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922 results on '"Ali, Hashim"'

1. Augmentation through Laundering Attacks for Audio Spoof Detection

Author

Ali, Hashim, Subramani, Surya, and Malik, Hafiz

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Artificial Intelligence, Computer Science - Sound
Abstract

Recent text-to-speech (TTS) developments have made voice cloning (VC) more realistic, affordable, and easily accessible. This has given rise to many potential abuses of this technology, including Joe Biden's New Hampshire deepfake robocall. Several methodologies have been proposed to detect such clones. However, these methodologies have been trained and evaluated on relatively clean databases. Recently, ASVspoof 5 Challenge introduced a new crowd-sourced database of diverse acoustic conditions including various spoofing attacks and codec conditions. This paper is our submission to the ASVspoof 5 Challenge and aims to investigate the performance of Audio Spoof Detection, trained using data augmentation through laundering attacks, on the ASVSpoof 5 database. The results demonstrate that our system performs worst on A18, A19, A20, A26, and A30 spoofing attacks and in the codec and compression conditions of C08, C09, and C10.

Published
2024

2. Is Audio Spoof Detection Robust to Laundering Attacks?

Author

Ali, Hashim, Subramani, Surya, Sudhir, Shefali, Varahamurthy, Raksha, and Malik, Hafiz

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing, Electrical Engineering and Systems Science - Signal Processing
Abstract

Voice-cloning (VC) systems have seen an exceptional increase in the realism of synthesized speech in recent years. The high quality of synthesized speech and the availability of low-cost VC services have given rise to many potential abuses of this technology. Several detection methodologies have been proposed over the years that can detect voice spoofs with reasonably good accuracy. However, these methodologies are mostly evaluated on clean audio databases, such as ASVSpoof 2019. This paper evaluates SOTA Audio Spoof Detection approaches in the presence of laundering attacks. In that regard, a new laundering attack database, called the ASVSpoof Laundering Database, is created. This database is based on the ASVSpoof 2019 (LA) eval database comprising a total of 1388.22 hours of audio recordings. Seven SOTA audio spoof detection approaches are evaluated on this laundered database. The results indicate that SOTA systems perform poorly in the presence of aggressive laundering attacks, especially reverberation and additive noise attacks. This suggests the need for robust audio spoof detection., Comment: Conference Paper

Published
2024
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3. Review of Heart Disease Prediction Using AGI Models: Advancements and Challenges

Author

Haq, Rashid Ul, Ali, Hashim, Malik, Mehak Mushtaq, Akbar, Abdullah, Ouaissa, Mariya, Ouaissa, Mariyam, Khan, Inam Ullah, Kumar, Amit, Series Editor, Suganthan, Ponnuthurai Nagaratnam, Series Editor, Haase, Jan, Series Editor, Senatore, Sabrina, Editorial Board Member, Gao, Xiao-Zhi, Editorial Board Member, Mozar, Stefan, Editorial Board Member, Srivastava, Pradeep Kumar, Editorial Board Member, El Hajjami, Salma, editor, Kaushik, Keshav, editor, and Khan, Inam Ullah, editor

Published
2025
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6. Protecting Voice-Controlled Devices against LASER Injection Attacks

Author

Ali, Hashim, Khuttan, Dhimant, Refat, Rafi Ud Daula, and Malik, Hafiz

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Voice-Controllable Devices (VCDs) have seen an increasing trend towards their adoption due to the small form factor of the MEMS microphones and their easy integration into modern gadgets. Recent studies have revealed that MEMS microphones are vulnerable to audio-modulated laser injection attacks. This paper aims to develop countermeasures to detect and prevent laser injection attacks on MEMS microphones. A time-frequency decomposition based on discrete wavelet transform (DWT) is employed to decompose microphone output audio signal into n + 1 frequency subbands to capture photo-acoustic related artifacts. Higher-order statistical features consisting of the first four moments of subband audio signals, e.g., variance, skew, and kurtosis are used to distinguish between acoustic and photo-acoustic responses. An SVM classifier is used to learn the underlying model that differentiates between an acoustic- and laser-induced (photo-acoustic) response in the MEMS microphone. The proposed framework is evaluated on a data set of 190 audios, consisting of 19 speakers. The experimental results indicate that the proposed framework is able to correctly classify $98\%$ of the acoustic- and laser-induced audio in a random data partition setting and $100\%$ of the audio in speaker-independent and text-independent data partition settings., Comment: 6 pages, 7 figures

Published
2023

7. Reaching machine learning leverage to advance performance of electrocatalytic CO2 conversion in non-aqueous deep eutectic electrolytes

Author

Ahmed Halilu, Mohamed Kamel Hadj-Kali, Hanee Farzana Hizaddin, Mohd Ali Hashim, Emad M. Ali, and Suresh Bhargava

Subjects
Deep eutectic electrolytes, CO2 capture and utilization, Machine learning, Artificial Intelligence, Electrocatalysis, Reaction Engineering, Medicine, Science
Abstract

Abstract Deep eutectic electrolytes (DEEs) show promise for future electrochemical systems due to their adjustable buffer capacities. This study utilizes machine learning algorithms to analyse the carbon dioxide reduction reaction (CO2RR) in DEEs with a buffer capacity of approximately 10.21 mol/pH. The objective is to minimize undesired hydrogen evolution reactions (HER) and render CO2RR dominant in a membrane cell. The CO2RR process was found to be non-adiabatic, as the time of nuclear motion for CO3 2− in K2CO3 product, through CO2 ●− trapping, is 0.368 femtoseconds shorter than the 1.856 × 10−3s charge transfer relaxation time. Microkinetic analysis reveals that the rate of CO2RR to CO2 ●− is 2.14 × 103 mol/cm2/s2 with a rate constant of 2.1 × 1010 cm/s. Our findings demonstrate that ensemble and k-Nearest Neighbours algorithms learn the CO2RR dataset, achieving a prediction accuracy of over 99%. The models were verified visually and quantitatively by overlaying predicted and experimental dataset. Diagnostic and SHAP analyses highlighted the gradient boost ensemble algorithm, predicting asymptotic current densities of -4.114 mA/cm2 or -13.340 mA/cm2, with high turnover frequencies (TOF) of 3.79 × 1010 h-1 or 12.30 × 1010 h-1 for CO2 ●− or K2CO3 generation on silver electrodes, respectively. These results consider both accuracy and robustness against overfitting, providing an opportunity to optimize future non-aqueous electrolytes for convenient TOF measurements at industrially relevant current densities.

Published
2024
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11. A biometric and physically unclonable function–Based authentication protocol for payload exchanges in internet of drones

Author

Vincent Omollo Nyangaresi, Istabraq M. Al-Joboury, Kareem Ali Al-sharhanee, Ali Hamzah Najim, Ali Hashim Abbas, and Hussein Muhi Hariz

Subjects
Attacks, Authentication, Drones, UAVs, Security, Privacy, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Internet of Drones (IoD) has been deployed in numerous military and civilian domains to offer services such as target surveillance, traffic monitoring, disaster handling and environmental monitoring. However, message exchanges among the drones and ground station servers are via insecure wireless channels. In addition, drones may be deployed in hostile and unattended locations. This renders the IoD susceptible to numerous privacy and security threats such as drone capture and cloning. Therefore, many security solutions have been developed based on techniques such as blockchain and public key infrastructure. However, majority of these schemes are still susceptible to many attacks while some of them are inefficient for the resource-limited IoD devices. In this paper, a Physically Unclonable Function (PUF) challenge-response and biometric based robust authentication protocol is presented. Its formal security is carried out using the Real or Random (RoR) model, which demonstrates the robustness of the negotiated session key. In addition, its semantic analysis shows that it can withstand typical IoD attacks such as impersonation, replay, de-synchronization and spoofing. In terms of performance, it is shown to incur lower computation, energy and communication costs.

Published
2024
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16. How to Supervise International PhD Students: A Narrative Inquiry Study

Author

Hussain, Mudassir and Ali, Hashim

Abstract

This narrative inquiry study was undertaken, recruiting 06 successful PhD students in China. The participants were invited and semi-structured interviews were taken one-by-one. The study aimed to explore information about supervisor-supervisee relationship and factors that motivate international PhD students to enhance their research outcomes in a cross-cultural environment. The qualitative data were coded, using QDA miner lite software. After the formation of initial codes, five major categories were emerged included: empowerment, usefulness, success, interest and caring. Each category represented the respective component of MUSIC model of academic motivation (Jones, 2009). The findings illustrated that International PhD students are satisfied with work and life. The supervisors used effective strategies to motivate international PhD supervisees to enhance academic outcomes. The study uncovered students' expectations which included: formal meetings, feedback, guidance, and team work. Based on study findings and results, the MUSIC model can be used as supervision strategy. It is a comprehensive model where all of its five components cover the supervisees' expectations.

Published
2019

20. Modeling and simulation of biomass anaerobic digestion for high biogas yield and CO2 mineralization

Author

Salim Mokraoui, Ahmed Halilu, Mohd Ali Hashim, and Mohamed Kamel Hadj-Kali

Subjects
Computation, Biogas, Waste-to-energy, Environmental remediation, CO2 conversion, Energy conservation, TJ163.26-163.5, Renewable energy sources, TJ807-830
Abstract

Abstract Bioenergy is one of several renewable energy options derived from biomass that can help satisfy our energy needs. Anaerobic digestion is a viable method for producing bioenergy in the form of biogas from biomass. The anaerobic digestion process is challenged with low biogas recovery, and low-quality effluent or CO2 emission, which contribute to environmental pollution and the carbon footprint in the atmosphere. Computational process modelling and simulation can provide realistic information for dealing with the technological challenges involved with anaerobic digestion. In this study, modeling and simulation of the simplified anaerobic digestion process were done using SuperPro Designer software fed with biomass feedstock containing carbohydrates, proteins, and fats, as well as yeast, at 37 °C mesophilic temperature. The anaerobic digestion process yielded 89.655% of CH4 and 10.345% of CO2 and confirmed that the carbohydrate feedstock produces more CH4 composition in the biogas. Mineralization of CO2 using MgO yielded 0.23% MgCO3, consuming > 99% of the CO2 produced during the anaerobic digestion process. Environmental impact assessment of the effluent discharge yielded 0.142 kg Slds/L volatile solid with 6.01% COD reduction per batch of the anaerobic digestion process in an anaerobic digester with 90% (1.925 kg/batch) feedstock dosage. The data indicate that single-batch effluent cannot be discharged into the environment, hence indicating the possible recycling for multiple anaerobic digestion processing. The results are a significant guide for the realistic scalable production of high-quality biogas for bioenergy application, CO2 mineralization, and environmental remediation.

Published
2023
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24. Prediction of geomechanical bearing capacity using autoregressive deep neural network in carbon capture and storage systems

Author

Sulieman Ibraheem Shelash Al-Hawary, Eyhab Ali, Suhair Mohammad Husein Kamona, Luma Hussain Saleh, Alzahraa S. Abdulwahid, Dahlia N. Al-Saidi, Muataz S. Alhassan, Fadhil A. Rasen, Hussein Abdullah Abbas, Ahmed Alawadi, Ali Hashim Abbas, and Mohammad Sina

Subjects
Carbon capture and storage (CCS), Machine learning, Shear wave velocity, Autoregressive deep neural network, Geomechanics, Petrophysics, Science (General), Q1-390, Social sciences (General), H1-99
Abstract

Carbon Capture and Storage (CCS) field is growing rapidly as a means to mitigate the accumulation of greenhouse gas emissions. However, the geomechanical stability of CCS systems, particularly related to bearing capacity, remains a critical challenge that requires accurate prediction models. In this research paper, we investigate the efficacy of employing an Autoregressive Deep Neural Network (ARDNN) algorithm to predict the geomechanical bearing capacity in CCS systems through shear wave velocity prediction as an index for bearing capacity evaluation of deep rock formations. The model utilizes a dataset consisting of 23,000 data points to train and test the ARDNN algorithm. Its scalability, use of deep learning techniques, automatic feature extraction, adaptability to changes in data, and versatility in various prediction tasks make it an attractive option for accurate predictions. The results demonstrate exceptional performance, as evidenced by an R-squared value of 0.9906 and a mean squared error of 0.0438 for the test data compared to the measured data. This research has significant practical implications for effectively predicting geomechanical stability in CCS systems, thus mitigating potential risks associated with their operation.

Published
2023
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25. Optimization of thermal biofuel production from biomass using CaO-based catalyst through different algorithm-based machine learning approaches

Author

Jiangbo Tang, Ali Kareem Abbas, Nisar Ahmad Koka, Naiser Sadoon, Jamal K. Abbas, Rasha Ali Abdalhuseen, Munther Abosaooda, Naked Mahmood Ahmed, and Ali Hashim Abbas

Subjects
Biodiesel, Optimization, Modeling, Machine learning, Energy, Engineering (General). Civil engineering (General), TA1-2040
Abstract

Optimization of biofuel production from algal oil through utilizing a CaO-based catalyst was carried out in this study. The optimal point for the highest yield of the reactions was determined using machine learning. To implement the optimization task, and to make predictions, we used three different methods, including Quantile regression, Logistic regression, and Gradient Boosted Decision Trees. The regression problem includes the amount of Catalyst, Reaction time, and Methanol/oil as input features, and FAME (fatty acid methyl ester) yield is the single output. We tuned the boosted version of these models with their important hyper-parameters and selected their best combination. Then different standard metrics are employed to assess their performance of them. Considering R2 score, Quantile regression, Logistic regression, and Gradient Boosted Decision Trees have error rates of 0.934, 0.996, and 0.998, and with MAE, they have 1.94, 1.68, and 1.17 errors, respectively. Also, Considering MAPE 2.14×10-2, 1.89×10-2, and 1.29×10-2 values obtained. Gradient Boosting is selected as the most appropriate model finally. Furthermore, the optimal output value with the proposed approach is 97.50, with the input vector being (x1 = 153, x2 = 0.625, x3 = 20).

Published
2023
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29. Stoma-free Survival After Rectal Cancer Resection With Anastomotic Leakage: Development and Validation of a Prediction Model in a Large International Cohort

Author

Greijdanus, Nynke G., Wienholts, Kiedo, Ubels, Sander, Talboom, Kevin, Hannink, Gerjon, Wolthuis, Albert, de Lacy, Francisco B., Lefevre, Jérémie H., Solomon, Michael, Frasson, Matteo, Rotholtz, Nicolas, Denost, Quentin, Perez, Rodrigo O., Konishi, Tsuyoshi, Panis, Yves, Rutegård, Martin, Hompes, Roel, Rosman, Camiel, van Workum, Frans, Tanis, Pieter J., de Wilt, Johannes H.W., Bremers, Andreas J.A., Ferenschild, Floris T., de Vriendt, Stefanie, D’Hoore, André, Bislenghi, Gabriele, Farguell, Jordi, Lacy, Antonio M., Atienza, Paula González, van Kessel, Charlotte S., Parc, Yann, Voron, Thibault, Collard, Maxime K., Muriel, Jorge Sancho, Cholewa, Hannia, Mattioni, Laura A., Frontali, Alice, Polle, Sebastiaan W., Polat, Fatih, Obihara, Ndidi J., Vailati, Bruna B., Kusters, Miranda, Tuynmann, Jurriaan B., Hazen, Sanne J.A., Grüter, Alexander A.J., Amano, Takahiro, Fujiwara, Hajime, Salomon, Mario, Ruiz, Hernán, Gonzalez, Ricardo, Estefanía, Diego, Avellaneda, Nicolas, Carrie, Augusto, Santillan, Mateo, Pantoja Pachajoa, Diana A., Parodi, Matias, Gielis, Manuel, Binder, Alf-Dorian, Gürtler, Thomas, Riedl, Peter, Badiani, Sarit, Berney, Christophe, Morgan, Matthew, Hollington, Paul, da Silva, Nigel, Nair, Gavin, Ho, Yiu M., Lamparelli, Michael, Kapadia, Raj, Kroon, Hidde M., Dudi-Venkata, Nagendra N., Liu, Jianliang, Sammour, Tarik, Flamey, Nicolas, Pattyn, Paul, Chaoui, Ahmed, Vansteenbrugge, Louis, van den Broek, Nathalie E.J., Vanclooster, Patrick, de Gheldere, Charles, Pletinckx, Pieter, Defoort, Barbara, Dewulf, Maxime, Slavchev, Mihail, Belev, Nikolay, Atanasov, Boyko, Krastev, Panche, Sokolov, Manol, Maslyankov, Svilen, Gribnev, Petar, Pavlov, Vasil, Ivanov, Tsvetomir, Karamanliev, Martin, Filipov, Emil, Tonchev, Pencho, Aigner, Felix, Mitteregger, Martin, Allmer, Caterina, Seitinger, Gerald, Colucci, Nicola, Buchs, Nicolas, Ris, Frédéric, Toso, Christian, Gialamas, Eleftherios, Vuagniaux, Aurélie, Chautems, Roland, Sauvain, Marc-Olivier, Daester, Silvio, von Flüe, Markus, Guenin, Marc-Olivier, Taha-Mehlitz, Stephanie, Hess, Gabriel F., Martínek, Lubomír, Skrovina, Matej, Machackova, Maria, Benčurik, Vladimir, Uluk, Deniz, Pratschke, Johann, Dittrich, Luca S., Guel-Klein, Safak, Perez, Daniel, Grass, Julia-Kristin, Melling, Nathaniel, Mueller, Simone, Iversen, Lene H., Eriksen, Jacob D., Baatrup, Gunnar, Al-Najami, Issam, Bjørsum-Meyer, Thomas, Teras, Jüri, Teras, Roland M., Monib, Fatma A., Abu Elnga Ahmed, Nagm Eldin, Alkady, Eithar, Ali, Ahmed K., Khedr, Gehan Abd Elatti, Abdelaal, Ahmed Samir, Bassyouni Ashoush, Fouad M., Ewedah, Moataz, Elshennawy, Eslam M., Hussein, Mohamed, Fernández-Martínez, Daniel, García-Flórez, Luis J., Fernández-Hevia, María, Suárez-Sánchez, Aida, del Hoyo Aretxabala, Izaskun, Docampo, Iria Losada, Zabala, Jesús Gómez, Tejedor, Patricia, Morales Bernaldo de Quirós, Javier T., Quiroga, Ignacio Bodega, Navarro-Sánchez, Antonio, Darias, Iván Soto, Fernández, Cristina López, de La Cruz Cuadrado, Cristina, Sánchez-Guillén, Luis, López-Rodríguez-Arias, Francisco, Soler-Silva, Álvaro, Arroyo, Antonio, Bernal-Sprekelsen, Juan C., Gómez-Abril, Segundo Á., Gonzálvez, Paula, Torres, María T., Sánchez, Teresa Rubio, Antona, Francisco Blanco, Sánchez Lara, Juan E., Alcázar Montero, José A., Fernández-Martínez, Daniel, García-Flórez, Luis J., Fernández-Hevia, María, Suárez-Sánchez, Aida, Colás-Ruiz, Enrique, Tasende-Presedo, Marta M., Fernández-Hurtado, Ignacio, Cifuentes-Ródenas, José A., Suárez, Marta Castro, Losada, Manuel, Hernández, Miguel, Alonso, Alfredo, Diéguez, Beatriz, Serralta, Daniel, Medina Quintana, Rita E., Gil Lopez, Jose M., Pinto, Francisca Lima, Nieto-Moreno, Elena, Bonito, Alba Correa, Santacruz, Carlos Cerdán, Marcos, Elena Bermejo, Septiem, Javier García, Calero-Lillo, Aránzazu, Alanez-Saavedra, Javier, Muñoz-Collado, Salvador, López-Lara, Manuel, Martínez, María Labalde, Herrero, Eduardo Ferrero, Borda, Francisco Javier García, Villar, Óscar García, Escartín, Jorge, Blas, Juan L., Ferrer, Rocío, Egea, Jorge García, Rodríguez-Infante, Antonio, Mínguez-Ruiz, Germán, Carreño-Villarreal, Guillermo, Pire-Abaitua, Gerardo, Dziakova, Jana, Rodríguez, Carlos Sáez-Cazallas, Pizarro Aranda, María J., Muguerza Huguet, José M., Borda-Arrizabalaga, Nerea, Enriquez-Navascués, José M., Echaniz, Garazi Elorza, Ansorena, Yolanda Saralegui, Estaire-Gómez, Mercedes, Martínez-Pinedo, Carlos, Barbero-Valenzuela, Alejandro, Ruíz-García, Pablo, Kraft, Miquel, Gómez-Jurado, María J., Pellino, Gianluca, Espín-Basany, Eloy, Cotte, Eddy, Panel, Nathalie, Goutard, Claire-Angéline, deÁngelis, Nicola, Lauka, Lelde, Shaikh, Shafaque, Osborne, Laura, Ramsay, George, Nichita, Vladimir-Ion, Bhandari, Santosh, Sarmah, Panchali, Bethune, Rob M., Pringle, Heather C.M., Massey, Lisa, Fowler, George E., Hamid, Hytham K.S., de Simone, Belinda D., Kynaston, James, Bradley, Nicholas, Stienstra, Roxane M., Gurjar, Shashank, Mukherjee, Tanmoy, Chandio, Ashfaq, Ahmed, Safia, Singh, Baljit, Runau, Francois, Chaudhri, Sanjay, Siaw, Oliver, Sarveswaran, Janahan, Miu, Victor, Ashmore, Daniel, Darwich, Haitham, Singh-Ranger, Deepak, Singh, Nirbhaibir, Shaban, Mohamed, Gareb, Fahed, Petropolou, Thalia, Polydorou, Adreas, Dattani, Mit, Afzal, Asma, Bavikatte, Akshay, Sebastian, Boby, Ward, Nicholas, Mishra, Amitabh, Manatakis, Dimitrios, Agalianos, Christos, Tasis, Nikolaos, Antonopoulou, Maria-Ioanna, Karavokyros, Ioannis, Charalabopoulos, Alexandros, Schizas, Dimitrios, Baili, Efstratia, Syllaios, Athanasios, Karydakis, Lysandros, Vailas, Michail, Balalis, Dimitrios, Korkolis, Dimitrios, Plastiras, Aris, Rompou, Aliki, Xenaki, Sofia, Xynos, Evangelos, Chrysos, Emmanuel, Venianaki, Maria, Christodoulidis, Grigorios, Perivoliotis, Konstantinos, Tzovaras, George, Baloyiannis, Ioannis, Ho, Man-Fung, Ng, Simon Siu-man, Mak, Tony Wing-chung, Futaba, Kaori, Šantak, Goran, Šimleša, Damir, osić, Jurica, Zukanović, Goran, Kelly, Michael E., Larkin, John O., McCormick, Paul H., Mehigan, Brian J., Connelly, Tara M., Neary, Peter, Ryan, Jessica, McCullough, Peter, Al-Juaifari, Maytham A., Hammoodi, Hayder, Abbood, Ali Hashim, Calabrò, Marcello, Muratore, Andrea, Terra, Antonio La, Farnesi, Francesca, Feo, Carlo V., Fabbri, Nicolò, Pesce, Antonio, Fazzin, Marta, Roscio, Francesco, Clerici, Federico, Lucchi, Andrea, Vittori, Laura, Agostinelli, Laura, Ripoli, Maria Cristina, Sambucci, Daniele, Porta, Andrea, Sinibaldi, Giovanni, Crescentini, Giacomo, larcinese, Antonella, Picone, Emanuele, Persiani, Roberto, Biondi, Alberto, Pezzuto, Roberto, Lorenzon, Laura, Rizzo, Gianluca, Coco, Claudio, D’Agostino, Luca, Spinelli, Antonino, Sacchi, Matteo M., Carvello, Michele, Foppa, Caterina, Spinelli, Antonino, Sacchi, Matteo M., Carvello, Michele, Foppa, Caterina, Maroli, Annalisa, Palini, Gian M., Garulli, Gianluca, Zanini, Nicola, Delrio, Paolo, Rega, Daniela, Carbone, Fabio, Aversano, Alessia, Pirozzolo, Giovanni, Recordare, Alfonso, DʼAlimonte, Lucrezia, Vignotto, Chiara, Corbellini, Carlo, Sampietro, Gianluca M., Lorusso, Leonardo, Manzo, Carlo A., Ghignone, Federico, Ugolini, Giampaolo, Montroni, Isacco, Pasini, Franceso, Pasini, Francesco, Ballabio, Michele, Bisagni, Pietro, Armao, Francesca T., Longhi, Marco, Ghazouani, Omar, Galleano, Raffaele, Tamini, Nicolò, Oldani, Massimo, Nespoli, Luca, Picciariello, Arcangelo, Altomare, Donato F., Tomasicchio, Giovanni, Lantone, Giuliano, Catena, Fausto, Giuffrida, Mario, Annicchiarico, Alfredo, Perrone, Gennaro, Grossi, Ugo, Santoro, Giulio A., Zanus, Giacomo, Iacomino, Alessandro, Novello, Simone, Passuello, Nicola, Zucchella, Martino, Puca, Lucia, deGiuli, Maurizio, Reddavid, Rossella, Scabini, Stefano, Aprile, Alessandra, Soriero, Domenico, Fioravanti, Emanuela, Rottoli, Matteo, Romano, Angela, Tanzanu, Marta, Belvedere, Angela, Mariani, Nicolò M., Ceretti, Andrea P., Opocher, Enrico, Gallo, Gaetano, Sammarco, Giuseppe, de Paola, Gilda, Pucciarelli, Salvatore, Marchegiani, Francesco, Spolverato, Gaya, Buzzi, Gianluca, Di Saverio, Salomone, Meroni, Paola, Parise, Cristiano, Bottazzoli, Elisa I., Lapolla, Pierfrancesco, Brachini, Gioia, Cirillo, Bruno, Mingoli, Andrea, Sica, Giuseppe, Siragusa, Leandro, Bellato, Vittoria, Cerbo, Daniele, de Pasqual, Carlo A., de Manzoni, Giovanni, di Cosmo, Maria A., Alrayes, Bourhan M.H., Qandeel, Mahmoud W. M., Hani, Mohammad Bani, Rabadi, Alexander, el Muhtaseb, Mohammad S., Abdeen, Basel, Karmi, Fahed, Žilinskas, Justas, Latkauskas, Tadas, Tamelis, Algimantas, Pikūnienė, Ingrida, Šlenfuktas, Vygintas, Poskus, Tomas, Kryzauskas, Marius, Jakubauskas, Matas, Mikalauskas, Saulius, Jakubauskiene, Lina, Hassan, Soha Y., Altrabulsi, Amani, Abdulwahed, Eman, Ghmagh, Reem, Deeknah, Abdulqudus, Alshareea, Entisar, Elhadi, Muhammed, Abujamra, Saleh, Msherghi, Ahmed A., Tababa, Osama W.E., Majbar, Mohammed A., Souadka, Amine, Benkabbou, Amine, Mohsine, Raouf, Echiguer, Sabrillah, Moctezuma-Velázquez, Paulina, Salgado-Nesme, Noel, Vergara-Fernández, Omar, Sainz-Hernández, Juan C., Alvarez-Bautista, Francisco E., Zakaria, Andee D., Zakaria, Zaidi, Wong, Michael P.K., Ismail, Razif, Ibrahim, Aini F., Abdullah, Nik A.N., Julaihi, Rokayah, Bhat, Sameer, OʼGrady, Greg, Bissett, Ian, Lamme, Bas, Musters, Gijsbert D., Dinaux, Anne M., Grotenhuis, Brechtje A., Steller Arend G.J. Aalbers, Ernst J., Leeuwenburgh, Marjolein M., Rutten, Harm J.T., Burger, Jacobus W.A., Bloemen, Johanne G., Ketelaers, Stijn H.J., Waqar, Usama, Chawla, Tabish, Rauf, Hareem, Rani, Pallavi, Talsma, Aaldert K., Scheurink, Lieke, van Praagh, Jasper B., Segelman, Josefin, Nygren, Jonas, Anderin, Kajsa, Tiefenthal, Marit, de Andrés, Beatriz, Beltrán de Heredia, Juan P., Vázquez, Andrea, Gómez, Tania, Golshani, Parisa, Kader, Rawaz, Mohamed, Abudi, Westerterp, Marinke, Marinelli, Andreas, Niemer, Quirine, Doornebosch, Pascal G., Shapiro, Joël, Vermaas, Maarten, de Graaf, Eelco J.R., van Westreenen, Hendrik L., Zwakman, Marije, van Dalsen, Annette D., Vles, Wouter J., Nonner, Joost, Toorenvliet, Boudewijn R., Janssen, Paul T.J., Verdaasdonk, Emiel G.G., Amelung, Femke J., Peeters Renu R. Bahadoer, Koen C.M.J., Holman, Fabian A., Heemskerk, Jeroen, Vosbeek, Noortje, Leijtens, Jeroen W.A., Taverne, Sophie B.M., Heijnen, Bob H.M., El-Massoudi, Youssef, de Groot-van Veen, Irene, Hoff, Christiaan, Jou-Valencia, Daniela, Consten, Esther C.J., Burghgraef, Thijs A., Geitenbeek, Ritch, Hulshof, Lorenzo G.W.L., Slooter, Gerrit D., Reudink, Muriël, Bouvy, Nicole D., Wildeboer, Aurelia C. L., Verstappen, Sonja, Pennings, Alexander J., van den Hengel, Berber, Wijma, Allard G., de Haan, Jael, de Nes, Lindsey C.F., Heesink, Vera, Karsten, Tom, Heidsma, Charlotte M., Koemans, Willem J., Dekker, Jan-Willem T., van der Zijden, Charlène J., Roos, Daphne, Demirkiran, Ahmet, van der Burg, Sjirk, Oosterling, Steven J., Hoogteijling, Tijs J., Wiering, Bastiaan, Smeeing, Diederik P.J., Havenga, Klaas, Lutfi, Hamid, Consten, Esther C.J., Tsimogiannis, Konstantinos, Sköldberg, Filip, Folkesson, Joakim, den Boer, Frank, van Schaik, Ted G., van Gerven, Pieter, Sietses, Colin, Hol, Jeroen C., Boerma, Evert-Jan G., Creemers, Davy M.J., Schultz, Johannes K., Frivold, Tone, Riis, Rolf, Gregussen, Hilde, Busund, Sondre, Sjo, Ole H., Gaard, Maria, Krohn, Nina, Ersryd, Amanda L., Leung, Edmund, Waqar, Usama, Chawla, Tabish, Rauf, Hareem, Rani, Pallavi, Sultan, Hytham, Hajjaj, Baraa Nabil, Alhisi, Ahmed Jehad, Khader, Ahmed A.E., Mendes, Ana Filipa Dias, Semião, Miguel, Faria, Luis Queiroz, Azevedo, Constança, da Costa Devesa, Helena M., Martins, Sónia Fortuna, Rodrigues Jarimba, Aldo M., Ribeiro Marques, Sónia M., Ferreira, Rita Marques, Oliveira, António, Ferreira, Cátia, Pereira, Ricardo, Surlin, Valeriu M., Graure, Giorgiana M., Ramboiu, Stefan Patrascu Sandu D., Negoi, Ionut, Ciubotaru, Cezar, Stoica, Bogdan, Tanase, Ioan, Stoica, Bogdan, Ciubotaru, Cezar, Negoita, Valentina M., Florea, Sabrina, Macau, Florin, Vasile, Mihai, Stefanescu, Victor, Dimofte, Gabriel-Mihail, Luncă, Sorinel, Roată, Cristian-Ene, Mu[Latin Small Letter s with comma below]ină, Ana-Maria, Garmanova, Tatiana, Agapov, Mikhail N., Markaryan, Daniil G., Eduard, Galliamov, Yanishev, Alexey, Abelevich, Alexander, Bazaev, Andrey, Rodimov, Sergey V., Filimonov, Victor B., Melnikov, Andrey A., Suchkov, Igor A., Drozdov, EvgeniyS., Kostromitskiy, Dmitriy N., Sjöström, Olle, Matthiessen, Peter, Baban, Bayar, Gadan, Soran, Jadid, Kaveh Dehlaghi, Staffan, Maria, Park, Jennifer M., Rydbeck, Daniel, Lydrup, Marie-Louise, Buchwald, Pamela, Jutesten, Henrik, Darlin, Lotten, Lindqvist, Ebba, Nilsson, Karl, Larsson, Per-Anders, Jangmalm, Staffan, Košir, Jurij A., Tomažič, Aleš, Grosek, Jan, Božič, Tajda Košir, Zazo, Aya, Zazo, Rama, Fares, Hala, Ayoub, Kusay, Niazi, Ammar, Mansour, Ali, Abbas, Ayman, Tantoura, Mohammad, Hamdan, Alaa, Hassan, Naya, Hasan, Bassam, Saad, Ahmad, Sebai, Amine, Haddad, Anis, Maghrebi, Houcine, Kacem, Montasser, Yalkın, Ömer, Samsa, Mehmet Veysi, Atak, İbrahim, Balci, Bengi, Haberal, Elifcan, Dogan, Lütfi, Gecim, Ibrahim E., Akyol, Cihangir, Koc, Mehmet A., Sivrikoz, Emre, Piyadeoğlu, Deniz, Larkin, John O., avanagh, Dara O., Sökmen, Selman, Bişgin, Tayfun, Günenç, Erşan, Güzel, Melek, Leventoğlu, Sezai, Yüksel, Osman, Kozan, Ramazan, Göbüt, Hüseyin, Cengiz, Fevzi, Erdinc, Kemal, Acar, Nihan Coşgun, Kamer, Erdinc, Özgür, İlker, Aydın, Oguzhan, Keskin, Metin, Bulut, Mehmet Türker, Kulle, Cemil B., Kara, Yasin, Sıbıç, Osman, Özata, İbrahim H., Buğra, Dursun, Balık, Emre, Kulle, Cemil B., Çakır, Murat, Alhardan, Anas, Colak, Elif, CiftciEngin Aybar, Ahmet B., Sari, Ahmet Can, Atici, Semra Demirli, Kaya, Tayfun, Dursun, Ayberk, Calik, Bulent, Özkan, Ömer Faruk, Ülgür, Hanife Şeyda, Düzgün, Özgül, Monson, John, George, Sarah, Woods, Kayla, Al-Eryani, Fatima, Albakry, Rudaina, Coetzee, Emile, Boutall, Adam, Herman, Ayesiga, Warden, Claire, Mugla, Naser, Forgan, Tim, Mia, Imraan, and Lambrechts, Anton

Published
2023
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32. Enhancement in structural, morphological, and optical properties of copper oxide for optoelectronic device applications

Author

Saleem Shahroz, Jabbar Abdullah Hasan, Jameel Muhammad Hasnain, Rehman Azka, Kareem Zahraa Hashim, Abbas Ali Hashim, Ghaffar Zunaira, Razzaq Saba Abdul, Pashameah Rami Adel, Alzahrani Eman, Ng Eng-Poh, and Sapuan Salit Mohd

Subjects
copper oxide, xrd, raman, sem, ftir, uv, optoelectronic devices, Technology, Chemical technology, TP1-1185, Physical and theoretical chemistry, QD450-801
Abstract

In this study, copper oxide (CuO) specimens were successfully prepared by the hydrothermal process at altered calcination temperatures; 350, 450, and 550°C. The synthesized samples were analyzed through X-ray powder diffraction (XRD), scanning electron microscope (SEM), Raman, Fourier-transform infrared spectroscopy (FTIR), and UV-Vis spectroscopy to analyze the impact of calcination temperature on the structural, morphological, vibration spectra, functional group, and optical properties of CuO for optoelectronic device applications. XRD confirms the pure single-phase monoclinic structure of synthesized samples with no impurity phases and has good crystallinity with the development in calcination temperature. The average crystalline size, lattice constant, and porosity were found in the range of 3.98–5.06 nm; a = 3.4357 Å, b = 3.9902 Å, c = 4.8977 Å – a = 3.0573 Å, b = 3.9573 Å, c = 4.6892 Å; and 3.37–1.03%, respectively. SEM exhibited a variation in morphology by increasing calcination temperature. Raman spectra revealed that the CuO sample calcinated at 550°C with a stone-like shape having a large grain size of 3.25 μm exhibited that Raman peak intensity and the multiphonon band became stronger and sharper and exhibited higher intensity compared to the samples calcinated at 350 and 450°C. FTIR spectra confirmed that these synthesized specimens exhibited the peaks associated with the typical stretching vibrations of the Cu–O bond between 400 and 500 cm−1 exhibiting the formation of CuO. The energy bandgap was slightly reduced from 1.61 to 1.43 eV with the increase in the calcination temperature. The optical studies revealed that the calcination temperature of 550°C improves the optical properties of CuO by tuning its optical bandgap. The modified structural, morphological, and optical characteristics of the prepared CuO samples make them an appropriate candidate for optoelectronic device applications.

Published
2022
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37. A DFT approach towards therapeutic potential of Novel borospherene as targeted drug delivery system for isoniazid drug.

Author

Hashim, Alaa A., Abdul-Reda Hussein, Uday, Bahir, Hala, Amir, Ahmed Ali, Muhammad, Faris Anad, Ahjel, Salam, Zabibah, Rahman S., Abbas, Ali Hashim, and Hason, Ahmad

Subjects
TARGETED drug delivery, DRUG carriers, DENSITY functional theory, ISONIAZID, AMINO acids
Abstract

The present work evaluates pristine and amino acid-functionalised C4B32 borospherenes for drug delivery purposes based on density functional theory (DFT). C atoms were doped on $ \textrm{B}_{36}^{ - 4} $ B 36 − 4 clusters to develop C4B32 borospherenes, and C4B32 clusters in pristine and alanine-modified forms have shown significant drug delivery performance. This study sought to evaluate the interactions of both pristine and alanine-modified C4B32 with isoniazid drug (IS) based on DFT calculations. The amino acid modification expectedly enabled biodrug carrier functionality and enhanced the adsorption of IS onto C4B32. The UV-Visible (UV-Vis) analysis revealed a redshift upon the adsorption of IS. It was concluded that alanine-modified C4B32 clusters could serve as a promising drug carrier. [ABSTRACT FROM AUTHOR]

Published
2024
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38. Comparative Analysis to the Performance of Three Mobile Ad-Hoc Network Routing Protocols in Time-Critical Events of Search and Rescue Missions

Author

Mostafa, Salama A., Mustapha, Aida, Ramli, Azizul Azhar, Jubair, Mohammed Ahmed, Hassan, Mustafa Hamid, Abbas, Ali Hashim, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Cassenti, Daniel N, editor, Scataglini, Sofia, editor, Rajulu, Sudhakar L., editor, and Wright, Julia L., editor

Published
2021
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39. A QoS Aware Cluster Head Selection and Hybrid Cryptography Routing Protocol for Enhancing Efficiency and Security of VANETs

Author

Mohammed Ahmed Jubair, Salama A. Mostafa, Dilovan Asaad Zebari, Hussein Muhee Hariz, Nejood Faisal Abdulsattar, Mustafa Hamid Hassan, Ali Hashim Abbas, Fatima Hashim Abbas, Areej Alasiry, and M. Turki-Hadj Alouane

Subjects
Vehicular ad hoc network (VANET), quality of service (QoS), network security, cluster head (CH), Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Nowadays, VANET (Vehicular Ad hoc Network) is one of the key aspects of developing advanced intelligent transportation systems. Due to its huge mobility and rapid topology alteration, the network exposes to link failure that affects the firmness of the network and causes delay and congestion. Additionally, the dynamic change in the network routing affects the network’s security, making it vulnerable to various attacks, and results data loss. An efficient and highly secured routing protocol is needed to overcome these drawbacks. Subsequently, this research proposes a new routing protocol that combines the Quality of Service (QoS)-aware Cluster Head (CH) selection and hybrid cryptography named QoS+. The QoS+ protocol is mainly divided into QoS-based CH selection and hybrid cryptography modules. The CH selection module based on QoS parameters attempts to provide reliable and stable clusters and improve the firmness and connectivity during the communication process of the network. The hybrid cryptography module contains Advanced Encryption Standard (AES) and Elliptic Curve Cryptosystems (ECC) algorithms. It attempts to improve the security and privacy of the network. The QoS+ protocol is evaluated by a developed VANET simulator using NS2 software. The simulator consists of a network model, a load model, and an attack model. Various speed and transmission ranges and gray hole and wormhole attacks are used in the simulator. The outcome calculated from the performance analysis shows that the proposed QoS+ protocol has a 7% to 24% higher message success rate, 500 to 800 higher packets normalized routing load, 350 to 550 Kbps higher throughput, 5% to 17% higher efficiency, and 50ms to 12ms lower end-to-end delay when compared with the earlier works of ECHS and KMSUNET. The proposed QoS+ protocol also achieves superior performance in terms of CH efficiency, cluster member efficiency, and average cluster number with various speeds and transmission ranges.

Published
2022
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40. The Evaluation of Rental Amount of Religious Endowments by Using Geomatic Techniques and Machine Learning Algorithms Hilla/ Iraq

Author

Oday, Jasim, Mohammed Mejbel, and Ali Hashim

Subjects
endowments, gis, regression endowment, machine learning, Science, Technology
Abstract

The religious endowments are one of the important sources, which acquire historical, cultural, and economic importance in all countries of the world. In particular, a religious endowment in Iraq includes several distributed real estates and lands that usually require efficient management systems. One of the most important factors affecting the management of real estates that belong to religious endowments is the rental amount of each real estate. In general, the estimation of the rental of real estates can support the future development of religious endowments. Governmental agencies are faced with some challenges in the management of religious endowments in terms of rental pricing due to numerous economic and geographic factors. The rapid development of artificial intelligence systems and Geomatic techniques can present a framework for rental amount estimation based on spatial and non-spatial factors. In this study, a machine learning algorithm (Support Vector Regression) will be combined with Geographic Information System (GIS) to predict and evaluate the rental amount of real estates that belong to a religious institution in Iraq (Shiite endowment in Hillah city). The final results indicated that the proposed method achieved an overall accuracy of 71%, a root mean square error of 0.2257 million Iraq, Dinar (IQD), and a correlation coefficient of 0.9272. This study can be used as an effective tool for the decision-makers to plan and manage the religious endowments in developing countries.

Published
2021
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48. Drugs that inhibit TMEM16 proteins block SARS-CoV-2 spike-induced syncytia

Author

Braga, Luca, Ali, Hashim, Secco, Ilaria, Chiavacci, Elena, Neves, Guilherme, Goldhill, Daniel, Penn, Rebecca, Jimenez-Guardeño, Jose M., Ortega-Prieto, Ana M., Bussani, Rossana, Cannatà, Antonio, Rizzari, Giorgia, Collesi, Chiara, Schneider, Edoardo, Arosio, Daniele, Shah, Ajay M., Barclay, Wendy S., Malim, Michael H., Burrone, Juan, and Giacca, Mauro

Published
2021
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49. Evaluation of sulfonated chitosan-g-sulfonated polyvinyl alcohol/polyethylene oxide/sulfated zirconia composite polyelectrolyte membranes for direct borohydride fuel cells: Solution casting against the electrospun membrane fabrication technique

Author

Marwa H. Gouda, Noha A. Elessawy, Mohamed Elnouby, Mohamed A. Ghorab, Islam Othman Radwan, Ali Hashim, M. Elsayed Youssef, and Diogo M. F. Santos

Subjects
proton exchange membranes, polyvinyl alcohol, polyethylene oxide, chitosan, electrospun, direct borohydride fuel cell, Technology
Abstract

To improve the mechanical properties of proton exchange membranes, consequently improving the performance of direct borohydride fuel cells, the present study prepared sulfonated chitosan-g-sulfonated polyvinyl alcohol/polyethylene oxide doped with sulfated zirconia composite (SCS-g-SPVA/PEO/SZrO2) polyelectrolyte membranes. Two fabrication techniques were followed, solution casting and electrospinning, to have the membranes in film and fiber forms and study the effect of the different forms on the membrane’s physicochemical properties. For the casting technique, different concentrations of SZrO2 (1-3 wt%) were used, while the optimum concentration of SZrO2 (3 wt%) was used in the electrospun one (SCS-g-SPVA/PEO/SZrO2-CF). SCS-g-SPVA/PEO/SZrO2-C membranes were prepared in a single step. The grafting and the crosslinking were carried out using glutaraldehyde and sulfosuccinic acid as sulfonating agents for chitosan and PVA and coupling agents simultaneously using click chemistry. On the other hand, SCS-g-SPVA/PEO/SZrO2-CF membranes were prepared in two steps. They were fabricated with electrospinning and then dipped into the coupling and crosslinking solutions. The casting membranes’ physicochemical properties were improved by increasing the SZrO2 content. The experimental results further show that the fabrication procedure significantly influences the physicochemical properties of the membranes. For instance, the composite fiber membrane demonstrated higher selectivity and higher ion exchange capacity (IEC) than the casting membrane. Furthermore, by using the response surface methodology model, the effects of ion exchange capacity, water uptake, and oxidative stability were optimized as three independent variables that affected the ionic conductivity of SCS-g-SPVA/PEO/SZrO2-3C. The optimized ionic conductivity of the SCS-g-SPVA/PEO/SZrO2-3C membrane was 13.6 mS cm−1, achieved at the maximum point of the polynomial model, with an IEC of 0.74 meq g−1, ∼92% water uptake, and about 93% oxidative stability.

Published
2022
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50. Application of acidic ionic liquids for the treatment of acidic low grade palm oil for biodiesel production

Author

Adeeb Hayyan, Andrew T.H. Yeow, Nashwan Abdullah Mohammed Alkahli, Jehad Saleh, Ahmaad Kadmouse Aldeehani, Khaled H. Alkandari, Falah DH. Alajmi, Yatimah Alias, Mohd Usman Mohd Junaidi, Mohd Ali Hashim, Wan Jefrey Basirun, and Maha A.A. Abdelrahman

Subjects
Esterification, Free fatty acid, Ionic liquids, Acidic crude palm oil, 1‑butyl‑3-methylimidazolium hydrogen sulfate, Chemistry, QD1-999
Abstract

Employing low quality feedstocks for biodiesel production can present cost reduction opportunities due to its wide availability and cost effectiveness. However, most low quality feedstocks have high free fatty acid (FFA) content which is not feasible for biodiesel production through direct transesterification due to soap formation. Thus, a pre-treatment step through FFA esterification could reduce the FFA content while simultaneously yielding fatty acid methyl esters (FAME). In this study, the catalytic activity of ten ionic liquids (ILs) were screened for the esterification of FFA in acidic crude palm oil (ACPO). 1‑butyl‑3-methylimidazolium hydrogen sulfate as IL exhibited the highest catalytic activity and was further investigated to determine the optimum operating parameters such as IL dosage, molar ratio, reaction temperature and time. Single factor optimization was employed for the laboratory scale batch esterification reaction. The FFA content was reduced from 10.77% to less than 1% using the following optimum conditions: 4% IL to oil, molar ratio of 10:1 (methanol to oil), 120 min reaction time and 60 °C reaction temperature. Under optimum conditions, the IL can be recycled five times with minimal loss in catalytic activity, highlighting its feasibility as an industrial catalyst for esterification of acidic oils and fats.

Published
2022
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