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The assessment of nutrient potential is crucial for enhancing tea production and achieving sustainable agricultural goals. Thus, the present study was carried out to assess and map the soil nutrient potential for tea plants through laboratory and geostatistical methods. A total of 74 random soil samples with 3 replications were collected from 0 to 9 cm (topsoil) and 9 to 18 cm (subsoil) depths from Oodaleah Tea Garden (OTG) which belongs to Fatikchhari Upazila of Chattogram District in Bangladesh. All the laboratory analyses were done following standard procedures and maps of individual soil indices were prepared through the Inverse Distance Weighted (IDW) interpolation technique using ArcGIS 10.7 software. The integration of maps for nutrient potential was performed following the Analytic Hierarchy Process (AHP). The mean concentrations of organic matter (OM), total nitrogen (TN), available phosphorus (AvP), available potassium (AvK), and available calcium (AvCa) were found higher at 0 to 9 cm compared to 9 to 18 cm soil depth with significant (p < 0.05) difference among the sampling sites. The maps of individual soil parameters revealed distinct spatial differences at 0 to 9 cm and 9 to 18 cm soil depths. The total study area covered 5.99 km2. Integrated nutrient potential mapping showed that moderately suitable (44.15%) accounted for the largest proportion, followed by highly suitable (26.03%), generally suitable (21.67%), marginally suitable (7.06%), and unsuitable area (1.08%) of the tea plantation suitability evaluation. The integrated map of nutrient potential will aid in better management of the tea soils.

The disclosure of insect occurrences correlating with meteorological data is pivotal in devising effective management strategies for rice fields. The study collected data on harmful and beneficial insects, along with meteorological variables, for three consecutive rice-growing seasons in 2021 and 2022. Month-wise insect distribution were delineated, with prominent species such as green leafhopper, brown planthopper, white-backed planthopper, rice whorl maggot, damselfly, parasitic wasps and green mirid bug observed consistently. Notably, most of the harmful insects were predominantly present during March–July and September–October. The spatial distribution, further segmented based on the rice-growing seasons, revealing the Aus season's preeminence in hosting harmful insects followed by Aman and Boro. The prevalence of most of the harmful insects were in tillering stage. Beneficial insects, displayed their dominance during specific months, growth stage, emphasizing their potential role in controlling harmful species and their prevalence were increased by increased of harmful insects. Furthermore, the study elucidated the correlation between climatic factors and insect abundance, emphasizing the role of temperature, rainfall and relative humidity. Temperature metrics and relative humidity manifested significant associations with several insects both harmful and beneficial species differently. While rainfall notably correlated with rice bug, short-horned grasshoppers, yellow stem borer, and non-significant association with other insects. The study underscored mutual dependencies between predator and prey species, emphasizing the ecological balance within the agricultural ecosystem. This comprehensive analysis indicated the need for integrated pest management strategies, considering both harmful and beneficial insect dynamics, to promote sustainable practices for optimizing rice production in changing climatic scenarios.

This dataset offers a comprehensive analysis of rice grain yield and quality traits over a period of three years, spanning 27 different environments in Bangladesh. The focus is on evaluating the performance of various genotypes (G), environments (E), and their interactions (GEI). The study's goal is to identify a stable and adaptive rice genotype that not only exhibits high-quality traits but also has a low glycemic index. The combined ANOVA showed significant effects of G, E, and GEI on grain yield (p ≤ 0.001). Genotype BRC266-5-1-1-1 achieved the highest average yield at 7.62 t/ha, while BRRI dhan28 had the lowest yield at 6.87 t/ha. In the Y2E4 environment, genotype BRC266-5-1-1-1 reached the highest yield at 8.86 t/ha. Genetic parameter estimation revealed significant effects of G, E, and GEI (p < 0.05), with GEI accounting for 82.85 % of the variance. The broad-sense heritability was low at 12 %, but the heritability of means was 0.80, indicating high selection accuracy (0.89). AMMI analysis showed significant effects from E (62.62 %) and GEI (16.19 %), with the first two principal components explaining 100 % of the variance. AMMI-based stability analysis identified BRC266-5-1-1-1 as the most stable genotype, with the highest AMMI-based stability parameter (ASTAB) at 2.11 and AMMI stability value (ASV) at 3.04. GGE biplot analysis revealed that BRC266-5-1-1-1 performed best in 14 environments. Quality trait analysis showed that BR16 and BRC266-5-1-1-1 had the highest milling outturn (70.90 %), with BRC266-5-1-1-1 also having the highest head rice yield (66.5 %). BRRIdhan28 had the highest glycemic index value (GIV) at 75.7, followed by BRC266-5-1-1-1 (55.04). These findings emphasize the significance of choosing appropriate genotypes for varietal release and taking environmental factors into account in rice breeding programs.

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.

The role of genetic variation is utmost importance in the selection of a desirable genotype for breeding programs. In the northern region of Bangladesh, the performance of five short duration boro rice varieties (BRRI dhan28, BRRI dhan74, BRRI dhan81, BRRI dhan88, and Bangabandhu dhan100) on yield and yield-contributing traits were evaluated in four locations. Yield attributes tiller hill-1 (TN), panicle hill-1 (PN), filled grain panicle-1 (FG), panicle length (PL), 1000-grain weight (TGW), and harvest index (HI), showed a positive correlation with grain yield (GY). Conversely, spikelet sterility (SSP) and unfilled grains panicle-1 (UFG) showed negative associations with grain yield. Principal component analysis (PCA) revealed that the first three components accounted for 89.60% of variability. The highest positive eigenvalue was observed in DF, DM, BY, HI, PN, SY, TN, PL and GY in PC1, indicating their significant influence on the overall genotype variations. PC2 was primarily driven by plant height (PH) and FG, while PC3 driven by TGW and UFG. The results suggest that grain yield is highly influenced by FG, PH, DM, DF, PN, TN, PL, TGW, SY, BY, and HI. These components highlighted the importance in distinguishing rice genotypes with higher grain yield potential. By considering these traits, BRRI dhan74 have outperformed other varieties, followed by BRRI dhan88 and Bangabandhu dhan100. BRRI dhan74 is most suitable for all four location followed by BRRI dhan88. The traits such as PN, PL, FG, TGW, PH, & HI significantly influence rice yield and should be prioritized in breeding programs aimed at developing high-yielding rice varieties.

Effective management of fertilizers is essential in influencing the prevalence of insects in rice (Oryza sativa L.) fields. Over two years (2019-20 and 2020-21), an experiment conducted at Bangladesh Rice Research Institute (BRRI), Habiganj, during the boro season aimed to identify the most effective multidimensional treatment (EMT) by testing various combinations of chemical fertilizers and its effect on rice insects. The goal was to optimize rice grain yield while minimizing harmful insect infestation and supporting natural enemies. Eight different chemical fertilizer applications were used as follows: T1 contained a full mix of nitrogen (N), phosphorus (P), potassium (K), and sulfur (S); T2 had PKS but lacked N; T3 had NKS but lacked P; T4 had NPS but lacked K; T5 had NPK but lacked S; T6 had KS but lacked N and P; T7 had PS but lacked N and K; and T8 lacked all four elements - N, P, K, and S.The relationship between the dynamics of harmful insects and natural enemies was highly positively correlated (r = 0.72 to 0.97). In two consecutive growing years, the 2020-21 season exhibited notably higher counts of harmful insects, with Rice Leafroller (RLR) dominating in the booting stage and White Backed Planthopper (WBPH) in mid-tillering, while Green Mirid Bug (GMB) prevailed among natural enemies across both stages, surpassing insect pest counts, notably GMB, Lady bird beetle (LBB), Carabid beetle (CDB), and Staphylinid (STD). However, the yield was notably higher in the 2019-20 growing season despite these pest pressures. Throughout the mid-tillering and booting stages, T1 consistently exhibited the highest average populations of harmful insects and natural enemies, while T7 demonstrated the lowest count of harmful insects, followed by T2 at both growth stages. Additionally, the highest grain yield (GY) was consistently recorded in T1, followed by T5, T6, and T3, with yields of 7.98 t/ha, 7.63 t/ha, 7.38 t/ha, and 7.33 t/ha, respectively. In both stages, beneficial insects prevailed over harmful ones in all fertilizer applications, with significant declines noted in T2 and T7. Factor analysis showed successful selection for EMT in the MGIDI index for all variables except INT and GY during the 2019-20 season, with selection differentials (SD) ranging from −0.10 to 8.29. However, in 2020-21, selection was achieved for all variables with SD ranging from 0.37 to 6.08. According to the MGIDI index, the top-ranked EMTs were identified as T4 and T3 for the 2019-20 period, and T3 and T5 for the 2020-21 period. The EMT shared in both years, T3, proved effective because of its positive impact on enhancing natural enemies throughout both periods (with SD ranging from 4.76 to 8.29 for 2019-20 and 3.03 to 6.08 for 2020-21), and its notable contribution to rice grain yield (SD = 0.37) in 2020-21. This study uniquely integrates EMT to optimize rice grain yield while simultaneously managing harmful insect infestations and supporting natural enemies, addressing a critical need in sustainable rice cultivation. The suggestion is to give preference to fertilizer application T3, which omits P but contains N and K, to improve rice grain yield and boost natural enemies, thereby reducing harmful insect infestation. Moreover, future investigations should concentrate on refining fertilizer blends to strike a harmony between maximizing yield and fostering ecological robustness in rice cultivation.

The dataset focuses on evaluating the performance of 17 sweet potato varieties (G) released by the Bangladesh Agricultural Research Institute (BARI) in terms of storage root yield and stability across five locations (E) in Bangladesh—Gazipur, Bogura, Jamalpur, Jashore, and Chattogram. The result revealed that BARI Mistialu-12 exhibited the highest average storage root yield at 45.35 t/ha, closely followed by BARI Mistialu-16 at 44.64 t/ha. Conversely, BARI Mistialu-1 had the lowest mean yield of 25.99 t/ha. Among the locations, Bogura recorded the highest mean root yield at 37.05 t/ha, while Chattogram exhibited the lowest at 31.27 t/ha. A combined analysis of variance revealed the presence of variability in storage root yield attributed to the genotype-location (environment) interaction (GEI). To delve deeper into this interaction, additive and multiplicative interaction effect models (AMMI) along with a linear mixed model (LMM) were employed for further investigations to confirm the significant contribution of GEI variance to root yield. The LMM results showed genetic variance (%), heritability (%), selection accuracy (%), and GEI correlation coefficients of 52.27, 54, 94, and 30, respectively. The AMMI analysis indicated that the first two principal components accounted for 74.60 % of GEI, with 20.16 % attributed to it. Assessing significant Interaction Principal Component Analyses (IPCAs) through the Weighted Average of Absolute Scores (WAAS) indicated that BARI Mistialu-12 is the most stable genotype, followed by BARI Mistialu-16 and BARI Mistialu-8, all displaying above-average root yield. The mega-environment analysis associated the highest root production of BARI Mistialu-11 and BARI Mistialu-2 with the Jamalpur location, while Gazipur, Bogura, and Jashore were linked with the superior performance of BARI Mistialu-12 and BARI Mistialu-16 genotypes. These findings are crucial for future breeding programs and the rapidly growing sweet potato industry, given the stable high-yield potential across diverse agro-ecological conditions. However, it is imperative to repeat the study to ensure reliable outcomes.

The dataset provided details on how tillage methods and nutrient management impacted the productivity of the four crops (mustard>mungbean>Transplanting (T.) aus >Transplanting (T.) aman) cropping system and the overall soil health. The specific tillage techniques examined were minimum tillage (MT), conventional tillage (CT), and deep tillage (DT). Regarding nutrient management, NM1 utilized 100 % soil test-based (STB) fertilization following fertilizer gradient generation (FRG); NM2 applied 125 % of STB after FRG-2018; NM3 consisted of 100 % STB (with 80 % from chemical fertilizers and 20 % from cow dung); and NM4 relied on native fertility without any fertilization. Over three consecutive seasonal years (2018–19, 2019–20, and 2020–21), twelve treatments were replicated three times following a factorial totally randomized design. The comparative analysis of crop yield, rice equivalent yield, system productivity and production efficiency indicated superior performance of MT over both CT and DT. Furthermore, in relation to agricultural productivity metrics, the application of the nutrition package NM3 demonstrated performance levels exceeding the average. The adoption of MT and the incorporation of the NM3 nutrition package led to notable advancements in organic matter, field capacity, microbial biomass nitrogen, microbial biomass carbon and soil nutrient levels (N, P, K, S, Zn, and B). Consequently, the synthesis of the NM3 with MT is posited as a strategic approach for soil enhancement and the augmentation of crop productivity.

The dataset primarily focused on selecting genotypes of sweet oranges based on their phenotypic performances. The dataset resulted significant variations in the best linear unbiased predictions (BLUPs) of 20 out of 21 traits, including leaves, flowers, fruits, and seeds. A strong positive correlation (r= 0.73 to 0.95) was observed among the majority of morphological traits. The sweet orange genotypes demonstrated considerable genetic variance, surpassing 65% for almost all traits, with a selection accuracy exceeding 92%. Using the multi-trait genotype-ideotype distance index (MGIDI), CS Jain-001 emerged as the top-ranked genotype, followed by BAU Malta-3 and CS Jain-002 in order of desirability. The broad sense heritability of selected traits was above 75.60%, and the selection gain reached a maximum of 12.60. These identified genotypes show promise as potential parent donors in breeding programs, leveraging their strengths and weaknesses to develop promising varieties in Bangladesh.

Gas sensing is essential for detecting and measuring gas concentrations across various environments, with applications in environmental monitoring, industrial safety, and healthcare. The integration of two-dimensional (2D) materials, organic materials, and metal oxides has significantly advanced gas sensor technology, enhancing its sensitivity, selectivity, and response times at room temperature. This review examines the progress in optically activated gas sensors, with emphasis on 2D materials, metal oxides, and organic materials, due to limited studies on their use in optically activated gas sensors, in contrast to other traditional gas-sensing technologies. We detail the unique properties of these materials and their impact on improving the figures of merit (FoMs) of gas sensors. Transition metal dichalcogenides (TMDCs), with their high surface-to-volume ratio and tunable band gap, show exceptional performance in gas detection, especially when activated by UV light. Graphene-based sensors also demonstrate high sensitivity and low detection limits, making them suitable for various applications. Although organic materials and hybrid structures, such as metal–organic frameworks (MoFs) and conducting polymers, face challenges related to stability and sensitivity at room temperature, they hold potential for future advancements. Optically activated gas sensors incorporating metal oxides benefit from photoactive nanomaterials and UV irradiation, further enhancing their performance. This review highlights the potential of the advanced materials in developing the next generation of gas sensors, addressing current research gaps and paving the way for future innovations.

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.

The dataset explores the impact of waterlogging stress on sesame plants during the pre-flowering stage, recognizing its global impact on crop yield and the identification of tolerant genotypes using the MGIDI index. Carried out in Bangladesh, the research assesses the survival status, grain yield, and stress tolerance indices of 40 sesame genotypes, revealing that twelve of them demonstrated resilience under 72 h of waterlogging stress at the pre-flowering stage. There were variations in genotypic grain yield, and G15 exhibited the highest yields, recording 5.22 g/plant under normal conditions and 4.10 g/plant under waterlogging stress. The MGIDI index, evaluating waterlogging tolerance, identified G4 as the most favorable genotype, followed by G5 and G12. Factor analysis within the MGIDI index uncovered distinct tolerance and susceptibility indices, highlighting strengths and weaknesses in the selected genotypes. The selection gain percentages of these genotypes ranged from 12.9 to 37.4, indicating high broad-sense heritability (≥0.97). These results underscore the potential of genotype selection based on waterlogging stress indices, providing valuable insights for breeders addressing stress-related crop challenges in the face of changing climatic conditions.

In this study, we constructed an UV-C illumination chamber using commercially available germicidal lamps and other locally available low-cost components for general-purpose biological decontamination purposes. The illumination chamber provides uniform illumination of around 1 J/cm2 in under 5 min across the chamber. The control mechanism was developed to automate the on/off process and make it more secure minimizing health and other electrical safety. To validate the decontamination efficacy of the UV-C Illumination Chamber we performed the Geobacillus spore strip culture assay. Additionally, we performed the viral load measurement by identifying the COVID-19-specific N-gene and ORF1 gene on surgical masks. The gold standard RT-qPCR measurement was performed to detect and quantify the COVID-19-specific gene on the mask sample. The biochemical assay was conducted on the control and test group to identify the presence of different types of bacteria, and fungi before and after exposure under the illumination chamber. The findings of our study revealed satisfactory decontamination efficacy test results. Therefore, it could be an excellent device in healthcare settings as a disinfection tool for biological decontamination such as SAR-CoV-2 virus, personal protection equipment (PPE), (including n95, k95 respirators, and surgical masks), and other common pathogens.

A study was conducted in five regions of Bangladesh, specifically Gazipur, Bogura, Jamalpur, Jashore, and Chattogram, each characterized by suitable agro-ecologies for sweet potato cultivation. The purpose of this data article was to demonstrate the correlations between traits and the selection of stable varieties based on the multi-trait stability index (MTSI). The data indicated a direct link between multiple characteristics and both the yield and factors contributing to yield. This implies that enhancing these traits might result in a higher overall production of sweet potato storage roots. Furthermore, the factor analysis for MTSI demonstrated that the desired goal for selection was achieved for all traits, except for mean vine length (VL) and storage root dry weight (DW). The broad sense heritability ranged from 0 to 0.97, and the selection gain percentage ranged from 0 to 42.8. The MTSI analysis identified the sweet potato variety BARI Mistialu-15 as the most stable among the other studied varieties.

A cropping system that is based on three or four crops is currently a widely favored option for augmenting crop productivity to address the escalating global food demand. However, the improper fertilizer management and undue tillage adversely impacts both the productivity of crops and the fertility of the soil. A research investigation was conducted on tillage and nutrient management within the mustard-mungbean-Transplanting aus (T.aus)-Transplanting aman (T.aman) cropping system to examine the impact of fertilizer packages and tillage techniques on the overall productivity of cropping systems, as well as the condition of the soil in grey terrace soil. The research included tillage techniques viz; minimum tillage (MT), conventional tillage (CT) and deep tillage (DT); while nutrient management; NM1: 100 % STB (Soil test based) following FRG (Fertilizer Recommendation Guide-2018), all from chemical fertilizer, NM2: 125 % of STB following FRG- 2018, all from chemical fertilizer, NM3: 100 % STB (80 % from chemical fertilizers and 20 % from cowdung), and NM4: Native fertility (no fertilization). A total of twelve treatments replicated three times following the factorial completely randomized design for three consecutive seasonal years (2018–19, 2019–20, and 2020–21). MT outperformed DT and CT in terms of crop yield, rice equivalent yield (REY), system productivity (SP), and production efficiency (PE). Moreover, NM3 exhibited enhanced performance in terms of agricultural productivity measures. Field capacity (FC), soil organic matter (OM), microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), and soil nutrients (N, P, K, S, Zn and B) observed an enhancement as a result of the implementation of tillage MT and nutrition package NM3. The investigation indicates that implementing minimum tillage (MT) coupled with an integrated plant nutrition system package (NM3) can assist in the improvement of soil and the enhancement of crop productivity.

Urban growth in the developing world has prompted researchers to seek alternatives to fine aggregate due to the severe environmental impact of extensive natural sand depletion. On top of that, the accumulation of non-biodegradable dumps, solid trash such as scrapped copper wire (SCW), and industrial remnants like granite dust (GD) have reached alarming levels. Therefore, incorporating these two waste materials in concrete offers a potentially sustainable solution. The study aims at substituting natural fine aggregate with GD as well as incorporating SCW for predicting and optimizing the compressive and splitting tensile strength of concrete using response surface methodology (RSM). Two independent variables, the volumetric percentages of GD (10 %, 20 %, and 30 %) and SCW (0.1 %, 0.3 %, and 0.5 %) in a concrete mix ratio of 1:1.5:3, were utilized to create probabilistic models for compressive and splitting tensile strength at 7 and 28 days. The experimental design employed central composite design (CCD) of RSM and the results of both ANOVA and regression analysis in terms of several statistical functions demonstrated a strong correlation between the predicted values of the responses and the actual experimental results. The developed models were validated by conducting experiments using optimized proportions of GD (23.32 %) and SCW (0.37 %). Finally, the strengths of the optimum content mix yielding 25.12 MPa and 3.266 MPa, respectively for compressive and splitting tensile at 28 days ensure the efficiency of the models due to the substantial similarity between experimental and predicted values. Therefore, integrating GD and SCW for higher-strength concrete in mass production can be a cost-effective alternative, fostering increased recycling of waste and supporting sustainable growth in building construction.

Silicon carbide (SiC) is widely used in abrasives, heating devices, light emitting diodes and high-power electronics, because of its unique physical-chemical properties including high hardness, high melting point, excellent thermal conductivity, and wide bandgap. In recent years, its color centers have also been used as potential sources of single photons. Given that borides and carbides are refractory materials, we decided to study a heterostructure formed by an n-doped silicon carbide substrate and a layer of tantalum monoboride (TaB) for near ultraviolet (UV) photodetection. It is shown that the addition of TaB creates a rectifying device that can handle currents higher than 300 mA, showing its potential for high-power optoelectronic devices. The detector also has a high responsivity of 2.9 A/W at the free-space wavelength of 405 nm and a fast frequency response of 610 kHz. The high performance of the detector is attributed to the high thermal and electrical conductivity, large bandgap of SiC, and its moderately close match in atomic arrangement with TaB. Such heterostructure offers a simple method to produce high-performance near UV photodetectors operating under hard conditions such as high power, high temperature and corrosive or oxidizing environments.

A study was carried out in five sweet potato growing regions of Bangladesh, each characterized by suitable agro-ecologies, in order to demonstrate the most favorable varietal performance and trait correlations. A completely randomized block design with three replications was used to compare the varietal performance of BARI (Bangladesh Agricultural Research Institute) released sweet potato varieties (viz. BARI Mistialu-9, BARI Mistialu-10, BARI Mistialu-12, BARI Mistialu-15 and BARI Mistialu-17). During the 2021-22 cropping season, sweet potato varieties were tested in five districts of Bangladesh, namely Gazipur, Bogura, Jamalpur, Jashore, and Chattogram. The findings revealed that the BARI Mistialu-12 variety exhibited remarkable attributes, including a high marketable storage root yield of 39.88 t/ha. Additionally, it demonstrated exceptional performance in various yield components such as vine length, average storage root weight, and dry weight of the root. Furthermore, a positive correlation was observed between several traits and yield, as well as yield-attributing characteristics. This correlation suggests that enhancing these traits could potentially contribute to an overall increase in the storage root yield of sweet potatoes.

The SARS-CoV-2 3CLpro protein is one of the key therapeutic targets of interest for COVID-19 due to its critical role in viral replication, various high-quality protein crystal structures, and as a basis for computationally screening for compounds with improved inhibitory activity, bioavailability, and ADMETox properties. The ChEMBL and PubChem database contains experimental data from screening small molecules against SARS-CoV-2 3CLpro, which expands the opportunity to learn the pattern and design a computational model that can predict the potency of any drug compound against coronavirus before in-vitro and in-vivo testing. In this study, Utilizing several descriptors, we evaluated 27 machine learning classifiers. We also developed a neural network model that can correctly identify bioactive and inactive chemicals with 91% accuracy, on CheMBL data and 93% accuracy on combined data on both CheMBL and Pubchem. The F1-score for inactive and active compounds was 93% and 94%, respectively. SHAP (SHapley Additive exPlanations) on XGB classifier to find important fingerprints from the PaDEL descriptors for this task. The results indicated that the PaDEL descriptors were effective in predicting bioactivity, the proposed neural network design was efficient, and the Explanatory factor through SHAP correctly identified the important fingertips. In addition, we validated the effectiveness of our proposed model using a large dataset encompassing over 100,000 molecules. This research employed various molecular descriptors to discover the optimal one for this task. To evaluate the effectiveness of these possible medications against SARS-CoV-2, more in-vitro and in-vivo research is required.

In this paper, we numerically demonstrate a low-cost plasmonic refractive index sensor using two-sided open-channels that can operate in both visible and near-infrared region. Widely used finite element method (FEM) software is employed to characterize the sensing properties. A thin coating of gold layer is used inside the open channels of the photonic crystal fiber (PCF) to create the plasmons. It is reported that the highest wavelength sensitivity of 5000 nm/RIU with a sensor resolution of 2.0 × 10−5 RIU can be achieved at an analyte refractive of 1.38. Besides, it is also achievable to have the highest amplitude sensitivity of 396 RIU−1. The proposed sensor shows excellent linear characteristics with the highest figure of merit (FOM) of 47 RIU−1 between a refractive index of 1.33 and 1.39. Due to structural simplicity and improved sensitivity, the proposed plasmonic sensor is highly applicable in biological and biochemical analyte detections. Keywords: Finite element method, Surface plasmon resonance, Biosensors, Amplitude sensitivity, Resolution

In this paper, we numerically demonstrate a two-layer circular lattice photonic crystal fiber (PCF) biosensor based on the principle of surface plasmon resonance (SPR). The finite element method (FEM) with circular perfectly matched layer (PML) boundary condition is applied to evaluate the performance of the proposed sensor. A thin gold layer is deposited outside the PCF structure, which acts as the plasmonic material for this design. The sensing layer (analyte) is implemented in the outermost layer, which permits easy and more practical fabrication process compared to analyte is put inside the air holes. It is demonstrated that, at gold layer thickness of 40 nm, the proposed sensor shows maximum sensitivity of 2200 nm/RIU using the wavelength interrogation method in the sensing range between 1.33–1.36. Besides, using an amplitude interrogation method, a maximum sensitivity of 266 RIU−1 and a maximum sensor resolution of 3.75 × 10−5 RIU are obtained. We also discuss how phase matching points are varied with different fiber parameters. Owing to high sensitivity and simple design, the proposed sensor may find important applications in biochemical and biological analyte detection.

Tests or examinations are an essential part of education, creating test anxiety among students. Several studies identified a negative correlation between test anxiety and Science, Technology, Engineering, and Mathematics (STEM) students' academic performance in higher education, and female students in STEM subjects showed more test anxiety before and during an examination. Studies also identified that STEM students could pay less attention to their examinations due to test anxiety. Such evidence in the context of Bangladeshi universities is yet to be understood. This study investigated the level of test anxiety among Bangladeshi university students of STEM subjects, including gender differences and the relationship between test anxiety and academic performance. We considered the quantitative research design to understand the status of test anxiety among undergraduate STEM students. A survey was conducted online from two STEM departments of a private university in Bangladesh using the shorter version of the CAEX test anxiety instrument, and a total of 246 undergraduate students participated. The findings show that Bangladeshi undergraduate STEM students generally felt slight anxiety during or before the examination. Female students were more anxious before or during a test than male students, and no significant relationship was found between test anxiety and academic achievement. The study reflects the need to conduct further research to understand the physiological, cognitive, and psychological factors leading to test anxiety among university students, as well as establishes a demand to identify the reasons behind having no relationship between test anxiety and students' academic performance. [ABSTRACT FROM AUTHOR]