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An overview on human exposure, toxicity, solid-phase microextraction and adsorptive removal of perfluoroalkyl carboxylic acids (PFCAs) from water matrices

Zango ZU, Ethiraj B, Al-Mubaddel FS, Alam MM, Lawal MA, Kadir HA, et al.

Environ Res, 2023 Aug 15;231(Pt 2):116102.
PMID: 37196688 DOI: 10.1016/j.envres.2023.116102

Perfluoroalkyl carboxylic acids (PFCAs) are sub-class of perfluoroalkyl substances commonly detected in water matrices. They are persistent in the environment, hence highly toxic to living organisms. Their occurrence at trace amount, complex nature and prone to matrix interference make their extraction and detection a challenge. This study consolidates current advancements in solid-phase extraction (SPE) techniques for the trace-level analysis of PFCAs from water matrices. The advantages of the methods in terms of ease of applications, low-cost, robustness, low solvents consumption, high pre-concentration factors, better extraction efficiency, good selectivity and recovery of the analytes have been emphasized. The article also demonstrated effectiveness of some porous materials for the adsorptive removal of the PFCAs from the water matrices. Mechanisms of the SPE/adsorption techniques have been discussed. The success and limitations of the processes have been elucidated.
Fulltext Towards Efficient Wireless Body Area Network Using Two-Way Relay Cooperation

Waheed M, Ahmad R, Ahmed W, Drieberg M, Alam MM

Sensors (Basel), 2018 Feb 13;18(2).
PMID: 29438278 DOI: 10.3390/s18020565

The fabrication of lightweight, ultra-thin, low power and intelligent body-borne sensors leads to novel advances in wireless body area networks (WBANs). Depending on the placement of the nodes, it is characterized as in/on body WBAN; thus, the channel is largely affected by body posture, clothing, muscle movement, body temperature and climatic conditions. The energy resources are limited and it is not feasible to replace the sensor's battery frequently. In order to keep the sensor in working condition, the channel resources should be reserved. The lifetime of the sensor is very crucial and it highly depends on transmission among sensor nodes and energy consumption. The reliability and energy efficiency in WBAN applications play a vital role. In this paper, the analytical expressions for energy efficiency (EE) and packet error rate (PER) are formulated for two-way relay cooperative communication. The results depict better reliability and efficiency compared to direct and one-way relay communication. The effective performance range of direct vs. cooperative communication is separated by a threshold distance. Based on EE calculations, an optimal packet size is observed that provides maximum efficiency over a certain link length. A smart and energy efficient system is articulated that utilizes all three communication modes, namely direct, one-way relay and two-way relay, as the direct link performs better for a certain range, but the cooperative communication gives better results for increased distance in terms of EE. The efficacy of the proposed hybrid scheme is also demonstrated over a practical quasi-static channel. Furthermore, link length extension and diversity is achieved by joint network-channel (JNC) coding the cooperative link.
Fulltext Climate change and COVID-19: shared challenges, divergent perspectives, and proposed collaborative solutions

Wahaj Z, Alam MM, Al-Amin AQ

Environ Sci Pollut Res Int, 2022 Mar;29(11):16739-16748.
PMID: 34989992 DOI: 10.1007/s11356-021-18402-5

Pandemics leave their mark quickly. This is true for all pandemics, including COVID-19. Its multifarious presence has wreaked havoc on people's physical, economic, and social life since late 2019. Despite the need for social science to save lives, it is also critical to ensure future generations are protected. COVID-19 appeared as the world grappled with the epidemic of climate change. This study suggests policymakers and practitioners address climate change and COVID-19 together. This article offers a narrative review of both pandemics' impacts. Scopus and Web of Science were sought databases. The findings are reported analytically using important works of contemporary social theorists. The analysis focuses on three interconnected themes: technology advancements have harmed vulnerable people; pandemics have macro- and micro-dimensions; and structural disparities. To conclude, we believe that collaborative effort is the key to combating COVID-19 and climate change, while understanding the lessons learnt from the industrialised world. Finally, policymakers can decrease the impact of global catastrophes by addressing many socioeconomic concerns concurrently.
Exploring the contribution of energy price to carbon emissions in African countries

Umar B, Alam MM, Al-Amin AQ

Environ Sci Pollut Res Int, 2021 Jan;28(2):1973-1982.
PMID: 32862348 DOI: 10.1007/s11356-020-10641-2

The increasing level of greenhouse gas carbon emission currently exacerbates the devastating effect of global warming on the Earth's ecosystem. Energy usage is one of the most important determinants that is increasing the amount of carbon gases being released. Simultaneously, the level of energy usage is derived by the price, and therefore, this study examines the contribution of energy price to carbon gas emissions in thirteen African nations for the period spanning 1990 to 2017. It does this by utilising the cross-sectional dependence (CD), augmented mean group (AMG) and pooled mean group (PMG) panel modelling methods. The findings of the AMG model suggest that a 1% increase in energy price leads to a 0.02% decrease in carbon emission. The results further reveal that a 1% increase in energy intensity and technological innovation leads to 0.04% and 3.65% increase in carbon emission, respectively, in the selected African countries. Findings will help policymakers to implement effective energy price policies to reduce carbon emissions and achieve sustainable development goals especially in the emerging economies of Africa.
Fulltext Achieving Reliability in Cloud Computing by a Novel Hybrid Approach

Shahid MA, Alam MM, Su'ud MM

Sensors (Basel), 2023 Feb 09;23(4).
PMID: 36850563 DOI: 10.3390/s23041965

Cloud computing (CC) benefits and opportunities are among the fastest growing technologies in the computer industry. Cloud computing's challenges include resource allocation, security, quality of service, availability, privacy, data management, performance compatibility, and fault tolerance. Fault tolerance (FT) refers to a system's ability to continue performing its intended task in the presence of defects. Fault-tolerance challenges include heterogeneity and a lack of standards, the need for automation, cloud downtime reliability, consideration for recovery point objects, recovery time objects, and cloud workload. The proposed research includes machine learning (ML) algorithms such as naïve Bayes (NB), library support vector machine (LibSVM), multinomial logistic regression (MLR), sequential minimal optimization (SMO), K-nearest neighbor (KNN), and random forest (RF) as well as a fault-tolerance method known as delta-checkpointing to achieve higher accuracy, lesser fault prediction error, and reliability. Furthermore, the secondary data were collected from the homonymous, experimental high-performance computing (HPC) system at the Swiss Federal Institute of Technology (ETH), Zurich, and the primary data were generated using virtual machines (VMs) to select the best machine learning classifier. In this article, the secondary and primary data were divided into two split ratios of 80/20 and 70/30, respectively, and cross-validation (5-fold) was used to identify more accuracy and less prediction of faults in terms of true, false, repair, and failure of virtual machines. Secondary data results show that naïve Bayes performed exceptionally well on CPU-Mem mono and multi blocks, and sequential minimal optimization performed very well on HDD mono and multi blocks in terms of accuracy and fault prediction. In the case of greater accuracy and less fault prediction, primary data results revealed that random forest performed very well in terms of accuracy and fault prediction but not with good time complexity. Sequential minimal optimization has good time complexity with minor differences in random forest accuracy and fault prediction. We decided to modify sequential minimal optimization. Finally, the modified sequential minimal optimization (MSMO) algorithm with the fault-tolerance delta-checkpointing (D-CP) method is proposed to improve accuracy, fault prediction error, and reliability in cloud computing.
Fulltext Optimizing energy efficiency in heterogeneous networks: An integrated stochastic geometry approach with novel sleep mode strategies and QoS framework

Shabbir A, Rizvi S, Alam MM, Shirazi F, Su'ud MM

PLoS One, 2024;19(2):e0296392.
PMID: 38408070 DOI: 10.1371/journal.pone.0296392

The quest for energy efficiency (EE) in multi-tier Heterogeneous Networks (HetNets) is observed within the context of surging high-speed data demands and the rapid proliferation of wireless devices. The analysis of existing literature underscores the need for more comprehensive strategies to realize genuinely energy-efficient HetNets. This research work contributes significantly by employing a systematic methodology, utilizing This model facilitates the assessment of network performance by considering the spatial distribution of network elements. The stochastic nature of the PPP allows for a realistic representation of the random spatial deployment of base stations and users in multi-tier HetNets. Additionally, an analytical framework for Quality of Service (QoS) provision based on D-DOSS simplifies the understanding of user-base station relationships and offers essential performance metrics. Moreover, an optimization problem formulation, considering coverage, energy maximization, and delay minimization constraints, aims to strike a balance between key network attributes. This research not only addresses crucial challenges in creating EE HetNets but also lays a foundation for future advancements in wireless network design, operation, and management, ultimately benefiting network operators and end-users alike amidst the growing demand for high-speed data and the increasing prevalence of wireless devices. The proposed D-DOSS approach not only offers insights for the systematic design and analysis of EE HetNets but also systematically outperforms other state-of-the-art techniques presented. The improvement in energy efficiency systematically ranges from 67% (min side) to 98% (max side), systematically demonstrating the effectiveness of the proposed strategy in achieving higher energy efficiency compared to existing strategies. This systematic research work establishes a strong foundation for the systematic evolution of energy-efficient HetNets. The systematic methodology employed ensures a comprehensive understanding of the complex interplay of network dynamics and user requirements in a multi-tiered environment.
Fulltext Assessment of heavy metals contamination and human health risk in shrimp collected from different farms and rivers at Khulna-Satkhira region, Bangladesh

Sarkar T, Alam MM, Parvin N, Fardous Z, Chowdhury AZ, Hossain S, et al.

Toxicol Rep, 2016;3:346-350.
PMID: 28959555 DOI: 10.1016/j.toxrep.2016.03.003

This study is aimed to assess the heavy metals contamination and health risk in Shrimp (Macrobrachium rosenbergii and Penaeus monodon) collected from Khulna-Satkhira region in Bangladesh. The results showed that the Pb concentrations (0.52-1.16 mg/kg) in all shrimp samples of farms were higher than the recommended limit. The Cd levels (0.05-0.13 mg/kg) in all samples and Cr levels in all farms except tissue content at Satkhira farm were higher than the permissible limits. The individual concentration of Pb, Cd, and Cr between shrimp tissue and shell in all rivers and farms were not statistically significant (P > 0.05). Target hazard quotient (THQ) and hazard index (HI) were estimated to assess the non-carcinogenic health risks. Shrimp samples from all locations under the current study were found to be safe for consumption, the possibility of health risk associated with non-carcinogenic effect is very low for continuous consumption for 30 years.
Potential application of 2D nano-layered MXene in analysing and remediating endocrine disruptor compounds and heavy metals in water

Rozaini MNH, Khoo KS, Abdah MAAM, Ethiraj B, Alam MM, Anwar AF, et al.

Environ Geochem Health, 2024 Mar 11;46(3):111.
PMID: 38466501 DOI: 10.1007/s10653-024-01917-4

With the advancement of technologies and growth of the economy, it is inevitable that more complex processes are deployed, producing more heterogeneous wastewater that comes from biomedical, biochemical and various biotechnological industries. While the conventional way of wastewater treatment could effectively reduce the chemical oxygen demand, pH and turbidity of wastewater, trace pollutants, specifically the endocrine disruptor compounds (EDCs) that exist in µg L-1 or ng L-1 have further hardened the detection and removal of these biochemical pollutants. Even in small amounts, EDC could interfere human's hormone, causing severe implications on human body. Hence, this review elucidates the recent insights regarding the effectiveness of an advanced 2D material based on titanium carbide (Ti3C2Tx), also known as MXene, in detecting and removing EDCs. MXene's highly tunable feature also allows its surface chemistry to be adjusted by adding chemicals with different functional groups to adsorb different kinds of EDCs for biochemical pollution mitigation. At the same time, the incorporation of MXene into sample matrices also further eases the analysis of trace pollutants down to ng L-1 levels, thereby making way for a more cleaner and comprehensive wastewater treatment. In that sense, this review also highlights the progress in synthesizing MXene from the conventional method to the more modern approaches, together with their respective key parameters. To further understand and attest to the efficacy of MXene, the limitations and current gaps of this potential agent are also accentuated, targeting to seek resolutions for a more sustainable application.
Environmental impact assessment via life cycle analysis on ultrafiltration membrane fabricated from polyethylene terephthalate waste to treat microalgal cultivation wastewater for reusability

Rawindran H, Khoo KS, Ethiraj B, Lim JW, Liew CS, Goh PS, et al.

Environ Res, 2024 Mar 16;251(Pt 2):118687.
PMID: 38493853 DOI: 10.1016/j.envres.2024.118687

The current study had conducted the life cycle analysis (LCA) to assess the environmental impact of microalgal wastewater treatment via an integrated membrane bioreactor. The functional unit selected for this analysis was 1 kg of treated microalgal wastewater with contaminants eliminated by ultrafiltration membrane fabricated from recycled polyethylene terephthalate waste. Meanwhile, the applied system boundary in this study was distinguished based on two scenarios, namely, cradle-to-gate encompassed wastewater treatment only and cradle-to-cradle which included the reutilization of treated wastewater to cultivate microalgae again. The environmental impacts and hotspots associated with the different stages of the wastewater treatment process had clearly elucidated that membrane treatment had ensued the highest impact, followed by microalgal harvesting, and finally cultivation. Among the environmental impact categories, water-related impact was found to be prominent in the following series: freshwater ecotoxicity, freshwater eutrophication and marine ecotoxicity. Notably, the key performance indicator of all environmental impact, i.e., the global warming potential was found to be very much lower at 2.94 × 10-4 kg CO2 eq as opposed to other literatures reported on the LCA of wastewater treatments using membranes. Overall, this study had proffered insights into the environmental impact of microalgal wastewater treatment and its stimulus for sustainable wastewater management. The findings of this study can be instrumental in making informed decision for optimizing microalgal wastewater treatment and reutilization assisted by membrane technology with an ultimate goal of enhancing sustainability.
Fulltext Improvements in the Engineering Properties of Cementitious Composites Using Nano-Sized Cement and Nano-Sized Additives

Rahman I, Singh P, Dev N, Arif M, Yusufi FNK, Azam A, et al.

Materials (Basel), 2022 Nov 15;15(22).
PMID: 36431551 DOI: 10.3390/ma15228066

The findings of an extensive experimental research study on the usage of nano-sized cement powder and other additives combined to form cement-fine-aggregate matrices are discussed in this work. In the laboratory, dry and wet methods were used to create nano-sized cements. The influence of these nano-sized cements, nano-silica fumes, and nano-fly ash in different proportions was studied to the evaluate the engineering properties of the cement-fine-aggregate matrices concerning normal-sized, commercially available cement. The composites produced with modified cement-fine-aggregate matrices were subjected to microscopic-scale analyses using a petrographic microscope, a Scanning Electron Microscope (SEM), and a Transmission Electron Microscope (TEM). These studies unravelled the placement and behaviour of additives in controlling the engineering properties of the mix. The test results indicated that nano-cement and nano-sized particles improved the engineering properties of the hardened cement matrix. The wet-ground nano-cement showed the best result, 40 MPa 28th-day compressive strength, without mixing any additive compared with ordinary and dry-ground cements. The mix containing 50:50 normal and wet-ground cement exhibited 37.20 MPa 28th-day compressive strength. All other mixes with nano-sized dry cement, silica fume, and fly ash with different permutations and combinations gave better results than the normal-cement-fine-aggregate mix. The petrographic studies and the Scanning Electron Microscope (SEM) and Transmission Electron Microscope (TEM) analyses further validated the above findings. Statistical analyses and techniques such as correlation and stepwise multiple regression analysis were conducted to compose a predictive equation to calculate the 28th-day compressive strength. In addition to these methods, a repeated measures Analysis of Variance (ANOVA) was also implemented to analyse the statistically significant differences among three differently timed strength readings.
Fulltext Determinants of neonatal, infant and under-five mortalities: evidence from a developing country, Bangladesh

Murad MW, Abdullah ABM, Islam MM, Alam MM, Reaiche C, Boyle S

J Public Health Policy, 2023 Jun;44(2):230-241.
PMID: 37117262 DOI: 10.1057/s41271-023-00413-w

We investigated the macroeconomic determinants of neonatal, infant, and under-five mortalities in Bangladesh for the period 1991-2018 and discuss implications of the United Nations' Sustainable Development Goal 3 (SDG 3) and Millennium Development Goal 4 (MDG 4) for developing countries. We used annual time series data and the econometric techniques of Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) regressions for analysis. Determinants most effective in combating neonatal, infant, and under-five mortalities include variables such as 'protecting newborns against tetanus', 'increasing healthcare expenditure', and 'making sure births are attended by skilled healthcare staff'. Employing more healthcare workers and assuring more and improved healthcare provisions can further reduce the neonatal, infant, and under-five mortalities. Developing countries with similar macroeconomic profiles can achieve similar SDG 3 and MDG 4 outcomes by emulating the policies and strategies Bangladesh applied to reducing child mortalities over the last three decades.
Advancements of microalgal upstream technologies: Bioengineering and application aspects in the paradigm of circular bioeconomy

Leong WH, Rawindran H, Ameen F, Alam MM, Chai YH, Ho YC, et al.

Chemosphere, 2023 Oct;339:139699.
PMID: 37532206 DOI: 10.1016/j.chemosphere.2023.139699

Sustainable energy transition has brought the attention towards microalgae utilization as potential feedstock due to its tremendous capabilities over its predecessors for generating more energy with reduced carbon footprint. However, the commercialization of microalgae feedstock remains debatable due to the various factors and considerations taken into scaling-up the conventional microalgal upstream processes. This review provides a state-of-the-art assessment over the recent developments of available and existing microalgal upstream cultivation systems catered for maximum biomass production. The key growth parameters and main cultivation modes necessary for optimized microalgal growth conditions along with the fundamental aspects were also reviewed and evaluated comprehensively. In addition, the advancements and strategies towards potential scale-up of the microalgal cultivation technologies were highlighted to provide insights for further development into the upstream processes aimed at sustainable circular bioeconomy.
Fulltext Isolation of a Novel Recombinant Canine Coronavirus From a Visitor to Haiti: Further Evidence of Transmission of Coronaviruses of Zoonotic Origin to Humans

Lednicky JA, Tagliamonte MS, White SK, Blohm GM, Alam MM, Iovine NM, et al.

Clin Infect Dis, 2022 Aug 24;75(1):e1184-e1187.
PMID: 34718467 DOI: 10.1093/cid/ciab924

We isolated a novel coronavirus from a medical team member presenting with fever and malaise after travel to Haiti. The virus showed 99.4% similarity with a recombinant canine coronavirus recently identified in a pneumonia patient in Malaysia, suggesting that infection with this virus and/or recombinant variants occurs in multiple locations.
Fulltext Socio-demographic predictors and average annual rates of caesarean section in Bangladesh between 2004 and 2014

Khan MN, Islam MM, Shariff AA, Alam MM, Rahman MM

PLoS One, 2017;12(5):e0177579.
PMID: 28493956 DOI: 10.1371/journal.pone.0177579

BACKGROUND: Globally the rates of caesarean section (CS) have steadily increased in recent decades. This rise is not fully accounted for by increases in clinical factors which indicate the need for CS. We investigated the socio-demographic predictors of CS and the average annual rates of CS in Bangladesh between 2004 and 2014.
METHODS: Data were derived from four waves of nationally representative Bangladesh Demographic and Health Survey (BDHS) conducted between 2004 and 2014. Rate of change analysis was used to calculate the average annual rate of increase in CS from 2004 to 2014, by socio-demographic categories. Multi-level logistic regression was used to identify the socio-demographic predictors of CS in a cross-sectional analysis of the 2014 BDHS data.
RESULT: CS rates increased from 3.5% in 2004 to 23% in 2014. The average annual rate of increase in CS was higher among women of advanced maternal age (≥35 years), urban areas, and relatively high socio-economic status; with higher education, and who regularly accessed antenatal services. The multi-level logistic regression model indicated that lower (≤19) and advanced maternal age (≥35), urban location, relatively high socio-economic status, higher education, birth of few children (≤2), antenatal healthcare visits, overweight or obese were the key factors associated with increased utilization of CS. Underweight was a protective factor for CS.
CONCLUSION: The use of CS has increased considerably in Bangladesh over the survey years. This rising trend and the risk of having CS vary significantly across regions and socio-economic status. Very high use of CS among women of relatively high socio-economic status and substantial urban-rural difference call for public awareness and practice guideline enforcement aimed at optimizing the use of CS.
Fulltext A computational classification method of breast cancer images using the VGGNet model

Khan A, Khan A, Ullah M, Alam MM, Bangash JI, Suud MM

Front Comput Neurosci, 2022;16:1001803.
PMID: 36405784 DOI: 10.3389/fncom.2022.1001803

Cancer is one of the most prevalent diseases worldwide. The most prevalent condition in women when aberrant cells develop out of control is breast cancer. Breast cancer detection and classification are exceedingly difficult tasks. As a result, several computational techniques, including k-nearest neighbor (KNN), support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT), and genetic algorithms, have been applied in the current computing world for the diagnosis and classification of breast cancer. However, each method has its own limitations to how accurately it can be utilized. A novel convolutional neural network (CNN) model based on the Visual Geometry Group network (VGGNet) was also suggested in this study. The 16 layers in the current VGGNet-16 model lead to overfitting on the training and test data. We, thus, propose the VGGNet-12 model for breast cancer classification. The VGGNet-16 model has the problem of overfitting the breast cancer classification dataset. Based on the overfitting issues in the existing model, this research reduced the number of different layers in the VGGNet-16 model to solve the overfitting problem in this model. Because various models of the VGGNet, such as VGGNet-13 and VGGNet-19, were developed, this study proposed a new version of the VGGNet model, that is, the VGGNet-12 model. The performance of this model is checked using the breast cancer dataset, as compared to the CNN and LeNet models. From the simulation result, it can be seen that the proposed VGGNet-12 model enhances the simulation result as compared to the model used in this study. Overall, the experimental findings indicate that the suggested VGGNet-12 model did well in classifying breast cancer in terms of several characteristics.
Fulltext Cardiovascular and Diabetes Diseases Classification Using Ensemble Stacking Classifiers with SVM as a Meta Classifier

Khan A, Khan A, Khan MM, Farid K, Alam MM, Su'ud MBM

Diagnostics (Basel), 2022 Oct 26;12(11).
PMID: 36359438 DOI: 10.3390/diagnostics12112595

Cardiovascular disease includes coronary artery diseases (CAD), which include angina and myocardial infarction (commonly known as a heart attack), and coronary heart diseases (CHD), which are marked by the buildup of a waxy material called plaque inside the coronary arteries. Heart attacks are still the main cause of death worldwide, and if not treated right they have the potential to cause major health problems, such as diabetes. If ignored, diabetes can result in a variety of health problems, including heart disease, stroke, blindness, and kidney failure. Machine learning methods can be used to identify and diagnose diabetes and other illnesses. Diabetes and cardiovascular disease both can be diagnosed using several classifier types. Naive Bayes, K-Nearest neighbor (KNN), linear regression, decision trees (DT), and support vector machines (SVM) were among the classifiers employed, although all of these models had poor accuracy. Therefore, due to a lack of significant effort and poor accuracy, new research is required to diagnose diabetes and cardiovascular disease. This study developed an ensemble approach called "Stacking Classifier" in order to improve the performance of integrated flexible individual classifiers and decrease the likelihood of misclassifying a single instance. Naive Bayes, KNN, Linear Discriminant Analysis (LDA), and Decision Tree (DT) are just a few of the classifiers used in this study. As a meta-classifier, Random Forest and SVM are used. The suggested stacking classifier obtains a superior accuracy of 0.9735 percent when compared to current models for diagnosing diabetes, such as Naive Bayes, KNN, DT, and LDA, which are 0.7646 percent, 0.7460 percent, 0.7857 percent, and 0.7735 percent, respectively. Furthermore, for cardiovascular disease, when compared to current models such as KNN, NB, DT, LDA, and SVM, which are 0.8377 percent, 0.8256 percent, 0.8426 percent, 0.8523 percent, and 0.8472 percent, respectively, the suggested stacking classifier performed better and obtained a higher accuracy of 0.8871 percent.
Fulltext Cytotoxic and photocatalytic studies of hexagonal boron nitride nanotubes: a potential candidate for wastewater and air treatment

Khalid A, Ahmad P, Khan A, Khandaker MU, Kebaili I, Alam MM, et al.

RSC Adv, 2022 Feb 22;12(11):6592-6600.
PMID: 35424596 DOI: 10.1039/d2ra00300g

Boron nitride (BN) nanomaterials are rapidly being investigated for potential applications in biomedical sciences due to their exceptional physico-chemical characteristics. However, their safe use demands a thorough understanding of their possible environmental and toxicological effects. The cytotoxicity of boron nitride nanotubes (BNNTs) was explored to see if they could be used in living cell imaging. It was observed that the cytotoxicity of BNNTs is higher in cancer cells (65 and 80%) than in normal cell lines (40 and 60%) for 24 h and 48 h respectively. The influence of multiple experimental parameters such as pH, time, amount of catalyst, and initial dye concentration on percentage degradation efficiency was also examined for both catalyst and dye. The degradation effectiveness decreases (92 to 25%) as the original concentration of dye increases (5-50 ppm) due to a decrease in the availability of adsorption sites. Similarly, the degradation efficiency improves up to 90% as the concentration of catalyst increases (0.01-0.05 g) due to an increase in the adsorption sites. The influence of pH was also investigated, the highest degradation efficiency for MO dye was observed at pH 4. Our results show that lower concentrations of BNNTs can be employed in biomedical applications. Dye degradation properties of BNNTs suggest that it can be a potential candidate as a wastewater and air treatment material.
Fulltext Effect of Cu Doping on ZnO Nanoparticles as a Photocatalyst for the Removal of Organic Wastewater

Khalid A, Ahmad P, Khan A, Muhammad S, Khandaker MU, Alam MM, et al.

Bioinorg Chem Appl, 2022;2022:9459886.
PMID: 35873731 DOI: 10.1155/2022/9459886

Environmental problems with chemical and biological water pollution have become a major concern for society. Providing people with safe and affordable water is a grand challenge of the 21st century. The study investigates the photocatalytic degradation capabilities of hydrothermally prepared pure and Cu-doped ZnO nanoparticles (NPs) for the elimination of dye pollutants. A simple, cost-effective hydrothermal process is employed to synthesize the Cu-doped ZnO NPs. The photocatalytic dye degradation activity of the synthesized Cu-doped ZnO NPs is tested by using methylene blue (MB) dye. In addition, the parameters that affect photodegradation efficiency, such as catalyst concentration, starting potential of hydrogen (pH), and dye concentration, were also assessed. The dye degradation is found to be directly proportional to the irradiation time, as 94% of the MB dye is degraded in 2 hrs. Similarly, the dye degradation shows an inverse relation to the MB dye concentration, as the degradation reduced from 94% to 20% when the MB concentration increases from 5 ppm to 80 ppm. The synthesized cost-effective and environmentally friendly Cu-doped ZnO NPs exhibit improved photocatalytic activity against MB dye and can therefore be employed in wastewater treatment materials.
Resource Allocation Schemes for 5G Network: A Systematic Review

Kamal MA, Raza HW, Alam MM, Su'ud MM, Sajak ABAB

Sensors (Basel), 2021 Oct 02;21(19).
PMID: 34640908 DOI: 10.3390/s21196588

Fifth-generation (5G) communication technology is intended to offer higher data rates, outstanding user exposure, lower power consumption, and extremely short latency. Such cellular networks will implement a diverse multi-layer model comprising device-to-device networks, macro-cells, and different categories of small cells to assist customers with desired quality-of-service (QoS). This multi-layer model affects several studies that confront utilizing interference management and resource allocation in 5G networks. With the growing need for cellular service and the limited resources to provide it, capably handling network traffic and operation has become a problem of resource distribution. One of the utmost serious problems is to alleviate the jamming in the network in support of having a better QoS. However, although a limited number of review papers have been written on resource distribution, no review papers have been written specifically on 5G resource allocation. Hence, this article analyzes the issue of resource allocation by classifying the various resource allocation schemes in 5G that have been reported in the literature and assessing their ability to enhance service quality. This survey bases its discussion on the metrics that are used to evaluate network performance. After consideration of the current evidence on resource allocation methods in 5G, the review hopes to empower scholars by suggesting future research areas on which to focus.
Fulltext An Improved Strategy for Task Scheduling in the Parallel Computational Alignment of Multiple Sequences

Ishaq M, Khan A, Su'ud MM, Alam MM, Bangash JI, Khan A

Comput Math Methods Med, 2022;2022:8691646.
PMID: 35126641 DOI: 10.1155/2022/8691646

Task scheduling in parallel multiple sequence alignment (MSA) through improved dynamic programming optimization speeds up alignment processing. The increased importance of multiple matching sequences also needs the utilization of parallel processor systems. This dynamic algorithm proposes improved task scheduling in case of parallel MSA. Specifically, the alignment of several tertiary structured proteins is computationally complex than simple word-based MSA. Parallel task processing is computationally more efficient for protein-structured based superposition. The basic condition for the application of dynamic programming is also fulfilled, because the task scheduling problem has multiple possible solutions or options. Search space reduction for speedy processing of this algorithm is carried out through greedy strategy. Performance in terms of better results is ensured through computationally expensive recursive and iterative greedy approaches. Any optimal scheduling schemes show better performance in heterogeneous resources using CPU or GPU.

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