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2. Efficacy and safety of fixed dose combination of Sitagliptin, metformin, and pioglitazone in type 2 Diabetes (IMPACT study): a randomized controlled trial

Author

Mondal Aashish, Naskar Arindam, Sheelu Shafiq Siddiqi, Deepak Bhosle, V. J. Mallikarjuna, Dange Amol, Sorate Sanket, Gavali Omkar, Patel Parth, Hasnani Dhruvi, Prasad Durga, Dalwadi Pradeep, Kumar Suresh, Pathak Vaishali, Chaudhari Mayura, Basu Indraneel, Shembalkar Jayashri, Fariooqui Arif, S. K. Raghavendra, Varade Deepak, Thakkar Ravindra, Bhanushali Shaishav, Gaikwad Vijay, Kamran Khan, V. V. Mahajani, A. D. Sharma, Mayur Mayabhate, R. R. Pawar, A. S. Aiwale, and Shahavi Vinayaka

Subjects
Type 2 diabetes mellitus, IMPACT study, Pioglitazone, Metformin, Sitagliptin, Triple therapy, Diseases of the endocrine glands. Clinical endocrinology, RC648-665
Abstract

Abstract Background Due to the progressive decline in β-cell function, it is often necessary to utilize multiple agents with complementary mechanisms of action to address various facets and achieve glycemic control. Thus, this study aimed to evaluate the efficacy and safety of a fixed-dose combination (FDC) of metformin/sitagliptin/pioglitazone (MSP) therapy vs. metformin/sitagliptin (MS) in type 2 diabetes mellitus (T2DM). Methods In this phase 3, multicenter, double-blind study, patients with T2DM who exhibited inadequate glycemic control with HbA1c of 8.0–11.0% while taking ≥1500 mg/day metformin for at least 6 weeks were randomized to receive either FDC of MSP (1000/100/15 mg) or MS (1000/100 mg) per day for 24 weeks. The primary outcome measure was the change in HbA1c, and secondary outcomes included changes in fasting plasma glucose (FPG), postprandial plasma glucose (PPG), and body weight from baseline to 24 weeks along with safety and tolerability. Results Among the 236 patients randomized, 207 (87.71%) successfully completed the study. All baseline characteristics were comparable between the FDC of MSP and MS groups. There was a subsequent significant reduction of HbA1c in FDC of MSP (− 1.64) vs. MS (− 1.32); between groups was [− 0.32% (95% CI, − 0.59, − 0.05)], P = 0.0208. Similar reductions were found in FPG [− 13.2 mg/dL (95% CI, − 22.86, − 3.71)], P = 0.0068, and PPG [− 20.83 mg/dL (95% CI, − 34.11, − 7.55)], P = 0.0023. There were no significant changes in body weight. A total of 27 adverse effects (AEs) and one severe AE were reported, none of which were related to the study drug. Conclusion The FDC of MSP demonstrated significant efficacy in managing glycemic indices and could serve as a valuable tool for physicians in the management of Indian patients with T2DM. Trial registration Clinical Trials Registry of India, CTRI/2021/10/037461.

Published
2024
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3. Prevalence of chronic cough, its risk factors and population attributable risk in the Burden of Obstructive Lung Disease (BOLD) study: a multinational cross-sectional study

Author

Hafizi, Hasan, Aliko, Anila, Bardhi, Donika, Tafa, Holta, Thanasi, Natasha, Mezini, Arian, Teferici, Alma, Todri, Dafina, Nikolla, Jolanda, Kazasi, Rezarta, Cherkaski, Hamid Hacene, Bengrait, Amira, Haddad, Tabarek, Zgaoula, Ibtissem, Ghit, Maamar, Roubhia, Abdelhamid, Boudra, Soumaya, Atoui, Feryal, Yakoubi, Randa, Benali, Rachid, Bencheikh, Abdelghani, Ait-Khaled, Nadia, Jenkins, Christine, Marks, Guy, Bird, Tessa, Espinel, Paola, Hardaker, Kate, Toelle, Brett, Studnicka, Michael, Dawes, Torkil, Lamprecht, Bernd, Schirhofer, Lea, Islam, Akramul, Ahmed, Syed Masud, Islam, Shayla, Islam, Qazi Shafayetul, Mesbah-Ul-Haque, Chowdhury, Tridib Roy, Chatterjee, Sukantha Kumar, Mia, Dulal, Chandra Das, Shyamal, Rahman, Mizanur, Islam, Nazrul, Uddin, Shahaz, Islam, Nurul, Khatun, Luiza, Parvin, Monira, Khan, Abdul Awal, Islam, Maidul, Lawin, Herve, Kpangon, Arsene, Kpossou, Karl, Agodokpessi, Gildas, Ayelo, Paul, Fayomi, Benjamin, Mbatchou, Bertrand, Ashu, Atongno Humphrey, Tan, Wan C., Wang, Wen, Zhong, NanShan, Liu, Shengming, Lu, Jiachun, Ran, Pixin, Wang, Dali, Zheng, Jin-ping, Zhou, Yumin, Jogi, Rain, Laja, Hendrik, Ulst, Katrin, Zobel, Vappu, Lill, Toomas-Julius, Adegnika, Ayola Akim, Welte, Tobias, Bodemann, Isabelle, Geldmacher, Henning, SchwedaLinow, Alexandra, Gislason, Thorarinn, Benedikdtsdottir, Bryndis, Jorundsdottir, Kristin, Lovisa Gudmundsdottir, Gudmundsdottir, Sigrun, Gudmundsson, Gunnar, Rao, Mahesh, Koul, Parvaiz A., Malik, Sajjad, Hakim, Nissar A., Khan, Umar Hafiz, Chowgule, Rohini, Shetye, Vasant, Raphael, Jonelle, Almeda, Rosel, Tawde, Mahesh, Tadvi, Rafiq, Katkar, Sunil, Kadam, Milind, Dhanawade, Rupesh, Ghurup, Umesh, Juvekar, Sanjay, Hirve, Siddhi, Sambhudas, Somnath, Chaidhary, Bharat, Tambe, Meera, Pingale, Savita, Umap, Arati, Umap, Archana, Shelar, Nitin, Devchakke, Sampada, Chaudhary, Sharda, Bondre, Suvarna, Walke, Savita, Gawhane, Ashleshsa, Sapkal, Anil, Argade, Rupali, Gaikwad, Vijay, Salvi, Sundeep, Brashier, Bill, Londhe, Jyoti, Madas, Sapna, Aquart-Stewart, Althea, Aikman, Akosua Francia, Sooronbaev, Talant M., Estebesova, Bermet M., Akmatalieva, Meerim, Usenbaeva, Saadat, Kydyrova, Jypara, Bostonova, Eliza, Sheraliev, Ulan, Marajapov, Nuridin, Toktogulova, Nurgul, Emilov, Berik, Azilova, Toktogul, Beishekeeva, Gulnara, Dononbaeva, Nasyikat, Tabyshova, Aijamal, Mortimer, Kevin, Nyapigoti, Wezzie, Mwangoka, Ernest, Kambwili, Mayamiko, Chipeta, Martha, Banda, Gloria, Mkandawire, Suzgo, Banda, Justice, Loh, Li-Cher, Rashid, Abdul, Sholehah, Siti, Benjelloun, Mohamed C., Nejjari, Chakib, Elbiaze, Mohamed, El Rhazi, Karima, Wouters, E.F.M., Wesseling, G.J., Obaseki, Daniel, Erhabor, Gregory, Awopeju, Olayemi, Adewole, Olufemi, Gulsvik, Amund, Endresen, Tina, Svendsen, Lene, Nafees, Asaad A., Irfan, Muhammad, Fatmi, Zafar, Zahidie, Aysha, Shaukat, Natasha, Iqbal, Meesha, Idolor, Luisito F., de Guia, Teresita S., Francisco, Norberto A., Roa, Camilo C., Ayuyao, Fernando G., Tady, Cecil Z., Tan, Daniel T., Banal-Yang, Sylvia, Balanag, Vincent M., Jr., Reyes, Maria Teresita N., Dantes, Renato B., Amarillo, Lourdes, Berratio, Lakan U., Fernandez, Lenora C., Garcia, Gerard S., Naval, Sullian S., Reyes, Thessa, Roa, Camilo C., Jr., Sanchez, Flordeliza, Simpao, Leander P., Nizankowska-Mogilnicka, Ewa, Frey, Jakub, Harat, Rafal, Mejza, Filip, Nastalek, Pawel, Pajak, Andrzej, Skucha, Wojciech, Szczeklik, Andrzej, Twardowska, Magda, Barbara, Cristina, Rodrigues, Fatima, Dias, Herminia, Cardoso, Joao, Almeida, João, Matos, Maria Joao, Simão, Paula, Santos, Moutinho, Ferreira, Reis, Al Ghobain, M., Alorainy, H., El-Hamad, E., Al Hajjaj, M., Hashi, A., Dela, R., Fanuncio, R., Doloriel, E., Marciano, I., Safia, L., Bateman, Eric, Jithoo, Anamika, Adams, Desiree, Barnes, Edward, Freeman, Jasper, Hayes, Anton, Hlengwa, Sipho, Johannisen, Christine, Koopman, Mariana, Louw, Innocentia, Ludick, Ina, Olckers, Alta, Ryck, Johanna, Storbeck, Janita, Gunasekera, Kirthi, Wickremasinghe, Rajitha, Elsony, Asma, Elsadig, Hana A., Osman, Nada Bakery, Noory, Bandar Salah, Mohamed, Monjda Awad, Akasha Ahmed Osman, Hasab Alrasoul, Moham ed Elhassan, Namarig, El Zain, Abdel Mu’is, Mohamaden, Marwa Mohamed, Khalifa, Suhaiba, Elhadi, Mahmoud, Hassan, Mohand, Abdelmonam, Dalia, Janson, Christer, Olafsdottir, Inga Sif, Nisser, Katarina, SpetzNystrom, Ulrike, Hagg, Gunilla, Lund, GunMarie, Seemungal, Terence, Lutchmansingh, Fallon, Conyette, Liane, Harrabi, Imed, Denguezli, Myriam, Tabka, Zouhair, Daldoul, Hager, Boukheroufa, Zaki, Chouikha, Firas, Khalifa, Wahbi Belhaj, Kocabas, Ali, Hancioglu, Attila, Hanta, Ismail, Kuleci, Sedat, Turkyilmaz, Ahmet Sinan, Umut, Sema, Unalan, Turgay, Burney, Peter G.J., Gnatiuc, Louisa, Azar, Hadia, Patel, Jaymini, Amor, Caron, Potts, James, Tumilty, Michael, McLean, Fiona, Dudhaiya, Risha, Buist, A. Sonia, McBurnie, Mary Ann, Vollmer, William M., Gillespie, Suzanne, Sullivan, Sean, Lee, Todd A., Weiss, Kevin B., Jensen, Robert L., Crapo, Robert, Enright, Paul, Mannino, David M., Cain, John, Copeland, Rebecca, Hazen, Dana, Methvin, Jennifer, Abozid, Hazim, Burney, Peter, Hartl, Sylvia, Breyer-Kohansal, Robab, Al Ghobain, Mohammed, Denguezli, Meriam, Loh, Li Cher, Paraguas, Stefanni Nonna, Franssen, Frits M.E., Mannino, David, Anand, Mahesh Padukudru, Buist, Sonia, El Sony, Asma, Breyer, Marie-Kathrin, Burghuber, Otto C., Wouters, Emiel F.M., and Amaral, Andre F.S.

Published
2024
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5. Improving performance of IoT applications using edge computing.

Author

Gaikwad, Vijay, Patel, Nehali, Dhikale, Aarti, Utekar, Sejal, and Dogra, Manik

Abstract

This paper describes about various paradigms of Edge Computing (EC) for applications of Internet of Things (IoT), as the data generated by IoT is huge in amount thus the need of Big Data and Cloud was identified. Cloud Computing (CC) started to face the problem of latency, higher bandwidth, response time, security etc. and this was the moment where it was realized that EC can be a better alternative. Limitations caused due to CC were recovered by EC. This paper discusses about four important platforms of EC that is Cloudlet, Fog Computing, Mobile Edge Computing (MEC) and FemtoCloud. The working and applications are also described in the proposed work. Comparison of these four platforms is presented and important conclusions are drawn to identify the paradigm which can work on diverse platforms as well as provide efficient nodal communication. [ABSTRACT FROM AUTHOR]

Published
2024
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6. Choosing the best metrics for quantifying the quality of the model in skewed binary classification problems.

Author

Hedaoo, Anushka, Gaikwad, Vijay, Ghadekar, Premanand, and Jalnekar, Rajesh

Abstract

Evaluating the performance of a predictive model trained on an imbalanced class distribution has encountered significant challenges as most of the widely used metrics are designed considering balanced class distribution datasets. This paper presents a study on four different biasing of target class variables in train set: (50% majority class, 50% minority class), (60% majority class, 40% minority class), (70% majority class, 30% minority class), (80% majority class, 20% minority class), and focuses on choosing the right metrics out of accuracy, precision, recall, f1 score and AUC score for a binary classification problem with a skewed class distribution. The algorithms considered are Logistic Regression, K Nearest Neighbors, Naïve Bayes. Analysis of all four biasing cases, five evaluation metrics for three algorithms have been presented in this paper. [ABSTRACT FROM AUTHOR]

Published
2024
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7. Overdiagnosis of COPD in Subjects With Unobstructed Spirometry: A BOLD Analysis

Author

Zhong, NanShan, Liu, Shengming, Lu, Jiachun, Ran, Pixin, Wang, Dali, Zheng, Jingping, Zhou, Yumin, Kocabaş, Ali, Hancioglu, Attila, Hanta, Ismail, Kuleci, Sedat, Turkyilmaz, Ahmet Sinan, Umut, Sema, Unalan, Turgay, Studnicka, Michael, Dawes, Torkil, Lamprecht, Bernd, Sator, Lea, Bateman, Eric, Jithoo, Anamika, Adams, Desiree, Barnes, Edward, Freeman, Jasper, Hayes, Anton, Hlengwa, Sipho, Johannisen, Christine, Koopman, Mariana, Louw, Innocentia, Ludick, Ina, Olckers, Alta, Ryck, Johanna, Storbeck, Janita, Gislason, Thorarinn, Benedikdtsdottir, Bryndis, Jörundsdottir, Kristin, Gudmundsdottir, Lovisa, Gudmundsdottir, Sigrun, Gundmundsson, Gunnar, Nizankowska-Mogilnicka, Ewa, Frey, Jakub, Harat, Rafal, Mejza, Filip, Nastalek, Pawel, Pajak, Andrzej, Skucha, Wojciech, Szczeklik, Andrzej, Twardowska, Magda, Welte, Tobias, Bodemann, Isabelle, Geldmacher, Henning, Schweda-Linow, Alexandra, Gulsvik, Amund, Endresen, Tina, Svendsen, Lene, Tan, Wan C., Wang, Wen, Mannino, David M., Cain, John, Copeland, Rebecca, Hazen, Dana, Methvin, Jennifer, Dantes, Renato B., Amarillo, Lourdes, Berratio, Lakan U., Fernandez, Lenora C., Francisco, Norberto A., Garcia, Gerard S., de Guia, Teresita S., Idolor, Luisito F., Naval, Sullian S., Reyes, Thessa, Roa, Camilo C., Jr., Sanchez, Ma. Flordeliza, Simpao, Leander P., Jenkins, Christine, Marks, Guy, Bird, Tessa, Espinel, Paola, Hardaker, Kate, Toelle, Brett, Burney, Peter G.J., Amor, Caron, Potts, James, Tumilty, Michael, McLean, Fiona, Wouters, E.F.M., Wesseling, G.J., Bárbara, Cristina, Rodrigues, Fátima, Dias, Hermínia, Cardoso, João, Almeida, João, Matos, Maria João, Simão, Paula, Santos, Moutinho, Ferreira, Reis, Janson, Christer, Olafsdottir, Inga Sif, Nisser, Katarina, Spetz-Nyström, Ulrike, Hägg, Gunilla, Lund, Gun-Marie, Jõgi, Rain, Laja, Hendrik, Ulst, Katrin, Zobel, Vappu, Lill, Toomas-Julius, Koul, Parvaiz A., Malik, Sajjad, Hakim, Nissar A., Khan, Umar Hafiz, Chowgule, Rohini, Shetye, Vasant, Raphael, Jonelle, Almeda, Rosel, Tawde, Mahesh, Tadvi, Rafiq, Katkar, Sunil, Kadam, Milind, Dhanawade, Rupesh, Ghurup, Umesh, Harrabi, Imed, Denguezli, Myriam, Tabka, Zouhair, Daldoul, Hager, Boukheroufa, Zaki, Chouikha, Firas, Khalifa, Wahbi Belhaj, Roa, Camilo C., Ayuyao, Fernando G., Tady, Cecil Z., Tan, Daniel T., Banal-Yang, Sylvia, Balanag, Vincent M., Jr., Reyes, Maria Teresita N., Juvekar, Sanjay, Hirve, Siddhi, Sambhudas, Somnath, Chaidhary, Bharat, Tambe, Meera, Pingale, Savita, Umap, Arati, Umap, Archana, Shelar, Nitin, Devchakke, Sampada, Chaudhary, Sharda, Bondre, Suvarna, Walke, Savita, Gawhane, Ashleshsa, Sapkal, Anil, Argade, Rupali, Gaikwad, Vijay, Salvi, Sundeep, Brashier, Bill, Londhe, Jyoti, Madas, Sapna, Obaseki, Daniel, Erhabor, Gregory, Awopeju, Olayemi, Adewole, Olufemi, Horner, Andreas, Kaiser, Bernhard, McBurnie, Mary Ann, Buist, A. Sonia, Gnatiuc, Luisa, Bateman, Eric D., and Burney, Peter

Published
2019
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9. Smart Agriculture and Plant Disease Prediction.

Author

Gaikwad, Vijay, Patil, Gaurav N., Patil, Gaurav R., Phad, Chaitanya, Pujari, Himaja, and Rathod, Payal

Subjects
MACHINE learning, SUSTAINABLE agriculture, SUSTAINABILITY, AGRICULTURE, AGRICULTURAL technology, FEATURE extraction
Abstract

Smart agriculture and plant disease prediction have become important areas of research due to the increasing demand for food production and the need for sustainable farming practices. The paper provides a review of smart agricultural practises and how machine learning algorithms are used to predict plant diseases. Smart agriculture is based on the use of technology such as the Internet of Things (IoT) which enable farmers to monitor and manage their farms remotely. Additionally, machine learning algorithms can be used to accurately identify plant diseases through analysing the input plant image. Three machine learning algorithms have been used in this paper i.e. SVM, VGG19, and KNN. VGG16 is used for the feature extraction. With the 13 different types of diseases, highest accuracy system achieved is 99%. [ABSTRACT FROM AUTHOR]

Published
2023

10. Smart Agriculture System using IoT.

Author

Gaikwad, Vijay, Kate, Sumedh, Khandelwal, Dipkul, Khandetod, Arya, Khaple, Siddesh, and Kharade, Aishwarya

Subjects
INTERNET of things, CROP yields, AGRICULTURE, SOIL temperature, SYSTEMS development
Abstract

Agriculture plays a vital role in the development of India, about 70% of the population is directly or indirectly dependent on agriculture, agriculture contributes 20% of India's GDP. Smart Agriculture is an application of IoT(Internet of Things) in agricultural practices. Use of the technology will be very helpful and beneficial for farmers and thereby for the country. This project will automate various aspects of the agricultural domain and thus help to reduce the burden of farmers. In the Smart Agriculture project, there has been a use of various sensors, NodeMCU, LCD and many other peripherals used in IoT. These devices help to detect various parameters involved in the agricultural field. IoT modernization helps in gathering information on circumstances like climate, dampness, temperature and fruitfulness of soil, by detecting these readings farmers can change their farming practices accordingly, to get maximum yield and a good crop. As the population is predicted to reach 10 billion people in 2060, and as a result of the population's rapid rise, there is a sudden surge in demand for food. Unfortunately, this increase in demand is only tangentially related to population growth. That is why there is a need for development of a system that will sustain this rising demand. [ABSTRACT FROM AUTHOR]

Published
2023

11. Chronic obstructive pulmonary disease mortality and prevalence: the associations with smoking and poverty—a BOLD analysis

Author

Burney, Peter, Jithoo, Anamika, Kato, Bernet, Janson, Christer, Mannino, David, Niżankowska-Mogilnicka, Ewa, Studnicka, Michael, Tan, Wan, Bateman, Eric, Koçabas, Ali, Vollmer, William M, Gislason, Thorarrin, Marks, Guy, Koul, Parvaiz A, Harrabi, Imed, Gnatiuc, Louisa, Buist, Sonia, Zhong, NanShan, Liu, Shengming, Lu, Jiachun, Ran, Pixin, Wang, Dali, Zheng, Jingping, Zhou, Yumin, Kocabaş, Ali, Hancioglu, Attila, Hanta, Ismail, Kuleci, Sedat, Turkyilmaz, Ahmet Sinan, Umut, Sema, Unalan, Turgay, Studnicka, Michael, Dawes, Torkil, Lamprecht, Bernd, Schirhofer, Lea, Bateman, Eric, Jithoo, Anamika, Adams, Desiree, Barnes, Edward, Freeman, Jasper, Hayes, Anton, Hlengwa, Sipho, Johannisen, Christine, Koopman, Mariana, Louw, Innocentia, Ludick, Ina, Olckers, Alta, Ryck, Johanna, Storbeck, Janita, Gislason, Thorarinn, Benedikdtsdottir, Bryndis, Jörundsdottir, Kristin, Gudmundsdottir, Lovisa, Gudmundsdottir, Sigrun, Gundmundsson, Gunnar, Nizankowska-Mogilnicka, Ewa, Frey, Jakub, Harat, Rafal, Mejza, Filip, Nastalek, Pawel, Pajak, Andrzej, Skucha, Wojciech, Szczeklik, Andrzej, Twardowska, Magda, Welte, Tobias, Bodemann, Isabelle, Geldmacher, Henning, Schweda-Linow, Alexandra, Gulsvik, Amund, Endresen, Tina, Svendsen, Lene, Tan, Wan C, Wang, Wen, Mannino, David M, Cain, John, Copeland, Rebecca, Hazen, Dana, Methvin, Jennifer, Dantes, Renato B, Amarillo, Lourdes, Berratio, Lakan U, Fernandez, Lenora C, Francisco, Norberto A, Garcia, Gerard S, de Guia, Teresita S, Idolor, Luisito F, Naval, Sullian S, Reyes, Thessa, Roa, Camilo C, Sanchez, Ma Flordeliza, Simpao, Leander P, Jenkins, Christine, Marks, Guy, Bird, Tessa, Espinel, Paola, Hardaker, Kate, Toelle, Brett, Burney, Peter G J, Amor, Caron, Potts, James, Tumilty, Michael, McLean, Fiona, Wouters, E F M, Wesseling, G J, Bárbara, Cristina, Rodrigues, Fátima, Dias, Hermínia, Cardoso, João, Almeida, João, Matos, Maria João, Simão, Paula, Santos, Moutinho, Ferreira, Reis, Janson, Christer, Olafsdottir, Inga Sif, Nisser, Katarina, Spetz-Nyström, Ulrike, Hägg, Gunilla, Lund, Gun-Marie, Jõgi, Rain, Laja, Hendrik, Ulst, Katrin, Zobel, Vappu, Lill, Toomas-Julius, Koul, Parvaiz A, Malik, Sajjad, Hakim, Nissar A, Khan, Umar Hafiz, Chowgule, Rohini, Shetye, Vasant, Raphael, Jonelle, Almeda, Rosel, Tawde, Mahesh, Tadvi, Rafiq, Katkar, Sunil, Kadam, Milind, Dhanawade, Rupesh, Ghurup, Umesh, Harrabi, Imed, Denguezli, Myriam, Tabka, Zouhair, Daldoul, Hager, Boukheroufa, Zaki, Chouikha, Firas, Khalifa, Wahbi Belhaj, Idolor, Luisito F, de Guia, Teresita S, Francisco, Norberto A, Roa, Camilo C, Ayuyao, Fernando G, Tady, Cecil Z, Tan, Daniel T, Banal-Yang, Sylvia, Balanag, Vincent M, Reyes, Maria Teresita N, Dantes, Renato B, Salvi, Sundeep, Hirve, Siddhi, Brashier, Bill, Londhe, Jyoti, Madas, Sapna, Sambhudas, Somnath, Chaidhary, Bharat, Tambe, Meera, Pingale, Savita, Umap, Arati, Umap, Archana, Shelar, Nitin, Devchakke, Sampada, Chaudhary, Sharda, Bondre, Suvarna, Walke, Savita, Gawhane, Ashleshsa, Sapkal, Anil, Argade, Rupali, Gaikwad, Vijay, Crapo, R O, Jensen, R L, Enright, Paul, and Harnoncourt, Georg

Published
2014
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12. COPD: Should Diagnosis Match Physiology?

Author

Zhong, NanShan, Liu, Shengming, Lu, Jiachun, Ran, Pixin, Wang, Dali, Zheng, Jingping, Zhou, Yumin, Kocabaş, Ali, Hancioglu, Attila, Hanta, Ismail, Kuleci, Sedat, Turkyilmaz, Ahmet Sinan, Umut, Sema, Unalan, Turgay, Studnicka, Michael, Dawes, Torkil, Lamprecht, Bernd, Sator, Lea, Bateman, Eric, Jithoo, Anamika, Adams, Desiree, Barnes, Edward, Freeman, Jasper, Hayes, Anton, Hlengwa, Sipho, Johannisen, Christine, Koopman, Mariana, Louw, Innocentia, Ludick, Ina, Olckers, Alta, Ryck, Johanna, Storbeck, Janita, Gislason, Thorarinn, Benedikdtsdottir, Bryndis, Jörundsdottir, Kristin, Gudmundsdottir, Lovisa, Gudmundsdottir, Sigrun, Gundmundsson, Gunnar, Nizankowska-Mogilnicka, Ewa, Frey, Jakub, Harat, Rafal, Mejza, Filip, Nastalek, Pawel, Pajak, Andrzej, Skucha, Wojciech, Szczeklik, Andrzej, Twardowska, Magda, Welte, Tobias, Bodemann, Isabelle, Geldmacher, Henning, Schweda-Linow, Alexandra, Gulsvik, Amund, Endresen, Tina, Svendsen, Lene, Tan, Wan C., Wang, Wen, Mannino, David M., Cain, John, Copeland, Rebecca, Hazen, Dana, Methvin, Jennifer, Dantes, Renato B., Amarillo, Lourdes, Berratio, Lakan U., Fernandez, Lenora C., Francisco, Norberto A., Garcia, Gerard S., de Guia, Teresita S., Idolor, Luisito F., Naval, Sullian S., Reyes, Thessa, Roa, Camilo C., Jr, Sanchez, Ma. Flordeliza, Simpao, Leander P., Jenkins, Christine, Marks, Guy, Bird, Tessa, Espinel, Paola, Hardaker, Kate, Toelle, Brett, Burney, Peter G.J., Amor, Caron, Potts, James, Tumilty, Michael, McLean, Fiona, Wouters, E.F.M., Wesseling, G.J., Bárbara, Cristina, Rodrigues, Fátima, Dias, Hermínia, Cardoso, João, Almeida, João, Matos, Maria João, Simão, Paula, Santos, Moutinho, Ferreira, Reis, Janson, Christer, Olafsdottir, Inga Sif, Nisser, Katarina, Spetz-Nyström, Ulrike, Hägg, Gunilla, Lund, Gun-Marie, Jõgi, Rain, Laja, Hendrik, Ulst, Katrin, Zobel, Vappu, Lill, Toomas-Julius, Koul, Parvaiz A., Malik, Sajjad, Hakim, Nissar A., Khan, Umar Hafiz, Chowgule, Rohini, Shetye, Vasant, Raphael, Jonelle, Almeda, Rosel, Tawde, Mahesh, Tadvi, Rafiq, Katkar, Sunil, Kadam, Milind, Dhanawade, Rupesh, Ghurup, Umesh, Harrabi, Imed, Denguezli, Myriam, Tabka, Zouhair, Daldoul, Hager, Boukheroufa, Zaki, Chouikha, Firas, Khalifa, Wahbi Belhaj, Roa, Camilo C., Ayuyao, Fernando G., Tady, Cecil Z., Tan, Daniel T., Banal-Yang, Sylvia, Balanag, Vincent M., Jr, Reyes, Maria Teresita N., Dantes, Renato. B., Juvekar, Sanjay, Hirve, Siddhi, Sambhudas, Somnath, Chaidhary, Bharat, Tambe, Meera, Pingale, Savita, Umap, Arati, Umap, Archana, Shelar, Nitin, Devchakke, Sampada, Chaudhary, Sharda, Bondre, Suvarna, Walke, Savita, Gawhane, Ashleshsa, Sapkal, Anil, Argade, Rupali, Gaikwad, Vijay, Salvi, Sundeep, Brashier, Bill, Londhe, Jyoti, Madas, Sapna, Obaseki, Daniel, Erhabor, Gregory, Awopeju, Olayemi, Adewole, Olufemi, Horner, Andreas, and Buist, A. Sonia

Published
2020
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13. COPD: should diagnosis match physiology?

Author

Studnicka, Michael, Horner, Andreas, Sator, Lea, Buist, A. Sonia, Lamprecht, Bernd, Zhong, NanShan, Liu, Shengming, Lu, Jiachun, Ran, Pixin, Wang, Dali, Zheng, Jingping, Zhou, Yumin, Kocabaş, Ali, Hancioglu, Attila, Hanta, Ismail, Kuleci, Sedat, Turkyilmaz, Ahmet Sinan, Umut, Sema, Unalan, Turgay, Dawes, Torkil, Bateman, Eric, Jithoo, Anamika, Adams, Desiree, Barnes, Edward, Freeman, Jasper, Hayes, Anton, Hlengwa, Sipho, Johannisen, Christine, Koopman, Mariana, Louw, Innocentia, Ludick, Ina, Olckers, Alta, Ryck, Johanna, Storbeck, Janita, Gislason, Thorarinn, Benedikdtsdottir, Bryndis, Jörundsdottir, Kristin, Gudmundsdottir, Lovisa, Gudmundsdottir, Sigrun, Gundmundsson, Gunnar, Nizankowska-Mogilnicka, Ewa, Frey, Jakub, Harat, Rafal, Mejza, Filip, Nastalek, Pawel, Pajak, Andrzej, Skucha, Wojciech, Szczeklik, Andrzej, Twardowska, Magda, Welte, Tobias, Bodemann, Isabelle, Geldmacher, Henning, Schweda-Linow, Alexandra, Gulsvik, Amund, Endresen, Tina, Svendsen, Lene, Tan, Wan C., Wang, Wen, Mannino, David M., Cain, John, Copeland, Rebecca, Hazen, Dana, Methvin, Jennifer, Dantes, Renato B., Amarillo, Lourdes, Berratio, Lakan U., Fernandez, Lenora C., Francisco, Norberto A., Garcia, Gerard S., de Guia, Teresita S., Idolor, Luisito F., Naval, Sullian S., Reyes, Thessa, Roa, Camilo C., Sanchez, Ma. Flordeliza, Simpao, Leander P., Jenkins, Christine, Marks, Guy, Bird, Tessa, Espinel, Paola, Hardaker, Kate, Toelle, Brett, Burney, Peter G.J., Amor, Caron, Potts, James, Tumilty, Michael, McLean, Fiona, Wouters, E.F.M., Wesseling, G.J., Bárbara, Cristina, Rodrigues, Fátima, Dias, Hermínia, Cardoso, João, Almeida, João, Matos, Maria João, Simão, Paula, Santos, Moutinho, Ferreira, Reis, Janson, Christer, Olafsdottir, Inga Sif, Nisser, Katarina, Spetz-Nyström, Ulrike, Hägg, Gunilla, Lund, Gun-Marie, Jõgi, Rain, Laja, Hendrik, Ulst, Katrin, Zobel, Vappu, Lill, Toomas-Julius, Koul, Parvaiz A., Malik, Sajjad, Hakim, Nissar A., Khan, Umar Hafiz, Chowgule, Rohini, Shetye, Vasant, Raphael, Jonelle, Almeda, Rosel, Tawde, Mahesh, Tadvi, Rafiq, Katkar, Sunil, Kadam, Milind, Dhanawade, Rupesh, Ghurup, Umesh, Harrabi, Imed, Denguezli, Myriam, Tabka, Zouhair, Daldoul, Hager, Boukheroufa, Zaki, Chouikha, Firas, Khalifa, Wahbi Belhaj, Ayuyao, Fernando G., Tady, Cecil Z., Tan, Daniel T., Banal-Yang, Sylvia, Balanag, Vincent M., Reyes, Maria Teresita N., Dantes, Renato. B., Juvekar, Sanjay, Hirve, Siddhi, Sambhudas, Somnath, Chaidhary, Bharat, Tambe, Meera, Pingale, Savita, Umap, Arati, Umap, Archana, Shelar, Nitin, Devchakke, Sampada, Chaudhary, Sharda, Bondre, Suvarna, Walke, Savita, Gawhane, Ashleshsa, Sapkal, Anil, Argade, Rupali, Gaikwad, Vijay, Salvi, Sundeep, Brashier, Bill, Londhe, Jyoti, Madas, Sapna, Obaseki, Daniel, Erhabor, Gregory, Awopeju, Olayemi, and Adewole, Olufemi

Subjects
Spirometry, Chronic obstructive pulmonary disease, Humans
Abstract

Submitted by Maria da Luz Antunes (mluz.antunes@estesl.ipl.pt) on 2020-12-08T16:58:31Z No. of bitstreams: 1 COPD_should diagnosis match physiology.pdf: 83490 bytes, checksum: bbbfcb5fa3e4d33e5d07d47e299641c6 (MD5) Made available in DSpace on 2020-12-08T16:58:31Z (GMT). No. of bitstreams: 1 COPD_should diagnosis match physiology.pdf: 83490 bytes, checksum: bbbfcb5fa3e4d33e5d07d47e299641c6 (MD5) Previous issue date: 2020-02 info:eu-repo/semantics/publishedVersion

Published
2020

14. On the role of upset pressure during friction welding of IN713LC and AISI 4140.

Author

Gaikwad, Vijay T., Mishra, M. K., Tripathi, Abhishek, and Singh, R. K. P.

Subjects
*FRICTION welding, *MATERIAL plasticity, *WELDING, *HEAT resistant alloys, *MICROSTRUCTURE
Abstract

Effect of upset pressure on evolution of microstructure and mechanical properties for rotary friction welding of IN713LC superalloy and AISI 4140 steel have been investigated. Microstructure observation shows that with increasing upset pressure, grain refining effect of plastic deformation overrides grain growth effect of heat input, leading to finer prior austenite grains on AISI 4140 side. Dendritic structure of IN713LC transformed into fine equiaxed grains. Weld joints fabricated using low upset pressure showed unbonded regions with curved shape profile. However, weld joints made using high upset pressure showed nearly straight-line joint profile and was found to be favourable for adequate plastic deformation and achieving better bonding. Improvement in mechanical properties of weld joint was observed with increasing upset pressure. [ABSTRACT FROM AUTHOR]

Published
2024
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18. Vision Based Pedestrian Detection for Advanced Driver assistance.

Author

Gaikwad, Vijay and Lokhande, Shashikant

Subjects
PEDESTRIANS, TRAFFIC safety, REAL-time computing, REAL-time programming, ONLINE data processing, COMPUTER network resources
Abstract

In pedestrian detection intricate feature descriptors are used to improve the detection rate at the cost of computational complexity. In this paper, we propose a detector based on simple, robust edgelet features to enhance the detection rate and classifier based on k-means clustering approach to reduce computational complexity. The proposed framework consists of extraction of candidate features of pedestrian detection using edgelet features and use of the cascade structure of k-means clustering for classification enabling high detection accuracy at low false positives. Experimental results show that the proposed method requires less processing time per frame, making it suitable for real-time systems. [ABSTRACT FROM AUTHOR]

Published
2015
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19. Lane Departure Identification for Advanced Driver Assistance.

Author

Gaikwad, Vijay and Lokhande, Shashikant

Abstract

In this paper, a technique for the identification of the unwanted lane departure of a traveling vehicle on a road is proposed. A piecewise linear stretching function (PLSF) is used to improve the contrast level of the region of interest (ROI). Lane markings on the road are detected by dividing the ROI into two subregions and applying the Hough transform in each subregion independently. This segmentation approach improves the computational time required for lane detection. For lane departure identification, a distance-based departure measure is computed at each frame, and a necessary warning message is issued to the driver when such measure exceeds a threshold. The novelty of the proposed algorithm is the identification of the lane departure only using three lane-related parameters based on the Euclidean distance transform to estimate the departure measure. The use of the Euclidean distance transform in combination with the PLSF keeps the false alarm around 3% and the lane detection rate above 97% under various lighting conditions. Experimental results indicate that the proposed system can detect lane boundaries in the presence of several image artifacts, such as lighting changes, poor lane markings, and occlusions by a vehicle, and it issues an accurate lane departure warning in a short time interval. The proposed technique shows the efficiency with some real video sequences. [ABSTRACT FROM PUBLISHER]

Published
2015
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20. An improved lane departure method for Advanced Driver Assistance System.

Author

Gaikwad, Vijay and Lokhande, Shashikant

Abstract

This paper proposes an improved lane departure algorithm based on region of interest (ROI) segmentation. ROI of a test image is extracted first. Based on statistical parameters a departure warning is generated. Hough transform is used to detect the lines in the segmented ROI. Distance between Hough origin and lane-line midpoint is estimated. Based on the difference between these distances, a lane departure decision is made. A typical road may have multiple lanes which may cause difficulty in generating appropriate departure decision with good accuracy and in short time. Experiments show that the proposed algorithm eliminates this problem. It is simple to implement and robust, and can detect the lane markings accurately and quickly. [ABSTRACT FROM PUBLISHER]

Published
2012
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