg/l), AA, Pro (100 mg/l), and NaHS (500 μM) treatments were very helpful in increasing the tolerance index for air pollution (APTI) in pear leaves. Exogenous application of Pro, SA (200 mg/l), and AA (2000 mg/l) increased Pro in leaves. Pear trees' yield, fruit firmness, TSS, TSS/acid, V.C., total sugars, total phenolics, antioxidants, peroxidase, and superoxide dismutase were increased. Fruit from trees planted at 10 m deferred in ripening and had higher total phenolics, antioxidants, peroxidase, and superoxide dismutase

Chest wall reconstruction still has place in Today’s modern practice:" a tertiary center experience"

Background: The key factor following chest wall resection is the preservation of the stability and integrity of the chest wall to support the respiration and protect the underlying organs. The present study aims to evaluate the use of the available grafts and prosthetic materials at our center in chest wall reconstruction with adherence to the proper surgical techniques, good perioperative and postoperative care to obtain good results. Methods: This is a retrospective single center study that concludes all patients underwent chest wall reconstruction for a variety of defects resulting from resection of tumors, trauma due to primarily firearms or motor car accidents, resection of radio necrotic tissues, infection and dehiscence of median sternotomy wounds after cardiac surgery.  Results: Study population consisted of 30 patients between January 2015and may 2018, among them were 20 male (70%) and 10 female patients (30%), with a median age of 43 ± 16.3 years, resection and reconstruction was performed in 23 cases (15 neoplastic,5 infective and  3 firearm cases) while reconstruction alone was performed in 7 (traumatic flail chest)  cases. Eighteen patients, underwent rib resection with an average 4.18 ± 2.2 ribs (range 2-6). Associated lung resection was performed in 5 patients (27.8 %): diaphragmatic resection was done in 2 cases in addition total sternal resection was performed in 5 cases. Most of the patients (96.7%) had primary healing of their wounds. there was one death (3.3%) in the early postoperative period. The average length of hospital stay for all patients was 8.7 days (range: 5–15). Respiratory complications occurred in three cases in the form of atelectasis and pneumonia at the ipsilateral side of reconstruction. Three cases suffered wound seroma which successfully managed by daily dressing and antibiotic coverage. Conclusions: according to our study and the analysis of similar studies, adequate perioperative preparation of patient undergoing chest wall resection and reconstruction with adherence to effective surgical techniques allowed us to use the available materials at our center for chest wall reconstruction with good and effective results without adding burden in terms of cost on the patient