Design and evaluation of a power tiller vegetable seedling transplanter with dibbler and furrow type.

Vegetable production plays a vital role in ensuring food security in Bangladesh. However, the majority of vegetable seedlings are currently transplanted manually, which is not only time-consuming but also labor-intensive and costly. In this context, a semi-automated transplanter can be considered as an alternative solution for mechanized seedling transplanting. To mechanize seedling operations, two types of transplanters were designed, fabricated and tested: the power tiller-operated semi-automatic dibbler vegetable seedling (DVS) transplanter and the furrow opener vegetable seedling (FVS) transplanter. The goal was to evaluate their performance and impact on field crop productivity. In the DVS transplanter design, the larger sprocket was adjusted to enhance the precision of hole-making by pressing the dibbler into the soil, creating holes where seedlings would be transplanted. On the other hand, the FVS transplanter utilized a furrow opener to create furrows, and the seedling is placed in these furrow at a specific distance from the furrow opener wall, where the distance between seedlings within the furrow could be adjusted based on the specific requirements of the seedling crop. The results of the evaluation indicated that both transplanters successfully planted seedlings without any missing placements, while hole covering was achieved at 115 and 118.2% for the DVS and FVS transplanters, respectively. The field capacity and field efficiency for both transplanters were determined to be 0.05 ha h-1 and 61.18%, respectively, with a coefficient of variation of 5% or less. Field tests conducted with brinjal crops at a forward speed of 1.2 km h-1 and a spacing of 0.7 × 0.6 m demonstrated that both designs yielded higher yield productivity compared to manual transplantation. Additionally, no issues related to vegetative development were observed. Both transplanters exhibited promising performance and significant potential in terms of accurately transplanting seedlings, and ensuring satisfactory transplantation quality. Furthermore, these transplanters offer several advantages, including less time-consuming, lower labor demands and even distribution of seedlings. This design encourages small to medium-level farmers seeking to engage in mechanized vegetable farming practices.

Performance Evaluation of Phenol-Resin-Based Adsorbents for Heat Transformation Applications.

Phenol resins (PRs) are considered as relatively inexpensive adsorbents synthesized from agricultural biomass via employing a variety of synthesized procedures. The performance of PR for heat transformation application is not widely investigated. In this regard, the present study aims to evaluate the four PR derivative/refrigerant pairs, namely (i) KOH6-PR/CO2, (ii) SAC-2/HFC, (iii) KOH4-PR/ethanol, and (iv) KOH6-PR/ethanol, for adsorption cooling and adsorption heating applications. Ideal cycle analyses and/or thermodynamic modelling approaches were utilized comprising governing heat and mass balance equations and adsorption equilibrium models. The performance of the AHP system is explored by means of specific cooling energy (SCE), specific heating energy (SHE), and coefficient of performance (COP), both for cooling and heating applications, respectively. It has been realized that KOH6-PR/ethanol could produce a maximum SCE of 1080 kJ/kg/cycle and SHE of 2141 kJ/kg/cycle at a regeneration temperature (Treg) and condenser temperature (Tcond) of 80 °C, and 10 °C, respectively, followed by KOH4-PR/ethanol, SAC-2/HFC-32, and KOH6-PR/CO2. The maximum COP values were estimated to be 1.78 for heating and 0.80 for cooling applications, respectively, at Treg = 80 °C and Tcond = 10 °C. In addition, the study reveals that, corresponding to increase/decrease in condenser/evaporator pressure, both SCE and SHE decrease/increase, respectively; however, this varies in magnitude due to adsorption equilibrium of the studied PR derivative/refrigerant pairs.

Coumarin regulated redox homeostasis to facilitate phytoremediation of saline and alkaline soils by bitter gourd (Momordica charantia L.).

The use of coumarin (COU) to alleviate the phytotoxic effects of salinity has great potential in improving the phytoremediation of saline and alkaline soils. 30-day bitter gourd plants were exposed to 15 dS m‒1 salinity of neutral (NaCl and Na2SO4) and alkaline (Na2CO3 and NaHCO3) salts. 60-day plants were harvested to record different growth, physiological and biochemical attributes. Salinity significantly subsided plant growth, chlorophyll, photosynthesis, and nutrient acquisition. Salinity induced notable oxidative damage in plants that displayed higher relative membrane permeability (RMP), accumulated elevated ROS (H2O2 and O2•‒) and MDA levels alongside intensified lipoxygenase (LOX) activity. The production of cytotoxic methylglyoxal was also significantly higher in plants under salinity. COU seed priming (50, 100 and 150 mg L‒1) promoted plant growth by circumventing oxidative injury and intensifying oxidative defense. Further, COU maintained the intricate balance between reduced (GSH) and oxidized (GSSG) glutathione to diminish ion excess toxicity, thereby facilitating the phytoremediation of saline soils. The lower doses of COU promoted methylglyoxal and ROS detoxification systems that, in turn, lessened the phytotoxic effects of salinity. COU restored ions homeostasis by augmenting osmotic adjustment in plants under salinity.

Effect of seed priming by taurine on growth and chromium (Cr) uptake in canola (Brassica napus L.) under Cr stress.

Taurine is a recently recognized plant growth regulator under abiotic stress. However, the information on taurine-mediated plant defense responses is scarce, particularly on taurine-mediated regulation of the glyoxalase system. There is currently no report available on the use of taurine as seed priming under stress. Chromium (Cr) toxicity considerably subsided growth characteristics, photosynthetic pigments, and relative water content. Furthermore, plants encountered intensified oxidative injury due to a significant increase in relative membrane permeability, H2O2, O2•‒, and MDA production. The amount of antioxidant compounds and the functioning of antioxidant enzymes rose, but imbalance due to over ROS generation frequently depleted antioxidant compounds. Taurine seed priming (50, 100, 150, and 200 mg L‒1) notably diminished oxidative injury, strengthened the antioxidant system, and conspicuously subsided methylglyoxal levels through enhanced activities of glyoxalase enzymes. The accumulation of Cr content was minimal in plants administered taurine as seed priming. In conclusion, our research demonstrates that taurine priming effectively mitigated the adverse effects of Cr toxicity on canola. Taurine reduced oxidative damage, leading to improved growth, enhanced chlorophyll levels, optimized ROS metabolism, and enhanced methylglyoxal detoxification. These findings highlight the potential of taurine as a promising strategy to enhance the tolerance of canola plants to Cr toxicity.

Refractive index based optically transparent biosensor device design for early detection of coronavirus.

For the quick detection of the new Coronavirus (COVID-19), a highly sensitive D-shaped gold-coated surface Plasmon resonance (SPR) biosensor is presented. The COVID-19 virus may be quickly and accurately identified using the SPR-based biosensor, which is essential for halting the spread of this excruciating epidemic. The suggested biosensor is used for detection of the IBV i.e. infectious bronchitis viruses contaminated cell that belongs to the family of COVID-19 having a refractive index of - 0.96, - 0.97, - 0.98, - 0.99, - 1 that is observed with the change in EID concentration. Some important optical parameter variations are examined in the investigation process. Multiphysics version 5.3 with the Finite element method is used for the proposed biosensor. The proposed sensor depicts maximum wavelength sensitivity of 40,141.76 nm/RIU. Some other parameters such as confinement loss, crosstalk, and insertion loss are also analyzed for the proposed sensor. The reported minimum insertion loss for the refractive index (RI) - 1 is 2.9 dB. Simple design, good sensitivity, and lower value of losses make the proposed sensor proficient for the detection of infectious bronchitis viruses belonging to COVID-19.

Unraveling Bonding Mechanisms and Electronic Structure of Pyridine Oximes on Fe(110) Surface: Deeper Insights from DFT, Molecular Dynamics and SCC-DFT Tight Binding Simulations.

The development of corrosion inhibitors with outstanding performance is a never-ending and complex process engaged in by researchers, engineers and practitioners. The computational assessment of organic corrosion inhibitors' performance is a crucial step towards the design of new task-specific materials. Herein, the electronic features, adsorption characteristics and bonding mechanisms of two pyridine oximes, namely 2-pyridylaldoxime (2POH) and 3-pyridylaldoxime (3POH), with the iron surface were investigated using molecular dynamics (MD), and self-consistent-charge density-functional tight-binding (SCC-DFTB) simulations. SCC-DFTB simulations revealed that the 3POH molecule can form covalent bonds with iron atoms in its neutral and protonated states, while the 2POH molecule can only bond with iron through its protonated form, resulting in interaction energies of -2.534, -2.007, -1.897, and -0.007 eV for 3POH, 3POH+, 2POH+, and 2POH, respectively. Projected density of states (PDOSs) analysis of pyridines-Fe(110) interactions indicated that pyridine molecules were chemically adsorbed on the iron surface. Quantum chemical calculations (QCCs) revealed that the energy gap and Hard and Soft Acids and Bases (HSAB) principles were efficient in predicting the bonding trend of the molecules investigated with an iron surface. 3POH had the lowest energy gap of 1.706 eV, followed by 3POH+ (2.806 eV), 2POH+ (3.121 eV), and 2POH (3.431 eV). In the presence of a simulated solution, MD simulation showed that the neutral and protonated forms of molecules exhibited a parallel adsorption mode on an iron surface. The excellent adsorption properties and corrosion inhibition performance of 3POH may be attributed to its low stability compared to 2POH molecules.

Pollution trends and ecological risks of heavy metal(loid)s in coastal zones of Bangladesh: A chemometric review.

Heavy metal(loid)s inputs contribute to human and environmental stresses in the coastal zones of Bangladesh. Several studies have been conducted on metal(loid)s pollution in sediment, soil, and water in the coastal zones. However, they are sporadic, and no attempt has been made in coastal zones from the standpoint of chemometric review. The current work aims to provide a chemometric assessment of the pollution trend of metal(loid)s, namely arsenic (As), chromium (Cr), cadmium (Cd), lead (Pb), copper (Cu), zinc (Zn), and nickel (Ni) in sediments, soils, and water across the coastal zones from 2015 to 2022. The findings showed that 45.7, 15.2, and 39.1 % of studies on heavy metal(loid)s were concentrated in the eastern, central, and western zones of coastal Bangladesh. The obtained data were further modeled using chemometric approaches, such as the contamination factor, pollution load index, geoaccumulation index, degree of contamination, Nemerow's pollution index, and ecological risk index. The results revealed that metal(loid)s, primarily Cd, have severely polluted the sediments (contamination factor, CF = 5.20) and soils (CF = 9.35) of coastal regions. Water was moderately polluted (Nemerow's pollution index, PN=5.22 ± 6.26) in the coastal area. The eastern zone was the most polluted compared to other zones, except for a few observations in the central zone. The overall ecological risks posed by metal(loid)s highlighted the significant ecological risk in sediments (ecological risk index, RI = 123.50) and soils (RI = 238.93) along the eastern coast. The coastal zone may have higher pollution levels due to the proximity of industrial effluent, residential sewage discharge, agricultural activities, sea transport, metallurgical industries, shipbreaking and recycling operations, and seaport activities, which are the major sources of metal(loid)s. This study will provide useful information to the relevant authorities and serve as the foundation for future management and policy decisions to reduce metal(loid) pollution in the coastal zones of southern Bangladesh.

Enhancing the Performance of Piezoelectric Wind Energy Harvester Using Curve-Shaped Attachments on the Bluff Body.

This paper presents a piezoelectric wind energy harvester that operates by a galloping mechanism with different shaped attachments attached to a bluff body. A comparison is made between harvesters that consist of different shaped attachments on a bluff body; these include triangular, circular, square, Y-shaped, and curve-shaped attachments. Simulation of the pressure field and the velocity field variation around the different shaped bluff bodies is performed and it is found that a high pressure difference creates a high lift force on the bluff body with curve-shaped attachments. A theoretical model based on a galloping mechanism is presented, which is verified by experiments. It is observed that the proposed harvester with curve-shaped attachments provides the best performance, where the harvester with a curve-shaped attachments provides the highest voltage and power output compared to the other shaped harvesters examined in this study. This paper provides a new concept for improving the power performance of the piezoelectric wind energy harvesters with modifications made on the bluff body.

Identifying factors affecting irrigation metrics in the Haor basin using integrated Shannon's entropy, fuzzy logic and automatic linear model.

The sustainability of agricultural practices is seriously threatened by the quality of water used for irrigation. This paper aims to evaluate the suitability of irrigation water and identify the region suitable for agricultural use in the Haor basin of Bangladesh using conventional irrigation indices such as sodium adsorption ratio (SAR), percent sodium (Na%), magnesium hazard ratio (MHR), permeability index (PI), and Kelly's ratio (KR), as well as novel irrigation indices such as, Shannon's entropy index for irrigation water quality (EWQ) and fuzzy logic index for irrigation water quality (FIWQI). The main influences of groundwater and surface water parameters on irrigation indices were predicted using automatic linear modeling (ALM). Forty water samples were collected from shallow tube wells, rivers, canals, ponds, and drainage systems within agricultural land sampled and analyzed for cations and anions. SAR and KR show that 52.5% and 60% of the samples exceeded the allowable level, respectively, indicating that they were unsuitable for irrigation. According to EWQI, about 55% of the analyzed samples were of good quality, while 45% were of medium quality. ALM predicted that KR (0.98), Na% (0.87), and MHR (0.14) were the main significant factors affecting SAR and KR. ALM shows that elevated sodium, magnesium, and calcium are the most important factors affecting irrigation water suitability. The EWQI and FIWQI integrated models showed that water from nearly 30% of the sampling sites would need treatment before use. A new suitability map created by overlaying all parameters showed that surface water and some groundwater in the western and southwestern portions are suitable for agriculture. The north-central part is unsuitable for irrigation due to excessive sodium and magnesium levels. This paper will highlight the irrigation pattern for regional water resource use, identify new suitable regions, and improve sustainable agricultural practices in the Haor basin.

A critical review and prospect of NO2 and SO2 pollution over Asia: Hotspots, trends, and sources.

Nitrogen dioxide (NO2) and sulfur dioxide (SO2) are two major atmospheric pollutants that significantly threaten human health, the environment, and ecosystems worldwide. Despite this, only some studies have investigated the spatiotemporal hotspots of NO2 and SO2, their trends, production, and sources in Asia. Our study presents a literature review covering the production, trends, and sources of NO2 and SO2 across Asian countries (e.g., Bangladesh, China, India, Iran, Japan, Pakistan, Malaysia, Kuwait, and Nepal). Based on the findings of the review, NO2 and SO2 pollution are increasing due to industrial activity, fossil fuel burning, biomass burning, heavy traffic movement, electricity generation, and power plants. There is significant concern about health risks associated with NO2 and SO2 emissions in Bangladesh, China, India, Malaysia, and Iran, as they pay less attention to managing and controlling pollution. Even though the lack of quality datasets and adequate research in most Asian countries further complicates the management and control of NO2 and SO2 pollution. This study has NO2 and SO2 pollution scenarios, including hotspots, trends, sources, and their influences on Asian countries. This study highlights the existing research gaps and recommends new research on identifying integrated sources, their variations, spatiotemporal trends, emission characteristics, and pollution level. Finally, the present study suggests a framework for controlling and monitoring these two pollutants' emissions.

Drug Repurposing and Systems Biology approaches of Enzastaurin can target potential biomarkers and critical pathways in Colorectal Cancer.

Colorectal cancer (CRC) is a severe health concern that results from a cocktail of genetic, epigenetic, and environmental abnormalities. Because it is the second most lethal malignancy in the world and the third-most common malignant tumor, but the treatment is unavailable. The goal of the current study was to use bioinformatics and systems biology techniques to determine the pharmacological mechanism underlying putative important genes and linked pathways in early-onset CRC. Computer-aided methods were used to uncover similar biological targets and signaling pathways associated with CRC, along with bioinformatics and network pharmacology techniques to assess the effects of enzastaurin on CRC. The KEGG and gene ontology (GO) pathway analysis revealed several significant pathways including in positive regulation of protein phosphorylation, negative regulation of the apoptotic process, nucleus, nucleoplasm, protein tyrosine kinase activity, PI3K-Akt signaling pathway, pathways in cancer, focal adhesion, HIF-1 signaling pathway, and Rap1 signaling pathway. Later, the hub protein module identified from the protein-protein interactions (PPIs) network, molecular docking and molecular dynamics simulation represented that enzastaurin showed strong binding interaction with two hub proteins including CASP3 (-8.6 kcal/mol), and MCL1 (-8.6 kcal/mol), which were strongly implicated in CRC management than other the five hub proteins. Moreover, the pharmacokinetic features of enzastaurin revealed that it is an effective therapeutic agent with minimal adverse effects. Enzastaurin may inhibit the potential biological targets that are thought to be responsible for the advancement of CRC and this study suggests a potential novel therapeutic target for CRC.

Federated Machine Learning for Detection of Skin Diseases and Enhancement of Internet of Medical Things (IoMT) Security.

Human skin disease, the most infectious dermatological ailment globally, is initially diagnosed by sight. Some clinical screening and dermoscopic analysis of skin biopsies and scrapings for accurate classification are medically compulsory. Classification of skin diseases using medical images is more challenging because of the complex formation and variant colors of the disease and data security concerns. Both the Convolution Neural Network (CNN) for classification and a federated learning approach for data privacy preservation show significant performance in the realm of medical imaging fields. In this paper, a custom image dataset was prepared with four classes of skin disease, a CNN model was suggested and compared with several benchmark CNN algorithms, and an experiment was carried out to ensure data privacy using a federated learning approach. An image augmentation strategy was followed to enlarge the dataset and make the model more general. The proposed model achieved a precision of 86%, 43%, and 60%, and a recall of 67%, 60%, and 60% for acne, eczema, and psoriasis. In the federated learning approach, after distributing the dataset among 1000, 1500, 2000, and 2500 clients, the model showed an average accuracy of 81.21%, 86.57%, 91.15%, and 94.15%. The CNN-based skin disease classification merged with the federated learning approach is a breathtaking concept to classify human skin diseases while ensuring data security.

Forecasting of energy consumption by G20 countries using an adjacent accumulation grey model.

This paper studies an adjacent accumulation discrete grey model to improve the prediction of the grey model and enhance the utilization of new data. The impact of COVID-19 on the global economy is also discussed. Two cases are discussed to prove the stability of the adjacent accumulation discrete grey model, which helped the studied model attain higher forecasting accuracy. Using the adjacent accumulation discrete grey model, non-renewable energy consumption in G20 countries from 2022 to 2026 is predicted based on their consumption data from 2011 to 2021. It is proven that the adjacent accumulation exhibits sufficient accuracy and precision. Forecasting results obtained in this paper show that energy consumption of all the non-renewable sources other than coal has an increasing trend during the forecasting period, with the USA, Russia, and China being the biggest consumers. Natural gas is the most consumed non-renewable energy source between 2022 and 2026, whereas hydroelectricity is the least consumed. The USA is the biggest consumer of Nuclear energy among the G20 countries, whereas Argentina consumed only 0.1 Exajoules of nuclear energy, placing it at the end of nuclear energy consumers.

MLBioIGE: integration and interplay of machine learning and bioinformatics approach to identify the genetic effect of SARS-COV-2 on idiopathic pulmonary fibrosis patients.

SARS-CoV-2, the virus that causes COVID-19, is a current concern for people worldwide. The virus has recently spread worldwide and is out of control in several countries, putting the outbreak into a terrifying phase. Machine learning with transcriptome analysis has advanced in recent years. Its outstanding performance in several fields has emerged as a potential option to find out how SARS-CoV-2 is related to other diseases. Idiopathic pulmonary fibrosis (IPF) disease is caused by long-term lung injury, a risk factor for SARS-CoV-2. In this article, we used a variety of combinatorial statistical approaches, machine learning, and bioinformatics tools to investigate how the SARS-CoV-2 affects IPF patients' complexity. For this study, we employed two RNA-seq datasets. The unique contributions include common genes identification to identify shared pathways and drug targets, PPI network to identify hub-genes and basic modules, and the interaction of transcription factors (TFs) genes and TFs-miRNAs with common differentially expressed genes also placed on the datasets. Furthermore, we used gene ontology and molecular pathway analysis to do functional analysis and discovered that IPF patients have certain standard connections with the SARS-CoV-2 virus. A detailed investigation was carried out to recommend therapeutic compounds for IPF patients affected by the SARS-CoV-2 virus.

Cellulose, clay and sodium alginate composites for the removal of methylene blue dye: Experimental and DFT studies.

Cellulose/clay/sodium alginate composites were prepared and employed for the removal of methylene blue (MB) dye. Cellulose was extracted from a paper mill waste and used for composite preparation with sodium alginate (Na-Alg) and clay. MB dye removal was analyzed at different operating conditions (pH, initial concentration, temperature, composite dose). The dye was adsorbed up to 90% for an equilibrium time of 60 min at optimum level of adsorbent dose (0.05 g), temperature (30 °C) and pH (i.e., 7 and 11 for cellulose-Na-Alg and cellulose-Na-Alg-clay, respectively). Kinetics and isotherms of MB adsorption were quantified and modeled. Results showed that MB dye adsorption data followed the pseudo-first order kinetics and a statistical physics model was used to analyze the adsorption mechanism. Thermodynamic calculation revealed that the MB dye adsorption on these composites was an exothermic, spontaneous and feasible process. The composites were regenerated with HCl thus contributing to their reutilization in subsequent adsorption cycles. The DFT (density functional theory) calculations were executed to explain the interactions responsible for the adsorption of MB dye on the composites. Results revealed that the Na-Alg-cellulose composites were effective for the MB dye removal. Therefore, these composites can be considered as low-cost alternative adsorbents for the pollution remediation caused by dyes in industrial effluents.

Multivariate analysis of accumulation and critical risk analysis of potentially hazardous elements in forage crops.

Potentially hazardous element (PHE) contamination of aquifers is an issue of global concern, as this not only affects soil and plants but also exerts a negative impact on livestock. The current study assessed the extent of PHE (cadmium, copper, nickel, and lead) contamination of groundwater, soil, and forage crops in Shorkot, Punjab, Pakistan. Low concentrations of PHEs, particularly Cd and Cu, were found in drinking water which remained below detection limits. The concentrations of Ni and Pb in water samples were 0.1 and 0.06 mg L-1, respectively. Calculated risk indices showed that there was a high carcinogenic and non-carcinogenic risk to livestock (sheep and cow/buffalo) from the ingestion of Ni- and Pb-contaminated water. Soil irrigation with contaminated water resulted in PHE accumulation (Cd: 0.4 mg kg-1, Cu: 16.8 mg kg-1, Ni: 17.6 mg kg-1, Pb: 7.7 mg kg-1) in soil and transfer to forage crops. The potential impact of PHE contamination of the groundwater on fodder plants was estimated for animal health by calculating the average daily dose (ADD), the hazard quotient (HQ), and the cancer risk (CR). While none of the PHEs in forage plants showed any carcinogenic or non-carcinogenic risk to livestock, a high exposure risk occurred from contaminated water (HQ: 12.9, CR: 0.02). This study provides baseline data for future research on the risks of PHE accumulation in livestock and their food products. Moreover, future research is warranted to fully understand the transfer of PHEs from livestock products to humans.

Heavy metals contamination, potential pathways and risks along the Indus Drainage System of Pakistan.

Riverine water exposed to heavy metals (HMs) pollution is a major concern in the world because of its serious effects on ecosystem and human health. This study assessed the pollution status, sources, diffusion and potential risks of Mn, Co, Cu, Zn, Cr, Ni, Cd, Hg and Pb for the first time along the entire Indus Drainage System of Pakistan. The concentrations of nine HMs in the riverine water ranged from 5.05-101.59 µg/L with a mean value of 41.51 µg/L. The overall metals quantification along the drainage was significantly high (27% of the total) in River Chenab followed by River Indus (26%) > Soan (20%) > Ravi (19%) > Kabul (5%) > Swat (3%). The potential sources of contamination were identified to be the surrounding geogenic activities, industrial/municipal wastewater discharges, agricultural and surface runoffs by using multivariate statistics including metals correlation analysis, hierarchical cluster analysis and principal component analysis. The average mass flux of ∑HMs in the entire drainage was approximately 10.24 tons/year, to which the River Indus contributed 84% of the total, Chenab 11%, Ravi 3%, Kabul 1%, and Soan 1% with more prevalence of biological essential (Zn&Mn) and non-essential (Ni&Cr) metals. In terms of ecological risk, the riverine water metals contamination (1.59 to 57.06) was within the risk threshold (ERI < 110), while the risks of ∑carcinogenic metals for exposed children and adults along the basin were significantly influenced between acceptable to high cancer risk by Cd, Co, Ni, Cr and Pb.

Porous carbon supported calcium oxide for CO2 adsorption and separation of CO2/CH4.

Calcium oxide incorporated porous carbon materials were synthesized by the impregnation method to study CO2 adsorption and separation of CO2/CH4. The X-ray diffraction, Raman analysis, N2 isotherms at 77 K, and SEM with EDX analysis were used to characterize synthesized materials. XRD and N2 isotherm results have confirmed that synthesized carbon has porosity, and EDX analysis has reported that the presence of CaO on porous carbon. 10CaO/porous carbon has shown 31 cm3 g-1 of CO2 adsorption which was higher than bare porous carbon CO2 adsorption 17.5 cm3 g-1 at 298 K, 1 bar. It was attributed to electrostatic interaction between CaO and CO2. However, CH4 adsorption was decreased by a decrease in surface area. The selectivity of CO2/CH4 was higher for 10CaO/porous carbon and the heat of CO2 adsorption was 36 KJ/mol at high adsorption of CO2. Moreover, CO2 adsorption was the same in each adsorption cycle.

Machine learning-based statistical analysis for early stage detection of cervical cancer.

Cervical cancer (CC) is the most common type of cancer in women and remains a significant cause of mortality, particularly in less developed countries, although it can be effectively treated if detected at an early stage. This study aimed to find efficient machine-learning-based classifying models to detect early stage CC using clinical data. We obtained a Kaggle data repository CC dataset which contained four classes of attributes including biopsy, cytology, Hinselmann, and Schiller. This dataset was split into four categories based on these class attributes. Three feature transformation methods, including log, sine function, and Z-score were applied to these datasets. Several supervised machine learning algorithms were assessed for their performance in classification. A Random Tree (RT) algorithm provided the best classification accuracy for the biopsy (98.33%) and cytology (98.65%) data, whereas Random Forest (RF) and Instance-Based K-nearest neighbor (IBk) provided the best performance for Hinselmann (99.16%), and Schiller (98.58%) respectively. Among the feature transformation methods, logarithmic gave the best performance for biopsy datasets whereas sine function was superior for cytology. Both logarithmic and sine functions performed the best for the Hinselmann dataset, while Z-score was best for the Schiller dataset. Various Feature Selection Techniques (FST) methods were applied to the transformed datasets to identify and prioritize important risk factors. The outcomes of this study indicate that appropriate system design and tuning, machine learning methods and classification are able to detect CC accurately and efficiently in its early stages using clinical data.

Spatiotemporal nexus between vegetation change and extreme climatic indices and their possible causes of change.

Climate extremes have a significant impact on vegetation. However, little is known about vegetation response to climatic extremes in Bangladesh. The association of Normalized Difference Vegetation Index (NDVI) with nine extreme precipitation and temperature indices was evaluated to identify the nexus between vegetation and climatic extremes and their associations in Bangladesh for the period 1986-2017. Moreover, detrended fluctuation analysis (DFA) and Morlet wavelet analysis (MWA) were employed to evaluate the possible future trends and decipher the existing periodic cycles, respectively in the time series of NDVI and climate extremes. Besides, atmospheric variables of ECMWF ERA5 were used to examine the casual circulation mechanism responsible for climatic extremes of Bangladesh. The results revealed that the monthly NDVI is positively associated with extreme rainfall with spatiotemporal heterogeneity. Warm temperature indices showed a significant negative association with NDVI on the seasonal scale, while precipitation and cold temperature extremes showed a positive association with yearly NDVI. The DEA revealed a continuous increase in temperature extreme in the future, while no change in precipitation extremes. NDVI also revealed a significant association with extreme temperature indices with a time lag of one month and with precipitation extreme without time lag. Spatial analysis indicated insensitivity of marshy vegetation type to climate extremes in winter. The study revealed that elevated summer geopotential height, no visible anticyclonic center, reduced high cloud cover, and low solar radiation with higher humidity contributed to climatic extremes in Bangladesh. The nexus between NDVI and climatic extremes established in this study indicated that increasing warm temperature extremes due to global warming might have severe implications on Bangladesh's ecology and the environment in the future.