4,620 research outputs found
Adeel Khalid
No full textDr. Adeel Khalid is a Professor of Systems Engineering at Kennesaw State University (KSU) in Marietta, Georgia. His expertise includes Multidisciplinary design and optimization of Aerospace systems. His industry experience includes working as a systems engineer at Avidyne Corporation. Dr. Khalid received his Ph.D. in Aerospace Engineering from Georgia Institute of Technology. He holds Master of Science degrees in the discipline of Mechanical Engineering from Michigan State University, and Industrial, and Aerospace Engineering from Georgia Institute of Technology. His research is focused on system level design optimization and integration of disciplinary analyses.https://commons.erau.edu/ntas-bios/1148/thumbnail.jp
Willmar Area Minority Business Survey: An Analysis of the Experiences of Local Minority-Owned Businesses, Based on a Representative Survey
No full text1 electronic resource (PDF). This archival publication may not reflect current scientific knowledge or recommendations. Current information available from the University of Minnesota Extension: https://www.extension.umn.edu.University of Minnesota Crookston EDA CenterAhmed, Adeel. (2012). Willmar Area Minority Business Survey: An Analysis of the Experiences of Local Minority-Owned Businesses, Based on a Representative Survey. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/171658
Sustainable synthesis of graphene-based materials and applications
No full textAdeel Zafar manufactured graphene and graphene-composites at ambient conditions from plant extracts, and for the first time nitrogen-doped graphene-oxide using a single step is fabricated. He developed electrochemical sensors for the detection of various insecticides and pesticides. This work is significant to achieve sustainability in the field of nanotechnology
جمیل احمد عدیل کے افسانوں میں مابعد الطبیعیاتی عناصر: Metaphysical elements in the fiction of Jameel Ahmad Adeel
No full textThe metaphysics is an important branch of philosophy. Spirituality has a fundamental position in this domain. It deals with the internal and immaterial affairs of the world. Metaphysical elements can be found in world Literature. Significant features of the writings of seventeenth-century Western metaphysical writers and poets, such as John Dunn, George Herbert, Crouch, and Marvel. Jameel Ahmad Adeel also holds an important place in Urdu fiction in the sense that his fiction has a unique style. He has deep social consciousness as well as mythology, symbolism and allegorical references as part of his fiction. That is why the metaphysical elements in his fictions appear to add to the reader a new magical and spiritual realm. Jameel Ahmed Adeel has a strong sense of modern awareness in his fiction. Till date, he has explored and understood Elliott's ideas well, but they all serve as a background for the evolution of his consciousness and for the depth and maturity of his art
Selfish Mules: Social Profit Maximization in Sparse Sensornets using Rationally-Selfish Human Relays
No full textFuture smart cities will require sensing on a scale hitherto unseen. Fixed infrastructures have limitations regarding sensor maintenance, placement and connectivity. Employing the ubiquity of mobile phones is one approach to overcoming some of these problems. Here, mobility and social patterns of phone owners can be exploited to optimize data forwarding efficiency. The question remains, how can we stimulate phone owners to serve as data relays? In this paper, we combine network science principles and Lyapunov optimization techniques, to maximize global social profit across this hybrid sensor and mobile phone network. Sensor data packets are produced and traded (transmitted) over a virtual economic network using a lightweight social-economic-aware backpressure algorithm, combining rate control, routing, and resource pricing. Phone owners can get benefits through relaying sensor data. Our algorithm is fully distributed and makes no probabilistic/stochastic assumptions regarding mobility, topology, and channel conditions, nor does it require prediction. The global social profit achieved by our algorithm can perform close to (or better than) an ideal algorithm with perfect prediction-- proven by rigorous theoretical analysis. Simulation results further demonstrate that the proposed algorithm outperforms pure backpressure and social-aware schemes; highlighting the advantage of building systems combining communication with other types of networks
Upper Aero Digestive Tract Cancer Diagnosis using Deep Learning Methods
No full textObjective: Narrow band imaging (NBI) and white light (WL) are endoscopic techniques to visualize upper aero digestive tract (UADT) cancers. However, these imaging techniques are less effective for diagnosing tumors in less competent centers since they depend on skilled medical experts. Recently, there has been evidence that deep learning (DL) has potential applications in UADT video endoscopy. This research aims to develop a DL for the automatic identification and delineation of UADT cancer.
Approach: In both WL and NBI frames, the YOLO DL model (YOLOv5s with YOLOv5m) ensemble, was used to diagnose laryngeal squamous cell carcinoma (LSCC). Six external LSCC laryngoscopy videos were tested in real-time for cancer detection. The SegMENT is a segmentation convolution neural networks (CNN), model proposed based on a modified DeepLabV3+ model for precise UADT delineation using an in-domain transfer learning ensemble technique. Its accuracy was further validated on external datasets with NBI images of oral cavity SCC (OSCC) and oropharyngeal SCC (OPSCC). The SegMENT-Plus is the improved version of SegMENT model designed for large LSCC datasets. SegMENT-Plus used EfficientNetB5 backbone as an encoder with a modified atrous spatial pyramid pooling (m-ASPP) block. The attentions blocks (SE and CBAM) were integrated into m-ASPP module to improve cancer segmentation. The m-ASPP was used to extract local and global LSCC features to overcome the limitation of conventional ASPP modules in literature. SegMENT-Plus was evaluated using a multi-center dataset from three hospitals (Genoa, Brescia, Seoul South Korea). The model was tested on LSCC frames, the delineation performance was compared with three otolaryngology experts. The unseen intraoperative laryngoscopy videos also validated for real-time performance. The SegMENT-Plus was compared with its predecessor SegMENT and other DL models (UNET, ResUNET, DeepLabv3+, DoubleUET,).
Main results: In the LSCC detection task, 219 patients from Genoa, Italy were enrolled, and were provided 624 LSCC video frames. YOLO models were trained using an 82.6% training set, an 8.2% validation set, and a 9.2% testing set. The ensemble algorithm (YOLOv5s with YOLOv5m —Test Time Augmentation) achieved top LSCC detection with 66% Precision, 62% Recall, and 63% mean Average Precision at 0.5 intersection over union (IoU). The average computation time per frame on laryngoscopy videos was 0.026 seconds. The SegMENT model for the UADT cancer delineation was developed using 219 patients (624 larynx frames), and external validation from Brescia, Italy for the OPSCC and OCSCC cohorts involved 116 and 102 NBI images, respectively. The SegMENT model achieved 0.68% IoU and 0.81% dice coefficient (DSC), outperforming other DL models. The DSC values in the OCSCC and OPSCC datasets improved significantly, with median DSC values of 10.3% and 11.9%, respectively. This study includes 557 patients with 3933 laryngeal images from Genoa, Italy to the development of SegMENT-Plus to improve LDCC delineation. The optimal performance and generalization of the algorithm were confirmed by external testing cohorts from Seoul, South Korea, and Brescia, Italy. The external cohorts showed DSC between 81.4% and 84.9% and IoU between 81.8% and 85.7%.
Significance: The study identified a suitable CNN model for LSCC detection in WL and NBI video laryngoscopes. SegMENT outperformed previous results in external validation cohorts, showing promise for precise tumor segmentation. SegMENT-Plus holds the potential for improved early tumor detection and delineation, laying the foundation for a clinical system in LSCC margin delineation
Development of Multifunctional Nanomaterials for Biomedical Applications
No full textLa ricerca condotta in questa tesi si è focalizzata su due principali tematiche. La prima ha riguardato lo sviluppo di sensori e biosensori elettrochimici, basati su materiali nanostrutturati, per la determinazione di molecole di interesse biologico. La seconda è stata rivolta a definire nuove strategie da applicare nella terapia del cancro.
Per quanto riguarda la prima tematica, sono stati sviluppati sensori elettrochimici, basati su materiali 2D, tra cui metal organic frameworks, per la determinazione enzyme-less del glucosio. I nanomateriali sintetizzati, definiti nanozymes, sono in grado di catalizzare direttamente (cioè agiscono come enzimi artificiali) l'ossidazione del glucosio. I sensori sono stati impiegati per la determinazione di glucosio in tampone fosfato e in campioni di plasma diluito, sia a pH fisiologici che alcalini. Un biosensore, basato sull’impiego di substrati costituiti di flexible carbon cloths, è stato sviluppato per la determinazione del Covid-19 nella fase iniziale. A tale scopo, è stato immobilizzando sul substrato grafitico, un anticorpo capace di intercettare in modo selettivo la proteina spike del SARS-CoV-2. Il sensore sviluppato, essendo flessibile e “vestibile” si presta ad essere integrato in mascherine facciali protettive, per l'automonitoraggio dell'insorgenza dell'infezione Covid-19.
Il secondo argomento ha riguardato lo sviluppo di nuove strategie e sistemi terapeutici per curare il cancro ovarico grave di alto grado (HGSOC). A tal fine, sono stati studiati materiali con funzionalità operative diversificate. Un sistema innovativo di drug delivery sviluppato, comprendeva del materiale a base di hydroxylated-boron nitride-nanosheets, capace di incorporare la di doxorubicina e rilasciarla in prossimità del tumore. Inoltre, al fine di evitare alcuni problemi legati a questa modalità di somministrazione dei farmaci, sono stati sviluppati composti di tipo carrier-free, costituiti di farmaci con caratteristiche altamente idrofobiche (MAGL, composti 17, VS1 e VS10), per differenti target terapeutici (PIN1 &STARD3) di cancro ovarico. A tale scopo Le molecole dei farmaci molto idrofobiche sono state solubilizzate, riducendone contemporaneamente le dimensioni degli aggregati molecolari, con diversi tensioattivi, e quindi ricoperte con nanocristalli di albumina per ottenere sistemi di trasporto in vivo stabili e sicuri. È stato anche studiato e proposto per scopi pratici o clinici un nanomateriale autoterapeutico, avente cioè funzioni medicinali, che può agire come un "nano proiettile magico" privo di composti terapeutici extra o il cui funzionamento possa dipendere da stimoli esterni
Measuring Return on Investment of Tourism Marketing: A Review of Sixteen State Tourism Offices
No full textpp 24. State travel offices’ justifications for funding are increasingly being scrutinized as states seek tobalance budgets. These justifications are expected to contain well‐stated objectives and measurable results, including indications of cost effectiveness. Advertising is a big portion of the budget for state travel offices and is perhaps the budgetary consideration most frequently investigated for its cost effectiveness. In order for Explore Minnesota Tourism (EMT), the state agency responsible for promoting travel to and within Minnesota, to better gauge the return oninvestment (ROI) of its recent marketing activities, a review of state‐level reports and assessments for the tourism marketing of Minnesota and 15 other states was completed in December 2010 and is presented in this report. Ahmed, Adeel. (2010). Measuring Return on Investment of Tourism Marketing: A Review of Sixteen State Tourism Offices. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/167913
INSIGHTS INTO THE IMPACT OF MEGA TRANSPORT INFRASTRUCTURE PROJECTS ON THE TRANSFORMATION OF THE URBAN FABRIC OF LAHORE
No full textThe goal of this Ph.D. research is to gain insights into the impacts of mass transit systems on the transformation of surrounding urban fabric in relation to transit-oriented development (TOD) in Lahore. It emphasizes the fact that diversified arrangements of TOD can be taken into account conferring the varying characters of different urban areas instead of one-size-fits-all approach of TOD. It also focuses on the urban streetscape transformation processes that
resulted from the construction of mass transit corridors, taking into account the aspects of proximity, accessibility benefits, and characteristics of collective spaces. Lahore, the second largest city of Pakistan with more than 11 million inhabitants, is characterized by a mix of high and low-density developments. To effectively meet the transport demand and to provide the city with a high-quality transit facilities system an integrated mass transit system has been
developed in the form of Bus Rapid Transit (BRT). These mass transit corridors balance mobility and amenity, providing more optimal economic outcomes to metropolitan conglomerations in general: residential and commercial activities increase in value due to the proximity to transport corridors. The primary aim of this research is to provide in-depth insights into the spatial transformations processes that took place around BRT corridor stations. This research concentrates on the intermediate scale to investigate phenomena of land use transformation, streetscape activity shifts, reconfiguration of properties, densification and land use revitalization within a 500-meter buffer around BRT transit stations. It uses both quantitative and qualitative analysis techniques to detect changes in the urban fabric around BRT stations. Qualitative analysis helps to analyze stakeholders’ perspectives and issues and provided in-depth
knowledge about the gentrification and displacement pressure induced after the implementation of the BRT project. Quantitative analysis allows to estimate the changes in the urban fabric and to identify certain factors that contributed to land use transformations, such as dynamic socio-economic conditions, varying population densities, road widths and plot sizes. Lastly, to understand the multiplicity of sustainable TOD patterns around BRT stations of varying character, an understanding of preferences of the users of roads and adjacent buildings is critically assessed through the technique of visual stated preference surveys. Based on the detailed insights gained during this research, diversified arrangements of sustainable TOD is proposed for old urban tissues and newly developed prosperous urban areas. This research concludes that, instead of applying a uniform smart growth strategy, the local urban planners and designers of Lahore must translate their visions of TOD through area sensitive master plans which are socially inclusive, market sensitive, and rooted in fiscal realities and thoroughly consulted with stakeholders
Accelerazione basata sull’indice: join in tempo reale e query ibride
No full textL’analisi dei dati in tempo reale `e diventata sempre pi`u importante con la crescita di sistemi interconnessi. Un’applicazione comune `e il mon- l’elaborazione dei dati energetici. Questi dati sono costantemente generati dai sensori installato su diversi dispositivi che producono e consumano energia. Di nuova generazione I dati devono essere elaborati frequentemente per offrire informazioni significative subito. L’approccio tipico alla lavorazione coinvolge produttore e consumatore modelli computazionali. Sono stati utilizzati numerosi quadri di elaborazione dei dati proposto di consumare flussi di dati (dati in tempo reale) da vari input eseguire calcoli distribuiti, combinare risultati individuali e Fornisci approfondimenti. Questi framework utilizzano in genere il parallelismo della pipeline sui dati in entrata ed effettuare varie operazioni online come l’adesione, aggregazione e filtraggio.
I dati in streaming sono gestiti all'interno di finestre (scorrevoli, a cascata, di sessione, ecc.), dove le tuple vengono continuamente aggiunte e quelle scadute rimosse. L'unione di flussi è fondamentale per i dati in tempo reale, ma presenta sfide computazionali maggiori rispetto all'unione di batch tradizionali, a causa della continua ricerca, aggiunta ed eliminazione di dati. Gli operatori di unione comuni includono unioni di uguaglianza e disuguaglianza (theta), con le unioni di disuguaglianza che risultano particolarmente intensive. Per affrontare queste sfide, si propongono due intuizioni chiave: 1) identificare distribuzioni di dati distorte in tempo reale e implementare strutture di indicizzazione dedicate per ridurre i costi di aggiornamento; 2) sfruttare strutture di dati ottimizzate, con strutture mutabili efficienti per l'inserimento e immutabili per la ricerca, per ottimizzare il processo di unione dei flussi.
In questo lavoro di dottorato propongo nuove soluzioni per l’elaborazione di join di flussi distribuiti. Uno dei contributi chiave `e un metodo di indicizzazione che utilizza un filtro dedicato efficiente in termini di spazio per monitorare la frequenza delle chiavi di input in tempo reale. Questo metodo, chiamato STA-Join, adatta la logica di elaborazione dei dati in base all’asimmetria dei dati. Inoltre, ho ampiamente confrontato questa tecnica con gli approcci esistenti. Inoltre, ho anche introdotto una struttura dati a due stadi per gestire ed elaborare efficacemente elementi della finestra scorrevole (contenuti streaming delimitati) con operatori di disuguaglianza complessi. Questo approccio, denominato SPO-Join, divide la finestra scorrevole in strutture dati mutabili (efficienti per l’inserimento) e immutabili (efficienti per la ricerca). Nonostante le sfide affrontate, come la gestione dello stato per l’elaborazione distribuita, le garanzie di elaborazione e i meccanismi di concorrenza efficienti, i risultati sperimentali dei sistemi di elaborazione di flussi distribuiti dimostrano che le soluzioni proposte superano i metodi all’avanguardia esistenti.
Allo stesso modo, man mano che i modelli di intelligenza artificiale generativa si diffondono in vari settori, tra cui quello energetico, i database vettoriali vengono sempre più utilizzati per archiviare dati industriali multidimensionali e fornire suggerimenti efficaci a questi modelli. Le prestazioni e l'accuratezza del I modelli dipendono in gran parte dalla qualità dei suggerimenti. Tuttavia, il recupero efficiente di vettori rilevanti, in particolare per le query ibride con un elevato richiamo, è un'attività complessa. Propongo una soluzione sensibile alla frequenza per una struttura di dati indice per affrontare questo problema.Real-time data analysis has become increasingly important with the growth of interconnected systems. One common application is the continuous monitoring of energy data. This data is constantly generated by the sensors installed on different energy-producing and consuming devices. Newly generated data need to be processed frequently to offer meaningful insights promptly. The typical processing approach involves producer and consumer computational patterns. Numerous data processing frameworks have been proposed to consume streaming (real-time) data from various input devices, perform distributed computation, combine individual results, and provide insights. These frameworks commonly employ pipeline parallelism on incoming data and carry out various online operations such as joining, aggregation, and filtering.
Streaming data is confined to windows (sliding, tumbling, session, etc.), where newly arriving tuples are continually inserted and expired tuples are removed frequently. Stream join is an essential operation for handling real-time data, however, it comes with additional computational challenges compared to traditional batch join, due to the continuous look-up, add, and delete data points from streaming windows. Common join operators include equality or inequality (theta) joins. The stream inequality join is particularly computationally intensive because it requires additional overhead to hold the contents of the streaming window using index data structures. To tackle this challenge, we identify two key insights: 1) identifying skewed data distributions in real-time and implementing dedicated indexing structures for skewed keys to reduce index update costs; 2) leveraging optimized data structures, including insert-efficient mutable and search-efficient immutable structures to optimize the search stream join process.
In this Ph.D. work, I propose novel solutions for distributed stream join processing. One of the key contributions is an indexing method that uses a space-efficient dedicated filter to monitor the frequency of input keys in real-time. This method, called STA-Join, adapts the data processing logic based on the skewness of the data. Additionally, I have extensively compared this technique with existing approaches. Moreover, I have also introduced a two-stage data structure for handling and processing sliding window items (bounded streaming contents) with complex inequality operators. This approach, named SPO-Join, divides the sliding window into mutable (insert-efficient) and immutable (search-efficient) data structures. Despite facing challenges such as state management for distributed processing, processing guarantees, and efficient concurrency mechanisms, experimental results from distributed stream processing systems demonstrate that the proposed solutions outperform existing state-of-the-art methods.
Similarly, as generative AI models become more widespread in various industries, including the energy sector, vector databases are increasingly being used to store multidimensional industry data and provide effective prompts to these models. The performance and accuracy of the models depend largely on the quality of the prompts. However, efficiently retrieving relevant vectors, especially for hybrid queries (vectors and predicate conditions) with high recall, is a challenging task. I propose a frequency-aware solution for an index-data structure to address this issue to facilitate approximate nearest neighbor (ANN) searches in high-dimensional spaces, especially for hybrid queries. I have extensively compared this solution with state-of-the-art vector indexing approaches for various types of queries (point, range, and mixed), and the results show that it performs better than the alternatives
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