E2BNAR: Energy Efficient Backup Node Assisted Routing for Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), each sensor node can only use so much power before recharging. If energy is depleted too quickly, nodes will fail one by one, bringing down the network as a whole. To this end, a design is needed to reduce the burden on the sensor nodes' power supplies while extending the network's useful life. This paper proposes a new approach, called Energy Efficient Backup Node Assisted Routing, to accomplish this (E2BNAR). Each primary node in the network has a group of backup nodes to ensure the network continues functioning. Assuming that the sensor nodes are capable of energy harvesting, E2BNAR finds the best backup node by analyzing the statistical relationship between energy harvesting and consumption rates. Periodically, residual energy is used to analyze the current energy consumption rate. When evaluating performance, several different indicators are taken into account. These include the Packet Delivery Ratio, Throughput, Average Energy Consumption, and Number of Awakened Sensor Nodes. Through analysis and experimentation in several settings, the proposed method's efficacy has been established

Tactile force-sensing for dynamic gripping using piezoelectric force- sensors

Thesis (M. Tech.) -- Central University of Technology, Free State, 200

Comparison of longitudinal changes in resting state functional magnetic resonance imaging between alzheimer’s and healthy controls

Resting State Functional Magnetic Resonance Imaging (rs-fMRI) is a technique that is widely used for analyzing brain function using different approaches and methods. This study involves rs-fMRI analysis of Blood Oxygenation Level Dependent (BOLD) signals acquired from Alzheimer’s disease (AD) Patients and Healthy Controls (HC). Each subject in the study had both functional and anatomical images with at least one rs-fMRI scan with their Anatomical (T1) scans. Previous rs-fMRI studies have demonstrated that AD shows differences in Amplitude of Low Frequency (\u3c0.1 Hz) Fluctuations (ALFF), and Regional Homogeneity (ReHo) measures according to HCs. The aim of the study is to investigate individual and group level differences using ReHo and mALFF related measures in a longitudinal analysis. The hypothesis is that with the age and group (AD or HC) of the subject, it is possible to separate AD and HC subjects from each other using 3 different ROIs (DMN – MT – MV), These regions are known to show abnormalities in AD patients but clinical wise never been identified as neuroimaging biomarkers. This study tries to check these ROIs to see if there are significant differences between the AD patients and HCs using 3 different features

Strategy to situate air quality monitoring stations in Arequipa industrial park

El desarrollo industrial en las zonas urbanas es un problema continuo para los ciudadanos y las industrias, esto debido a las constantes quejas por contaminación del aire y las enfermedades respiratorias. En Arequipa se carece de datos para la orientación ambiental de la calidad del aire y la protección de la salud pública. La propuesta en este informe es determinar las ubicaciones óptimas de las estaciones de monitoreo en el Parque Industrial de Arequipa. Utilizando el método de Proceso Jerarquía Analítica (AHP), evaluamos sus preferencias relativas para ubicar cada estación de monitoreo. La estrategia incluye tres actividades principales 1) la identificación del uso del suelo (es decir, industrial, estación de autobuses, urbano), y la recopilación de datos (meteorología y contaminantes químicos), 2) desarrollo de la jerarquía analítica de los criterios por AHP, y 3) el diseño de la red de monitoreo utilizando los métodos de superposición ponderada y sistema de información geográfica. En primer lugar, se evalúan tres criterios sostenibilidad (medioambientales, sociales y económicos) considerando nueve subcriterios. Después, con las ponderaciones asignadas, se obtuvo un índice aleatorio de 0,58, un índice de consistencia de 0,04 y un ratio de consistencia de 0,07. Con esta confirmación, la sostenibilidad se priorizó de la siguiente manera: ambiental (61,9%), social (28,4%) y económica (9,6%). Por último, proponemos una red de vigilancia de la sostenibilidad, que incluye tres estaciones de vigilancia (GIS). Las partes competentes pueden utilizar esta propuesta para desarrollar una capacidad de diagnóstico rápido, así como los episodios de contaminación, del impacto en la salud de la población del parque industrial de Arequip

In-situ monitoring of laser powder bed fusion applied to defect detection

Additive manufacturing technologies, particularly laser powder bed fusion (LPBF), have received much attention recently due to their numerous advantages over conventional manufacturing methods. However, the use of LPBF is still quite restricted, mainly due to two factors: its typically low productivity, which makes the technology less competitive in applications with moderate to high production volumes, and its limited reliability, particularly relevant for applications where high performance is required from the materials.The issue of low productivity is addressed in this thesis by adjusting the main LPBF process parameters. An equation for the build rate was formulated based on these parameters, determining their contributions and enabling strategies for build rate maximization. The changes in microstructure and defect populations associated with increasing productivity were determined.The reliability issue was explored by investigating defect formation, detectability and mitigation, since a major factor compromising reliability and materials’ performance is the presence of defects. Internal defects were deliberately created in LPBF-manufactured material to assess their detectability via in-situ monitoring. Two main routes of deliberate defect formation have been identified while preserving defect formation mechanisms; therefore, this thesis can be divided into two parts according to the approach employed to create defects.Defects are generated systematically if suboptimal process parameters are employed. The types, quantities, and sizes of defects in nickel-based alloy Hastelloy X resulting from varying processing conditions were thoroughly characterized. Analyzing data obtained from in-situ monitoring made it possible to distinguish virtually defect-free material from defective material.Defects are generated stochastically due to the redeposition of process by-products on the powder bed. With the aid of in-situ monitoring data, the presence of these defects can be inferred from the detection of the process by-products responsible for their formation. The comparison of data obtained in-situ with data obtained through ex-situ material characterization allowed determining how precisely detections corresponded to actual defects. The impact of these defects on the mechanical properties of Hastelloy X was assessed. A couple of in-process mitigation strategies were investigated, and their performances were evaluated. By establishing means to use LPBF process monitoring to distinguish high-quality from defective material and detect random, unavoidable defects, this thesis enables the prediction of LPBF material quality. It creates conditions necessary for the first-time-right production of defect-free material at increased build rates

Finding firms for joint development sites : an algorithm for integrated business location planning

Thesis (M.C.P.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 1982.MICROFICHE COPY AVAILABLE IN ARCHIVES AND ROTCHIncludes bibliographical references.by Susan J. MacCracken.M.C.P

Ecological Urban Design for Performance

The manner and extent to which urban design can be re-positioned as a performance-based practice that incorporates the uncertainty inherent in the future of cities by providing adaptive responses to it using foresight and scenario creation

GUIDE FOR THE COLLECTION OF INSTRUSION DATA FOR MALWARE ANALYSIS AND DETECTION IN THE BUILD AND DEPLOYMENT PHASE

During the COVID-19 pandemic, when most businesses were not equipped for remote work and cloud computing, we saw a significant surge in ransomware attacks. This study aims to utilize machine learning and artificial intelligence to prevent known and unknown malware threats from being exploited by threat actors when developers build and deploy applications to the cloud. This study demonstrated an experimental quantitative research design using Aqua. The experiment\u27s sample is a Docker image. Aqua checked the Docker image for malware, sensitive data, Critical/High vulnerabilities, misconfiguration, and OSS license. The data collection approach is experimental. Our analysis of the experiment demonstrated how unapproved images were prevented from running anywhere in our environment based on known vulnerabilities, embedded secrets, OSS licensing, dynamic threat analysis, and secure image configuration. In addition to the experiment, the forensic data collected in the build and deployment phase are exploitable vulnerability, Critical/High Vulnerability Score, Misconfiguration, Sensitive Data, and Root User (Super User). Since Aqua generates a detailed audit record for every event during risk assessment and runtime, we viewed two events on the Audit page for our experiment. One of the events caused an alert due to two failed controls (Vulnerability Score, Super User), and the other was a successful event meaning that the image is secure to deploy in the production environment. The primary finding for our study is the forensic data associated with the two events on the Audit page in Aqua. In addition, Aqua validated our security controls and runtime policies based on the forensic data with both events on the Audit page. Finally, the study’s conclusions will mitigate the likelihood that organizations will fall victim to ransomware by mitigating and preventing the total damage caused by a malware attack

A PVC substance flow analysis for Sweden. Part III: Methodological backgrounds

Industrial Ecolog