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6,264 results on '"KHAN, MOHAMMED"'

2. Finding Blind Spots in Evaluator LLMs with Interpretable Checklists

Author

Doddapaneni, Sumanth, Khan, Mohammed Safi Ur Rahman, Verma, Sshubam, and Khapra, Mitesh M.

Subjects
Computer Science - Computation and Language
Abstract

Large Language Models (LLMs) are increasingly relied upon to evaluate text outputs of other LLMs, thereby influencing leaderboards and development decisions. However, concerns persist over the accuracy of these assessments and the potential for misleading conclusions. In this work, we investigate the effectiveness of LLMs as evaluators for text generation tasks. We propose FBI, a novel framework designed to examine the proficiency of Evaluator LLMs in assessing four critical abilities in other LLMs: factual accuracy, instruction following, coherence in long-form writing, and reasoning proficiency. By introducing targeted perturbations in answers generated by LLMs, that clearly impact one of these key capabilities, we test whether an Evaluator LLM can detect these quality drops. By creating a total of 2400 perturbed answers covering 22 perturbation categories, we conduct a comprehensive study using different evaluation strategies on five prominent LLMs commonly used as evaluators in the literature. Our findings reveal significant shortcomings in current Evaluator LLMs, which failed to identify quality drops in over 50\% of cases on average. Single-answer and pairwise evaluations demonstrated notable limitations, whereas reference-based evaluations showed comparatively better performance. These results underscore the unreliable nature of current Evaluator LLMs and advocate for cautious implementation in practical applications. Code and data are available at https://github.com/AI4Bharat/FBI.

Published
2024

3. ALINA: Advanced Line Identification and Notation Algorithm

Author

Khan, Mohammed Abdul Hafeez, Ganeriwala, Parth, Bhattacharyya, Siddhartha, Neogi, Natasha, and Muthalagu, Raja

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Labels are the cornerstone of supervised machine learning algorithms. Most visual recognition methods are fully supervised, using bounding boxes or pixel-wise segmentations for object localization. Traditional labeling methods, such as crowd-sourcing, are prohibitive due to cost, data privacy, amount of time, and potential errors on large datasets. To address these issues, we propose a novel annotation framework, Advanced Line Identification and Notation Algorithm (ALINA), which can be used for labeling taxiway datasets that consist of different camera perspectives and variable weather attributes (sunny and cloudy). Additionally, the CIRCular threshoLd pixEl Discovery And Traversal (CIRCLEDAT) algorithm has been proposed, which is an integral step in determining the pixels corresponding to taxiway line markings. Once the pixels are identified, ALINA generates corresponding pixel coordinate annotations on the frame. Using this approach, 60,249 frames from the taxiway dataset, AssistTaxi have been labeled. To evaluate the performance, a context-based edge map (CBEM) set was generated manually based on edge features and connectivity. The detection rate after testing the annotated labels with the CBEM set was recorded as 98.45%, attesting its dependability and effectiveness., Comment: Paper has been accepted to The 3rd CVPR Workshop on Vision Datasets Understanding, 2024

Published
2024

4. Wobbling and Migrating Ferrofluid Droplets

Author

Aggarwal, Aaveg, Chen, Shih-Yuan, Kirkinis, Eleftherios, Khan, Mohammed Imran, Fan, Bei, Driscoll, Michelle M, and de la Cruz, Monica Olvera

Subjects
Physics - Fluid Dynamics, Condensed Matter - Soft Condensed Matter
Abstract

Active components incorporated in materials generate motion by inducing conformational changes in response to external fields. Magnetic fields are particularly interesting as they can actuate materials remotely. Millimeter-sized ferrofluid droplets placed on a solid surface, surrounded by an ambient gas phase, are shown here to migrate under a rotating magnetic field due to the periodic deformation of the liquid-gas interface. This interface wobbling leads to droplet migration with speeds that increase as the amplitude and frequency of the magnetic field increase. In addition to migrating in a controlled manner, we demonstrate the ability of magnetic droplets to clean surface impurities and transport cargo.

Published
2024

6. Overcoming LLM Challenges using RAG-Driven Precision in Coffee Leaf Disease Remediation

Author

S, Dr. Selva Kumar, Khan, Afifah Khan Mohammed Ajmal, Banday, Imadh Ajaz, Gada, Manikantha, and Shanbhag, Vibha Venkatesh

Subjects
Computer Science - Information Retrieval, Computer Science - Computation and Language
Abstract

This research introduces an innovative AI-driven precision agriculture system, leveraging YOLOv8 for disease identification and Retrieval Augmented Generation (RAG) for context-aware diagnosis. Focused on addressing the challenges of diseases affecting the coffee production sector in Karnataka, The system integrates sophisticated object detection techniques with language models to address the inherent constraints associated with Large Language Models (LLMs). Our methodology not only tackles the issue of hallucinations in LLMs, but also introduces dynamic disease identification and remediation strategies. Real-time monitoring, collaborative dataset expansion, and organizational involvement ensure the system's adaptability in diverse agricultural settings. The effect of the suggested system extends beyond automation, aiming to secure food supplies, protect livelihoods, and promote eco-friendly farming practices. By facilitating precise disease identification, the system contributes to sustainable and environmentally conscious agriculture, reducing reliance on pesticides. Looking to the future, the project envisions continuous development in RAG-integrated object detection systems, emphasizing scalability, reliability, and usability. This research strives to be a beacon for positive change in agriculture, aligning with global efforts toward sustainable and technologically enhanced food production., Comment: 6 pages, 3 figures

Published
2024

7. IoT-Driven Cloud-based Energy and Environment Monitoring System for Manufacturing Industry

Author

Saha, Nitol, Aulia, Md Masruk, Rahman, Md. Mostafizur, and Khan, Mohammed Shafiul Alam

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

This research focused on the development of a cost-effective IoT solution for energy and environment monitoring geared towards manufacturing industries. The proposed system is developed using open-source software that can be easily deployed in any manufacturing environment. The system collects real-time temperature, humidity, and energy data from different devices running on different communication such as TCP/IP, Modbus, etc., and the data is transferred wirelessly using an MQTT client to a database working as a cloud storage solution. The collected data is then visualized and analyzed using a website running on a host machine working as a web client.

Published
2024

8. IndicLLMSuite: A Blueprint for Creating Pre-training and Fine-Tuning Datasets for Indian Languages

Author

Khan, Mohammed Safi Ur Rahman, Mehta, Priyam, Sankar, Ananth, Kumaravelan, Umashankar, Doddapaneni, Sumanth, G, Suriyaprasaad, G, Varun Balan, Jain, Sparsh, Kunchukuttan, Anoop, Kumar, Pratyush, Dabre, Raj, and Khapra, Mitesh M.

Subjects
Computer Science - Computation and Language
Abstract

Despite the considerable advancements in English LLMs, the progress in building comparable models for other languages has been hindered due to the scarcity of tailored resources. Our work aims to bridge this divide by introducing an expansive suite of resources specifically designed for the development of Indic LLMs, covering 22 languages, containing a total of 251B tokens and 74.8M instruction-response pairs. Recognizing the importance of both data quality and quantity, our approach combines highly curated manually verified data, unverified yet valuable data, and synthetic data. We build a clean, open-source pipeline for curating pre-training data from diverse sources, including websites, PDFs, and videos, incorporating best practices for crawling, cleaning, flagging, and deduplication. For instruction-fine tuning, we amalgamate existing Indic datasets, translate/transliterate English datasets into Indian languages, and utilize LLaMa2 and Mixtral models to create conversations grounded in articles from Indian Wikipedia and Wikihow. Additionally, we address toxicity alignment by generating toxic prompts for multiple scenarios and then generate non-toxic responses by feeding these toxic prompts to an aligned LLaMa2 model. We hope that the datasets, tools, and resources released as a part of this work will not only propel the research and development of Indic LLMs but also establish an open-source blueprint for extending such efforts to other languages. The data and other artifacts created as part of this work are released with permissive licenses.

Published
2024

9. EVALUATING HEALTHCARE PERFORMANCE USING FUZZY LOGIC

Author

Khan, Mohammed Sadique, Mansour, M. A., Khadar, S. D., and Mallick, Z .

Subjects
healthcare service quality, fuzzy logic, prioritization, Industrial engineering. Management engineering, T55.4-60.8
Abstract

The basic determining elements of healthcare services are the patient’s satisfaction with the service provided by hospitals, which includes behavioural and sentimental aspects and the quality and efficiency of the hospitals themselves. Patients are sometimes very confused, and so express their views very vaguely. These imprecise responses of patients add to the complexity of evaluating quality. The involvement of multiple criteria, uncertainty, and qualitative factors significantly complicates the evaluation of the quality of a healthcare service. Fuzzy logic is a method by which indistinct or hazy responses can be taken up for quality analysis, such as the prioritisation of hospitals, departments, dimensions, etc. A pilot study was carried out in this study comprising 18 private hospitals with more than 100 beds that were selected in the twin city of Bhubaneswar and Cuttack in the state of Odisha, India. Nine quality dimensions were also selected from those used in the literature. A questionnaire survey was conducted in different departments of the hospitals using the nine dimensions. Patients’ responses on a five-point Likert scale were first analysed statistically. Then ranking the dimensions and the hospitals was carried out using fuzzy analysis. The results could be used by healthcare service providers continually to improve their organisation.

Published
2020
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12. The genomic and evolutionary landscapes of anaplastic thyroid carcinoma

Author

Zeng, Peter YF, Prokopec, Stephenie D, Lai, Stephen Y, Pinto, Nicole, Chan-Seng-Yue, Michelle A, Clifton-Bligh, Roderick, Williams, Michelle D, Howlett, Christopher J, Plantinga, Paul, Cecchini, Matthew J, Lam, Alfred K, Siddiqui, Iram, Wang, Jianxin, Sun, Ren X, Watson, John D, Korah, Reju, Carling, Tobias, Agrawal, Nishant, Cipriani, Nicole, Ball, Douglas, Nelkin, Barry, Rooper, Lisa M, Bishop, Justin A, Garnis, Cathie, Berean, Ken, Nicolson, Norman G, Weinberger, Paul, Henderson, Ying C, Lalansingh, Christopher M, Tian, Mao, Yamaguchi, Takafumi N, Livingstone, Julie, Salcedo, Adriana, Patel, Krupal, Vizeacoumar, Frederick, Datti, Alessandro, Xi, Liu, Nikiforov, Yuri E, Smallridge, Robert, Copland, John A, Marlow, Laura A, Hyrcza, Martin D, Delbridge, Leigh, Sidhu, Stan, Sywak, Mark, Robinson, Bruce, Fung, Kevin, Ghasemi, Farhad, Kwan, Keith, MacNeil, S Danielle, Mendez, Adrian, Palma, David A, Khan, Mohammed I, Shaikh, Mushfiq, Ruicci, Kara M, Wehrli, Bret, Winquist, Eric, Yoo, John, Mymryk, Joe S, Rocco, James W, Wheeler, David, Scherer, Steve, Giordano, Thomas J, Barrett, John W, Faquin, William C, Gill, Anthony J, Clayman, Gary, Boutros, Paul C, and Nichols, Anthony C

Subjects
Biological Sciences, Genetics, Human Genome, Cancer, Rare Diseases, Aetiology, 2.1 Biological and endogenous factors, Humans, Thyroid Carcinoma, Anaplastic, Thyroid Neoplasms, Mutation, Adenocarcinoma, Genomics, CP: Cancer, CP: Genomics, anaplastic thyroid cancer, cancer progression, genomics, tumour evolution, tumour heterogeneity, Biochemistry and Cell Biology, Medical Physiology, Biological sciences
Abstract

Anaplastic thyroid carcinoma is arguably the most lethal human malignancy. It often co-occurs with differentiated thyroid cancers, yet the molecular origins of its aggressivity are unknown. We sequenced tumor DNA from 329 regions of thyroid cancer, including 213 from patients with primary anaplastic thyroid carcinomas. We also whole genome sequenced 9 patients using multi-region sequencing of both differentiated and anaplastic thyroid cancer components. Using these data, we demonstrate thatanaplastic thyroid carcinomas have a higher burden of mutations than other thyroid cancers, with distinct mutational signatures and molecular subtypes. Further, different cancer driver genes are mutated in anaplastic and differentiated thyroid carcinomas, even those arising in a single patient. Finally, we unambiguously demonstrate that anaplastic thyroid carcinomas share a genomic origin with co-occurring differentiated carcinomas and emerge from a common malignant field through acquisition of characteristic clonal driver mutations.

Published
2024

13. Airavata: Introducing Hindi Instruction-tuned LLM

Author

Gala, Jay, Jayakumar, Thanmay, Husain, Jaavid Aktar, M, Aswanth Kumar, Khan, Mohammed Safi Ur Rahman, Kanojia, Diptesh, Puduppully, Ratish, Khapra, Mitesh M., Dabre, Raj, Murthy, Rudra, and Kunchukuttan, Anoop

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

We announce the initial release of "Airavata," an instruction-tuned LLM for Hindi. Airavata was created by fine-tuning OpenHathi with diverse, instruction-tuning Hindi datasets to make it better suited for assistive tasks. Along with the model, we also share the IndicInstruct dataset, which is a collection of diverse instruction-tuning datasets to enable further research for Indic LLMs. Additionally, we present evaluation benchmarks and a framework for assessing LLM performance across tasks in Hindi. Currently, Airavata supports Hindi, but we plan to expand this to all 22 scheduled Indic languages. You can access all artifacts at https://ai4bharat.github.io/airavata., Comment: Work in progress

Published
2024

14. Language Detection for Transliterated Content

Author

S, Selva Kumar, Khan, Afifah Khan Mohammed Ajmal, Manjeshwar, Chirag, and Banday, Imadh Ajaz

Subjects
Computer Science - Computation and Language, C.m, I.2
Abstract

In the contemporary digital era, the Internet functions as an unparalleled catalyst, dismantling geographical and linguistic barriers particularly evident in texting. This evolution facilitates global communication, transcending physical distances and fostering dynamic cultural exchange. A notable trend is the widespread use of transliteration, where the English alphabet is employed to convey messages in native languages, posing a unique challenge for language technology in accurately detecting the source language. This paper addresses this challenge through a dataset of phone text messages in Hindi and Russian transliterated into English utilizing BERT for language classification and Google Translate API for transliteration conversion. The research pioneers innovative approaches to identify and convert transliterated text, navigating challenges in the diverse linguistic landscape of digital communication. Emphasizing the pivotal role of comprehensive datasets for training Large Language Models LLMs like BERT, our model showcases exceptional proficiency in accurately identifying and classifying languages from transliterated text. With a validation accuracy of 99% our models robust performance underscores its reliability. The comprehensive exploration of transliteration dynamics supported by innovative approaches and cutting edge technologies like BERT, positions our research at the forefront of addressing unique challenges in the linguistic landscape of digital communication. Beyond contributing to language identification and transliteration capabilities this work holds promise for applications in content moderation, analytics and fostering a globally connected community engaged in meaningful dialogue., Comment: 4 Pages, 6 diagrams

Published
2024

24. Readiness and Perception of Pacific Students to Mobile Phones for Higher Education

Author

Reddy, Emmenual V., Reddy, Pritika, Sharma, Bibhya, Reddy, Karuna, and Khan, Mohammed G. M.

Abstract

The emergence and advancement of Information Communication Technologies have transformed facilitation and content delivery in higher education worldwide, the Pacific region being no exception. The extensive use of mobile phones in the Pacific, especially with the student-aged populace, is gradually creating a niche for mobile learning in the education landscape. However, there is a growing concern on the effectiveness of this innovative intervention keeping in mind the digital intelligence of Pacific students. This paper explores the readiness and perception of the Pacific students using mobile devices for learning. An online questionnaire was used to collect data for this exploratory research. While the results revealed the student's willingness and positive perception to leverage on mobile phones for learning in higher education, their readiness for the mobile-driven education is rather dependent on various factors which are explored in detail. The paper concludes with recommendations for the higher education institutes and education ministries in the Pacific region.

Published
2023
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25. A Low-Complexity Diversity-Preserving Universal Bit-Flipping Enhanced Hard Decision Decoder for Arbitrary Linear Codes

Author

Bere, Praveen Sai, Khan, Mohammed Zafar Ali, and Hanzo, Lajos

Subjects
Computer Science - Information Theory
Abstract

V2X (Vehicle-to-everything) communication relies on short messages for short-range transmissions over a fading wireless channel, yet requires high reliability and low latency. Hard-decision decoding sacrifices the preservation of diversity order, leading to pronounced performance degradation in fading channels. By contrast, soft-decision decoding retains diversity order, albeit at the cost of increased computational complexity. We introduce a novel enhanced hard-decision decoder termed as the Diversity Flip decoder (DFD) designed for preserving the diversity order. Moreover, it exhibits 'universal' applicability to all linear block codes. For a $\mathscr{C}(n,k)$ code having a minimum distance ${d_{\min}}$, the proposed decoder incurs a worst-case complexity order of $2^{({d_{\min}}-1)}-1$. Notably, for codes having low ${d_{\min}}$, this complexity represents a significant reduction compared to the popular soft and hard decision decoding algorithms. Due to its capability of maintaining diversity at a low complexity, it is eminently suitable for applications such as V2X (Vehicle-to-everything), IoT (Internet of Things), mMTC (Massive Machine type Communications), URLLC (Ultra-Reliable Low Latency Communications) and WBAN (Wireless Body Area Networks) for efficient decoding with favorable performance characteristics. The simulation results provided for various known codes and decoding algorithms validate the performance versus complexity benefits of the proposed decoder., Comment: Journal of 23 pages

Published
2023
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26. TextMI: Textualize Multimodal Information for Integrating Non-verbal Cues in Pre-trained Language Models

Author

Hasan, Md Kamrul, Islam, Md Saiful, Lee, Sangwu, Rahman, Wasifur, Naim, Iftekhar, Khan, Mohammed Ibrahim, and Hoque, Ehsan

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

Pre-trained large language models have recently achieved ground-breaking performance in a wide variety of language understanding tasks. However, the same model can not be applied to multimodal behavior understanding tasks (e.g., video sentiment/humor detection) unless non-verbal features (e.g., acoustic and visual) can be integrated with language. Jointly modeling multiple modalities significantly increases the model complexity, and makes the training process data-hungry. While an enormous amount of text data is available via the web, collecting large-scale multimodal behavioral video datasets is extremely expensive, both in terms of time and money. In this paper, we investigate whether large language models alone can successfully incorporate non-verbal information when they are presented in textual form. We present a way to convert the acoustic and visual information into corresponding textual descriptions and concatenate them with the spoken text. We feed this augmented input to a pre-trained BERT model and fine-tune it on three downstream multimodal tasks: sentiment, humor, and sarcasm detection. Our approach, TextMI, significantly reduces model complexity, adds interpretability to the model's decision, and can be applied for a diverse set of tasks while achieving superior (multimodal sarcasm detection) or near SOTA (multimodal sentiment analysis and multimodal humor detection) performance. We propose TextMI as a general, competitive baseline for multimodal behavioral analysis tasks, particularly in a low-resource setting.

Published
2023

27. Ternary oxides of $\textit{s}$- and $\textit{p}$-block metals for photocatalytic solar-to-hydrogen conversion

Author

Gelin, Simon, Kirchner-Hall, Nicole E., Katzbaer, Rowan R., Theibault, Monica J., Xiong, Yihuang, Zhao, Wayne, Khan, Mohammed M., Andrewlavage, Eric, Orbe, Paul, Baksa, Steven M., Cococcioni, Matteo, Timrov, Iurii, Campbell, Quinn, Abruña, Héctor, Schaak, Raymond E., and Dabo, Ismaila

Subjects
Condensed Matter - Materials Science
Abstract

Oxides containing metals or metalloids from the {\it p}-block of the periodic table ({\it e.g.}, In, Sn, Sb, Pb, Bi) are of technological interest as transparent conductors and light absorbers for solar energy conversion due to the tunability of their electronic conductivity and optical absorption. Comparatively, these oxides have found limited applications in hydrogen photoelectrolysis primarily due to their high electronegativity, which impedes electron transfer for reducing protons into hydrogen. We have shown recently that inserting {\it s}-block cations into {\it p}-block metal oxides is effective at lowering electronegativities while affording further control of band gaps. Here, we explain the origins of this dual tunability by demonstrating the mediator role of {\it s}-block cations in modulating orbital hybridization while not contributing to frontier electronic states. From this result, we carry out a comprehensive computational study of 109 ternary oxides of {\it s}- and {\it p}-block metal elements as candidate photocatalysts for solar hydrogen generation. We downselect the most desirable materials using band gaps and band edges obtained from Hubbard-corrected density-functional theory with Hubbard parameters computed entirely from first principles, evaluate the stability of these oxides in aqueous conditions, and characterize experimentally four of the remaining materials, synthesized with high phase uniformity, to validate and further develop the computational models. We thus propose nine oxide semiconductors, including CsIn$_3$O$_5$, Sr$_2$In$_2$O$_5$, and KSbO$_2$ which, to the extent of our literature review, have not been previously considered as water-splitting photocatalysts., Comment: 14 pages, 5 figures, 1 supplemental material

Published
2023

28. Total RNA sequencing reveals gene expression and microbial alterations shared by oral pre-malignant lesions and cancer.

Author

Khan, Mohammed, Frustino, Jennifer, Nguyen, Bach-Cuc, Woo, Sook-Bin, Johnson, William, Varelas, Xaralabos, Kukuruzinska, Maria, Monti, Stefano, and Villa, Alessandro

Subjects
Early detection, Oral cancer, Oral microbiota, Pre-malignant lesions, Humans, Mouth Neoplasms, Carcinoma, Squamous Cell, Precancerous Conditions, Transcriptome, Sequence Analysis, RNA
Abstract

Head and neck cancers are a complex malignancy comprising multiple anatomical sites, with cancer of the oral cavity ranking among the deadliest and the most disfiguring cancers globally. Oral cancer (OC) constitutes a subset of head and neck cancer cases, presenting primarily as tobacco- and alcohol-associated oral squamous cell carcinoma (OSCC), with a 5-year survival rate of ~ 65%, partly due to the lack of early detection and effective treatments. OSCC arises from premalignant lesions (PMLs) in the oral cavity through a multi-step series of clinical and histopathological stages, including varying degrees of epithelial dysplasia. To gain insights into the molecular mechanisms associated with the progression of PMLs to OSCC, we profiled the whole transcriptome of 66 human PMLs comprising leukoplakia with dysplasia and hyperkeratosis non-reactive (HkNR) pathologies, alongside healthy controls and OSCC. Our data revealed that PMLs were enriched in gene signatures associated with cellular plasticity, such as partial EMT (p-EMT) phenotypes, and with immune response. Integrated analyses of the host transcriptome and microbiome further highlighted a significant association between differential microbial abundance and PML pathway activity, suggesting a contribution of the oral microbiome toward PML evolution to OSCC. Collectively, this study reveals molecular processes associated with PML progression that may help early diagnosis and disease interception at an early stage.

Published
2023

30. Understanding the Redox Mechanism of Layered Transition Metal Oxide During Electrochemical Cycling in Sodium-Ion Batteries

Author

Bhardwaj, Nikita, Khan, Mohammed Saquib, Jhankal, Deependra, Choudhary, Deepika, Preeti, Kushwaha, Himmat Singh, Sachdev, Kanupriya, Ghosh, Arindam, Series Editor, Chua, Daniel, Series Editor, de Souza, Flavio Leandro, Series Editor, Aktas, Oral Cenk, Series Editor, Han, Yafang, Series Editor, Gong, Jianghong, Series Editor, Jawaid, Mohammad, Series Editor, Khan, Zishan Husain, editor, Jackson, Mark, editor, and Salah, Numan A., editor

Published
2024
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31. Integrated global assessment of the natural forest carbon potential

Author

Mo, Lidong, Zohner, Constantin M., Reich, Peter B., Liang, Jingjing, de Miguel, Sergio, Nabuurs, Gert-Jan, Renner, Susanne S., van den Hoogen, Johan, Araza, Arnan, Herold, Martin, Mirzagholi, Leila, Ma, Haozhi, Averill, Colin, Phillips, Oliver L., Gamarra, Javier G. P., Hordijk, Iris, Routh, Devin, Abegg, Meinrad, Adou Yao, Yves C., Alberti, Giorgio, Almeyda Zambrano, Angelica M., Alvarado, Braulio Vilchez, Alvarez-Dávila, Esteban, Alvarez-Loayza, Patricia, Alves, Luciana F., Amaral, Iêda, Ammer, Christian, Antón-Fernández, Clara, Araujo-Murakami, Alejandro, Arroyo, Luzmila, Avitabile, Valerio, Aymard, Gerardo A., Baker, Timothy R., Bałazy, Radomir, Banki, Olaf, Barroso, Jorcely G., Bastian, Meredith L., Bastin, Jean-Francois, Birigazzi, Luca, Birnbaum, Philippe, Bitariho, Robert, Boeckx, Pascal, Bongers, Frans, Bouriaud, Olivier, Brancalion, Pedro H. S., Brandl, Susanne, Brearley, Francis Q., Brienen, Roel, Broadbent, Eben N., Bruelheide, Helge, Bussotti, Filippo, Cazzolla Gatti, Roberto, César, Ricardo G., Cesljar, Goran, Chazdon, Robin L., Chen, Han Y. H., Chisholm, Chelsea, Cho, Hyunkook, Cienciala, Emil, Clark, Connie, Clark, David, Colletta, Gabriel D., Coomes, David A., Cornejo Valverde, Fernando, Corral-Rivas, José J., Crim, Philip M., Cumming, Jonathan R., Dayanandan, Selvadurai, de Gasper, André L., Decuyper, Mathieu, Derroire, Géraldine, DeVries, Ben, Djordjevic, Ilija, Dolezal, Jiri, Dourdain, Aurélie, Engone Obiang, Nestor Laurier, Enquist, Brian J., Eyre, Teresa J., Fandohan, Adandé Belarmain, Fayle, Tom M., Feldpausch, Ted R., Ferreira, Leandro V., Finér, Leena, Fischer, Markus, Fletcher, Christine, Frizzera, Lorenzo, Gianelle, Damiano, Glick, Henry B., Harris, David J., Hector, Andrew, Hemp, Andreas, Hengeveld, Geerten, Hérault, Bruno, Herbohn, John L., Hillers, Annika, Honorio Coronado, Eurídice N., Hui, Cang, Ibanez, Thomas, Imai, Nobuo, Jagodziński, Andrzej M., Jaroszewicz, Bogdan, Johannsen, Vivian Kvist, Joly, Carlos A., Jucker, Tommaso, Jung, Ilbin, Karminov, Viktor, Kartawinata, Kuswata, Kearsley, Elizabeth, Kenfack, David, Kennard, Deborah K., Kepfer-Rojas, Sebastian, Keppel, Gunnar, Khan, Mohammed Latif, Killeen, Timothy J., Kim, Hyun Seok, Kitayama, Kanehiro, Köhl, Michael, Korjus, Henn, Kraxner, Florian, Kucher, Dmitry, Laarmann, Diana, Lang, Mait, Lu, Huicui, Lukina, Natalia V., Maitner, Brian S., Malhi, Yadvinder, Marcon, Eric, Marimon, Beatriz Schwantes, Marimon-Junior, Ben Hur, Marshall, Andrew R., Martin, Emanuel H., Meave, Jorge A., Melo-Cruz, Omar, Mendoza, Casimiro, Mendoza-Polo, Irina, Miscicki, Stanislaw, Merow, Cory, Monteagudo Mendoza, Abel, Moreno, Vanessa S., Mukul, Sharif A., Mundhenk, Philip, Nava-Miranda, María Guadalupe, Neill, David, Neldner, Victor J., Nevenic, Radovan V., Ngugi, Michael R., Niklaus, Pascal A., Oleksyn, Jacek, Ontikov, Petr, Ortiz-Malavasi, Edgar, Pan, Yude, Paquette, Alain, Parada-Gutierrez, Alexander, Parfenova, Elena I., Park, Minjee, Parren, Marc, Parthasarathy, Narayanaswamy, Peri, Pablo L., Pfautsch, Sebastian, Picard, Nicolas, Piedade, Maria Teresa F., Piotto, Daniel, Pitman, Nigel C. A., Poulsen, Axel Dalberg, Poulsen, John R., Pretzsch, Hans, Ramirez Arevalo, Freddy, Restrepo-Correa, Zorayda, Rodeghiero, Mirco, Rolim, Samir G., Roopsind, Anand, Rovero, Francesco, Rutishauser, Ervan, Saikia, Purabi, Salas-Eljatib, Christian, Saner, Philippe, Schall, Peter, Schelhaas, Mart-Jan, Schepaschenko, Dmitry, Scherer-Lorenzen, Michael, Schmid, Bernhard, Schöngart, Jochen, Searle, Eric B., Seben, Vladimír, Serra-Diaz, Josep M., Sheil, Douglas, Shvidenko, Anatoly Z., Silva-Espejo, Javier E., Silveira, Marcos, Singh, James, Sist, Plinio, Slik, Ferry, Sonké, Bonaventure, Souza, Alexandre F., Stereńczak, Krzysztof J., Svenning, Jens-Christian, Svoboda, Miroslav, Swanepoel, Ben, Targhetta, Natalia, Tchebakova, Nadja, ter Steege, Hans, Thomas, Raquel, Tikhonova, Elena, Umunay, Peter M., Usoltsev, Vladimir A., Valencia, Renato, Valladares, Fernando, van der Plas, Fons, Van Do, Tran, van Nuland, Michael E., Vasquez, Rodolfo M., Verbeeck, Hans, Viana, Helder, Vibrans, Alexander C., Vieira, Simone, von Gadow, Klaus, Wang, Hua-Feng, Watson, James V., Werner, Gijsbert D. A., Wiser, Susan K., Wittmann, Florian, Woell, Hannsjoerg, Wortel, Verginia, Zagt, Roderik, Zawiła-Niedźwiecki, Tomasz, Zhang, Chunyu, Zhao, Xiuhai, Zhou, Mo, Zhu, Zhi-Xin, Zo-Bi, Irie C., Gann, George D., and Crowther, Thomas W.

Published
2023
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34. Sensing the Environment with 5G Scattered Signals (5G-CommSense): A Feasibility Analysis

Author

Jana, Sandip, Mishra, Amit Kumar, and Khan, Mohammed Zafar Ali

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

By making use of the sensors and AI (SensAI) algorithms for a specialized task, Application Specific INstrumentation (ASIN) framework uses less computational overhead and gives a good performance. This work evaluates the feasibility of the ASIN framework dependent Communication based Sensing (CommSense) system using 5th Generation New Radio (5G NR) infrastructure. Since our proposed system is backed up by 5G NR infra, this system is termed as 5G-CommSense. In this paper, we have used NR channel models specified by the 3rd Generation Partnership Project (3GPP) and added white Gaussian noise (AWGN) to vary the signal to noise ratio at the receiver. Finally, from our simulation result, we conclude that the proposed system is practically feasible., Comment: 3 pages, Accepted in conference

Published
2023

35. Effectiveness of robot-based pivotal response treatment for children with autism – A systematic review

Author

Minhajzafar Nasirabadi, Arman Mehrukh, Mazher Ali, Khan Mohammed Zeeshan Ali, and Arifuddin Ahmed

Subjects
autism, pivotal response therapy, robot-based pivotal response therapy, Psychiatry, RC435-571
Abstract

Introduction: Autism spectrum disorder has shown rising incidence and increasing cost of healthcare over the past few years. Robot-based interventions are an attempt to overcome the shortage of trained therapists for children with autism. Materials and Methods: This systematic review aims at examining the efficacy of robot-based pivotal response treatment (PRT) in the treatment of children with autism. It aimed to answer the following questions – which robots are used to administer PRT, which aspects of PRT can be targeted by robots, and what factors influence outcome in robot-based PRT. Peer-reviewed clinical trials and randomized controlled trials from the last 10 years were included. Results: The social robot NAO was the most commonly used intervention. Joint attention was the most commonly studied aspect. Outcome depended on the design of the intervention and the baseline functioning of the child with autism. Conclusion: The review found that there are modest gains in robot-based PRT and that it can be beneficial in providing therapy to children with autism.

Published
2024
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37. Performance enhancement in hydroponic and soil compound prediction by deep learning techniques

Author

Mustufa Haider Abidi, Sanjay Chintakindi, Ateekh Ur Rehman, and Muneer Khan Mohammed

Subjects
Hydroponic and soil compound prediction, Plant growth, Weighted features, Iteration-assisted enhanced mother optimization algorithm, Multi-scale feature fusion-based convolution autoencoder with gated recurrent unit, Electronic computers. Computer science, QA75.5-76.95
Abstract

The soil quality plays a crucial role in providing essential nutrients for crop growth and ensuring a bountiful yield. Identifying the soil composition, which includes sand, silt particles, and the mixture of clay in specific proportions, is vital for making informed decisions about crop selection and managing weed growth. Furthermore, soil pollution from emerging contaminants presents a substantial risk to water resource management and food production. Developing numerical models to comprehensively describe the transport and reactions of chemicals within both the plants and soil is of utmost importance in crafting effective mitigation strategies. To address the limitations of traditional models, this paper devises an innovative approach that leverages deep learning to predict hydroponic and soil compound dynamics during plant growth. This method not only enhances the understanding of how plants interact with their environment but also aids in making more informed decisions about agriculture, ultimately contributing to more sustainable and efficient crop production. The data needed to perform the developed hydroponic and soil compound prediction model is acquired from online resources. After that, this data is forwarded to the feature extraction phase. The weighted features, deep belief network (DBN) features, and the original features are achieved in the feature extraction stage. To get the weighted features, the weights are optimally obtained using the Iteration-assisted Enhanced Mother Optimization Algorithm (IEMOA). Subsequently, these extracted features are fed into the Multi-Scale feature fusion-based Convolution Autoencoder with a Gated Recurrent Unit (MS-CAGRU) network for hydroponic and soil compound prediction. Thus, the hydroponic and soil compound prediction data is attained in the end. Finally, the performance evaluation of the suggested work is conducted and contrasted with numerous conventional models to showcase the system’s efficacy.

Published
2024
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39. Global patterns and environmental drivers of forest functional composition

Author

Bouchard, Elise, Searle, Eric B, Drapeau, Pierre, Liang, Jingjing, Gamarra, Javier GP, Abegg, Meinrad, Alberti, Giorgio, Zambrano, Angelica Almeyda, Alvarez‐Davila, Esteban, Alves, Luciana F, Avitabile, Valerio, Aymard, Gerardo, Bastin, Jean‐François, Birnbaum, Philippe, Bongers, Frans, Bouriaud, Olivier, Brancalion, Pedro, Broadbent, Eben, Bussotti, Filippo, Gatti, Roberto Cazzolla, Češljar, Goran, Chisholm, Chelsea, Cienciala, Emil, Clark, Connie J, Corral‐Rivas, José Javier, Crowther, Thomas W, Dayanandan, Selvadurai, Decuyper, Mathieu, Gasper, André L, de‐Miguel, Sergio, Derroire, Géraldine, DeVries, Ben, Djordjević, Ilija, Van Do, Tran, Dolezal, Jiri, Fayle, Tom M, Fridman, Jonas, Frizzera, Lorenzo, Gianelle, Damiano, Hemp, Andreas, Hérault, Bruno, Herold, Martin, Imai, Nobuo, Jagodziński, Andrzej M, Jaroszewicz, Bogdan, Jucker, Tommaso, Kepfer‐Rojas, Sebastian, Keppel, Gunnar, Khan, Mohammed Latif, Kim, Hyun Seok, Korjus, Henn, Kraxner, Florian, Laarmann, Diana, Lewis, Simon, Lu, Huicui, Maitner, Brian S, Marcon, Eric, Marshall, Andrew R, Mukul, Sharif A, Nabuurs, Gert‐Jan, Nava‐Miranda, María Guadalupe, Parfenova, Elena I, Park, Minjee, Peri, Pablo L, Pfautsch, Sebastian, Phillips, Oliver L, Piedade, Maria Teresa F, Piotto, Daniel, Poulsen, John R, Poulsen, Axel Dalberg, Pretzsch, Hans, Reich, Peter B, Rodeghiero, Mirco, Rolim, Samir, Rovero, Francesco, Saikia, Purabi, Salas‐Eljatib, Christian, Schall, Peter, Schepaschenko, Dmitry, Schöngart, Jochen, Šebeň, Vladimír, Sist, Plinio, Slik, Ferry, Souza, Alexandre F, Stereńczak, Krzysztof, Svoboda, Miroslav, Tchebakova, Nadezhda M, Steege, Hans, Tikhonova, Elena V, Usoltsev, Vladimir A, Valladares, Fernando, Viana, Helder, Vibrans, Alexander C, Wang, Hua‐Feng, Westerlund, Bertil, Wiser, Susan K, Wittmann, Florian, Wortel, Verginia, Zawiła‐Niedźwiecki, Tomasz, and Zhou, Mo

Subjects
Environmental Sciences, Biological Sciences, Ecology, biogeography, climate, environmental gradients, functional traits, seed mass, species abundance, specific leaf area, trees, wood density
Abstract

Abstract: Aim: To determine the relationships between the functional trait composition of forest communities and environmental gradients across scales and biomes and the role of species relative abundances in these relationships. Location: Global. Time period: Recent. Major taxa studied: Trees. Methods: We integrated species abundance records from worldwide forest inventories and associated functional traits (wood density, specific leaf area and seed mass) to obtain a data set of 99,953 to 149,285 plots (depending on the trait) spanning all forested continents. We computed community‐weighted and unweighted means of trait values for each plot and related them to three broad environmental gradients and their interactions (energy availability, precipitation and soil properties) at two scales (global and biomes). Results: Our models explained up to 60% of the variance in trait distribution. At global scale, the energy gradient had the strongest influence on traits. However, within‐biome models revealed different relationships among biomes. Notably, the functional composition of tropical forests was more influenced by precipitation and soil properties than energy availability, whereas temperate forests showed the opposite pattern. Depending on the trait studied, response to gradients was more variable and proportionally weaker in boreal forests. Community unweighted means were better predicted than weighted means for almost all models. Main conclusions: Worldwide, trees require a large amount of energy (following latitude) to produce dense wood and seeds, while leaves with large surface to weight ratios are concentrated in temperate forests. However, patterns of functional composition within‐biome differ from global patterns due to biome specificities such as the presence of conifers or unique combinations of climatic and soil properties. We recommend assessing the sensitivity of tree functional traits to environmental changes in their geographic context. Furthermore, at a given site, the distribution of tree functional traits appears to be driven more by species presence than species abundance.

Published
2023

40. Immune-based classification of HPV-associated oropharyngeal cancer with implications for biomarker-driven treatment de-intensification

Author

Zeng, Peter YF, Cecchini, Matthew J, Barrett, John W, Shammas-Toma, Matthew, De Cecco, Loris, Serafini, Mara S, Cavalieri, Stefano, Licitra, Lisa, Hoebers, Frank, Brakenhoff, Ruud H, Leemans, C René, Scheckenbach, Kathrin, Poli, Tito, Wang, Xiaowei, Liu, Xinyi, Laxague, Francisco, Prisman, Eitan, Poh, Catherine, Bose, Pinaki, Dort, Joseph C, Shaikh, Mushfiq H, Ryan, Sarah EB, Dawson, Alice, Khan, Mohammed I, Howlett, Christopher J, Stecho, William, Plantinga, Paul, da Silva, Sabrina Daniela, Hier, Michael, Khan, Halema, MacNeil, Danielle, Mendez, Adrian, Yoo, John, Fung, Kevin, Lang, Pencilla, Winquist, Eric, Palma, David A, Ziai, Hedyeh, Amelio, Antonio L, Li, Shawn S-C, Boutros, Paul C, Mymryk, Joe S, and Nichols, Anthony C

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Immunology, Cancer, Prevention, Infectious Diseases, Precision Medicine, Radiation Oncology, Digestive Diseases, Sexually Transmitted Infections, Dental/Oral and Craniofacial Disease, Good Health and Well Being, Humans, Papillomavirus Infections, Retrospective Studies, Neoplasm Recurrence, Local, Oropharyngeal Neoplasms, Squamous Cell Carcinoma of Head and Neck, Prognosis, Biomarkers, Human Papillomavirus Viruses, Head and Neck Neoplasms, Papillomaviridae, Head and neck squamous cell carcinoma, Transcriptomics, Cancer immunology, HPV, De-escalation, Clinical Sciences, Public Health and Health Services, Clinical sciences, Epidemiology
Abstract

BackgroundThere is significant interest in treatment de-escalation for human papillomavirus-associated (HPV+) oropharyngeal squamous cell carcinoma (OPSCC) patients given the generally favourable prognosis. However, 15-30% of patients recur after primary treatment, reflecting a need for improved risk-stratification tools. We sought to develop a molecular test to risk stratify HPV+ OPSCC patients.MethodsWe created an immune score (UWO3) associated with survival outcomes in six independent cohorts comprising 906 patients, including blinded retrospective and prospective external validations. Two aggressive radiation de-escalation cohorts were used to assess the ability of UWO3 to identify patients who recur. Multivariate Cox models were used to assess the associations between the UWO3 immune class and outcomes.FindingsA three-gene immune score classified patients into three immune classes (immune rich, mixed, or immune desert) and was strongly associated with disease-free survival in six datasets, including large retrospective and prospective datasets. Pooled analysis demonstrated that the immune rich group had superior disease-free survival compared to the immune desert (HR = 9.0, 95% CI: 3.2-25.5, P = 3.6 × 10-5) and mixed (HR = 6.4, 95% CI: 2.2-18.7, P = 0.006) groups after adjusting for age, sex, smoking status, and AJCC8 clinical stage. Finally, UWO3 was able to identify patients from two small treatment de-escalation cohorts who remain disease-free after aggressive de-escalation to 30 Gy radiation.InterpretationWith additional prospective validation, the UWO3 score could enable biomarker-driven clinical decision-making for patients with HPV+ OPSCC based on robust outcome prediction across six independent cohorts. Prospective de-escalation and intensification clinical trials are currently being planned.FundingCIHR, European Union, and the NIH.

Published
2022

41. Introduction and expression of PIK3CAE545K in a papillary thyroid cancer BRAFV600E cell line leads to a dedifferentiated aggressive phenotype

Author

Pinto, Nicole, Ruicci, Kara M, Khan, Mohammed Imran, Shaikh, Mushfiq Hassan, Zeng, Yu Fan Peter, Yoo, John, Fung, Kevin, MacNeil, S Danielle, Mendez, Adrian, Mymryk, Joe S, Barrett, John W, Boutros, Paul C, and Nichols, Anthony C

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Rare Diseases, Cancer, Aetiology, 2.1 Biological and endogenous factors, Animals, Cell Line, Cell Line, Tumor, Class I Phosphatidylinositol 3-Kinases, Humans, Mice, Mice, Nude, Mutation, Phenotype, Proto-Oncogene Proteins B-raf, Thyroid Cancer, Papillary, Thyroid Neoplasms, Papillary thyroid cancer, Anaplastic thyroid cancer, Disease progression, Dedifferentiation, Otorhinolaryngology, Clinical sciences
Abstract

Anaplastic thyroid cancer (ATC) is a rare, aggressive form of undifferentiated thyroid cancer, which exhibits rapid progression and is almost universally fatal. At least a subset of ATC is thought to arise from pre-existing well-differentiated thyroid cancer, most frequently papillary thyroid cancer (PTC). While PIK3CA mutations are rare in PTC, they are common in ATC and tend to co-occur with BRAF mutations. This provided the rationale for our study to identify the potential role of PIK3CA mutations in the progression from well-differentiated to undifferentiated thyroid cancer. We introduced PIK3CAE545K into the LAM1 PTC cell line, which carries a BRAFV600E mutation. In culture, the engineered cell line (LAM1:PIK3CAE545K) proliferated faster and demonstrated increased clonogenic potential relative to the parental line carrying an empty vector (LAM1EV). Both the LAM1EV and LAM1:PIK3CAE545K edited lines were implanted into hind flanks of athymic nude mice for in vivo determination of disease progression. While tumour weights and volumes were not significantly higher in LAM1:PIK3CAE545K mice, there was a decrease in expression of thyroid differentiation markers TTF-1, thyroglobulin, PAX8 and B-catenin, suggesting that introduction of PIK3CAE545K led to dedifferentiation in vivo. Collectively, this study provides evidence of a role for PIK3CAE545K in driving disease progression from a well-differentiated to an undifferentiated thyroid cancer; however, over-expression was not a determinant of an accelerated growth phenotype in ATC.

Published
2022

42. The global biogeography of tree leaf form and habit

Author

Ma, Haozhi, Crowther, Thomas W., Mo, Lidong, Maynard, Daniel S., Renner, Susanne S., van den Hoogen, Johan, Zou, Yibiao, Liang, Jingjing, de-Miguel, Sergio, Nabuurs, Gert-Jan, Reich, Peter B., Niinemets, Ülo, Abegg, Meinrad, Adou Yao, Yves C., Alberti, Giorgio, Almeyda Zambrano, Angelica M., Alvarado, Braulio Vilchez, Alvarez-Dávila, Esteban, Alvarez-Loayza, Patricia, Alves, Luciana F., Ammer, Christian, Antón-Fernández, Clara, Araujo-Murakami, Alejandro, Arroyo, Luzmila, Avitabile, Valerio, Aymard, Gerardo A., Baker, Timothy R., Bałazy, Radomir, Banki, Olaf, Barroso, Jorcely G., Bastian, Meredith L., Bastin, Jean-Francois, Birigazzi, Luca, Birnbaum, Philippe, Bitariho, Robert, Boeckx, Pascal, Bongers, Frans, Bouriaud, Olivier, Brancalion, Pedro H. S., Brandl, Susanne, Brearley, Francis Q., Brienen, Roel, Broadbent, Eben N., Bruelheide, Helge, Bussotti, Filippo, Cazzolla Gatti, Roberto, César, Ricardo G., Cesljar, Goran, Chazdon, Robin, Chen, Han Y. H., Chisholm, Chelsea, Cho, Hyunkook, Cienciala, Emil, Clark, Connie, Clark, David, Colletta, Gabriel D., Coomes, David A., Valverde, Fernando Cornejo, Corral-Rivas, José J., Crim, Philip M., Cumming, Jonathan R., Dayanandan, Selvadurai, de Gasper, André L., Decuyper, Mathieu, Derroire, Géraldine, DeVries, Ben, Djordjevic, Ilija, Dolezal, Jiri, Dourdain, Aurélie, Engone Obiang, Nestor Laurier, Enquist, Brian J., Eyre, Teresa J., Fandohan, Adandé Belarmain, Fayle, Tom M., Feldpausch, Ted R., Ferreira, Leandro V., Finér, Leena, Fischer, Markus, Fletcher, Christine, Fridman, Jonas, Frizzera, Lorenzo, Gamarra, Javier G. P., Gianelle, Damiano, Glick, Henry B., Harris, David J., Hector, Andrew, Hemp, Andreas, Hengeveld, Geerten, Hérault, Bruno, Herbohn, John L., Herold, Martin, Hillers, Annika, Honorio Coronado, Eurídice N., Hui, Cang, Ibanez, Thomas T., Amaral, Iêda, Imai, Nobuo, Jagodziński, Andrzej M., Jaroszewicz, Bogdan, Johannsen, Vivian Kvist, Joly, Carlos A., Jucker, Tommaso, Jung, Ilbin, Karminov, Viktor, Kartawinata, Kuswata, Kearsley, Elizabeth, Kenfack, David, Kennard, Deborah K., Kepfer-Rojas, Sebastian, Keppel, Gunnar, Khan, Mohammed Latif, Killeen, Timothy J., Kim, Hyun Seok, Kitayama, Kanehiro, Köhl, Michael, Korjus, Henn, Kraxner, Florian, Kucher, Dmitry, Laarmann, Diana, Lang, Mait, Lewis, Simon L., Lu, Huicui, Lukina, Natalia V., Maitner, Brian S., Malhi, Yadvinder, Marcon, Eric, Marimon, Beatriz Schwantes, Marimon-Junior, Ben Hur, Marshall, Andrew R., Martin, Emanuel H., Meave, Jorge A., Melo-Cruz, Omar, Mendoza, Casimiro, Merow, Cory, Monteagudo Mendoza, Abel, Moreno, Vanessa S., Mukul, Sharif A., Mundhenk, Philip, Nava-Miranda, María Guadalupe, Neill, David, Neldner, Victor J., Nevenic, Radovan V., Ngugi, Michael R., Niklaus, Pascal A., Oleksyn, Jacek, Ontikov, Petr, Ortiz-Malavasi, Edgar, Pan, Yude, Paquette, Alain, Parada-Gutierrez, Alexander, Parfenova, Elena I., Park, Minjee, Parren, Marc, Parthasarathy, Narayanaswamy, Peri, Pablo L., Pfautsch, Sebastian, Phillips, Oliver L., Picard, Nicolas, Piedade, Maria Teresa F., Piotto, Daniel, Pitman, Nigel C. A., Mendoza-Polo, Irina, Poulsen, Axel D., Poulsen, John R., Pretzsch, Hans, Ramirez Arevalo, Freddy, Restrepo-Correa, Zorayda, Rodeghiero, Mirco, Rolim, Samir G., Roopsind, Anand, Rovero, Francesco, Rutishauser, Ervan, Saikia, Purabi, Salas-Eljatib, Christian, Saner, Philippe, Schall, Peter, Schelhaas, Mart-Jan, Schepaschenko, Dmitry, Scherer-Lorenzen, Michael, Schmid, Bernhard, Schöngart, Jochen, Searle, Eric B., Seben, Vladimír, Serra-Diaz, Josep M., Sheil, Douglas, Shvidenko, Anatoly Z., Silva-Espejo, Javier E., Silveira, Marcos, Singh, James, Sist, Plinio, Slik, Ferry, Sonké, Bonaventure, Souza, Alexandre F., Miścicki, Stanislaw, Stereńczak, Krzysztof J., Svenning, Jens-Christian, Svoboda, Miroslav, Swanepoel, Ben, Targhetta, Natalia, Tchebakova, Nadja, ter Steege, Hans, Thomas, Raquel, Tikhonova, Elena, Umunay, Peter M., Usoltsev, Vladimir A., Valencia, Renato, Valladares, Fernando, van der Plas, Fons, Van Do, Tran, van Nuland, Michael E., Vasquez, Rodolfo M., Verbeeck, Hans, Viana, Helder, Vibrans, Alexander C., Vieira, Simone, von Gadow, Klaus, Wang, Hua-Feng, Watson, James V., Werner, Gijsbert D. A., Westerlund, Bertil, Wiser, Susan K., Wittmann, Florian, Woell, Hannsjoerg, Wortel, Verginia, Zagt, Roderick, Zawiła-Niedźwiecki, Tomasz, Zhang, Chunyu, Zhao, Xiuhai, Zhou, Mo, Zhu, Zhi-Xin, Zo-Bi, Irie C., and Zohner, Constantin M.

Published
2023
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43. Native diversity buffers against severity of non-native tree invasions

Author

Delavaux, Camille S., Crowther, Thomas W., Zohner, Constantin M., Robmann, Niamh M., Lauber, Thomas, van den Hoogen, Johan, Kuebbing, Sara, Liang, Jingjing, de-Miguel, Sergio, Nabuurs, Gert-Jan, Reich, Peter B., Abegg, Meinrad, Adou Yao, Yves C., Alberti, Giorgio, Almeyda Zambrano, Angelica M., Alvarado, Braulio Vilchez, Alvarez-Dávila, Esteban, Alvarez-Loayza, Patricia, Alves, Luciana F., Ammer, Christian, Antón-Fernández, Clara, Araujo-Murakami, Alejandro, Arroyo, Luzmila, Avitabile, Valerio, Aymard, Gerardo A., Baker, Timothy R., Bałazy, Radomir, Banki, Olaf, Barroso, Jorcely G., Bastian, Meredith L., Bastin, Jean-Francois, Birigazzi, Luca, Birnbaum, Philippe, Bitariho, Robert, Boeckx, Pascal, Bongers, Frans, Bouriaud, Olivier, Brancalion, Pedro H. S., Brandl, Susanne, Brienen, Roel, Broadbent, Eben N., Bruelheide, Helge, Bussotti, Filippo, Gatti, Roberto Cazzolla, César, Ricardo G., Cesljar, Goran, Chazdon, Robin, Chen, Han Y. H., Chisholm, Chelsea, Cho, Hyunkook, Cienciala, Emil, Clark, Connie, Clark, David, Colletta, Gabriel D., Coomes, David A., Cornejo Valverde, Fernando, Corral-Rivas, José J., Crim, Philip M., Cumming, Jonathan R., Dayanandan, Selvadurai, de Gasper, André L., Decuyper, Mathieu, Derroire, Géraldine, DeVries, Ben, Djordjevic, Ilija, Dolezal, Jiri, Dourdain, Aurélie, Engone Obiang, Nestor Laurier, Enquist, Brian J., Eyre, Teresa J., Fandohan, Adandé Belarmain, Fayle, Tom M., Feldpausch, Ted R., Ferreira, Leandro V., Fischer, Markus, Fletcher, Christine, Frizzera, Lorenzo, Gamarra, Javier G. P., Gianelle, Damiano, Glick, Henry B., Harris, David J., Hector, Andrew, Hemp, Andreas, Hengeveld, Geerten, Hérault, Bruno, Herbohn, John L., Herold, Martin, Hillers, Annika, Honorio Coronado, Eurídice N., Hui, Cang, Ibanez, Thomas T., Amaral, Iêda, Imai, Nobuo, Jagodziński, Andrzej M., Jaroszewicz, Bogdan, Johannsen, Vivian Kvist, Joly, Carlos A., Jucker, Tommaso, Jung, Ilbin, Karminov, Viktor, Kartawinata, Kuswata, Kearsley, Elizabeth, Kenfack, David, Kennard, Deborah K., Kepfer-Rojas, Sebastian, Keppel, Gunnar, Khan, Mohammed Latif, Killeen, Timothy J., Kim, Hyun Seok, Kitayama, Kanehiro, Köhl, Michael, Korjus, Henn, Kraxner, Florian, Laarmann, Diana, Lang, Mait, Lewis, Simon L., Lu, Huicui, Lukina, Natalia V., Maitner, Brian S., Malhi, Yadvinder, Marcon, Eric, Marimon, Beatriz Schwantes, Marimon-Junior, Ben Hur, Marshall, Andrew R., Martin, Emanuel H., Martynenko, Olga, Meave, Jorge A., Melo-Cruz, Omar, Mendoza, Casimiro, Merow, Cory, Mendoza, Abel Monteagudo, Moreno, Vanessa S., Mukul, Sharif A., Mundhenk, Philip, Nava-Miranda, María Guadalupe, Neill, David, Neldner, Victor J., Nevenic, Radovan V., Ngugi, Michael R., Niklaus, Pascal A., Oleksyn, Jacek, Ontikov, Petr, Ortiz-Malavasi, Edgar, Pan, Yude, Paquette, Alain, Parada-Gutierrez, Alexander, Parfenova, Elena I., Park, Minjee, Parren, Marc, Parthasarathy, Narayanaswamy, Peri, Pablo L., Pfautsch, Sebastian, Phillips, Oliver L., Picard, Nicolas, Piedade, Maria Teresa T. F., Piotto, Daniel, Pitman, Nigel C. A., Polo, Irina, Poorter, Lourens, Poulsen, Axel D., Pretzsch, Hans, Ramirez Arevalo, Freddy, Restrepo-Correa, Zorayda, Rodeghiero, Mirco, Rolim, Samir G., Roopsind, Anand, Rovero, Francesco, Rutishauser, Ervan, Saikia, Purabi, Salas-Eljatib, Christian, Saner, Philippe, Schall, Peter, Schepaschenko, Dmitry, Scherer-Lorenzen, Michael, Schmid, Bernhard, Schöngart, Jochen, Searle, Eric B., Seben, Vladimír, Serra-Diaz, Josep M., Sheil, Douglas, Shvidenko, Anatoly Z., Silva-Espejo, Javier E., Silveira, Marcos, Singh, James, Sist, Plinio, Slik, Ferry, Sonké, Bonaventure, Souza, Alexandre F., Miscicki, Stanislaw, Stereńczak, Krzysztof J., Svenning, Jens-Christian, Svoboda, Miroslav, Swanepoel, Ben, Targhetta, Natalia, Tchebakova, Nadja, ter Steege, Hans, Thomas, Raquel, Tikhonova, Elena, Umunay, Peter M., Usoltsev, Vladimir A., Valencia, Renato, Valladares, Fernando, van der Plas, Fons, Do, Tran Van, van Nuland, Michael E., Vasquez, Rodolfo M., Verbeeck, Hans, Viana, Helder, Vibrans, Alexander C., Vieira, Simone, von Gadow, Klaus, Wang, Hua-Feng, Watson, James V., Werner, Gijsbert D. A., Wiser, Susan K., Wittmann, Florian, Woell, Hannsjoerg, Wortel, Verginia, Zagt, Roderik, Zawiła-Niedźwiecki, Tomasz, Zhang, Chunyu, Zhao, Xiuhai, Zhou, Mo, Zhu, Zhi-Xin, Zo-Bi, Irie C., and Maynard, Daniel S.

Published
2023
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46. Evaluation of zirconia ceramics fabricated through DLP 3d printing process for dental applications

Author

Muneer Khan Mohammed, Abdulrahman Alahmari, Hisham Alkhalefah, and Mustufa Haider Abidi

Subjects
Zirconia, Ceramics, 3d printing, Mechanical properties, Characterization, Additive manufacturing, Science (General), Q1-390, Social sciences (General), H1-99
Abstract

Zirconia ceramics are versatile materials with remarkable properties such as a high thermal resistance, high fracture strength, and low thermal conductivity. They are chemically inert and highly wear- and corrosion-resistant, making them ideal for a wide range of applications in the aerospace, automotive, and biomedical fields. In dentistry, zirconia ceramics are used for veneers, crowns, bridges, and implants because of their biocompatibility. Despite the various benefits of zirconia ceramics, they are difficult to process because of their high hardness and brittleness. Additive manufacturing (AM) has proven to be a viable alternative to conventional fabrication processes, particularly for the processing of difficult-to-cut materials. AM of ceramics has gained significant attention in recent years because of its flexibility and ability to produce customized geometries rapidly and economically. In this study, the digital light processing (DLP) technique was employed to 3D print yttria-stabilized zirconia. The fabricated zirconia was evaluated and characterized for use in dental applications. Thermogravimetric analysis (TGA) and differential thermogravimetry (DTG) were performed on the green body to assess the decomposition of the additives in the slurry and determine the debinding temperatures. The as-built parts were subjected to debinding and sintering to obtain fully dense zirconia parts. The parts tended to shrink after sintering; therefore, the shrinkage ratios were evaluated and found to be 1.2817, 1.2900, and 1.3388 in the x-, y-, and z-directions, respectively. The average density after sintering was 6.031 g/cc. The flexural strength determined using four-point bending tests was 451.876 MPa, and the tensile and compressive strengths were 143 MPa and 298.4 MPa, respectively.

Published
2024
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47. Student Adoption of E-Learning in Higher Education Institutions in Saudi Arabia: Opportunities and Challenges

Author

Hasan, Asif, Habib, Sufyan, Khan, Mohammed Arshad, and Hamadneh, Nawaf N.

Abstract

This study aims to explore the various factors influencing students in adopting e-learning in educational institutions in Saudi Arabia and analyze the relationship between factors of students' adoption of e-learning and student behavior intention. The study also analyzes perceived opportunities and challenges faced by students in adopting an e-learning system in higher education. A well-structured questionnaire was developed, and information was collected from 509 respondents. The study found that students' behavioral intention to adopt e-learning is highly influenced by performance expectancy, effort expectancy, social influence, facilitating conditions, computer self-efficacy, and internet knowledge. The study confirms the mediating role of student engagement that can act as an alternate path for strengthening the relationship between factors of adoption of e-learning and behavioral intention. In addition, several implications and new lines of investigation are recommended for meeting the educational transformation needs and future sustainability.

Published
2023
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48. Advancing IoT Cybersecurity: Adaptive Threat Identification with Deep Learning in Cyber-Physical Systems

Author

C. Atheeq, Ruhiat Sultana, Syeda Asfiya Sabahath, and Murtuza Ahmed Khan Mohammed

Subjects
cyber-attacks, deep learning, threat detection, industrial control system, industrial IoT, cyber-physical systems, Engineering (General). Civil engineering (General), TA1-2040, Technology (General), T1-995, Information technology, T58.5-58.64
Abstract

Securing Internet of Things (IoT)-enabled Cyber-Physical Systems (CPSs) can be challenging because security solutions intended for typical IT/OT systems may not be as effective in a CPS setting. The goal of this study is to create a mechanism for identifying and attributing two-level ensemble attacks that are specifically designed for use against Industrial Control Systems (ICSs). An original ensemble deep representation learning model is combined with decision tree algorithm to identify assaults on unbalanced ICS environments at the first level. An attack attribution network, which constitutes a collection of deep neural networks, is formed at the second level. The proposed model is tested using real-world datasets, notably those pertaining to water purification and gas pipelines. The results demonstrate that the proposed strategy outperforms other strategies with comparable computing complexity and that the recommended model outperforms the existing mechanisms.

Published
2024
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