Pembangunan instrumen penerimaan e-pembelajaran pelajar pascasiswazah menggunakan analisis Rasch

Kajian ini bertujuan untuk menguji kesahan dan kebolehpercayaan instrumen penerimaan e-pembelajaran bagi pelajar pascasiswazah dengan menggunakan analisis Rasch. Objektif kajian ini adalah mengukur instrumen dengan menggunakan ujian-ujian dalam analisis Rasch seperti polariti item, varians reja terpiawai, kebolehpercayaan dan juga peta taburan individu-item. Instrumen ini mengandungi 16 item yang menggunakan skala Likert lima mata dan berasaskan Model UTAUT. Soal selidik ini ditadbir atas 97 orang pelajar pascasiswazah Fakulti Pendidikan UKM Bangi. Program Statistical Package for the Social Science (SPSS) versi 23.0 telah digunakan untuk memasukkan data dan membuat analisis statistik deskriptif sebelum dianalisis menggunakan program Winstep v3.68.2 untuk analisis Rasch. Dapatan kajian menunjukkan kesemua 16 item yang telah dibina memenuhi keperluan Model Rasch dan sesuai digunakan untuk mengukur penerimaan e-pembelajaran. Namun begitu, pembaikan item dari sudut pelarasan bahasa dan struktur ayat serta penambahan bilangan sampel yang lebih besar perlu dilakukan bagi meningkatkan lagi kesahan dan kebolehpercayaan item-item soal selidik ini di masa akan datang. Kajian ini juga menunjukkan bahawa analisis Rasch boleh membantu penyelidik memperbaiki soal selidik yang dibina secara telus dan sistematik

First Class Futures: Specification and implementation of Update Strategies

International audienceA natural way to benefit from distribution is via asynchronous invocations to methods or services. Upon invocation, a request is enqueued at the destination side and the caller can continue its execution. But a question remains: “what if one wants to manipulate the result of an asynchronous invocation?” First-class futures provide a transparent and easy-to-program answer: a future acts as the placeholder for the result of an asynchronous invocation and can be safely transmitted between processes while its result is not needed. Synchronization occurs automatically upon an access to the result. As references to futures disseminate, a strategy is necessary to propagate the result of each request to the processes that need it. This paper studies the efficient transmission of results: it presents three strategies in a semi-formal manner, providing experimental results highlighting their benefits and drawbacks

Vehicular Wireless Communication Standards: Challenges and Comparison

Autonomous vehicles (AVs) are the future of mobility. Safe and reliable AVs are required for widespread adoption by a community which is only possible if these AVs can communicate with each other & with other entities in a highly efficient way. AVs require ultra-reliable communications for safety-critical applications to ensure safe driving. Existing vehicular communication standards, i.e., IEEE 802.11p (DSRC), ITS-G5, & LTE, etc., do not meet the requirements of high throughput, ultra-high reliability, and ultra-low latency along with other issues. To address these challenges, IEEE 802.11bd & 5G NR-V2X standards provide more efficient and reliable communication, however, these standards are in the developing stage. Existing literature generally discusses the features of these standards only and does not discuss the drawbacks. Similarly, existing literature does not discuss the comparison between these standards or discusses a comparison between any two standards only. However, this work comprehensively describes different issues/challenges faced by these standards. This work also comprehensively provides a comparison among these standards along with their salient features. The work also describes spectrum management issues comprehensively, i.e., interoperability issues, co-existence with Wi-Fi, etc. The work also describes different other issues comprehensively along with recommendations. The work describes that 802.11bd and 5G NR are the two potential future standards for efficient vehicle communications; however, these standards must be able to provide backward compatibility, interoperability, and co-existence with current and previous standards

Parametric and non-parametric approaches for runoff and rainfall regionalization

The information on river flows is important for a number of reasons including; the construction of hydraulic structures for water management, for equitable distribution of water and for a number of environmental issues. The flow measurement devices are generally installed across the workspace at various locations to get data on river flows but due to a number of technical and accessibility issues, it is not always possible to get continuous data. The amount rainfall in a basin area also contributes towards the river flows and intense rainfall can cause flooding. The extended rainfall maps for the study areas to analyze these extreme events can be of great practical and theoretical interest. This thesis can be generally regarded as a work on catchment hydrology and mapping rainfall extremes to estimate certain hydrological variables that are not only useful for future research but also for practical designing and management issues. We analyzed a number of existing techniques available in literature to extend the hydrological information from gauged basin to ungauged basin; and suggested improvements. The three main frontiers of our work are: Monthly runoff regime regionalization, Flow duration curves (FDCs) regionalization and preparing rainfall hazardous maps. The proposed methods of regionalization for runoff regime and FDCs are tested for the basins located in northern Italy; whereas for rainfall extremes, the procedure is applied to the data points located in northern part of Pakistan

Incorporating geomorphological analyses to identify potential criticalities for flood susceptibility in Romagna Plain

openEmilia-Romagna, Italy, suffered significant floods in May 2023 consequently, of torrential rains following a protracted drought. Levees, major flood defense infrastructure, were damaged yet played an important role throughout the event, illustrating the critical necessity for effective flood risk management techniques and emphasizing the essentials of comprehensive flood susceptibility assessment in the area. While flood susceptibility mapping takes into account a variety of factors such as land use, rainfall, and hydrological variables, the role of geomorphological features is yet underexplored. This thesis seeks to address this gap by incorporating geomorphological analyses into flood susceptibility assessments, by systematically defining geomorphological indicators and their relationship to flood processes. The methodology involves a multi-step approach, including the synthesis of data analysis, GIS integration, and the development of potential indicators for flood susceptibility, particularly for the Sillaro and Senio Rivers in the region. Historical topographic maps, Geomorphological maps, Digital Terrain Models and several other data were acquired from the online sources and regional authorities of Emilia Romagna. Using Geographic Information System (GIS), this work aims to leverage historical and geomorphological data to pinpoint areas susceptible to future flooding, by analyzing river channel shifts, sinuosity patterns, meander migration, potential cutoff locations and post-event criticalities. The insights of this study reveal a strong link between historical geomorphological evidences, current sinuosity patterns, and flood susceptibility. The identified critical locations from the post-event orthophoto aligns with the area highlighted in the geomorphological analyses, suggesting significant correlation of geomorphological aspects to flood susceptibility, especially for the Senio River. The patterns of riverbank expansion and contraction further emphasizes the dynamic nature of the river system in the plain. These insights underscore the essentials of incorporating geomorphological analyses into flood susceptibility mapping to enhance flood risk management in the region.Emilia-Romagna, Italy, suffered significant floods in May 2023 consequently, of torrential rains following a protracted drought. Levees, major flood defense infrastructure, were damaged yet played an important role throughout the event, illustrating the critical necessity for effective flood risk management techniques and emphasizing the essentials of comprehensive flood susceptibility assessment in the area. While flood susceptibility mapping takes into account a variety of factors such as land use, rainfall, and hydrological variables, the role of geomorphological features is yet underexplored. This thesis seeks to address this gap by incorporating geomorphological analyses into flood susceptibility assessments, by systematically defining geomorphological indicators and their relationship to flood processes. The methodology involves a multi-step approach, including the synthesis of data analysis, GIS integration, and the development of potential indicators for flood susceptibility, particularly for the Sillaro and Senio Rivers in the region. Historical topographic maps, Geomorphological maps, Digital Terrain Models and several other data were acquired from the online sources and regional authorities of Emilia Romagna. Using Geographic Information System (GIS), this work aims to leverage historical and geomorphological data to pinpoint areas susceptible to future flooding, by analyzing river channel shifts, sinuosity patterns, meander migration, potential cutoff locations and post-event criticalities. The insights of this study reveal a strong link between historical geomorphological evidences, current sinuosity patterns, and flood susceptibility. The identified critical locations from the post-event orthophoto aligns with the area highlighted in the geomorphological analyses, suggesting significant correlation of geomorphological aspects to flood susceptibility, especially for the Senio River. The patterns of riverbank expansion and contraction further emphasizes the dynamic nature of the river system in the plain. These insights underscore the essentials of incorporating geomorphological analyses into flood susceptibility mapping to enhance flood risk management in the region

Satellite based methane emission estimation for flaring activities in oil and gas industry: A data-driven approach(SMEEF-OGI)

Klimaendringer, delvis utløst av klimagassutslipp, utgjør en kritisk global utfordring. Metan, en svært potent drivhusgass med et globalt oppvarmings potensial på 80 ganger karbondioksid, er en betydelig bidragsyter til denne krisen. Kilder til metanutslipp inkluderer olje- og gassindustrien, landbruket og avfallshåndteringen, med fakling i olje- og gassindustrien som en betydelig utslippskilde. Fakling, en standard prosess i olje- og gassindustrien, antas ofte å være 98 % effektiv ved omdannelse av metan til mindre skadelig karbondioksid. Nyere forskning fra University of Michigan, Stanford, Environmental Defense Fund og Scientific Aviation indikerer imidlertid at den allment aksepterte effektiviteten på 98 % av fakling ved konvertering av metan til karbondioksid, en mindre skadelig klimagass, kan være unøyaktig. Denne undersøkelsen revurderer fakkelprosessens effektivitet og dens rolle i metankonvertering. Dette arbeidet fokuserer på å lage en metode for uavhengig å beregne metanutslipp fra olje- og gassvirksomhet for å løse dette problemet. Satellittdata, som er et nyttig verktøy for å beregne klimagassutslipp fra ulike kilder, er inkludert i den foreslåtte metodikken. I tillegg til standard overvåkingsteknikker, tilbyr satellittdata en uavhengig, ikke-påtrengende, rimelig og kontinuerlig overvåkingstilnærming. På bakgrunn av dette er problemstillingen for dette arbeidet følgende "Hvordan kan en datadrevet tilnærming utvikles for å forbedre nøyaktigheten og kvaliteten på estimering av metanutslipp fra faklingsaktiviteter i olje- og gassindustrien, ved å bruke satellittdata fra utvalgte plattformer for å oppdage og kvantifisere fremtidige utslipp basert på maskinlæring mer effektivt?" For å oppnå dette ble følgende mål og aktiviteter utført. * Teoretisk rammeverk og sentrale begreper * Teknisk gjennomgang av dagens toppmoderne satellittplattformer og eksisterende litteratur. * Utvikling av et Proof of Concept * Foreslå en evaluering av metoden * Anbefalinger og videre arbeid Dette arbeidet har tatt i bruk en systematisk tilnærming, som starter med et omfattende teoretisk rammeverk for å forstå bruken av fakling, de miljømessige implikasjonene av metan, den nåværende «state-of-the-art» av forskning, og «state-of-the-art» i felt for fjernmåling via satellitter. Basert på rammeverket utviklet i de innledende fasene av dette arbeidet, ble det formulert en datadrevet metodikk, som benytter VIIRS-datasettet for å få geografiske områder av interesse. Hyperspektrale data og metandata ble samlet fra Sentinel-2 og Sentinel-5P satellittdatasettet. Denne informasjonen ble behandlet via en foreslått rørledning, med innledende justering og forbedring. I dette arbeidet ble bildene forbedret ved å beregne den normaliserte brennindeksen. Resultatet var et datasett som inneholdt plasseringen av kjente fakkelsteder, med data fra både Sentinel-2 og Sentinel-5P-satellitten. Resultatene understreker forskjellene i dekningen mellom Sentinel-2- og Sentinel-5P-data, en faktor som potensielt kan påvirke nøyaktigheten av metanutslippsestimater. De anvendte forbehandlingsteknikkene forbedret dataklarheten og brukervennligheten markant, men deres effektivitet kan avhenge av fakkelstedenes spesifikke egenskaper og rådatakvaliteten. Dessuten, til tross for visse begrensninger, ga kombinasjonen av Sentinel-2 og Sentinel-5P-data effektivt et omfattende datasett egnet for videre analyse. Avslutningsvis introduserer dette prosjektet en oppmuntrende metodikk for å estimere metanutslipp fra fakling i olje- og gassindustrien. Den legger et grunnleggende springbrett for fremtidig forskning, og forbedrer kontinuerlig presisjonen og kvaliteten på data for å bekjempe klimaendringer. Denne metodikken kan sees i flytskjemaet nedenfor. Basert på arbeidet som er gjort i dette prosjektet, kan fremtidig arbeid fokusere på å innlemme alternative kilder til metan data, utvide interesseområdene gjennom industrisamarbeid og forsøke å trekke ut ytterligere detaljer gjennom bildesegmenteringsmetoder. Dette prosjektet legger et grunnlag, og baner vei for påfølgende utforskninger å bygge videre på.Climate change, precipitated in part by greenhouse gas emissions, presents a critical global challenge. Methane, a highly potent greenhouse gas with a global warming potential of 80 times that of carbon dioxide, is a significant contributor to this crisis. Sources of methane emissions include the oil and gas industry, agriculture, and waste management, with flaring in the oil and gas industry constituting a significant emission source. Flaring, a standard process in the Oil and gas industry is often assumed to be 98% efficient when converting methane to less harmful carbon dioxide. However, recent research from the University of Michigan, Stanford, the Environmental Defense Fund, and Scientific Aviation indicates that the widely accepted 98% efficiency of flaring in converting methane to carbon dioxide, a less harmful greenhouse gas, may be inaccurate. This investigation reevaluates the flaring process's efficiency and its role in methane conversion. This work focuses on creating a method to independently calculate methane emissions from oil and gas activities to solve this issue. Satellite data, which is a helpful tool for calculating greenhouse gas emissions from various sources, is included in the suggested methodology. In addition to standard monitoring techniques, satellite data offers an independent, non-intrusive, affordable, and continuous monitoring approach. Based on this, the problem statement for this work is the following “How can a data-driven approach be developed to enhance the accuracy and quality of methane emission estimation from flaring activities in the Oil and Gas industry, using satellite data from selected platforms to detect and quantify future emissions based on Machine learning more effectively?" To achieve this, the following objectives and activities were performed. * Theoretical Framework and key concepts * Technical review of the current state-of-the-art satellite platforms and existing literature. * Development of a Proof of Concept * Proposing an evaluation of the method * Recommendations and further work This work has adopted a systematic approach, starting with a comprehensive theoretical framework to understand the utilization of flaring, the environmental implications of methane, the current state-of-the-art of research, and the state-of-the-art in the field of remote sensing via satellites. Based upon the framework developed during the initial phases of this work, a data-driven methodology was formulated, utilizing the VIIRS dataset to get geographical areas of interest. Hyperspectral and methane data were aggregated from the Sentinel-2 and Sentinel-5P satellite dataset. This information was processed via a proposed pipeline, with initial alignment and enhancement. In this work, the images were enhanced by calculating the Normalized Burn Index. The result was a dataset containing the location of known flare sites, with data from both the Sentinel-2, and the Sentinel-5P satellite. The results underscore the disparities in coverage between Sentinel-2 and Sentinel-5P data, a factor that could potentially influence the precision of methane emission estimates. The applied preprocessing techniques markedly enhanced data clarity and usability, but their efficacy may hinge on the flaring sites' specific characteristics and the raw data quality. Moreover, despite certain limitations, the combination of Sentinel-2 and Sentinel-5P data effectively yielded a comprehensive dataset suitable for further analysis. In conclusion, this project introduces an encouraging methodology for estimating methane emissions from flaring activities within the oil and gas industry. It lays a foundational steppingstone for future research, continually enhancing the precision and quality of data in combating climate change. This methodology can be seen in the flow chart below. Based on the work done in this project, future work could focus on incorporating alternative sources of methane data, broadening the areas of interest through industry collaboration, and attempting to extract further features through image segmentation methods. This project signifies a start, paving the way for subsequent explorations to build upon. Climate change, precipitated in part by greenhouse gas emissions, presents a critical global challenge. Methane, a highly potent greenhouse gas with a global warming potential of 80 times that of carbon dioxide, is a significant contributor to this crisis. Sources of methane emissions include the oil and gas industry, agriculture, and waste management, with flaring in the oil and gas industry constituting a significant emission source. Flaring, a standard process in the Oil and gas industry is often assumed to be 98% efficient when converting methane to less harmful carbon dioxide. However, recent research from the University of Michigan, Stanford, the Environmental Defense Fund, and Scientific Aviation indicates that the widely accepted 98% efficiency of flaring in converting methane to carbon dioxide, a less harmful greenhouse gas, may be inaccurate. This investigation reevaluates the flaring process's efficiency and its role in methane conversion. This work focuses on creating a method to independently calculate methane emissions from oil and gas activities to solve this issue. Satellite data, which is a helpful tool for calculating greenhouse gas emissions from various sources, is included in the suggested methodology. In addition to standard monitoring techniques, satellite data offers an independent, non-intrusive, affordable, and continuous monitoring approach. Based on this, the problem statement for this work is the following “How can a data-driven approach be developed to enhance the accuracy and quality of methane emission estimation from flaring activities in the Oil and Gas industry, using satellite data from selected platforms to detect and quantify future emissions based on Machine learning more effectively?" To achieve this, the following objectives and activities were performed. * Theoretical Framework and key concepts * Technical review of the current state-of-the-art satellite platforms and existing literature. * Development of a Proof of Concept * Proposing an evaluation of the method * Recommendations and further work This work has adopted a systematic approach, starting with a comprehensive theoretical framework to understand the utilization of flaring, the environmental implications of methane, the current state-of-the-art of research, and the state-of-the-art in the field of remote sensing via satellites. Based upon the framework developed during the initial phases of this work, a data-driven methodology was formulated, utilizing the VIIRS dataset to get geographical areas of interest. Hyperspectral and methane data were aggregated from the Sentinel-2 and Sentinel-5P satellite dataset. This information was processed via a proposed pipeline, with initial alignment and enhancement. In this work, the images were enhanced by calculating the Normalized Burn Index. The result was a dataset containing the location of known flare sites, with data from both the Sentinel-2, and the Sentinel-5P satellite. The results underscore the disparities in coverage between Sentinel-2 and Sentinel-5P data, a factor that could potentially influence the precision of methane emission estimates. The applied preprocessing techniques markedly enhanced data clarity and usability, but their efficacy may hinge on the flaring sites' specific characteristics and the raw data quality. Moreover, despite certain limitations, the combination of Sentinel-2 and Sentinel-5P data effectively yielded a comprehensive dataset suitable for further analysis. In conclusion, this project introduces an encouraging methodology for estimating methane emissions from flaring activities within the oil and gas industry. It lays a foundational steppingstone for future research, continually enhancing the precision and quality of data in combating climate change. This methodology can be seen in the flow chart below. Based on the work done in this project, future work could focus on incorporating alternative sources of methane data, broadening the areas of interest through industry collaboration, and attempting to extract further features through image segmentation methods. This project signifies a start, paving the way for subsequent explorations to build upon

Designing Light Filters to Detect Skin Using a Low-powered Sensor

Detection of nudity in photos and videos, especially prior to uploading to the internet, is vital to solving many problems related to adolescent sexting, the distribution of child pornography, and cyber-bullying. The problem with using nudity detection algorithms as a means to combat these problems is that: 1) it implies that a digitized nude photo of a minor already exists (i.e., child pornography), and 2) there are real ethical and legal concerns around the distribution and processing of child pornography. Once a camera captures an image, that image is no longer secure. Therefore, we need to develop new privacy-preserving solutions that prevent the digital capture of nude imagery of minors. My research takes a first step in trying to accomplish this long-term goal: In this thesis, I examine the feasibility of using a low-powered sensor to detect skin dominance (defined as an image comprised of 50% or more of human skin tone) in a visual scene. By designing four custom light filters to enhance the digital information extracted from 300 scenes captured with the sensor (without digitizing high-fidelity visual features), I was able to accurately detect a skin dominant scene with 83.7% accuracy, 83% precision, and 85% recall. The long-term goal to be achieved in the future is to design a low-powered vision sensor that can be mounted on a digital camera lens on a teen\u27s mobile device to detect and/or prevent the capture of nude imagery. Thus, I discuss the limitations of this work toward this larger goal, as well as future research directions

Enhancing statistical wind speed forecasting models : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering at Massey University, Manawatū Campus, New Zealand

In recent years, wind speed forecasting models have seen significant development and growth. In particular, hybrid models have been emerging since the last decade. Hybrid models combine two or more techniques from several categories, with each model utilizing its distinct strengths. Mainly, data-driven models that include statistical and Artificial Intelligence/Machine Learning (AI/ML) models are deployed in hybrid models for shorter forecasting time horizons (< 6hrs). Literature studies show that machine learning models have gained enormous potential owing to their accuracy and robustness. On the other hand, only a handful of studies are available on the performance enhancement of statistical models, despite the fact that hybrid models are incomplete without statistical models. To address the knowledge gap, this thesis identified the shortcomings of traditional statistical models while enhancing prediction accuracy. Three statistical models are considered for analyses: Grey Model [GM(1,1)], Markov Chain, and Holt’s Double Exponential Smoothing models. Initially, the problems that limit the forecasting models' applicability are highlighted. Such issues include negative wind speed predictions, failure of predetermined accuracy levels, non-optimal estimates, and additional computational cost with limited performance. To address these concerns, improved forecasting models are proposed considering wind speed data of Palmerston North, New Zealand. Several methodologies have been developed to improve the model performance and fulfill the necessary and sufficient conditions. These approaches include adjusting dynamic moving window, self-adaptive state categorization algorithm, a similar approach to the leave-one-out method, and mixed initialization method. Keeping in view the application of the hybrid methods, novel MODWT-ARIMA-Markov and AGO-HDES models are further proposed as secondary objectives. Also, a comprehensive analysis is presented by comparing sixteen models from three categories, each for four case studies, three rolling windows, and three forecasting horizons. Overall, the improved models showed higher accuracy than their counter traditional models. Finally, the future directions are highlighted that need subsequent research to improve forecasting performance further

Antifungal and antispasmodic activities of the extracts of Euphorbia granulata

The dichloromethane and methanolic extracts of the plant Euphorbia granulata were investigated for their antifungal, antibacterial, phytotoxic, brine-shrimp cytotoxic, antioxidant, spasmolytic (antispasmodic) and acetylcholinestrase inhibitory activities. The dichloromethane extract showed strong inhibition against Microsporum canis (90%) and against Aspergillus flavus (50%). Both the extracts inhibited the spontaneous contractions in rabbit jejunum preparations with EC50 value of 0.17 and 1.3 mg/mL, respectively and also relaxed the K+-induced contractions with EC50 0.2 and 2.8 mg/mL, respectively, suggesting a calcium channel blocking activity. However, the extracts did not show antibacterial, phytotoxic, brine-shrimp cytotoxic, antioxidant and acetylcholinestrase inhibitory activities