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Article: A Study on High-Rate Performance of Graphite Nanostructures Produced by Ball Milling as Anode for Lithium-Ion Batteries.

Ghanooni Ahmadabadi, Vahide / Rahman, Md Mokhlesur / Chen, Ying

2023 Volume 14, Issue 1

Abstract: Graphite, with appealing features such as good stability, high electrical conductivity, and natural abundance, is still the main commercial anode material for lithium-ion batteries. The charge-discharge rate capability of graphite anodes is not ... ...

Abstract	Graphite, with appealing features such as good stability, high electrical conductivity, and natural abundance, is still the main commercial anode material for lithium-ion batteries. The charge-discharge rate capability of graphite anodes is not significant for the development of mobile devices and electric vehicles. Therefore, the feasibility investigation of the rate capability enhancement of graphite by manipulating the structure is worthwhile and of interest. In this study, an effective ball-milling process has been set up by which graphite nanostructures with a high surface area are produced. An in-depth investigation into the effect of ball milling on graphite structure as well as electrochemical performance, particularly rate capability, is conducted. Here, we report that graphite nanoflakes with 350 m
Language	English
Publishing date	2023-01-12
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2620864-7
ISSN	2072-666X
ISSN	2072-666X
DOI	10.3390/mi14010191
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Article ; Online: Associations between COVID-19 Pandemic, Lockdown Measures and Human Mobility: Longitudinal Evidence from 86 Countries.

Rahman, Md Mokhlesur / Thill, Jean-Claude

International journal of environmental research and public health

2022 Volume 19, Issue 12

Abstract: Recognizing an urgent need to understand the dynamics of the pandemic's severity, this longitudinal study is conducted to explore the evolution of complex relationships between the COVID-19 pandemic, lockdown measures, and social distancing patterns in a ...

Abstract	Recognizing an urgent need to understand the dynamics of the pandemic's severity, this longitudinal study is conducted to explore the evolution of complex relationships between the COVID-19 pandemic, lockdown measures, and social distancing patterns in a diverse set of 86 countries. Collecting data from multiple sources, a structural equation modeling (SEM) technique is applied to understand the interdependencies between independent variables, mediators, and dependent variables. Results show that lockdown and confinement measures are very effective to reduce human mobility at retail and recreation facilities, transit stations, and workplaces and encourage people to stay home and thereby control COVID-19 transmission at critical times. The study also found that national contexts rooted in socioeconomic and institutional factors influence social distancing patterns and severity of the pandemic, particularly with regard to the vulnerability of people, treatment costs, level of globalization, employment distribution, and degree of independence in society. Additionally, this study portrayed a mutual relationship between the COVID-19 pandemic and human mobility. A higher number of COVID-19 confirmed cases and deaths reduces human mobility and the countries with reduced personal mobility have experienced a deepening of the severity of the pandemic. However, the effect of mobility on pandemic severity is stronger than the effect of pandemic situations on mobility. Overall, the study displays considerable temporal changes in the relationships between independent variables, mediators, and dependent variables considering pandemic situations and lockdown regimes, which provides a critical knowledge base for future handling of pandemics. It has also accommodated some policy guidelines for the authority to control the transmission of COVID-19.
MeSH term(s)	COVID-19/epidemiology ; Communicable Disease Control ; Humans ; Longitudinal Studies ; Pandemics/prevention & control ; SARS-CoV-2
Language	English
Publishing date	2022-06-14
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2175195-X
ISSN	1660-4601 ; 1661-7827
ISSN (online)	1660-4601
ISSN	1661-7827
DOI	10.3390/ijerph19127317
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Article ; Online: Pandemic vulnerability index of US cities: A hybrid knowledge-based and data-driven approach.

Rahman, Md Shahinoor / Paul, Kamal Chandra / Rahman, Md Mokhlesur / Samuel, Jim / Thill, Jean-Claude / Hossain, Md Amjad / Ali, G G Md Nawaz

Sustainable cities and society

2023 Volume 95, Page(s) 104570

Abstract: Cities become mission-critical zones during pandemics and it is vital to develop a better understanding of the factors that are associated with infection levels. The COVID-19 pandemic has impacted many cities severely; however, there is significant ... ...

Abstract	Cities become mission-critical zones during pandemics and it is vital to develop a better understanding of the factors that are associated with infection levels. The COVID-19 pandemic has impacted many cities severely; however, there is significant variance in its impact across cities. Pandemic infection levels are associated with inherent features of cities (e.g., population size, density, mobility patterns, socioeconomic condition, and health & environment), which need to be better understood. Intuitively, the infection levels are expected to be higher in big urban agglomerations, but the measurable influence of a specific urban feature is unclear. The present study examines 41 variables and their potential influence on the incidence of COVID-19 infection cases. The study uses a multi-method approach to study the influence of variables, classified as demographic, socioeconomic, mobility and connectivity, urban form and density, and health and environment dimensions. This study develops an index dubbed the pandemic vulnerability index at city level (PVI-CI) for classifying the pandemic vulnerability levels of cities, grouping them into five vulnerability classes, from very high to very low. Furthermore, clustering and outlier analysis provides insights on the spatial clustering of cities with high and low vulnerability scores. This study provides strategic insights into levels of influence of key variables upon the spread of infections, along with an objective ranking for the vulnerability of cities. Thus, it provides critical wisdom needed for urban healthcare policy and resource management. The calculation method for the pandemic vulnerability index and the associated analytical process present a blueprint for the development of similar indices for cities in other countries, leading to a better understanding and improved pandemic management for urban areas, and more resilient planning for future pandemics in cities across the world.
Language	English
Publishing date	2023-04-11
Publishing country	Netherlands
Document type	Journal Article
ISSN	2210-6715
ISSN (online)	2210-6715
DOI	10.1016/j.scs.2023.104570
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Article ; Online: Drying methods effect on bioactive compounds, phenolic profile, and antioxidant capacity of mango powder

Shireen Akther / Jakia Sultana Jothi / Md. Rahim Badsha / Md. Mokhlesur Rahman / Goutam Buddha Das / Md. Abdul Alim

Journal of King Saud University: Science, Vol 35, Iss 1, Pp 102370- (2023)

1480

Abstract: The effects of various drying methods (spray drying, cabinet drying, vacuum drying, tunnel drying, rotary oven drying, and gas oven drying) on the phenolic profile, bioactive compounds, and antioxidant capacity of mango powder have been considered with ... ...

Abstract	The effects of various drying methods (spray drying, cabinet drying, vacuum drying, tunnel drying, rotary oven drying, and gas oven drying) on the phenolic profile, bioactive compounds, and antioxidant capacity of mango powder have been considered with the aim of discover the effective drying technique. To identify and quantify the phenolic profile, the Folin-Ciocalteu method was used, while 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging studies were utilized to elucidate the antioxidant capacity. Results showed that total phenolic content (TPC), total flavonoid content (TFC), total anthocyanin content (TAC) and total carotenoid content (TCC) showed substantial variations (p < 0.05) between different drying methods, and the antioxidant properties were influenced to different degrees by drying methods. The antioxidant capacity of dried mango powder is strongly correlated with phenolic compounds (polyphenol, flavonoids, and anthocyanin). Caffeic acid was predominant among the 16 phenolics in mango powder. Flavonoids had higher retention factors (RF) than phenolic acids. Finally, our findings indicate that both cabinet drying and vacuum drying are appropriate for mango powder production. However, taking into account both economic viability and phenolic compound quality, cabinet drying would be the supreme choice for the outturn of mango powder as antioxidant-rich flavor enhancers.
Keywords	Drying methods ; Mango powder ; Bioactive compound ; Antioxidant capacity ; HPLC ; UV-VIS spectrophotometer ; Science (General) ; Q1-390
Subject code	660
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
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Article ; Online: Hidden risk of terrestrial food chain contamination from organochlorine insecticides in a vegetable cultivation area of Northwest Bangladesh.

Akter, Mousumi / Alam, Md Shohidul / Yang, Xiaomei / Nunes, João Pedro / Zomer, Paul / Rahman, Md Mokhlesur / Mol, Hans / Ritsema, Coen J / Geissen, Violette

The Science of the total environment

2023 Volume 912, Page(s) 169343

Abstract: Organochlorine insecticide (OCI) exposures in terrestrial food chains from historical or current applications were studied in a vegetable production area in northwest Bangladesh. A total of 57 subsoil, 57 topsoil, and 57 vegetable samples, as well as 30 ... ...

Abstract	Organochlorine insecticide (OCI) exposures in terrestrial food chains from historical or current applications were studied in a vegetable production area in northwest Bangladesh. A total of 57 subsoil, 57 topsoil, and 57 vegetable samples, as well as 30 cow's milk samples, were collected from 57 farms. Multiple OCI residues were detected using GC-MS/MS with modified QuEChERS in 20 % of subsoils, 21 % of topsoils, 23 % of vegetables, and 7 % of cow's milk samples. Diversified OCI residues were detected in subsoils (17 residues with a concentration of 179.15 ± 148.61 μg kg
MeSH term(s)	Animals ; Cattle ; Female ; Humans ; Insecticides/analysis ; Vegetables ; Bangladesh ; Food Chain ; Tandem Mass Spectrometry ; Hydrocarbons, Chlorinated/analysis ; Pesticides ; Pesticide Residues/analysis ; Organic Chemicals ; Soil ; Food Contamination/analysis
Chemical Substances	Insecticides ; Hydrocarbons, Chlorinated ; Pesticides ; Pesticide Residues ; Organic Chemicals ; Soil
Language	English
Publishing date	2023-12-12
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	121506-1
ISSN	1879-1026 ; 0048-9697
ISSN (online)	1879-1026
ISSN	0048-9697
DOI	10.1016/j.scitotenv.2023.169343
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Article ; Online: In situ

Mateti, Srikanth / Rahman, Md Mokhlesur / Cizek, Pavel / Chen, Ying

RSC advances

2020 Volume 10, Issue 22, Page(s) 12754–12758

Abstract: A solvent-free, low-cost, high-yield and scalable single-step ball milling process is developed to construct 2D ... ...

Abstract	A solvent-free, low-cost, high-yield and scalable single-step ball milling process is developed to construct 2D MoS
Language	English
Publishing date	2020-04-14
Publishing country	England
Document type	Journal Article
ISSN	2046-2069
ISSN (online)	2046-2069
DOI	10.1039/d0ra01503b
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Article ; Online: Spatio-temporal prediction of the COVID-19 pandemic in US counties

Behnam Nikparvar / Md. Mokhlesur Rahman / Faizeh Hatami / Jean-Claude Thill

Scientific Reports, Vol 11, Iss 1, Pp 1-

modeling with a deep LSTM neural network

2021 Volume 12

Abstract: Abstract Prediction of complex epidemiological systems such as COVID-19 is challenging on many grounds. Commonly used compartmental models struggle to handle an epidemiological process that evolves rapidly and is spatially heterogeneous. On the other ... ...

Abstract	Abstract Prediction of complex epidemiological systems such as COVID-19 is challenging on many grounds. Commonly used compartmental models struggle to handle an epidemiological process that evolves rapidly and is spatially heterogeneous. On the other hand, machine learning methods are limited at the beginning of the pandemics due to small data size for training. We propose a deep learning approach to predict future COVID-19 infection cases and deaths 1 to 4 weeks ahead at the fine granularity of US counties. The multi-variate Long Short-term Memory (LSTM) recurrent neural network is trained on multiple time series samples at the same time, including a mobility series. Results show that adding mobility as a variable and using multiple samples to train the network improve predictive performance both in terms of bias and of variance of the forecasts. We also show that the predicted results have similar accuracy and spatial patterns with a standard ensemble model used as benchmark. The model is attractive in many respects, including the fine geographic granularity of predictions and great predictive performance several weeks ahead. Furthermore, data requirement and computational intensity are reduced by substituting a single model to multiple models folded in an ensemble model.
Keywords	Medicine ; R ; Science ; Q
Subject code	006
Language	English
Publishing date	2021-11-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Artificial neural network with Taguchi method for robust classification model to improve classification accuracy of breast cancer.

Rahman, Md Akizur / Muniyandi, Ravie Chandren / Albashish, Dheeb / Rahman, Md Mokhlesur / Usman, Opeyemi Lateef

PeerJ. Computer science

2021 Volume 7, Page(s) e344

Abstract: Artificial neural networks (ANN) perform well in real-world classification problems. In this paper, a robust classification model using ANN was constructed to enhance the accuracy of breast cancer classification. The Taguchi method was used to determine ... ...

Abstract	Artificial neural networks (ANN) perform well in real-world classification problems. In this paper, a robust classification model using ANN was constructed to enhance the accuracy of breast cancer classification. The Taguchi method was used to determine the suitable number of neurons in a single hidden layer of the ANN. The selection of a suitable number of neurons helps to solve the overfitting problem by affecting the classification performance of an ANN. With this, a robust classification model was then built for breast cancer classification. Based on the Taguchi method results, the suitable number of neurons selected for the hidden layer in this study is 15, which was used for the training of the proposed ANN model. The developed model was benchmarked upon the Wisconsin Diagnostic Breast Cancer Dataset, popularly known as the UCI dataset. Finally, the proposed model was compared with seven other existing classification models, and it was confirmed that the model in this study had the best accuracy at breast cancer classification, at 98.8%. This confirmed that the proposed model significantly improved performance.
Language	English
Publishing date	2021-01-25
Publishing country	United States
Document type	Journal Article
ISSN	2376-5992
ISSN (online)	2376-5992
DOI	10.7717/peerj-cs.344
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Article ; Online: Spatio-temporal prediction of the COVID-19 pandemic in US counties: modeling with a deep LSTM neural network.

Nikparvar, Behnam / Rahman, Md Mokhlesur / Hatami, Faizeh / Thill, Jean-Claude

Scientific reports

2021 Volume 11, Issue 1, Page(s) 21715

Abstract: Prediction of complex epidemiological systems such as COVID-19 is challenging on many grounds. Commonly used compartmental models struggle to handle an epidemiological process that evolves rapidly and is spatially heterogeneous. On the other hand, ... ...

Abstract	Prediction of complex epidemiological systems such as COVID-19 is challenging on many grounds. Commonly used compartmental models struggle to handle an epidemiological process that evolves rapidly and is spatially heterogeneous. On the other hand, machine learning methods are limited at the beginning of the pandemics due to small data size for training. We propose a deep learning approach to predict future COVID-19 infection cases and deaths 1 to 4 weeks ahead at the fine granularity of US counties. The multi-variate Long Short-term Memory (LSTM) recurrent neural network is trained on multiple time series samples at the same time, including a mobility series. Results show that adding mobility as a variable and using multiple samples to train the network improve predictive performance both in terms of bias and of variance of the forecasts. We also show that the predicted results have similar accuracy and spatial patterns with a standard ensemble model used as benchmark. The model is attractive in many respects, including the fine geographic granularity of predictions and great predictive performance several weeks ahead. Furthermore, data requirement and computational intensity are reduced by substituting a single model to multiple models folded in an ensemble model.
MeSH term(s)	Algorithms ; COVID-19/epidemiology ; Deep Learning ; Geography ; Humans ; Machine Learning ; Memory, Short-Term ; Models, Statistical ; Monte Carlo Method ; Neural Networks, Computer ; Population Dynamics ; Public Health Informatics ; Reproducibility of Results ; SARS-CoV-2 ; Time Factors ; United States/epidemiology
Language	English
Publishing date	2021-11-05
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-021-01119-3
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Article ; Online: Hidden risk of terrestrial food chain contamination from organochlorine insecticides in a vegetable cultivation area of Northwest Bangladesh

Akter, Mousumi / Alam, Md Shohidul / Yang, Xiaomei / Nunes, João Pedro / Zomer, Paul / Rahman, Md Mokhlesur / Mol, Hans / Ritsema, Coen J. / Geissen, Violette

Science of the Total Environment

2024 Volume 912

Abstract	Organochlorine insecticide (OCI) exposures in terrestrial food chains from historical or current applications were studied in a vegetable production area in northwest Bangladesh. A total of 57 subsoil, 57 topsoil, and 57 vegetable samples, as well as 30 cow's milk samples, were collected from 57 farms. Multiple OCI residues were detected using GC–MS/MS with modified QuEChERS in 20 % of subsoils, 21 % of topsoils, 23 % of vegetables, and 7 % of cow's milk samples. Diversified OCI residues were detected in subsoils (17 residues with a concentration of 179.15 ± 148.61 μg kg−1) rather than in topsoils (3 DDT residues with a concentration of 25.76 ± 20.19 μg kg−1). Isomeric ratios indicate intensive historical applications of OCIs. According to Dutch and Chinese standards, the lower concentrations of individual OCI residues in the soil indicate negligible to slight soil pollution, assuming local farmers follow local pesticide use regulations. However, a maximum of 78.24 μg kg−1 ΣAldrines and 35.57 μg kg−1 ΣHCHs were detected (1–4 residues) in 60 % of brinjal, 28 % of cucumber, 29 % of sponge gourd, and 20 % of lady's finger samples, which could be a result of either historical or current OCI applications, or both. A strong positive correlation between aldrines in subsoils and cucurbit vegetables indicates greater bioaccumulation. Cow milk samples contained up to 6.96 μg kg−1 ΣDDTs, which resulted either from rationing contaminated vegetables or grazing on contaminated land. Individual OCI in both vegetables and cow's milk was below the respective maximum residue limits of US and FAO/WHO CODEX and poses little or no risk to human health. However, combined exposure to multiple pesticides could increase human health risks. A cumulative health risk assessment of multiple pesticide residues is suggested to assess the suitability of those soils for cultivation and grazing, as well as the safety of vegetables and cow's milk for human consumption.
Keywords	Agricultural soils ; Cow's milk ; Multiple residues ; Organochlorine insecticides ; Vegetables
Language	English
Publishing country	nl
Document type	Article ; Online
ZDB-ID	121506-1
ISSN	1879-1026 ; 0048-9697
ISSN (online)	1879-1026
ISSN	0048-9697
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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