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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.
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.
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 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.
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 Potential use of halophytes to remediate saline soils

Hasanuzzaman M, Nahar K, Alam MM, Bhowmik PC, Hossain MA, Rahman MM, et al.

Biomed Res Int, 2014;2014:589341.
PMID: 25110683 DOI: 10.1155/2014/589341

Salinity is one of the rising problems causing tremendous yield losses in many regions of the world especially in arid and semiarid regions. To maximize crop productivity, these areas should be brought under utilization where there are options for removing salinity or using the salt-tolerant crops. Use of salt-tolerant crops does not remove the salt and hence halophytes that have capacity to accumulate and exclude the salt can be an effective way. Methods for salt removal include agronomic practices or phytoremediation. The first is cost- and labor-intensive and needs some developmental strategies for implication; on the contrary, the phytoremediation by halophyte is more suitable as it can be executed very easily without those problems. Several halophyte species including grasses, shrubs, and trees can remove the salt from different kinds of salt-affected problematic soils through salt excluding, excreting, or accumulating by their morphological, anatomical, physiological adaptation in their organelle level and cellular level. Exploiting halophytes for reducing salinity can be good sources for meeting the basic needs of people in salt-affected areas as well. This review focuses on the special adaptive features of halophytic plants under saline condition and the possible ways to utilize these plants to remediate salinity.
Seasonal dynamics of phytoplankton community and functional groups in a tropical river

Haque MA, Jewel MAS, Akhi MM, Atique U, Paul AK, Iqbal S, et al.

Environ Monit Assess, 2021 Oct 08;193(11):704.
PMID: 34623504 DOI: 10.1007/s10661-021-09500-5

Functional classification of phytoplankton could be a valuable tool in water quality monitoring in the eutrophic riverine ecosystems. This study is novel from the Bangladeshi perspective. In this study, phytoplankton cell density and diversity were studied with particular reference to the functional groups (FGs) approach during pre-monsoon, monsoon, and post-monsoon at four sampling stations in Karatoya River, Bangladesh. A total of 54 phytoplankton species were recorded under four classes, viz. Chlorophyceae (21 species) Cyanophyceae (16 species), Bacillariophyceae (15 species), and Euglenophyceae (2 species). A significantly higher total cell density of phytoplankton was detected during the pre-monsoon season (24.20 × 103 cells/l), while the lowest in monsoon (9.43 × 103 cells/l). The Shannon-Wiener diversity index varied significantly (F = 16.109, P = 000), with the highest value recorded during the post-monsoon season. Analysis of similarity (ANOSIM) identified significant variations among the three seasons (P
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 Facial Mask Detection Using Depthwise Separable Convolutional Neural Network Model During COVID-19 Pandemic

Asghar MZ, Albogamy FR, Al-Rakhami MS, Asghar J, Rahmat MK, Alam MM, et al.

Front Public Health, 2022;10:855254.
PMID: 35321193 DOI: 10.3389/fpubh.2022.855254

Deep neural networks have made tremendous strides in the categorization of facial photos in the last several years. Due to the complexity of features, the enormous size of the picture/frame, and the severe inhomogeneity of image data, efficient face image classification using deep convolutional neural networks remains a challenge. Therefore, as data volumes continue to grow, the effective categorization of face photos in a mobile context utilizing advanced deep learning techniques is becoming increasingly important. In the recent past, some Deep Learning (DL) approaches for learning to identify face images have been designed; many of them use convolutional neural networks (CNNs). To address the problem of face mask recognition in facial images, we propose to use a Depthwise Separable Convolution Neural Network based on MobileNet (DWS-based MobileNet). The proposed network utilizes depth-wise separable convolution layers instead of 2D convolution layers. With limited datasets, the DWS-based MobileNet performs exceptionally well. DWS-based MobileNet decreases the number of trainable parameters while enhancing learning performance by adopting a lightweight network. Our technique outperformed the existing state of the art when tested on benchmark datasets. When compared to Full Convolution MobileNet and baseline methods, the results of this study reveal that adopting Depthwise Separable Convolution-based MobileNet significantly improves performance (Acc. = 93.14, Pre. = 92, recall = 92, F-score = 92).
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.
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 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 Rice Crop Counting Using Aerial Imagery and GIS for the Assessment of Soil Health to Increase Crop Yield

Hassan SI, Alam MM, Zia MYI, Rashid M, Illahi U, Su'ud MM

Sensors (Basel), 2022 Nov 07;22(21).
PMID: 36366269 DOI: 10.3390/s22218567

Rice is one of the vital foods consumed in most countries throughout the world. To estimate the yield, crop counting is used to indicate improper growth, identification of loam land, and control of weeds. It is becoming necessary to grow crops healthy, precisely, and proficiently as the demand increases for food supplies. Traditional counting methods have numerous disadvantages, such as long delay times and high sensitivity, and they are easily disturbed by noise. In this research, the detection and counting of rice plants using an unmanned aerial vehicle (UAV) and aerial images with a geographic information system (GIS) are used. The technique is implemented in the area of forty acres of rice crop in Tando Adam, Sindh, Pakistan. To validate the performance of the proposed system, the obtained results are compared with the standard plant count techniques as well as approved by the agronomist after testing soil and monitoring the rice crop count in each acre of land of rice crops. From the results, it is found that the proposed system is precise and detects rice crops accurately, differentiates from other objects, and estimates the soil health based on plant counting data; however, in the case of clusters, the counting is performed in semi-automated mode.
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 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.
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.
Impact of artificial intelligence on human loss in decision making, laziness and safety in education

Ahmad SF, Han H, Alam MM, Rehmat MK, Irshad M, Arraño-Muñoz M, et al.

Humanit Soc Sci Commun, 2023;10(1):311.
PMID: 37325188 DOI: 10.1057/s41599-023-01787-8

This study examines the impact of artificial intelligence (AI) on loss in decision-making, laziness, and privacy concerns among university students in Pakistan and China. Like other sectors, education also adopts AI technologies to address modern-day challenges. AI investment will grow to USD 253.82 million from 2021 to 2025. However, worryingly, researchers and institutions across the globe are praising the positive role of AI but ignoring its concerns. This study is based on qualitative methodology using PLS-Smart for the data analysis. Primary data was collected from 285 students from different universities in Pakistan and China. The purposive Sampling technique was used to draw the sample from the population. The data analysis findings show that AI significantly impacts the loss of human decision-making and makes humans lazy. It also impacts security and privacy. The findings show that 68.9% of laziness in humans, 68.6% in personal privacy and security issues, and 27.7% in the loss of decision-making are due to the impact of artificial intelligence in Pakistani and Chinese society. From this, it was observed that human laziness is the most affected area due to AI. However, this study argues that significant preventive measures are necessary before implementing AI technology in education. Accepting AI without addressing the major human concerns would be like summoning the devils. Concentrating on justified designing and deploying and using AI for education is recommended to address the issue.
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.

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