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Epipremnum aureum, sometimes known as the Money Plant, is a popular houseplant known for its hearts-shaped leaves and durability. Commonly referred to as Golden Pothos or Devil's Ivy, it is also appreciated for its ornamental value and air cleaning ability. They say that these plants are attractive to many people owing to their tolerance to several conditions and easy care, therefore, it is no surprise that they are found in many households and workplaces. Money Plants are hardy, but like any other plant they can also be infected by various diseases, which may render them less attractive, or even unattractive. This work encompasses bacterial wilt, manganese poisoning aspects and together with a healthy leaves aspect presents all prevalent masses and offer a comprehensive image of diseases. A dataset of 224 × 224 pixel images is utilized to accomplish this work with the intention to further enhance support in Ornamental Horticulture practices and diagnose more accurately. This work not only contributes ideas and approaches in understanding the field of plants pathology but also stresses on the fact how image processing can be beneficial in looking after plants. The dataset serves as a solid foundation for deep learning approaches into Ornamental Agriculture and provides useful insights for researchers studying the cultivation of money plants.

The dataset extensively examines the factors considered when choosing sweet potato genotypes, considering various characteristics. Notably, Moz1.15 demonstrated the highest marketable root yield at 46.46 t/ha, H5.ej.10 exhibited the highest beta-carotene level at 48.94 mg/100 g, and Moz1.9 recorded the highest vitamin C content at 23.89 mg/100 g. Moreover, there were significant correlations (ranging from 0.21 to 0.84) among the yield and quality traits studied in sweet potatoes. Principal component analysis (PCA) confirmed the connections among these traits, identifying four distinct clusters of genotypes, each characterized by specific significant combinations of traits. Factor analysis using the multi-trait genotype-ideotype index (MGIDI) highlighted the considerable impact of sweet potato traits across two growing seasons (2020–21 and 2021–22), facilitating the selection of genotypes with potential genetic gains ranging from 1.86 % to 75.4 %. Broad-sense heritability (h2) varied from 64.9 % to 99.8 %. The use of the MGIDI index pinpointed several promising genotypes, with BARI Mistialu-12 and H9.7.12 consistently performing well over both years. These genotypes exhibited both strengths and weaknesses.

Ground heat exchanger (GHE) is the most crucial element of a ground source heat pump (GSHP) system for building cooling and heating by the utilization of geothermal energy. Therefore, intending to enhance the performance of GHE, the present study conducts a computational investigation of the thermal performance of modified spiral tube vertical GHEs. Several modifications of uniform-pitched spiral GHE are made to increase its thermal performance. Some modifications are introduced as variable-pitched spiral tube GHE where spiral inlet pipes are densified in the lower part of GHEs by reducing pitch distance. Conversely, in some modifications, the position of the outlet straight pipe is changed. Water is considered as the working fluid and the inlet temperature of the water is maintained fixed at 300.15 K. After extensive analysis, it is evident that, when the outlet pipe is placed outside of the spiral coil, there is a 7.67 % enhancement in the thermal performance than a traditional uniform-pitched spiral tube GHE. However, modifications like variable-pitched spiral tube GHEs are not significant to improve the thermal performance due to the quick saturation of the ground soil temperature around the GHE pipes. To have a balance between heat transfer rate and pressure drop, thermal performance capability (TPC) and coefficient of performance improvement (COPimprvt) criterion were evaluated and it is found that the uniform-pitched spiral tube GHE along with the outlet pipe at the outside of the spiral provides maximum thermal performance with a maximum TPC value of 1.062 and provides the positive value of COPimprvt criterion. The positive values of COPimprvt indicate that the spiral tube GHEs are energy efficient based on heat transfer and pressure drop. Moreover, spiral GHE with high-density polyethylene (HDPE), concrete pile, and sandy clay outperform the other materials for pipe, backfill, and soil, respectively. Specifically, HDPE pipe, concrete backfill, and sandy clay as soil offer around 7 %, 5 %, and 7.8 % higher thermal performance compared to polyethylene, sand silica, and clay, respectively.

Summary: Background: Globally, stunting affects ∼150 million children under five, while wasting affects nearly 50 million. Current interventions have had limited effectiveness in ameliorating long-term sequelae of undernutrition including stunting, cognitive deficits and immune dysfunction. Disrupted development of the gut microbiota has been linked to the pathogenesis of undernutrition, providing potentially new treatment approaches. Methods: 124 Bangladeshi children with moderate acute malnutrition (MAM) enrolled (at 12–18 months) in a previously reported 3-month RCT of a microbiota-directed complementary food (MDCF-2) were followed for two years. Weight and length were monitored by anthropometry, the abundances of bacterial strains were assessed by quantifying metagenome-assembled genomes (MAGs) in serially collected fecal samples and levels of growth-associated proteins were measured in plasma. Findings: Children who had received MDCF-2 were significantly less stunted during follow-up than those who received a standard ready-to-use supplementary food (RUSF) [linear mixed-effects model, βtreatment group x study week (95% CI) = 0.002 (0.001, 0.003); P = 0.004]. They also had elevated fecal abundances of Agathobacter faecis, Blautia massiliensis, Lachnospira and Dialister, plus increased levels of a group of 37 plasma proteins (linear model; FDR-adjusted P

In Bangladesh, sweet potato holds the fourth position as a crucial carbohydrate source, trailing rice, wheat, and potato. However, locally grown sweet potato varieties often display limited stability and yield. To tackle this challenge, diverse selection methods and statistical models were utilized to pinpoint sweet potato genotypes showcasing both stability and superior yield and quality traits. In the initial two years, multiple selection methods were employed to narrow down the collections based on preferences for yield and its contributing traits. Subsequently, a multi-environment trial (MET) was conducted in the following year to pinpoint superior and stable genotypes with desirable yield and quality characteristics. An integrated approach involving the Multi-Trait Genotype Ideotype Distance Index (MGIDI), Factor Analysis and Ideotype-Design (FAI-BLUP), and Smith-Hazel Index (SH) led to the identification of 71 superior sweet potato genotypes out of a total of 351 in the initial growing season. In the subsequent season, the MGIDI selection index was applied to the 71 genotypes, resulting in the selection of 11 top-performing genotypes. This selection process was complemented by a detailed analysis of the strengths and weaknesses of the selected genotypes. In the MET, the mixed effect model, specifically the linear mixed model (LMM), identified significant genotypic and genotype-environment interaction (GEI) variances. This points to elevated heritability and selection accuracy, ultimately boosting the model's reliability. By combining the strengths of LMM and additive main effects and multiplicative interaction (AMMI), the best linear unbiased prediction (BLUP) index identified H20 as the top-performing genotype for marketable root yield (MRY), H37 for dry weight of root (DW), H8 for beta carotene (BC) and H41 for vitamin c (VC). These genotypes surpassed the overall average in the WAAS index. For simultaneous stability and high performance, the WAASBY index selected H37 for MRY, H6 for DW, H61 for BC, and H3 for VC. Finally, genotypes H3 and H20 were selected using multi-trait stability index (MTSI), as they possessed high performance and stability. Based on the selection sense, the objective has been achieved with regards to the trait MRW, which serves as a major criterion for a superior variety of sweet potato.

In a very dense urban landscape, incorporating renewables becomes challenging due to a lack of space, planning, and mindset. Utilization of already existing large infrastructures in combination with existing technology and necessary adaptation can create the right synergy for harnessing renewables like solar. This paper proposes the installation of a solar power plant in Dhaka, Bangladesh, using available space on Metro Rail Line 6 to meet the increasing demand for clean and renewable energy. The proposed system involves the selection of suitable space, and PV panels, the calculation of annual electricity generation, and performing financial and environmental analyses. The proposed on-grid/grid-tied system offers the advantage of reducing dependence on power supplied to the grid, resulting in lower energy costs, and promoting the use of green energy. The system has a payback period of 7.7 years and a return on investment of 45.7 %. It is estimated that the system saves 14,053.203 tons of CO2 emissions per year and 281,064.06 tons of CO2 emissions over its lifetime. Also, the grid life cycle emission is 584 gCO2/kWh, and the system life cycle emission is 39,119.4 tCO2, which further proves that it is a feasible solution to meeting energy demands while reducing the dependency on fossil fuels and promoting sustainable energy utilization. The results of simulations run using PVsyst and HOMER confirm the economic viability of the proposed solar power station, supporting its viability. The levelized energy cost (LCOE), as projected by PVsyst, is $0.09 per kWh, nearly matching HOMER's prediction of $0.0835. This convergence of results from several simulation tools supports the solar power plant's predicted cost-effectiveness, demonstrating its potential as a key player in the effort to create a greener and more affordable energy landscape.

The quaternary compound copper manganese tin sulfide Cu2MnSnS4 is a potential absorber semiconductor material for fabricating thin film solar cells (TFSC) thanks to their promising optoelectronic parameters. This article numerically investigated the performance of Cu2MnSnS4 (CMTS)-based TFSC without and with tin sulphide (SnS) back surface field (BSF) thin-film layer. First, the impact of several major influential parameters such as the active material's thickness, doping concentration of photoactive materials, density of bulk and interface defect, working temperature, and metal contact, were studied systematically without a BSF layer. Thereafter, the photovoltaic performance of the optimized pristine cell was further investigated with an SnS as BSF inserted between the absorber (CMTS) with a Platinum back metal of an optimized heterostructure of Cu/ZnO:Al/i-ZnO/n-CdS/p-Cu2MnSnS4/Pt. Thus, the photoconversion efficiency (PCE) of 25.43% with a JSC of 34.41nullmA/cm2 and VOC of 0.883 V was achieved under AM1.5G solar spectrum without SnS BSF layer. Furthermore, an improved PCE of 31.4% with a JSC of 36.21nullmA/cm2 and VOC of 1.07 V was achieved with a quantum efficiency of over 85% in the wavelengths of 450–1000 nm by the addition of SnS BSF layer. Thus, this obtained systematic and consistent outcomes reveal immense potential of CMTS with SnS as absorber and BSF, respectively and provide imperious guidance for fabricating highly a massive potential efficient solar cell.

Enterocytozoon hepatopenaei (EHP) is a parasite that mostly affects shrimp; it is linked to spore-forming and obligatory intracellular microsporidia and causes growth retardation and body length fluctuation, which results in financial consequences for the shrimp industry in Asian nations. However, the introduction of technologies to detect EHP has been complicated by the absence of a thorough knowledge of the properties of spore wall proteins (SWPs) linked to host infection. Thus, characterization, feature identification, 3D modeling of protein sequence, prediction of transmembrane regions, motif analysis, as well as model validation of SWPs can help improve drug delivery in the future. Conservation analysis was performed using ClustalW. InterPro, MEMEsuite, and TMHMM Server v2.0 were used to perform motif analysis, family domain prognosis, and transmembrane region analysis, respectively. Finally, the Alphafold2 server was used for analogy modeling and 3D layout. Different model validation servers were used to validate the protein structural model. Physicochemical features for SWPs were analyzed and found molecular weight around 16.701 KDa-38.314 KDa, theoretical pI 5.25–9.05, instability index 32.04–48.89, aliphatic index 76.49–99.84, grand average of hydropathicity (GRAVY) – 0.059 to −0.892. Each SWP was discovered to be hydrophilic. There was no significant region detected in the conserved region analysis. Both a lower and higher E value were found by the motif analysis. For most of the proteins, there were no transmembrane regions. Finally, analogous 3D models for each protein were created and verified using various bioinformatics tools and software. This investigation could aid in the further research of diseases and the development of drug delivery systems.

The increasing disposal of waste tires and tubes represent a major environmental problem since they are non-biodegradable and require bulk storage space for open dumping. However, significant heating value, low ash content makes them a valuable source of energy. Pyrolysis is an environmentally friendly thermochemical process which produce useful products (oil, char, and gas) from solid wastes. The main purpose of this paper is to produce pyrolysis oil from waste tire and tube of bicycle/rickshaw by using lab scale fixed-bed internally heating reactor. Experiments were conducted to investigate the effect of reaction temperature (300–600 °C), feed size (0.5–2.0 cm3) and pyrolysis reaction time on the pyrolysis product yields. Moreover, the physiochemical properties of obtained oil also been discussed. The maximum liquid yield from the waste tire was 45.29 wt% at temperature of 450 °C with optimum sample size 1.5 cm3 and reaction time of 39 minute, while for tube pyrolysis, the maximum yields of liquid was 43.89 wt% at temperature of 550 °C with sample size of 2.0 cm3 and reaction time of 38 minute. The pyrolysis of waste tube, which is new in this study, the maximum liquid produced (43.89 wt%) is comparable with the maximum liquid yield (45.29 wt%) from tire pyrolysis. GCV obtained for tire and tube pyrolysis oil are 39.78 MJ/kg and 39.17 MJ/kg, respectively. Physiochemical properties of derived oil are comparable with that of commercial diesel fuel, and this obtained oil can be used as an alternative fuel in engine after proper treatment.

Smart Grid technology as a platform can encompass several advanced technological features across the spectrum of the power system. Smart Grid may introduce an early warning system for structural damage on the surface of large-scale power utility facilities providing crucial information to maintain the safety of the plant. To achieve such a cost-effective structural health monitoring system, a holistic smart inspection implementation framework is required. Using recently available sophisticated inspection technologies, high-resolution images covering the structural condition of large infrastructure can be routinely acquired as a remote monitoring process. Automated analysis of these inspection images can significantly reduce the inspection cost, provide an effective detection mechanism, and shorten reporting time, as a result, reducing overall maintenance costs, and improving safety measures. In this work, we have applied state of art deep learning based inspection image analysis methods for surface damage detection of various renewable energy power plants with a single unified model. We have achieved the state-of-the-art accuracy of 0.79 mean average precision on average even where input images are of varied modalities: from thermal images to visual images, from high- to low-resolution images, and from PV panels to wind turbines. All variations have been tackled with one single deep learning model to detect surface damages. Our results demonstrate the promise of effectively deploying a single trained model to inspect a wide range of energy installations while reducing the monitoring cost significantly. In addition, this work also published the reported dataset comprising four specific image sets for the research community.

The temporal distribution of drugs in the eye tissues after the topical dosing is required to study the efficacy of the drugs. Glaucoma is one of the primary causes of vision impairment around the world and timolol is used in the treatment of glaucoma in general. This paper contributes to developing a model to represent the more accurate biophysics of the human eye through the finite element method compared to other state-of-the-art simulation models. A 2D human-eye model is used to simulate the temporal distribution of timolol after topical dosing within the COMSOL Multiphysics environment. The biophysics of the human eye including temperature regulation, fluid flow in the anterior segment, convection, diffusion, reaction kinetics of timolol, and metabolic consumption are incorporated in the proposed simulation model. The temporal concentration of timolol is analyzed for seven ocular tissues (cornea, iris, sclera, aqueous humor, lens, ciliary body, and trabecular meshwork) in this work. The peak concentration of timolol takes place within 2500 s in the ocular tissues considered in the simulation and the peak in the anterior chamber is observed at 1150 s. The comparison based on the normalized concentration of timolol in the anterior chamber demonstrates that the results of the proposed ocular model comply close with the practical findings. The mean difference with the experimental results is lower in this study compared to the recent numerical works on timolol. The proposed model can be used for analyzing the physiological behavior of human eyes under different types of drug delivery with varying initial concentrations.

This study aims to investigate the drying kinetics, energy analysis and milling performance of two common rice varieties namely BRRI-28 and BR-16 by two stage drying. Parboiled paddy samples were dried employing two schemes of two stage drying. First scheme is first stage by fluidized bed drying at temperature profile of (150–170 °C) with fixed bed thickness of 10 cm, tempering (30 min) and followed by fluidized bed drying again using lower temperature 80±2 °C and second scheme is first stage fluidized bed drying (150–170 °C) and tempering and followed by sun drying method. In addition, sun drying method as complete single stage drying was considered as control drying. Drying results indicated that total drying time required in first scheme, second scheme and control drying are 1.4, 4.75 and 11–12 h, respectively. Energy analysis showed that for both paddy varieties, first scheme yielded the highest specific electrical (12.6 MJ/kg H2O evaporated) and thermal energy consumption (10.5–11.6 MJ/kg H2O evaporated) compare to second scheme (0.58–1.6 MJ/kg H2O evaporated) of drying. Milling quality as head rice yield (HRY) was significantly varied among the drying schemes. For BR-16 variety, first scheme yielded the highest HRY (66 ± 0.706%) compare to BRRI-28 variety that yielded the lowest HRY (43.9 ± 0.571%). Better milling recovery (75 ± 0.64%) was obtained in first scheme compare with control dried sample (71 ± 0.56%). Therefore, parboiled paddy is suggested to be dried by any scheme of two stage drying for achieving quality dried grain with minimum energy consumption.

Nanofluids have been extensively studied in recent decades and have been regarded as “next-generation heat transfer fluids” due to their superior properties. However, dispersion stability and application at higher temperatures are among the challenges that must be overcome. In this work, a new class of stable hybrid nanofluid based on multi-walled carbon nanotube (MWCNT) + cupric oxide (CuO) nanocomposite is produced with Therminol55 (TH55) as the base fluid. Nanofluids' thermophysical characteristics are investigated at varying concentrations (0.005–0.08 wt%), and they are subsequently employed as the heat transfer medium in a tube heat exchanger (HEX) for the turbulent flow regime. Thermal conductivity was significantly increased by 128.4% at the maximum nanocomposite concentration of 0.08 wt%. Despite this, nanocomposites enhanced the nanofluids' viscosity, which climbed gradually with concentration to a maximum enhancement of around 25% at 0.08 wt%. The heat transfer performance of the formulated nanofluids was numerically assessed and found to be good; for example, when compared to pure TH55, the heat transfer coefficient improved by up to 128%. The highest increase in Nu was 38.4%, while the maximum increase in pumping power was determined to be 103.88%. Furthermore, the maximum exergy efficiency was 47.84% at a 0.08 wt% concentration and a Reynolds number (Re) of 12500, which is somewhat higher than the 40.96% attained with pure TH55. The highest thermal performance factor was 1.31 for 0.08 wt %, exceeding the maximum thermal performance factors of 1.17, 1.11, 1.08, and 1.03 for 0.04, 0.02, 0.01, and 0.005 wt %, respectively. Consequently, a nanofluid made of MWCNT + CuO/TH55 might be a promising candidate for usage as a heat transfer fluid.

Smart Grid technology as a platform can encompass several advanced technological features across the spectrum of the power system. Effective Smart Grid system includes smart metering, robust communication infrastructure, deep integration of renewable energy sources, and distributed generation, among other important factors. The presented work in this paper highlights the required adaptation of the potential prime features of Smart Grid technology covering niftier equipment integration and internal cross-platform communication adaptable to the context of Bangladesh. This paper also focuses on the potential cost model for Smart Grid installation in Bangladesh based on the current scenario regarding the power sector. A study on India’s Smart Grid that simulates a quasi-similar circumstance helped better comprehend the essential steps required to implement Smart Grids in Bangladesh.

COVID-19 has elapsed all over the world with massive losses which indicate the lack of availability of medical equipment during the pandemic such as a ventilator. This is exemplified by the densely populated country Bangladesh who unable to maintain COVID-affected people because of the ventilator. Due to the higher price, unavailability, and manufacturing defection, most medical are unable to purchase this ventilator which causes terrible death for a respiratory problem. Of these cases, this paper represents a way to escape this problem and proposed a mechanical ventilator named “NISHASH” which will help to anticipate COVID affected people and higher price of the ventilator. Through the electromechanical instruments, a prototype lightweight easily moveable where preciously it automatically controls with digital feedback system ventilator which fulfills oxygen flow based on patient requirement are developed with different selection mode. The aim was to design and develop inexpensively automated easy to build to minimize the extreme shortage of the ventilator in Bangladesh. In this model of a mechanical ventilator, the cost is less than $90 where components are available all over the world.

Enhanced sustainability and efficiency are among the prime indicators to achieve in the power generation sector of any country. The decline in efficiency might be caused by improper utilization of fuels and increasing entropy in outdated generation units. Reduced efficiency in power generation is particularly detrimental for the sustainability in developing countries, as resources are limited and need to be carefully allocated and maintained. Exergy analysis based on the laws of thermodynamics are currently gaining traction among researchers to study the overall performances of power generation units. Taking Bangladesh as a case study, this work investigates the power generation data for the country since 2009 till 2019 to study the internal energy losses of natural gas, oil, high-speed diesel, coal, and hydro based plants and provides a baseline for investment policy formulation. Our study found that, due to lack of maintenance and proper planning during the initial phases, the overall energy efficiencies among these sectors are relatively low, staying in the range of 34.55%–36.1% while the exergy efficiencies are 35.07%–36.59%. Corresponding exergetic parameters as depletion number, exergy sustainability index, relative irreversibility, lack of production, cumulative exergy loss, waste exergy ratio, and environmental effect factor pointed to similar findings in terms of sustainability as well. Comparing these parameters, it is observed that hydroelectric plants are more sustainable and efficient compared to fossil fuel-based generation units. Cost estimation of fuels and financial losses in the power generation sector are also explored for a better understanding of the return on investment.

The current study investigates the effects of thermal radiation and Lorentz’s force on heat and mass flow in a nanofluid with a magnetic field and free convection, as it passes over a vertical permeable sheet. The dominant PDEs are turned into linked nonlinear ODEs using an appropriate similarity transformation. Using the MATLAB ODE45 tool, dimensionless ODEs are numerically computed using the finite difference procedure. Four different water-based nanofluids are considered, including copper, titanium dioxide, aluminum oxide, and silver. The influence of emerging dimensionless numbers and other parameters, such as the magnetic force parameter, the radiation parameter, the suction parameter, the Prandtl number, the Schmidt number, the Dufour number, as well as for constant values of the modified local Grashof number and local Grashof number on the concentration, velocity, and temperature profiles is emphasized and mentioned. Moreover, the visual representation of the effects of a volume percentage of up to 4% copper nanoparticles on the distributions of concentration, temperature, and velocity is also shown. The temperature profile, concentration profile, and velocity profile all grow with an increase in the volume percent of copper nanoparticles between 0.00 and 0.04. Furthermore, numerous scenarios are investigated for the distributions of the local Sherwood number, the local Nusselt number, and the local skin friction coefficient. The local Nusselt number decreases, and the local skin friction coefficient and Sherwood number increase about by 30 %, 45 %, and 60 % respectively due to increasing the value of volume fraction from 0 % to 4 %. The local skin friction coefficient increases and the local Nusselt number decreases by about 15 % and 39 % respectively for rising values of the thermal radiation number from 0.5 to 3.5. In addition, for magnetic force parameter values between 0.5 and 4.0, the local skin friction coefficient drops by about 13 %.

Bananas are among the most widely consumed fruits globally due to their appealing flavor, high nutritional value, and ease of digestion. In Bangladesh, bananas hold significant agricultural importance, being one of the most extensively cultivated fruits in terms of land coverage and ranking third in production volume. The banana image dataset presented in this article includes clear and detailed images of four common banana varieties in Bangladesh: Sagor Kola (Musa acuminate), Shabri Kola (Musa sapientum), Bangla Kola (Musa sp.), and Champa Kola (Musa sapientum), as well as four key stages of banana ripeness: Green, Semi-ripe, Ripe, and Overripe. The bananas were collected from wholesale markets and retail fruit shops located in different places in Bangladesh. Overall, the dataset has 2471 original images of different varieties of bananas and 820 original images of varying ripeness stages of bananas. All the images were carefully captured using a high-quality smartphone camera. Later, each image was manually reviewed, maintaining the quality standard throughout the dataset. The augmented version of the banana variety classification dataset contains 7413 images and the augmented banana ripeness stages dataset contains 2457 images. The dataset possesses immense potential in driving innovation and development of automated and efficient processes and mechanisms in several fields, including precision agriculture, food processing, and supply chain management. Machine Learning (ML) and Deep Learning (DL) models can be trained on this dataset to accurately categorize banana varieties and determine their ripeness stages. Such ML and DL models can be leveraged to develop automated systems to determine the optimal harvest time, establish standards for quality control of bananas, develop products and marketing strategies through analysis of consumer preferences for various banana varieties and ripeness levels, and streamline the banana supply chain through improvements in harvesting, sorting, packaging, and inventory management. Additionally, researchers aiming to contribute to developing Computer Vision technologies in food and agricultural sciences will find this dataset valuable in advancing precision farming and food processing mechanisms. Therefore, the dataset has a vast capacity for automating banana production and processing, minimizing the costs of manual labor, and improving overall efficiency.

Bacteriophages or bacteria infecting viruses are genetically diverse. Due to the emergence of antimicrobial-resistant bacteria, lytic bacteriophages are gaining enormous attention for treating superbug infections. Klebsiella pneumoniae is one of the eight most significant nosocomial pathogens and is addressed as a critical priority pathogen by WHO, requiring alternative treatment options. We reported two highly lytic bacteriophages, Klebsiella phage Kpn BM7 and the novel Klebsiella phage Kpn BU9, isolated from hospital wastewater and exhibiting lytic activity against different clinical isolates. Whole-genome analysis revealed that phages BM7 and BU9 belong to class Caudoviricetes. Phage BM7, with a genome length of 170,558 bp, is a member of the genus Marfavirus and the species Marfavirus F48. While phage BU9, with a genome length of 60,450 bp, remains unclassified. Neither phage harbors any lysogenic, toxin, or antimicrobial resistance genes. Both phages can steadily survive up to 40 °C and at pH 5–7. The optimal MOI was 0.1 for BM7 and 1 for BU9, with short latent periods of 10 and 25 min and burst sizes of 85 PFU/cell and 12 PFU/cell, respectively. This is the first carbapenem-resistant K. pneumoniae targeting lytic phages to be reported from Bangladesh. This study suggests that BM7 and BU9 are potential candidates for targeting carbapenem-resistant K. pneumoniae.

This article statistically investigates the impact of heat production on the unstable MHD natural convection transport of nanofluid flow via a perforated sheet. The ordinary differential equations (ODEs) are derived from the partial differential equations (PDEs) by using of the similarity transformation. The dimensionless ordinary differential equations (ODEs) may be numerically resolved with the help of the MATLAB ODE45 tool and the finite difference method (FDM) along with the shooting strategy. Four innovative water-based nanofluids such as TiO2, Cu, Al2O3, and Ag are considered nanoparticles. The numerical results have been explained for the role of numerous non-dimensional numbers or parameters such as heat generation or absorption (Q), nanoparticle volume fraction (φ), Dufour number (Df), Prandtl number (Pr), magnetic force parameter (M), Soret number (Sr), and Schmidt number (Sc) on the fluid flow, and heat and mass transfer rates. The fluid temperature drops but velocity is enhanced for higher amounts of Q. Copper nanoparticle volume fraction up to 4 % shows a rise in temperature, concentration, and velocity curves. Heat transfer rate ( − θ′(0)) diminishes by about 124 %, while the values of f′(0) promote by approximately 26 % owing to an increase in the values of Q (heat generation) from 1.0 to 2.0. The value of ( − θ′(0)) increases by 49 %, but f′0 decreases by 15 % due to a rise in Q (heat absorption) from -3.0 to -10.0. The local skin friction coefficient (f′(0)) diminishes by about 65.21 % due to an increase in the values of the magnetic force parameter (M) from 0.5 to 3.5 whereas the rate of heat and mass transfer remain unchanged. As φ increased from 0.01 to 0.04, the local skin friction coefficient (f′(0)) exhibited a 36 % increase, while the heat transport rate (θ′(0)) decreased around by 10 %. In conclusion, a comparison was made between our findings and those of the published research. The comparison indicates a high degree of consistency.

This study examined whether work-family conflict mediates the effects of perceived organizational support and perceived supervisor support on turnover intention among nurses in the healthcare sector in Bangladesh. Data collected from 383 participants were analyzed using structural equation modelling. Both perceived supervisor support and perceived organizational support were found to have a significant negative association with nurses' intention to leave. The study further revealed that the connection between perceived support and turnover intention among nurses in Bangladesh was positively mediated by work-family conflict; it was shown that work-family conflict increases turnover intention. This study recommended that, hospitals should focus on creating a socially interactive environment and addressing work-family conflicts, as selected variables can as these factors can mitigate nurses’ turnover intentions through job satisfaction and increase overall well-being among nurses. An increase in job satisfaction may ultimately improve patient care within healthcare organizations. This study aimed to provide support to human resource managers in the development of strategies that can effectively reduce nurse turnover, which is a critical issue in Bangladesh and other countries facing similar challenges.

This paper investigates the role of heat absorption or production on time-dependent free MHD convective transport over a vertical porous plate with thermal radiation. The PDEs are changed into non-dimensional couple ODEs by adopting proper similarity analysis. Then the finite difference method (FMD) is used for solving the converted non-dimensional coupled ODEs. The roles of the dimensionless parameters or numbers like the radiative parameter (R), internal heat absorption or generation(Q), the suction (v0), the magnetic force parameter (M), the Schmidt number (Sc), and Prandtl number (Pr) the on the numerical results of the temperature, velocity, and concentration distributions are explained in graphically. The results indicate that improving values of the heat absorption or production with thermal radiation improves the thermal boundary layer thickness. The local skin friction coefficient increases by about 11 % and the heat transfer rate reduces by about 85 % due to improving values of Q from 1.0 to 2.0. Growing values of the radiative parameter from 1.0 to 4.0 improves the local skin friction coefficient by about 13 %. The heat transfer rate lessens by about 41 %. Our numerical results are more compared with the published paper.

Antibiotic resistance has been recognized as a public health threat in recent years, and mortality due to resistance is increasing alarmingly every year. Antibiotic resistance, among many factors, may arise due to the consumption of substandard antibiotic brands that provide subnormal levels of the drug in the blood. Post-market evaluation can provide important information in assessing pharmaceutical products in terms of quality, purity, and therapeutic aspects. Ciprofloxacin, a broad-spectrum antibiotic, has been used against a wide range of infectious diseases in Bangladesh. The present study aimed to determine the quality attributes of twenty-two commonly prescribed brands of ciprofloxacin 500 mg tablet collected from Dhaka city and the rural regions of Jessore. RP-HPLC coupled with UV–visible spectrophotometry was used to determine the potency of ciprofloxacin in tablets, and the zone of inhibition was determined using Kirby-Bauer's disc diffusion method to assess the antimicrobial efficacy against different strains of microorganisms. We found that 95.45% of brands (21 out of 22 brands) of ciprofloxacin tablets met United States Pharmacopoeia (USP) and British Pharmacopoeia (BP) specified potency, whereas one brand failed. From dissolution studies, we observed that 68.2% of brands (15 out of 22 brands) followed USP/NF dissolution test specifications, whereas 31.8% (7 out of 22 brands) failed to release 80% of the labeled amount of drug within 30 min. Drug release kinetics data showed that most brands followed the Weibull drug release kinetic model. Fit factor analysis exhibited that 8 brands out of 22 (36.4%) failed to comply similar dissolution profiles with the reference product. Minimum inhibitory concentrations, assessed against five bacterial strains, further showed good antimicrobial sensitivity by all brands.

The effects of radiative and viscous dissipation on the transfer of unsteady magnetic-conductive heat-mass across a vertically porous sheet is studied in this article. The non-dimensional ODEs are solved by applying the Finite Difference Method (FDM) through the MATLAB software numerically. The fluid temperature and velocity enhance for uplifting values of the Eckert number. Enhancing values of the transpiration parameter the velocity, concentration, and temperature distributions reduce. The local skin friction enhances about 9%, and 18% due to increase the Eckert number (0.5–3.0) and Dufour number (0.5–4.0), respectively and reduces 17%, 38%, and 31% due to increase Prandtl number (0.71–7.0), magnetic force parameter (0.5–3.0), and suction parameter (0.5–3.0), respectively. Enhancing values of the Eckert number (0.5–3.0) reduces the heat transfer rate by 40%. The increasing value of the Prandtl number (0.71–7.0) and the suction parameter (0.5–3.0) increases the heat transfer rate by 27% and 92%, respectively. With an increase in the values of the Schmidt number (0.22–0.67), the mass transfer rate increased by approximately 94%. At last, the numerical results of this paper has compared with the previously published paper. We noticed that the comparison has an excellent acceptance.

Ventilators have always been common in medical scenarios but are very expensive to procure or develop. One of the main reasons for these is the components that are being used are expensive and require precise instrumentation, research, and development. This paper attempts to mitigate that problem by proposing a novel way to rapidly develop a portable ventilator that uses common 3D printing technology and off-the-shelf components. This turbine and valve-based ventilator feature most of the modes that are commonly used by healthcare professionals. A unique servo-based pressure release mechanism has been designed that makes the system around 36 times more efficient than solenoid-based systems. Reliability and efficiency have been increased further through the use of a novel positive end-expiratory pressure (PEEP) valve that does not contain any electromechanical component. Effective algorithms such as feed-forward and proportional–integral–derivative (PID) controllers were used alongside the unique ‘Sensor data filtration methodology’. The system also provides an interactive graphical user interface (GUI) via an android application that can be installed on any readily found tabs while the firmware manages the breathing detection algorithm using a flow meter and pressure sensor. This modular and portable ventilator also features a replaceable battery and holds the ability to run on solar power. This energy-efficient low-noise system can run for 5 to 6 h at a stretch without needing to be connected to the main’s supply.

Studies about the effects of foreign direct investment (FDI) on export processing zones (EPZs) in Bangladesh are very scarce. Most of the researchers have done research on the effects of FDI on economic growth in Bangladesh. Some of the researchers have also examined the effects of FDI on domestic investment, trade, and other sectors of the economy. But as far as we know, the effects of FDI on EPZs in Bangladesh have not been examined. The objective of this study is to analyze the effects of FDI inflows on EPZs in terms of exports and employment in EPZs using secondary data from 1997 to 2018 in Bangladesh. The study has applied the Generalized Method of Moments (GMM) model for the export of EPZ and the Auto Regressive Distributed Lag (ARDL) bound test approach for employment generation, respectively. Inward FDI is the key explanatory variable. There are also some control variables to overcome the problem of omitting variables. According to the GMM estimation, FDI and GDP growth both have a positive relationship in accelerating EPZ export. The coefficient of FDI inflows is 0.74%, which means that if the FDI inflows increase by 1%, the export of EPZ production will increase by 0.74%. The ARDL results show that, in the long-run and short-run, there is a positive association between FDI inflows and EPZ employment. The government and policymakers of the country should emphasize the real operation of the one-stop service to encourage foreign investors as well as local investors. If the government really starts a one-stop service for foreign investors, then most of the investors will be encouraged to invest in the country.

In this paper, the transpiration effect on unsteady natural convection boundary layer flow around a vertical slender body has been studied numerically. Applying the similarity transformation together with Boussinesq's approximation, the governing PDEs have been modified into ODEs. These equations have been solved by applying the shooting technique with the help of “ODE45 MATLAB” software. The influences of the pertinent parameters/numbers namely transpiration parameter (FW), the radius of slender body (R0), Prandtl number (Pr) and the driving parameter (β) on the velocity and temperature of the fluid within the boundary layer has been analyzed. The velocity and temperature decrease with increasing the values of the suction parameter but a reverse trend is found for increasing values of blowing parameter from the wall to the free stream. Rising values of the suction parameter (0.5–1.0) increase the local skin friction and the heat transfer rate by approximately about 35% and 31%. The velocity of the fluid decreases for uplifting values of the kinematic viscosity. When the thermal conductivity decreases then the temperature of the fluid decreases. Rising values of the body radius parameter the skin-friction and the rate of heat transfer increase.

The impact of the QCD critical point on the propagation of nonlinear waves has been studied. The effects have been investigated within the scope of second-order causal dissipative hydrodynamics by incorporating the critical point into the equation of state, and the scaling behavior of transport coefficients and of thermodynamic response functions. Near the critical point, the nonlinear waves are found to be significantly damped which may result in the disappearance of the Mach cone effects of the away side jet. Such damping may lead to enhancement in the fluctuations of elliptic and higher flow coefficients. Therefore, the disappearance of Mach cone effects and the enhancement of fluctuations in flow harmonics in the event-by-event analysis may be considered as signals of the critical endpoint.

The two component Monte-Carlo Glauber model predicts a knee-like structure in the centrality dependence of elliptic flow v2 in Uranium + Uranium collisions at sNN=193 GeV. It also produces a strong anti-correlation between v2 and dNch/dy in the case of top ZDC events. However, none of these features have been observed in data. We address these discrepancies by including the effect of nucleon shadowing to the two component Monte-Carlo Glauber model. Apart from addressing successfully the above issues, we find that the nucleon shadow suppresses the event by event fluctuation of various quantities, e.g. ε2 which is in accordance with expectation from the dynamical models of initial condition based on gluon saturation physics.

The levels of greenhouse gases, and in particular, carbon dioxide (CO2) emissions due to anthropogenic activities, have greatly inflated, and this has contributed to climate fluctuation and global warming. In 2023, the CO2 emissions went up by 1.1 % to arrive at a figure of 37.4 g/t. There is now a good prospect of converting CO2 into other products, thanks to the active research into the use of COFs for CO2 capture and conversion. COFs as a new class of porous crystalline materials are synthesized by organic units linked like benzene and triazine, sanines, and porphyrines. Production procedures may result in COFs impurities, therefore, an activation paragraph is required to outweigh the deficiency and improve the efficiency of the COFs. Even though it is difficult to achieve these characteristics in humid conditions where temperature and pressure are in the normal operating conditions of COFs, their low density, highly porous surface areas, large pore volume, and adjustable pore size, all vice versa are effective in carbon capture. This review focuses on the fact that COFs' structural properties are vital to the success of the CO2 capture and storage processes. It also assesses the possibility of creating cyclic carbonates or other organic compounds to solve environmental issues effectively.

Geothermal energy is a reliable and renewable form of energy that offers steady cooling and heating of buildings without relying on sunlight or wind, avoiding price fluctuations. Ground heat exchangers (GHEs) are designed to utilize geothermal energy for building heating and cooling. The present study conducted a numerical investigation on the thermal performance of Modified Vertical U-tube GHEs. Enhancement of the thermal performance of various configurations of GHEs by adjusting the leg spacing and incorporating spiral configuration of U-tube GHEs. The upper section's leg spacing varies to reduce thermal interference and the lower section's leg spacing is kept constant. Some designs incorporated a combination of spiral and straight U-tubes. All GHE models were considered a 20-meter depth. Performances of these GHEs are evaluated with polyethylene, high-density polyethylene, and low-density polyethylene tubes and boreholes filled with silica sand. The surrounding soil consisted of clay. In some configurations, the lower section was modeled with spiral geometry after 10 meters in depth. The design parameters for the spiral section were 70 mm and 100 mm diameter. The simulations were conducted using the ANSYS FLUENT 22.2 software program for cooling operation. The outlet temperature was lower for spiral configurations than for the other configurations after being simulated for 72 hours. Compared to the traditional U-tube configuration, the heat transfer rate achieved by each arrangement is significantly higher. Results indicated that the combination of spiral shape with traditional shape led to a 31.2% increase in average heat transfer rate. Leg spacing variation opens up the opportunity to combine multiple geometries in a single borehole, also providing the solution to reduce the thermal interference between the inlet and outlet pipe.

Contamination of harmful additives in fruits has become a concerning norm these days. Owing to the great popularity of fruits, dishonest vendors frequently use harmful chemicals to contaminate fruits to extend their shelf life, which is extremely dangerous for the general public's health. To mitigate this issue, machine-learning algorithms like Decision Tree Classifier, Naïve Bayes and a deep learning model named “DurbeenNet” are evaluated separately. Alongside, a computer vision-based detection method coupled with a hybrid model is proposed that combines deep learning and chemical sensor. Formaldehyde Detection Sensor is used in this experiment to take reading of the sensor data. Mango, Apple, Banana, and Malta are taken as sample fruits in this study. Sensor data for both fresh and chemical-mixed fruit is newly collected using Formaldehyde Detection Sensor. The above mentioned sensor data along with the previously captures images of both fresh and chemical-mixed state are being integrated to a hybrid model. Among two machine learning algorithms naïve bayes come up with 82 % accuracy. Using both sensor data and captured image data, the proposed model “SensorNet” provides highest accuracy of 97.03 % which is substantial than “DurbeenNet” model's accuracy. Through the utilization of these fruit samples, formaldehyde detection sensor provides instantaneous detection, identifying the specific toxic substances present in the contaminated fruits.

The elasmobranch population is declining in the Bay of Bengal of Bangladesh due to large-mesh gill net fishing, locally known as the Lakkha net, which primarily targets Indian threadfin (Leptomelanosoma indicum). This study was the first attempt to identify megafaunal bycatch in Lakkha fishing and assess its vulnerability using Productivity Susceptibility Analysis. A total of 40 elasmobranch bycatch species were identified, with sharks comprising 13 species from three families, while 27 rays belonged to six families, with the majority belonging to the Myliobatiformes order (60 %). Productivity and susceptibility scores were assigned to all identified species, with values ranging from 1.27 to 2.73 and 1.50 to 2.63, respectively. The target Lakkha fish exhibited the highest susceptibility score, followed by several pelagic sharks and eagle rays. Vulnerability assessment revealed that 31.7 % (n = 13) of species were highly vulnerable, while 43.9 % (n = 18) were classified as moderate, and 24.4 % (n = 10) were considered to have low vulnerability. All the high-risk megafauna species (n = 13) are classified as threatened by the global IUCN Red List. Sensitivity analysis highlighted susceptibility as a major contributor to species’ vulnerability. Alterations in susceptibility scores led to significant changes in the vulnerability status of many species. The overall data quality assessment indicated moderate data quality across species, with variability observed between productivity (76 % of species received a poor data quality score) and susceptibility attributes. However, vulnerability of these species can be reduced through adequate gear modification, shorter net deployment periods, adoption of safe discharge techniques, identification of critical habitats, and establishment of marine protected areas within this region. This study provides valuable insights into the species composition and vulnerability of elasmobranchs in the Lakkha gill net fishery, emphasizing the need for conservation measures to mitigate bycatch impacts on threatened species.

Water quality is a critical factor in shrimp farming, and the success of shrimp production is closely tied to the overall condition of the water. Challenges such as rapid population growth, environmental pollution, and global warming have led to a decline in fisheries production, particularly in the freshwater shrimp sector. This study addresses these challenges by monitoring multiple water parameters in shrimp farms, including pH, temperature, TDS, EC, and salinity. Traditional manual monitoring systems are known to be cumbersome, time-consuming, and lacking real-time capabilities. Consequently, a continuous and automated monitoring system becomes imperative for efficient and real-time metrics handling. This study introduces a real-time freshwater shrimp (locally named Galda, i.e., Macrobrachium Rosenbergii) farm monitoring system. The proposed system incorporates technologies such as microcontroller-based physical devices, IoT, cloud storage with service, machine learning models, and web applications. This integrated system enables users to remotely monitor shrimp farms and receive alerts when water parameters fall outside the optimal range. The physical implementation involves a set of sensors for collecting data on water metrics in shrimp farms. Regression analysis is employed for predicting next-day values, and a newly developed decision-based algorithm classifies shrimp production levels into low, medium, and maximum categories using six well-known classification algorithms. The system demonstrates a high success rate for next-day predictions (r2 of 0.94) by multiple linear regression, and the accuracy in classifying shrimp production is 97.84 % by Random Forest. Additionally, a ‘Smart Aquaculture Analytics’ web application has been developed, offering features such as real-time dashboards, historical data visualization, prediction and classification tools, and automated notifications to farmers in Bangladesh.

The study assessed the quality of four different edible fats and oils using standard analytical techniques. The presence of potentially toxic elements was determined using atomic absorption spectrometry. This study reveals that edible oils function admirably in terms of physical traits such as moisture content, boiling point, melting point, density, and specific gravity. Some edible fats and oils exceeded the standard limit of moisture, acid value, and peroxide value and these values were found in the range of 0.120–0.760 %, 0.220–2.45 mg KOH/g, and 1.23–21.7 meq/kg respectively. The iodine value for fats showed satisfactory results but for oils observed lower than the standard value varied from 68.2 to 104 g/100 g. The results of saponification value for most of the oils and fats were found satisfactory but others were lower than recommended limits and detected results were in the range of 167–224 mg KOH/g. Trace metals viz. Fe, Mn, Ni, Pb, Cd, Cu, and Co were measured in all samples and the concentration ranged from 0.070 to 47.0, 0.120–2.44, 0.540–27.1, 0.030–1.87, 0.010–4.63 and 0.060–8.39 ppm for iron, manganese, nickel, lead, copper, and cobalt respectively. The study found high levels of Fe, Mn, Ni, Cu, and Co in edible fats and oils in Bangladesh. No Cd was found, and Pb was not present in over half of the samples, which included the majority of mustard oils. The levels of Fe and Ni were higher than advised, but there was no discernible toxicological danger from Cd or Pb. The results of the health risk assessment indicated that there was no risk to children's health and possible hazards to adults' health.

The present study reports on a novel prototype of a wavy-tape insert designed to enhance heat transfer in pipes. The prototype features a dimpled channel pipe with a wavy-tape positioned centrally within it. The characteristics of flow and heat transmission are studied using computational fluid dynamics simulations. Full-scale simulations assuming turbulent flow regime with Re ranging from 10,000 to 25,000 are studied in the thermal entrance region under constant heat flux boundary conditions. The numerical results were validated against existing experimental data for a similar wavy-tape insert geometry, demonstrating good agreement within a ±3.5% deviation for the Nusselt number. The overall heat transfer enhancement ratio of the wavy-tape insert can reach up to 2.0. This insert induces a swirl flow structure within the pipe, which is observable. On the tape flanks, it has been seen that pairs of vortices are produced, improving the efficiency of pipe heat transfer. The impact of the wavy-tape’s amplitude and width on the thermal–hydraulic performance of the pipe is investigated through parametric analyses. Parametric studies reveal that the thermal performance of the channel improves with increasing tape width (W/D) but deteriorates with increasing Reynolds number (Re). Conversely, thermal performance decreases as the amplitude of the tape (A/W) increases. Notably, the Performance Evaluation Criterion (PEC) remains relatively insensitive to variations in tape thickness (t/D). Upon investigating different case studies, the optimized configuration of the channel was suggested. This study demonstrates the potential of the proposed design to enhance heat transfer in pipes and paves the way for developing high-performance heat exchangers for various applications.

The length-weight relationship (LWR) and relative condition factor are widely used as the most important biological parameters to infer the growth pattern and well-being of fishes. The aim of our study was to investigate the growth parameters of the LWR in different growth phases of Hilsa (Tenualosa ilisha), a flagship species of Bangladesh, and compare its relative condition factors across its major habitats. Fish from various rivers in Bangladesh and the Bay of Bengal were sampled, their length-weight growth parameters were measured and subjected to statistical analysis with pooled data from other studies. An isometric growth pattern in Hilsa was predominantly found in the size classes of 25 ≤ TL (total length) Bay of Bengal > Andharmanik > Biskhali > Tetulia > Padma. Additionally, the relative physiological well-being of Hilsa from the river Padma and Tetulia was poor compared to that of other habitats. A more in-depth analysis is required to determine the factors that can influence Hilsa's growth and well-being in relation to habitat quality. The present findings have significant relevance for fisheries biologists and managers in understanding and interpreting Hilsa's ecology, relative well-being of populations of same or contrasting habitats, demographic assessment, and for better management of Hilsa in the future.

Drying vegetables in a suitable dryer is an imperative concern in the food-processing sector. To address this issue, a cabinet-type tray dryer has been developed by modifying its design, employing a comprehensive approach ensuring uniform air velocity and temperature in all the trays, and naming it a modified tray dryer (MTD). A comparative analysis of drying performance, physical characteristics, functional attributes, and antioxidant capacity of mallow (Malva verticillata) vegetables dried in MTD and existing three drying methods such as sun, shade, and traditional cabinet drying was accomplished. The performance evaluation study revealed that MTD demonstrated a faster moisture removal capacity and required less drying time compared to other drying methods. Statistical analysis revealed that drying methods had a significant (p < 0.05) impact on the physical properties, functional qualities, and bioactive functions of the dried mallow powder (MP). The higher chlorophyll content (27.41 ± 0.84 mg/100 gm of dry matter, DM), total phenol content (15.71 ± 0.95 mg GAE/g DM), and DPPH activity (8.82 ± 0.81 mg TEAC/g DM) were noticed in the mallow vegetable powder dried in MTD than shade, sun and traditional cabinet drying methods. A slightly lower value (statistically insignificant) of ascorbic acid content (9.64 ± 0.78 mg/100 gm DM) and total flavonoid (1.44 ± 0.05 mg QE/g DM) was achieved in contrast to other drying methods. The antioxidant capacity of the analyzed data was lowest for shade drying compared to other drying methods, while MTD exhibited the highest capacity. Therefore, this study might assist in selecting a suitable dryer for drying vegetables. The findings suggest that MTD can be an alternative drying technique as a quicker drying technique and for obtaining better-quality dried products in terms of antioxidant and functional properties than the traditional and existing process of vegetable drying.

The impact of epigenetic modifications like DNA methylation on plant phenotypes has expanded the possibilities for crop development. DNA methylation plays a part in the regulation of both the chromatin structure and gene expression, and the enzyme involved, DNA methyltransferase, executes the methylation process within the plant genome. By regulating crucial biological pathways, epigenetic changes actively contribute to the creation of the phenotype. Therefore, epigenome editing may assist in overcoming some of the drawbacks of genome editing, which can have minor off-target consequences and merely facilitate the loss of a gene’s function. These drawbacks include gene knockout, which can have such off-target effects. This review provides examples of several molecular characteristics of DNA methylation, as well as some plant physiological processes that are impacted by these epigenetic changes in the plants. We also discuss how DNA alterations might be used to improve crops and meet the demands of sustainable and environmentally-friendly farming.

Soil parameters are crucial aspects in increasing agricultural production. Even though Bangladesh is heavily dependent on agriculture, little research has been done regarding its automation. And a vital aspect of agricultural automation is predicting soil parameters. Generally, sensors relating to soil parameters are quite expensive and are often done in a controlled environment such as a greenhouse. However, a large scale implementation of such expensive sensors is not very feasible. This work tries to find an inexpensive solution towards predicting soil parameters such as soil moisture and temperature, both of which are crucial to the growth of crops. We focus on finding a robust relation between the above mentioned soil parameters with the nearby weather parameters such as humidity and temperature, irrespective of the weather. We apply different machine learning models like multilayer perceptron (MLP), random forest, etc. to predict the soil parameters, given the humidity and temperature of the surrounding environment. For all the experiments we have used a custom made dataset, which contains around 9000 datapoints of soil moisture & temperature, ambient humidity & temperature. The data has been collected in an uncontrolled agriculture bed via inexpensive sensors. Our results show that XGBoost regressor achieves the best results with an R2 score of 0.93 and 0.99 for soil moisture and soil temperature data respectively. This suggests very high correlation between the weather parameters and soil parameters. The model also portrayed a very low root mean squared error and mean absolute error of 0.037 & 0.015 for soil moisture and 0.001 & 0.0008 for soil temperature. Our results show that it is indeed possible to find the soil parameters from the corresponding weather, which will have great impact on mass agricultural automation. The dataset has been made publicly available at https://github.com/Nadimulhaque0403/Soil_parameter_prediction_dataset.