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6,924 results on '"Afzal, Muhammad"'

51. Acoustic scattering from a wave-bearing cavity with flexible inlet and outlet

Author

Afzal, Muhammad, Bilal, Hazrat, Ahmed, Naveed, and Wahab, Abdul

Subjects
Physics - Classical Physics, Mathematical Physics, Mathematics - Analysis of PDEs
Abstract

In this article, we substantiate the appositeness of the \emph{mode-matching technique} to study the scattering response of bridging elastic plates connecting two flexible duct regions of different heights. We present two different solution schemes to analyze the structure-borne and fluid-borne radiations in the elastic plate-bounded waveguide. The first scheme supplements the mode-matching technique with the so-called \emph{tailored-Galerkin approach} which uses a solution ansatz with homogeneous and integral parts corresponding to the vibrations of the bridging elastic plate and the cavity, respectively. In the second scheme, we supplement the mode-matching technique with the \emph{modal approach} wherein the displacement of the bridging elastic plate is projected onto the eigenmodes of the cavity. To handle the non-orthogonality of the eigenfunctions, we invoke generalized orthogonality relations. An advantage of the proposed mode-matching schemes is that they provide a convenient way of incorporating a variety of edge conditions on the joints of the plates, including clamped, pin-joint, or restraint connections. The numerical analysis of the waveguide scattering problems substantiates that edge connections on the joints have a significant impact on the scattering energies and transmission loss., Comment: 27 pages, 5 figures

Published
2022

52. I2MVFormer: Large Language Model Generated Multi-View Document Supervision for Zero-Shot Image Classification

Author

Naeem, Muhammad Ferjad, Khan, Muhammad Gul Zain Ali, Xian, Yongqin, Afzal, Muhammad Zeshan, Stricker, Didier, Van Gool, Luc, and Tombari, Federico

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Recent works have shown that unstructured text (documents) from online sources can serve as useful auxiliary information for zero-shot image classification. However, these methods require access to a high-quality source like Wikipedia and are limited to a single source of information. Large Language Models (LLM) trained on web-scale text show impressive abilities to repurpose their learned knowledge for a multitude of tasks. In this work, we provide a novel perspective on using an LLM to provide text supervision for a zero-shot image classification model. The LLM is provided with a few text descriptions from different annotators as examples. The LLM is conditioned on these examples to generate multiple text descriptions for each class(referred to as views). Our proposed model, I2MVFormer, learns multi-view semantic embeddings for zero-shot image classification with these class views. We show that each text view of a class provides complementary information allowing a model to learn a highly discriminative class embedding. Moreover, we show that I2MVFormer is better at consuming the multi-view text supervision from LLM compared to baseline models. I2MVFormer establishes a new state-of-the-art on three public benchmark datasets for zero-shot image classification with unsupervised semantic embeddings.

Published
2022

56. Learning Attention Propagation for Compositional Zero-Shot Learning

Author

Khan, Muhammad Gul Zain Ali, Naeem, Muhammad Ferjad, Van Gool, Luc, Pagani, Alain, Stricker, Didier, and Afzal, Muhammad Zeshan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Compositional zero-shot learning aims to recognize unseen compositions of seen visual primitives of object classes and their states. While all primitives (states and objects) are observable during training in some combination, their complex interaction makes this task especially hard. For example, wet changes the visual appearance of a dog very differently from a bicycle. Furthermore, we argue that relationships between compositions go beyond shared states or objects. A cluttered office can contain a busy table; even though these compositions don't share a state or object, the presence of a busy table can guide the presence of a cluttered office. We propose a novel method called Compositional Attention Propagated Embedding (CAPE) as a solution. The key intuition to our method is that a rich dependency structure exists between compositions arising from complex interactions of primitives in addition to other dependencies between compositions. CAPE learns to identify this structure and propagates knowledge between them to learn class embedding for all seen and unseen compositions. In the challenging generalized compositional zero-shot setting, we show that our method outperforms previous baselines to set a new state-of-the-art on three publicly available benchmarks.

Published
2022

63. Global mortality associated with 33 bacterial pathogens in 2019: a systematic analysis for the Global Burden of Disease Study 2019

Author

Collaborators, GBD 2019 Antimicrobial Resistance, Ikuta, Kevin S, Swetschinski, Lucien R, Aguilar, Gisela Robles, Sharara, Fablina, Mestrovic, Tomislav, Gray, Authia P, Weaver, Nicole Davis, Wool, Eve E, Han, Chieh, Hayoon, Anna Gershberg, Aali, Amirali, Abate, Semagn Mekonnen, Abbasi-Kangevari, Mohsen, Abbasi-Kangevari, Zeinab, Abd-Elsalam, Sherief, Abebe, Getachew, Abedi, Aidin, Abhari, Amir Parsa, Abidi, Hassan, Aboagye, Richard Gyan, Absalan, Abdorrahim, Ali, Hiwa Abubaker, Acuna, Juan Manuel, Adane, Tigist Demssew, Addo, Isaac Yeboah, Adegboye, Oyelola A, Adnan, Mohammad, Adnani, Qorinah Estiningtyas Sakilah, Afzal, Muhammad Sohail, Afzal, Saira, Aghdam, Zahra Babaei, Ahinkorah, Bright Opoku, Ahmad, Aqeel, Ahmad, Araz Ramazan, Ahmad, Rizwan, Ahmad, Sajjad, Ahmad, Sohail, Ahmadi, Sepideh, Ahmed, Ali, Ahmed, Haroon, Ahmed, Jivan Qasim, Rashid, Tarik Ahmed, Ajami, Marjan, Aji, Budi, Akbarzadeh-Khiavi, Mostafa, Akunna, Chisom Joyqueenet, Hamad, Hanadi Al, Alahdab, Fares, Al-Aly, Ziyad, Aldeyab, Mamoon A, Aleman, Alicia V, Alhalaiqa, Fadwa Alhalaiqa Naji, Alhassan, Robert Kaba, Ali, Beriwan Abdulqadir, Ali, Liaqat, Ali, Syed Shujait, Alimohamadi, Yousef, Alipour, Vahid, Alizadeh, Atiyeh, Aljunid, Syed Mohamed, Allel, Kasim, Almustanyir, Sami, Ameyaw, Edward Kwabena, Amit, Arianna Maever L, Anandavelane, Nivedita, Ancuceanu, Robert, Andrei, Catalina Liliana, Andrei, Tudorel, Anggraini, Dewi, Ansar, Adnan, Anyasodor, Anayochukwu Edward, Arabloo, Jalal, Aravkin, Aleksandr Y, Areda, Demelash, Aripov, Timur, Artamonov, Anton A, Arulappan, Judie, Aruleba, Raphael Taiwo, Asaduzzaman, Muhammad, Ashraf, Tahira, Athari, Seyyed Shamsadin, Atlaw, Daniel, Attia, Sameh, Ausloos, Marcel, Awoke, Tewachew, Quintanilla, Beatriz Paulina Ayala, Ayana, Tegegn Mulatu, Azadnajafabad, Sina, Jafari, Amirhossein Azari, Darshan, BB, Badar, Muhammad, Badiye, Ashish D, Baghcheghi, Nayereh, Bagherieh, Sara, Baig, Atif Amin, Banerjee, Indrajit, Barac, Aleksandra, Bardhan, Mainak, and Barone-Adesi, Francesco

Subjects
Hematology, Sepsis, Emerging Infectious Diseases, Prevention, Lung, Vaccine Related, Biodefense, Pneumonia, Pneumonia & Influenza, Immunization, Infectious Diseases, 2.2 Factors relating to the physical environment, Aetiology, Infection, Good Health and Well Being, Child, Male, Female, Humans, Global Burden of Disease, Syndrome, Africa South of the Sahara, Global Health, Bacterial Infections, Risk Factors, Bacteria, GBD 2019 Antimicrobial Resistance Collaborators, Medical and Health Sciences, General & Internal Medicine
Abstract

BackgroundReducing the burden of death due to infection is an urgent global public health priority. Previous studies have estimated the number of deaths associated with drug-resistant infections and sepsis and found that infections remain a leading cause of death globally. Understanding the global burden of common bacterial pathogens (both susceptible and resistant to antimicrobials) is essential to identify the greatest threats to public health. To our knowledge, this is the first study to present global comprehensive estimates of deaths associated with 33 bacterial pathogens across 11 major infectious syndromes.MethodsWe estimated deaths associated with 33 bacterial genera or species across 11 infectious syndromes in 2019 using methods from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, in addition to a subset of the input data described in the Global Burden of Antimicrobial Resistance 2019 study. This study included 343 million individual records or isolates covering 11 361 study-location-years. We used three modelling steps to estimate the number of deaths associated with each pathogen: deaths in which infection had a role, the fraction of deaths due to infection that are attributable to a given infectious syndrome, and the fraction of deaths due to an infectious syndrome that are attributable to a given pathogen. Estimates were produced for all ages and for males and females across 204 countries and territories in 2019. 95% uncertainty intervals (UIs) were calculated for final estimates of deaths and infections associated with the 33 bacterial pathogens following standard GBD methods by taking the 2·5th and 97·5th percentiles across 1000 posterior draws for each quantity of interest.FindingsFrom an estimated 13·7 million (95% UI 10·9-17·1) infection-related deaths in 2019, there were 7·7 million deaths (5·7-10·2) associated with the 33 bacterial pathogens (both resistant and susceptible to antimicrobials) across the 11 infectious syndromes estimated in this study. We estimated deaths associated with the 33 bacterial pathogens to comprise 13·6% (10·2-18·1) of all global deaths and 56·2% (52·1-60·1) of all sepsis-related deaths in 2019. Five leading pathogens-Staphylococcus aureus, Escherichia coli, Streptococcus pneumoniae, Klebsiella pneumoniae, and Pseudomonas aeruginosa-were responsible for 54·9% (52·9-56·9) of deaths among the investigated bacteria. The deadliest infectious syndromes and pathogens varied by location and age. The age-standardised mortality rate associated with these bacterial pathogens was highest in the sub-Saharan Africa super-region, with 230 deaths (185-285) per 100 000 population, and lowest in the high-income super-region, with 52·2 deaths (37·4-71·5) per 100 000 population. S aureus was the leading bacterial cause of death in 135 countries and was also associated with the most deaths in individuals older than 15 years, globally. Among children younger than 5 years, S pneumoniae was the pathogen associated with the most deaths. In 2019, more than 6 million deaths occurred as a result of three bacterial infectious syndromes, with lower respiratory infections and bloodstream infections each causing more than 2 million deaths and peritoneal and intra-abdominal infections causing more than 1 million deaths.InterpretationThe 33 bacterial pathogens that we investigated in this study are a substantial source of health loss globally, with considerable variation in their distribution across infectious syndromes and locations. Compared with GBD Level 3 underlying causes of death, deaths associated with these bacteria would rank as the second leading cause of death globally in 2019; hence, they should be considered an urgent priority for intervention within the global health community. Strategies to address the burden of bacterial infections include infection prevention, optimised use of antibiotics, improved capacity for microbiological analysis, vaccine development, and improved and more pervasive use of available vaccines. These estimates can be used to help set priorities for vaccine need, demand, and development.FundingBill & Melinda Gates Foundation, Wellcome Trust, and Department of Health and Social Care, using UK aid funding managed by the Fleming Fund.

Published
2022

64. Age–sex differences in the global burden of lower respiratory infections and risk factors, 1990–2019: results from the Global Burden of Disease Study 2019

Author

Collaborators, GBD 2019 LRI, Kyu, Hmwe Hmwe, Vongpradith, Avina, Sirota, Sarah Brooke, Novotney, Amanda, Troeger, Christopher E, Doxey, Matthew C, Bender, Rose G, Ledesma, Jorge R, Biehl, Molly H, Albertson, Samuel B, Frostad, Joseph Jon, Burkart, Katrin, Bennitt, Fiona B, Zhao, Jeff T, Gardner, William M, Hagins, Hailey, Bryazka, Dana, Dominguez, Regina-Mae Villanueva, Abate, Semagn Mekonnen, Abdelmasseh, Michael, Abdoli, Amir, Abdoli, Gholamreza, Abedi, Aidin, Abedi, Vida, Abegaz, Tadesse M, Abidi, Hassan, Aboagye, Richard Gyan, Abolhassani, Hassan, Abtew, Yonas Derso, Ali, Hiwa Abubaker, Abu-Gharbieh, Eman, Abu-Zaid, Ahmed, Adamu, Kidist, Addo, Isaac Yeboah, Adegboye, Oyelola A, Adnan, Mohammad, Adnani, Qorinah Estiningtyas Sakilah, Afzal, Muhammad Sohail, Afzal, Saira, Ahinkorah, Bright Opoku, Ahmad, Aqeel, Ahmad, Araz Ramazan, Ahmad, Sajjad, Ahmadi, Ali, Ahmadi, Sepideh, Ahmed, Haroon, Ahmed, Jivan Qasim, Rashid, Tarik Ahmed, Akbarzadeh-Khiavi, Mostafa, Hamad, Hanadi Al, Albano, Luciana, Aldeyab, Mamoon A, Alemu, Bezatu Mengistie, Alene, Kefyalew Addis, Algammal, Abdelazeem M, Alhalaiqa, Fadwa Alhalaiqa Naji, Alhassan, Robert Kaba, Ali, Beriwan Abdulqadir, Ali, Liaqat, Ali, Musa Mohammed, Ali, Syed Shujait, Alimohamadi, Yousef, Alipour, Vahid, Al-Jumaily, Adel, Aljunid, Syed Mohamed, Almustanyir, Sami, Al-Raddadi, Rajaa M, Al-Rifai, Rami H Hani, AlRyalat, Saif Aldeen S, Alvis-Guzman, Nelson, Alvis-Zakzuk, Nelson J, Ameyaw, Edward Kwabena, Dehkordi, Javad Javad Aminian, Amuasi, John H, Amugsi, Dickson A, Anbesu, Etsay Woldu, Ansar, Adnan, Anyasodor, Anayochukwu Edward, Arabloo, Jalal, Areda, Demelash, Argaw, Ayele Mamo, Argaw, Zeleke Gebru, Arulappan, Judie, Aruleba, Raphael Taiwo, Asemahagn, Mulusew A, Athari, Seyyed Shamsadin, Atlaw, Daniel, Attia, Engi F, Attia, Sameh, Aujayeb, Avinash, Awoke, Tewachew, Ayana, Tegegn Mulatu, Ayanore, Martin Amogre, Azadnajafabad, Sina, Azangou-Khyavy, Mohammadreza, Azari, Samad, Jafari, Amirhossein Azari, Badar, Muhammad, and Badiye, Ashish D

Subjects
Prevention, Good Health and Well Being, Adult, Child, Female, Male, Humans, Child, Preschool, Aged, Aged, 80 and over, Global Burden of Disease, Bayes Theorem, Sex Characteristics, Pyridinolcarbamate, Global Health, Respiratory Tract Infections, Risk Factors, Particulate Matter, Quality-Adjusted Life Years, GBD 2019 LRI Collaborators, Clinical Sciences, Medical Microbiology, Public Health and Health Services, Microbiology
Abstract

BackgroundThe global burden of lower respiratory infections (LRIs) and corresponding risk factors in children older than 5 years and adults has not been studied as comprehensively as it has been in children younger than 5 years. We assessed the burden and trends of LRIs and risk factors across all age groups by sex, for 204 countries and territories.MethodsIn this analysis of data for the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we used clinician-diagnosed pneumonia or bronchiolitis as our case definition for LRIs. We included International Classification of Diseases 9th edition codes 079.6, 466-469, 470.0, 480-482.8, 483.0-483.9, 484.1-484.2, 484.6-484.7, and 487-489 and International Classification of Diseases 10th edition codes A48.1, A70, B97.4-B97.6, J09-J15.8, J16-J16.9, J20-J21.9, J91.0, P23.0-P23.4, and U04-U04.9. We used the Cause of Death Ensemble modelling strategy to analyse 23 109 site-years of vital registration data, 825 site-years of sample vital registration data, 1766 site-years of verbal autopsy data, and 681 site-years of mortality surveillance data. We used DisMod-MR 2.1, a Bayesian meta-regression tool, to analyse age-sex-specific incidence and prevalence data identified via systematic reviews of the literature, population-based survey data, and claims and inpatient data. Additionally, we estimated age-sex-specific LRI mortality that is attributable to the independent effects of 14 risk factors.FindingsGlobally, in 2019, we estimated that there were 257 million (95% uncertainty interval [UI] 240-275) LRI incident episodes in males and 232 million (217-248) in females. In the same year, LRIs accounted for 1·30 million (95% UI 1·18-1·42) male deaths and 1·20 million (1·07-1·33) female deaths. Age-standardised incidence and mortality rates were 1·17 times (95% UI 1·16-1·18) and 1·31 times (95% UI 1·23-1·41) greater in males than in females in 2019. Between 1990 and 2019, LRI incidence and mortality rates declined at different rates across age groups and an increase in LRI episodes and deaths was estimated among all adult age groups, with males aged 70 years and older having the highest increase in LRI episodes (126·0% [95% UI 121·4-131·1]) and deaths (100·0% [83·4-115·9]). During the same period, LRI episodes and deaths in children younger than 15 years were estimated to have decreased, and the greatest decline was observed for LRI deaths in males younger than 5 years (-70·7% [-77·2 to -61·8]). The leading risk factors for LRI mortality varied across age groups and sex. More than half of global LRI deaths in children younger than 5 years were attributable to child wasting (population attributable fraction [PAF] 53·0% [95% UI 37·7-61·8] in males and 56·4% [40·7-65·1] in females), and more than a quarter of LRI deaths among those aged 5-14 years were attributable to household air pollution (PAF 26·0% [95% UI 16·6-35·5] for males and PAF 25·8% [16·3-35·4] for females). PAFs of male LRI deaths attributed to smoking were 20·4% (95% UI 15·4-25·2) in those aged 15-49 years, 30·5% (24·1-36·9) in those aged 50-69 years, and 21·9% (16·8-27·3) in those aged 70 years and older. PAFs of female LRI deaths attributed to household air pollution were 21·1% (95% UI 14·5-27·9) in those aged 15-49 years and 18·2% (12·5-24·5) in those aged 50-69 years. For females aged 70 years and older, the leading risk factor, ambient particulate matter, was responsible for 11·7% (95% UI 8·2-15·8) of LRI deaths.InterpretationThe patterns and progress in reducing the burden of LRIs and key risk factors for mortality varied across age groups and sexes. The progress seen in children younger than 5 years was clearly a result of targeted interventions, such as vaccination and reduction of exposure to risk factors. Similar interventions for other age groups could contribute to the achievement of multiple Sustainable Development Goals targets, including promoting wellbeing at all ages and reducing health inequalities. Interventions, including addressing risk factors such as child wasting, smoking, ambient particulate matter pollution, and household air pollution, would prevent deaths and reduce health disparities.FundingBill & Melinda Gates Foundation.

Published
2022

66. SemAttNet: Towards Attention-based Semantic Aware Guided Depth Completion

Author

Nazir, Danish, Liwicki, Marcus, Stricker, Didier, and Afzal, Muhammad Zeshan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Depth completion involves recovering a dense depth map from a sparse map and an RGB image. Recent approaches focus on utilizing color images as guidance images to recover depth at invalid pixels. However, color images alone are not enough to provide the necessary semantic understanding of the scene. Consequently, the depth completion task suffers from sudden illumination changes in RGB images (e.g., shadows). In this paper, we propose a novel three-branch backbone comprising color-guided, semantic-guided, and depth-guided branches. Specifically, the color-guided branch takes a sparse depth map and RGB image as an input and generates color depth which includes color cues (e.g., object boundaries) of the scene. The predicted dense depth map of color-guided branch along-with semantic image and sparse depth map is passed as input to semantic-guided branch for estimating semantic depth. The depth-guided branch takes sparse, color, and semantic depths to generate the dense depth map. The color depth, semantic depth, and guided depth are adaptively fused to produce the output of our proposed three-branch backbone. In addition, we also propose to apply semantic-aware multi-modal attention-based fusion block (SAMMAFB) to fuse features between all three branches. We further use CSPN++ with Atrous convolutions to refine the dense depth map produced by our three-branch backbone. Extensive experiments show that our model achieves state-of-the-art performance in the KITTI depth completion benchmark at the time of submission.

Published
2022

67. The global burden of cancer attributable to risk factors, 2010–19: a systematic analysis for the Global Burden of Disease Study 2019

Author

Collaborators, GBD 2019 Cancer Risk Factors, Tran, Khanh Bao, Lang, Justin J, Compton, Kelly, Xu, Rixing, Acheson, Alistair R, Henrikson, Hannah Jacqueline, Kocarnik, Jonathan M, Penberthy, Louise, Aali, Amirali, Abbas, Qamar, Abbasi, Behzad, Abbasi-Kangevari, Mohsen, Abbasi-Kangevari, Zeinab, Abbastabar, Hedayat, Abdelmasseh, Michael, Abd-Elsalam, Sherief, Abdelwahab, Ahmed Abdelwahab, Abdoli, Gholamreza, Abdulkadir, Hanan Abdulkadir, Abedi, Aidin, Abegaz, Kedir Hussein, Abidi, Hassan, Aboagye, Richard Gyan, Abolhassani, Hassan, Absalan, Abdorrahim, Abtew, Yonas Derso, Ali, Hiwa Abubaker, Abu-Gharbieh, Eman, Achappa, Basavaprabhu, Acuna, Juan Manuel, Addison, Daniel, Addo, Isaac Yeboah, Adegboye, Oyelola A, Adesina, Miracle Ayomikun, Adnan, Mohammad, Adnani, Qorinah Estiningtyas Sakilah, Advani, Shailesh M, Afrin, Sumia, Afzal, Muhammad Sohail, Aggarwal, Manik, Ahinkorah, Bright Opoku, Ahmad, Araz Ramazan, Ahmad, Rizwan, Ahmad, Sajjad, Ahmad, Sohail, Ahmadi, Sepideh, Ahmed, Haroon, Ahmed, Luai A, Ahmed, Muktar Beshir, Rashid, Tarik Ahmed, Aiman, Wajeeha, Ajami, Marjan, Akalu, Gizachew Taddesse, Akbarzadeh-Khiavi, Mostafa, Aklilu, Addis, Akonde, Maxwell, Akunna, Chisom Joyqueenet, Hamad, Hanadi Al, Alahdab, Fares, Alanezi, Fahad Mashhour, Alanzi, Turki M, Alessy, Saleh Ali, Algammal, Abdelazeem M, Al-Hanawi, Mohammed Khaled, Alhassan, Robert Kaba, Ali, Beriwan Abdulqadir, Ali, Liaqat, Ali, Syed Shujait, Alimohamadi, Yousef, Alipour, Vahid, Aljunid, Syed Mohamed, Alkhayyat, Motasem, Al-Maweri, Sadeq Ali Ali, Almustanyir, Sami, Alonso, Nivaldo, Alqalyoobi, Shehabaldin, Al-Raddadi, Rajaa M, Al-Rifai, Rami H Hani, Al-Sabah, Salman Khalifah, Al-Tammemi, Ala'a B, Altawalah, Haya, Alvis-Guzman, Nelson, Amare, Firehiwot, Ameyaw, Edward Kwabena, Dehkordi, Javad Javad Aminian, Amirzade-Iranaq, Mohammad Hosein, Amu, Hubert, Amusa, Ganiyu Adeniyi, Ancuceanu, Robert, Anderson, Jason A, Animut, Yaregal Animut, Anoushiravani, Amir, Anoushirvani, Ali Arash, Ansari-Moghaddam, Alireza, Ansha, Mustafa Geleto, Antony, Benny, Antwi, Maxwell Hubert, Anwar, Sumadi Lukman, and Anwer, Razique

Subjects
Prevention, Cancer, Aetiology, 2.2 Factors relating to the physical environment, Good Health and Well Being, Female, Global Burden of Disease, Global Health, Humans, Male, Neoplasms, Quality-Adjusted Life Years, Risk Assessment, Risk Factors, Smoking, GBD 2019 Cancer Risk Factors Collaborators, Medical and Health Sciences, General & Internal Medicine
Abstract

BackgroundUnderstanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally.MethodsThe GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented.FindingsGlobally, in 2019, the risk factors included in this analysis accounted for 4·45 million (95% uncertainty interval 4·01-4·94) deaths and 105 million (95·0-116) DALYs for both sexes combined, representing 44·4% (41·3-48·4) of all cancer deaths and 42·0% (39·1-45·6) of all DALYs. There were 2·88 million (2·60-3·18) risk-attributable cancer deaths in males (50·6% [47·8-54·1] of all male cancer deaths) and 1·58 million (1·36-1·84) risk-attributable cancer deaths in females (36·3% [32·5-41·3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20·4% (12·6-28·4) and DALYs by 16·8% (8·8-25·0), with the greatest percentage increase in metabolic risks (34·7% [27·9-42·8] and 33·3% [25·8-42·0]).InterpretationThe leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden.FundingBill & Melinda Gates Foundation.

Published
2022

70. Global burden of bacterial antimicrobial resistance 1990–2021: a systematic analysis with forecasts to 2050

Author

Naghavi, Mohsen, Vollset, Stein Emil, Ikuta, Kevin S, Swetschinski, Lucien R, Gray, Authia P, Wool, Eve E, Robles Aguilar, Gisela, Mestrovic, Tomislav, Smith, Georgia, Han, Chieh, Hsu, Rebecca L, Chalek, Julian, Araki, Daniel T, Chung, Erin, Raggi, Catalina, Gershberg Hayoon, Anna, Davis Weaver, Nicole, Lindstedt, Paulina A, Smith, Amanda E, Altay, Umut, Bhattacharjee, Natalia V, Giannakis, Konstantinos, Fell, Frederick, McManigal, Barney, Ekapirat, Nattwut, Mendes, Jessica Andretta, Runghien, Tilleye, Srimokla, Oraya, Abdelkader, Atef, Abd-Elsalam, Sherief, Aboagye, Richard Gyan, Abolhassani, Hassan, Abualruz, Hasan, Abubakar, Usman, Abukhadijah, Hana J, Aburuz, Salahdein, Abu-Zaid, Ahmed, Achalapong, Sureerak, Addo, Isaac Yeboah, Adekanmbi, Victor, Adeyeoluwa, Temitayo Esther, Adnani, Qorinah Estiningtyas Sakilah, Adzigbli, Leticia Akua, Afzal, Muhammad Sohail, Afzal, Saira, Agodi, Antonella, Ahlstrom, Austin J, Ahmad, Aqeel, Ahmad, Sajjad, Ahmad, Tauseef, Ahmadi, Ali, Ahmed, Ayman, Ahmed, Haroon, Ahmed, Ibrar, Ahmed, Mohammed, Ahmed, Saeed, Ahmed, Syed Anees, Akkaif, Mohammed Ahmed, Al Awaidy, Salah, Al Thaher, Yazan, Alalalmeh, Samer O, AlBataineh, Mohammad T, Aldhaleei, Wafa A, Al-Gheethi, Adel Ali Saeed, Alhaji, Nma Bida, Ali, Abid, Ali, Liaqat, Ali, Syed Shujait, Ali, Waad, Allel, Kasim, Al-Marwani, Sabah, Alrawashdeh, Ahmad, Altaf, Awais, Al-Tammemi, Alaa B., Al-Tawfiq, Jaffar A, Alzoubi, Karem H, Al-Zyoud, Walid Adnan, Amos, Ben, Amuasi, John H, Ancuceanu, Robert, Andrews, Jason R, Anil, Abhishek, Anuoluwa, Iyadunni Adesola, Anvari, Saeid, Anyasodor, Anayochukwu Edward, Apostol, Geminn Louis Carace, Arabloo, Jalal, Arafat, Mosab, Aravkin, Aleksandr Y, Areda, Demelash, Aremu, Abdulfatai, Artamonov, Anton A, Ashley, Elizabeth A, Asika, Marvellous O, Athari, Seyyed Shamsadin, Atout, Maha Moh'd Wahbi, Awoke, Tewachew, Azadnajafabad, Sina, Azam, James Mba, Aziz, Shahkaar, Azzam, Ahmed Y., Babaei, Mahsa, Babin, Francois-Xavier, Badar, Muhammad, Baig, Atif Amin, Bajcetic, Milica, Baker, Stephen, Bardhan, Mainak, Barqawi, Hiba Jawdat, Basharat, Zarrin, Basiru, Afisu, Bastard, Mathieu, Basu, Saurav, Bayleyegn, Nebiyou Simegnew, Belete, Melaku Ashagrie, Bello, Olorunjuwon Omolaja, Beloukas, Apostolos, Berkley, James A, Bhagavathula, Akshaya Srikanth, Bhaskar, Sonu, Bhuyan, Soumitra S, Bielicki, Julia A, Briko, Nikolay Ivanovich, Brown, Colin Stewart, Browne, Annie J, Buonsenso, Danilo, Bustanji, Yasser, Carvalheiro, Cristina G, Castañeda-Orjuela, Carlos A, Cenderadewi, Muthia, Chadwick, Joshua, Chakraborty, Sandip, Chandika, Rama Mohan, Chandy, Sara, Chansamouth, Vilada, Chattu, Vijay Kumar, Chaudhary, Anis Ahmad, Ching, Patrick R, Chopra, Hitesh, Chowdhury, Fazle Rabbi, Chu, Dinh-Toi, Chutiyami, Muhammad, Cruz-Martins, Natalia, da Silva, Alanna Gomes, Dadras, Omid, Dai, Xiaochen, Darcho, Samuel D, Das, Saswati, De la Hoz, Fernando Pio, Dekker, Denise Myriam, Dhama, Kuldeep, Diaz, Daniel, Dickson, Benjamin Felix Rothschild, Djorie, Serge Ghislain, Dodangeh, Milad, Dohare, Sushil, Dokova, Klara Georgieva, Doshi, Ojas Prakashbhai, Dowou, Robert Kokou, Dsouza, Haneil Larson, Dunachie, Susanna J, Dziedzic, Arkadiusz Marian, Eckmanns, Tim, Ed-Dra, Abdelaziz, Eftekharimehrabad, Aziz, Ekundayo, Temitope Cyrus, El Sayed, Iman, Elhadi, Muhammed, El-Huneidi, Waseem, Elias, Christelle, Ellis, Sally J, Elsheikh, Randa, Elsohaby, Ibrahim, Eltaha, Chadi, Eshrati, Babak, Eslami, Majid, Eyre, David William, Fadaka, Adewale Oluwaseun, Fagbamigbe, Adeniyi Francis, Fahim, Ayesha, Fakhri-Demeshghieh, Aliasghar, Fasina, Folorunso Oludayo, Fasina, Modupe Margaret, Fatehizadeh, Ali, Feasey, Nicholas A, Feizkhah, Alireza, Fekadu, Ginenus, Fischer, Florian, Fitriana, Ida, Forrest, Karen M, Fortuna Rodrigues, Celia, Fuller, John E, Gadanya, Muktar A, Gajdács, Márió, Gandhi, Aravind P, Garcia-Gallo, Esteban E, Garrett, Denise O, Gautam, Rupesh K, Gebregergis, Miglas Welay, Gebrehiwot, Mesfin, Gebremeskel, Teferi Gebru, Geffers, Christine, Georgalis, Leonidas, Ghazy, Ramy Mohamed, Golechha, Mahaveer, Golinelli, Davide, Gordon, Melita, Gulati, Snigdha, Gupta, Rajat Das, Gupta, Sapna, Gupta, Vijai Kumar, Habteyohannes, Awoke Derbie, Haller, Sebastian, Harapan, Harapan, Harrison, Michelle L, Hasaballah, Ahmed I, Hasan, Ikramul, Hasan, Rumina Syeda, Hasani, Hamidreza, Haselbeck, Andrea Haekyung, Hasnain, Md Saquib, Hassan, Ikrama Ibrahim, Hassan, Shoaib, Hassan Zadeh Tabatabaei, Mahgol Sadat, Hayat, Khezar, He, Jiawei, Hegazi, Omar E, Heidari, Mohammad, Hezam, Kamal, Holla, Ramesh, Holm, Marianne, Hopkins, Heidi, Hossain, Md Mahbub, Hosseinzadeh, Mehdi, Hostiuc, Sorin, Hussein, Nawfal R, Huy, Le Duc, Ibáñez-Prada, Elsa D, Ikiroma, Adalia, Ilic, Irena M, Islam, Sheikh Mohammed Shariful, Ismail, Faisal, Ismail, Nahlah Elkudssiah, Iwu, Chidozie Declan, Iwu-Jaja, Chinwe Juliana, Jafarzadeh, Abdollah, Jaiteh, Fatoumatta, Jalilzadeh Yengejeh, Reza, Jamora, Roland Dominic G, Javidnia, Javad, Jawaid, Talha, Jenney, Adam W J, Jeon, Hyon Jin, Jokar, Mohammad, Jomehzadeh, Nabi, Joo, Tamas, Joseph, Nitin, Kamal, Zul, Kanmodi, Kehinde Kazeem, Kantar, Rami S, Kapisi, James Apollo, Karaye, Ibraheem M, Khader, Yousef Saleh, Khajuria, Himanshu, Khalid, Nauman, Khamesipour, Faham, Khan, Ajmal, Khan, Mohammad Jobair, Khan, Muhammad Tariq, Khanal, Vishnu, Khidri, Feriha Fatima, Khubchandani, Jagdish, Khusuwan, Suwimon, Kim, Min Seo, Kisa, Adnan, Korshunov, Vladimir Andreevich, Krapp, Fiorella, Krumkamp, Ralf, Kuddus, Mohammed, Kulimbet, Mukhtar, Kumar, Dewesh, Kumaran, Emmanuelle A P, Kuttikkattu, Ambily, Kyu, Hmwe Hmwe, Landires, Iván, Lawal, Basira Kankia, Le, Thao Thi Thu, Lederer, Ingeborg Maria, Lee, Munjae, Lee, Seung Won, Lepape, Alain, Lerango, Temesgen Leka, Ligade, Virendra S, Lim, Cherry, Lim, Stephen S, Limenh, Liknaw Workie, Liu, Chaojie, Liu, Xiaofeng, Liu, Xuefeng, Loftus, Michael J, M Amin, Hawraz Ibrahim, Maass, Kelsey Lynn, Maharaj, Sandeep B, Mahmoud, Mansour Adam, Maikanti-Charalampous, Panagiota, Makram, Omar M, Malhotra, Kashish, Malik, Ahmad Azam, Mandilara, Georgia D, Marks, Florian, Martinez-Guerra, Bernardo Alfonso, Martorell, Miquel, Masoumi-Asl, Hossein, Mathioudakis, Alexander G, May, Juergen, McHugh, Theresa A, Meiring, James, Meles, Hadush Negash, Melese, Addisu, Melese, Endalkachew Belayneh, Minervini, Giuseppe, Mohamed, Nouh Saad, Mohammed, Shafiu, Mohan, Syam, Mokdad, Ali H, Monasta, Lorenzo, Moodi Ghalibaf, AmirAli, Moore, Catrin E, Moradi, Yousef, Mossialos, Elias, Mougin, Vincent, Mukoro, George Duke, Mulita, Francesk, Muller-Pebody, Berit, Murillo-Zamora, Efren, Musa, Sani, Musicha, Patrick, Musila, Lillian A, Muthupandian, Saravanan, Nagarajan, Ahamarshan Jayaraman, Naghavi, Pirouz, Nainu, Firzan, Nair, Tapas Sadasivan, Najmuldeen, Hastyar Hama Rashid, Natto, Zuhair S, Nauman, Javaid, Nayak, Biswa Prakash, Nchanji, G Takop, Ndishimye, Pacifique, Negoi, Ionut, Negoi, Ruxandra Irina, Nejadghaderi, Seyed Aria, Nguyen, QuynhAnh P, Noman, Efaq Ali, Nwakanma, Davis C, O'Brien, Seamus, Ochoa, Theresa J, Odetokun, Ismail A, Ogundijo, Oluwaseun Adeolu, Ojo-Akosile, Tolulope R, Okeke, Sylvester Reuben, Okonji, Osaretin Christabel, Olagunju, Andrew T, Olivas-Martinez, Antonio, Olorukooba, Abdulhakeem Abayomi, Olwoch, Peter, Onyedibe, Kenneth Ikenna, Ortiz-Brizuela, Edgar, Osuolale, Olayinka, Ounchanum, Pradthana, Oyeyemi, Oyetunde T, P A, Mahesh Padukudru, Paredes, Jose L, Parikh, Romil R, Patel, Jay, Patil, Shankargouda, Pawar, Shrikant, Peleg, Anton Y, Peprah, Prince, Perdigão, João, Perrone, Carlo, Petcu, Ionela-Roxana, Phommasone, Koukeo, Piracha, Zahra Zahid, Poddighe, Dimitri, Pollard, Andrew J, Poluru, Ramesh, Ponce-De-Leon, Alfredo, Puvvula, Jagadeesh, Qamar, Farah Naz, Qasim, Nameer Hashim, Rafai, Clotaire Donatien, Raghav, Pankaja, Rahbarnia, Leila, Rahim, Fakher, Rahimi-Movaghar, Vafa, Rahman, Mosiur, Rahman, Muhammad Aziz, Ramadan, Hazem, Ramasamy, Shakthi Kumaran, Ramesh, Pushkal Sinduvadi, Ramteke, Pramod W, Rana, Rishabh Kumar, Rani, Usha, Rashidi, Mohammad-Mahdi, Rathish, Devarajan, Rattanavong, Sayaphet, Rawaf, Salman, Redwan, Elrashdy Moustafa Mohamed, Reyes, Luis Felipe, Roberts, Tamalee, Robotham, Julie V, Rosenthal, Victor Daniel, Ross, Allen Guy, Roy, Nitai, Rudd, Kristina E, Sabet, Cameron John, Saddik, Basema Ahmad, Saeb, Mohammad Reza, Saeed, Umar, Saeedi Moghaddam, Sahar, Saengchan, Weeravoot, Safaei, Mohsen, Saghazadeh, Amene, Saheb Sharif-Askari, Narjes, Sahebkar, Amirhossein, Sahoo, Soumya Swaroop, Sahu, Maitreyi, Saki, Morteza, Salam, Nasir, Saleem, Zikria, Saleh, Mohamed A, Samodra, Yoseph Leonardo, Samy, Abdallah M, Saravanan, Aswini, Satpathy, Maheswar, Schumacher, Austin E, Sedighi, Mansour, Seekaew, Samroeng, Shafie, Mahan, Shah, Pritik A, Shahid, Samiah, Shahwan, Moyad Jamal, Shakoor, Sadia, Shalev, Noga, Shamim, Muhammad Aaqib, Shamshirgaran, Mohammad Ali, Shamsi, Anas, Sharifan, Amin, Shastry, Rajesh P, Shetty, Mahabalesh, Shittu, Aminu, Shrestha, Sunil, Siddig, Emmanuel Edwar, Sideroglou, Theologia, Sifuentes-Osornio, Jose, Silva, Luís Manuel Lopes Rodrigues, Simões, Eric A F, Simpson, Andrew J H, Singh, Amit, Singh, Surjit, Sinto, Robert, Soliman, Sameh S M, Soraneh, Soroush, Stoesser, Nicole, Stoeva, Temenuga Zhekova, Swain, Chandan Kumar, Szarpak, Lukasz, T Y, Sree Sudha, Tabatabai, Shima, Tabche, Celine, Taha, Zanan Mohammed-Ameen, Tan, Ker-Kan, Tasak, Nidanuch, Tat, Nathan Y, Thaiprakong, Areerat, Thangaraju, Pugazhenthan, Tigoi, Caroline Chepngeno, Tiwari, Krishna, Tovani-Palone, Marcos Roberto, Tran, Thang Huu, Tumurkhuu, Munkhtuya, Turner, Paul, Udoakang, Aniefiok John, Udoh, Arit, Ullah, Noor, Ullah, Saeed, Vaithinathan, Asokan Govindaraj, Valenti, Mario, Vos, Theo, Vu, Huong T L, Waheed, Yasir, Walker, Ann Sarah, Walson, Judd L, Wangrangsimakul, Tri, Weerakoon, Kosala Gayan, Wertheim, Heiman F L, Williams, Phoebe C M, Wolde, Asrat Arja, Wozniak, Teresa M, Wu, Felicia, Wu, Zenghong, Yadav, Mukesh Kumar Kumar, Yaghoubi, Sajad, Yahaya, Zwanden Sule, Yarahmadi, Amir, Yezli, Saber, Yismaw, Yazachew Engida, Yon, Dong Keon, Yuan, Chun-Wei, Yusuf, Hadiza, Zakham, Fathiah, Zamagni, Giulia, Zhang, Haijun, Zhang, Zhi-Jiang, Zielińska, Magdalena, Zumla, Alimuddin, Zyoud, Sa'ed H. H, Zyoud, Samer H, Hay, Simon I, Stergachis, Andy, Sartorius, Benn, Cooper, Ben S, Dolecek, Christiane, and Murray, Christopher J L

Published
2024
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80. Comparison of dexmedetomidine and fentanyl as adjuvants to intrathecal isobaric levobupivacaine in lower segment caesarean section

Author

Rehman Zahid Talha, Hayat Arshad, Rashk-E-hinna, Bashir Rubina, Rauf Asghar Arshman, and Afzal Muhammad

Subjects
levobupivacaine, fentanyl, dexmedetomidine, spinal anaesthesia, adjunct, Medicine
Abstract

Background/Aim: Caesarean section is one the most common surgeries encountered in the operating room worldwide in the younger demo-graphic ages from 18-39 years of age. The objective of this study was to compare the efficacy of dexmedetomidine versus fentanyl as adjuvants to intrathecal levobupivacaine in the lower segment caesarean section. Methods: This quasi-experimental study was carried out at the Anaesthesia Department, Combined Military Hospital, Rawalpindi, Punjab, Pa-kistan from July 2021 to July 2023. A total of 240 patients were studied. They were divided into the dexmedetomidine group (n = 120) and fentanyl group (n = 120) group. Patients in both groups received 2.5 mL of 0.5 % of isobaric levobupivacaine with the dexmedetomidine group receiving 5 mcg of the drug and the fentanyl group 25 mcg of fentanyl to a total volume of 3 mL. Primary variables measured were: time to complete sensory and motor block, total duration of the block, time to first rescue analgesia after block regression in the post anaesthesia care unit (PACU) and mean PACU stay. Secondary variables observed were hypotension, nausea, vomiting and shivering. Results: The time on onset for a sensory block in the dexmedetomidine group was delayed compared to the fentanyl group (4.35 ± 0.14 min and 3.39 ± 0.11 min, respectively), (p < 0.0001). The duration of the block was longer for the dexmedetomidine group with a mean time of 327.26 ± 12.60 min versus 243.3 ± 22.75 min (p < 0.0001). When comparing the motor blockade, the time of onset to successfully reach Bromage score 3 was similarly delayed in the dexmedetomidine group with a mean time of onset of 3.33 ± 0.12 min versus 2.36 ± 0.09 min (p < 0.0001). A similar trend was seen in the duration of the block with a mean time of 262.17 ± 13.31 min versus 203.34 ± 1.47 min (p < 0.0001). Conclusion: Dexmedetomidine offered advantages over fentanyl as an adjunct to levobupivacaine spinal anaesthesia with a longer block duration and less adverse effects profile. It is recommended to use dexmedetomi-dine due to its better safety profile, longer duration and better hemodynamic stability. Fentanyl should be reserved when the early onset of the block is required in emergency cases.

Published
2024
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81. Current Status and Performance Analysis of Table Recognition in Document Images with Deep Neural Networks

Author

Hashmi, Khurram Azeem, Liwicki, Marcus, Stricker, Didier, Afzal, Muhammad Adnan, Afzal, Muhammad Ahtsham, and Afzal, Muhammad Zeshan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

The first phase of table recognition is to detect the tabular area in a document. Subsequently, the tabular structures are recognized in the second phase in order to extract information from the respective cells. Table detection and structural recognition are pivotal problems in the domain of table understanding. However, table analysis is a perplexing task due to the colossal amount of diversity and asymmetry in tables. Therefore, it is an active area of research in document image analysis. Recent advances in the computing capabilities of graphical processing units have enabled deep neural networks to outperform traditional state-of-the-art machine learning methods. Table understanding has substantially benefited from the recent breakthroughs in deep neural networks. However, there has not been a consolidated description of the deep learning methods for table detection and table structure recognition. This review paper provides a thorough analysis of the modern methodologies that utilize deep neural networks. This work provided a thorough understanding of the current state-of-the-art and related challenges of table understanding in document images. Furthermore, the leading datasets and their intricacies have been elaborated along with the quantitative results. Moreover, a brief overview is given regarding the promising directions that can serve as a guide to further improve table analysis in document images., Comment: 23 pages, 14 figures

Published
2021

82. Guided Table Structure Recognition through Anchor Optimization

Author

Hashmi, Khurram Azeem, Stricker, Didier, Liwicki, Marcus, Afzal, Muhammad Noman, and Afzal, Muhammad Zeshan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper presents the novel approach towards table structure recognition by leveraging the guided anchors. The concept differs from current state-of-the-art approaches for table structure recognition that naively apply object detection methods. In contrast to prior techniques, first, we estimate the viable anchors for table structure recognition. Subsequently, these anchors are exploited to locate the rows and columns in tabular images. Furthermore, the paper introduces a simple and effective method that improves the results by using tabular layouts in realistic scenarios. The proposed method is exhaustively evaluated on the two publicly available datasets of table structure recognition i.e ICDAR-2013 and TabStructDB. We accomplished state-of-the-art results on the ICDAR-2013 dataset with an average F-Measure of 95.05$\%$ (94.6$\%$ for rows and 96.32$\%$ for columns) and surpassed the baseline results on the TabStructDB dataset with an average F-Measure of 94.17$\%$ (94.08$\%$ for rows and 95.06$\%$ for columns)., Comment: 13 pages, 8 figures, 5 tables. Submitted to IEEE Access Journal

Published
2021

83. Cingulate dynamics track depression recovery with deep brain stimulation

Author

Alagapan, Sankaraleengam, Choi, Ki Sueng, Heisig, Stephen, Riva-Posse, Patricio, Crowell, Andrea, Tiruvadi, Vineet, Obatusin, Mosadoluwa, Veerakumar, Ashan, Waters, Allison C., Gross, Robert E., Quinn, Sinead, Denison, Lydia, O’Shaughnessy, Matthew, Connor, Marissa, Canal, Gregory, Cha, Jungho, Hershenberg, Rachel, Nauvel, Tanya, Isbaine, Faical, Afzal, Muhammad Furqan, Figee, Martijn, Kopell, Brian H., Butera, Robert, Mayberg, Helen S., and Rozell, Christopher J.

Published
2023
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87. Characterization and antioxidant potential of white mustard (Brassica hirta) leaf extract and stabilization of sunflower oil

Author

Riaz Muhammad, Nawaz Maria, Qadir Rahman, Hussain Shabbir, Roheen Taleeha, Afzal Muhammad, Perviaz Muhammad, Ali Akbar, Aziz Tariq, Alharbi Metab, Albekairi Thamer H., and Alasmari Abdullah F.

Subjects
phytochemistry, peroxide, phenolics, fee fatty acid, dpph, Chemistry, QD1-999
Abstract

The current study aimed to analyze the stabilization of sunflower oil using white mustard (Brassica hirta) leaf extracts as well as their antioxidant potential and cytotoxicity. Selected plant leaves were macerated in different solvents such as n-hexane, chloroform, ethyl acetate, n-butanol, and methanol. The total flavonoid contents, total phenolic contents, the antioxidant activity by DPPH assay, and the inhibition of linoleic acid oxidation were also examined. Moreover, the stabilization of sunflower oil was evaluated by calculating the conjugated dienes, conjugated trienes, peroxide value, p-ansidine, and free fatty acid values. The cytotoxicity study in terms of the hemolytic activity of B. hirta leaf extract was evaluated and the maximum percentage of lysis was found to be safe (less than 5%). The results of the present study revealed that among others, methanolic white mustard (B. hirta) leaf extract possessed higher antioxidant potential and might be safely employed for the stabilization of vegetable oils.

Published
2023
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95. Effect of Simulated Chemistry Practicals on Students' Performance at Secondary School Level

Author

Jabeen, Farkhanda and Afzal, Muhammad Tanveer

Abstract

The study was conducted to compare the performance of students working in chemistry laboratory with those working in chemistry laboratory supplemented with simulations at secondary school level. The study was experimental in nature and post-test only control group design was used. The sample comprised of 55 males and 60 female students and 02 Chemistry teachers of class IX of Public schools. At the end of the treatment, practical examination was conducted on the pattern of Peshawar Board of Intermediate and Secondary Education. The scores of both control and experimental groups were compared by using independent sample t-test in three main areas i.e. written, viva voce and notebook. The result of independent sample t test of school No 1(male) indicated that there is a significance difference between the performance of control group (M=8.9, SD=2.13) and experimental group (M=10.5, SD=3.04) at a=0.05 and df=53. The result of independent sample t test of school No 2(female) indicated that there is a significance difference between the performance of control group (M=10, SD=1.91) and experimental group (M=11.7, SD=2.13) at a=0.05 and df=58. The qualitative data was collected by means of interviews from chemistry teachers. Both the interviewees were motivated and showed keen interest in the simulated software. The performance of the students of experimental groups showed improvement results in the rejection of hypotheses that there is no significant difference between the performance of students taught by conventional demonstration in laboratory and laboratory work facilitated with simulation.

Published
2020

96. An Empirical Analysis of how Internet Access Influences Public Opinion towards Undocumented Immigrants and Unaccompanied Children

Author

Afzal, Muhammad Hassan Bin

Subjects
Economics - General Economics
Abstract

This research adds to the expanding field of data-driven analysis, scientific modeling, and forecasting on the impact of having access to the Internet and IoT on the general US population regarding immigrants and immigration policies. More specifically, this research focuses on the public opinion of undocumented immigrants in the United States and having access to the Internet in their local settings. The term Undocumented Immigrants refers to those who live in the United States without legal papers, documents, or visas. Undocumented immigrants may have come into the country unlawfully or with valid documentation, but their legal status has expired. Using the 2020 American National Election Studies (ANES) time series dataset, I investigated the relationship between internet access (A2I) and public perception of undocumented immigrants. According to my research and analysis, increasing internet access among non-Hispanic whites with at least a bachelors degree with an annual household income of less than 99K is more likely to oppose the deportation of undocumented immigrants and separating unaccompanied children from their families in borderland areas. The individuals with substantial Republican political ideology exhibit significantly lower opposing effects in deporting undocumented immigrants or separating unaccompanied children from their families. The evidence from multiple statistical models is resilient to a variety of factors. The findings show that increased internet access may improve undocumented immigrants social integration and acceptability. During health emergencies, it may be especially beneficial to make them feel safe, included, and supported in their local settings., Comment: 20 pages, 2 Figures, 2 Tables

Published
2021

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