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Integrating artificial intelligence (AI) into agriculture is a pivotal solution to address the pressing challenges posed by rapid population growth and escalating food demand. Traditional farming methods, unable to cope with this surge, often resort to harmful pesticides, deteriorating soil health. However, the advent of AI promises a transformative shift toward sustainable agricultural practices. In the context of the United States, AI's historical trajectory within the agricultural sector showcases a remarkable evolution from rudimentary applications to sophisticated systems focused on optimizing production and quality. The future of American agriculture lies in AI-driven innovations, spanning various facets such as image sensing for yield mapping, labor management, yield optimization, and decision support for farmers. Despite its numerous advantages, the deployment of AI in agriculture does not come without challenges. This paper delved into both the benefits and drawbacks of AI adoption in the agricultural domain, examining its impact on the agro-industry and the environment. It scrutinized the emergence of robot farmers and AI's role in reshaping farming practices while acknowledging the inherent problems associated with AI implementation, including accessibility, data privacy, and potential job displacement. Moreover, the study explored how AI tools can catalyze the development of agribusiness, offering insights into overcoming existing challenges through innovative solutions. By comprehensively understanding the opportunities and obstacles entailed in AI integration, stakeholders can navigate the agricultural landscape adeptly, fostering a more sustainable and resilient food system for future generations.

Blast and bacterial blight (BB) are the two major rice diseases in the world including Bangladesh. In this study, BB resistance genes (Xa21 and xa13) and blast resistance (Pi9 and Pb1) genes were pyramided into a mega variety, BRRI dhan29 through marker-assisted backcross breeding. IRBB58 was used as a BB-resistant donor and Pi9-US2, and Pb1-US2 were used as blast-resistant donors. Backcross was done between BRRI dhan29 and donor parents to develop BC3F1 population and then selfing was done to develop BC3F6 population. BC3F2 population was genotyped and phenotyped for segregation analysis and BC3F6 was evaluated for genotyping, phenotyping and morphological traits and yields. Chi-square analysis of BC3F2 data revealed that blast and BB resistance followed the single gene mendelian fashion (1:2:1 and 3:1). Two to four gene combinations were found in the advanced lines of the BC3F6 population. The yield of the advanced lines ranged from 6.42 (t ha−1) to 9.5 (t ha−1) and they showed resistant against blast and BB with mean disease scores ranging from 0.67 to 2.33 and 0.33–2.33, respectively. Finally, eight lines having four genes (xa13, Xa21, Pi9 and Pb1) were selected for multilocational (five locations) trials for yield performance and disease reaction. Mean yield data of eight advanced lines of all locations were varied from 6.48±0.15–8.38±0.11 t ha−1 and all the lines showed resistant reactions against blast (score 0.53–1) and BB (score 0.6–0.87) disease. The highest yield was found in BR (Path) 13800-BC3–224–12 (G28, 8.38±0.11 t ha−1) followed by BR (Path) 13800-BC3–134–252 (G26, 8.28±0.08 t ha−1) and BR (Path) 13800-BC3–136–115 (G12, 8.24±0.07 t ha−1). Pyramided advanced lines of this study could be released as BB and blast-resistant varieties or could be utilized as donor parents in resistant breeding.

Cerebral blood volume (CBV) is an essential metric that indicates and evaluates various healthy and pathologic conditions. Most methods of CBV measurement are cumbersome and have a poor temporal resolution. Recently, it has been proposed that signals and derived metrics from cerebral near-infrared spectroscopy (NIRS), a non-invasive sensor, can be used to estimate CBV. However, this association remains vastly unexplored. As such, this scoping review aimed to examine the literature on the relationship between cerebral NIRS signals and CBV. A search of six databases was conducted conforming to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to assess the following search question: What are the associations between various NIRS cerebral signals and CBV? The database search yielded 3350 unique results. Seven of these articles were included in this review based on the inclusion and exclusion criteria. An additional study was identified and included while examining the articles’ reference sections. Overall, the literature for this systematic scoping review shows extreme variation in the association between cerebral NIRS signals and CBV, with few sources objectively documenting a true statistical association between the two. This review highlights the current critical knowledge gap and emphasizes the need for further research in the area.

Dragon fruit has significant economic value in many countries due to has excellent nutritional content, health advantages, and adaptability to different climates, making it an important crop in the global fruit industry. This study aimed to gather comprehensive nutritional data on three dragon fruit cultivars by analysing the levels of micronutrients, fibre, carbohydrates, antioxidants, vitamins, and minerals in their pulps. Uniform dragon fruit samples underwent thorough analysis for proximate composition, mineral content, pigments, antioxidants, and vitamin C, with statistical methods used to assess significant differences among the parameters studied. The proximate composition analysis revealed significant differences among the three dragon fruit cultivars. Among the proximate components, protein (0.40 ± 0.02 g/100 g), moisture (91.33 ± 0.88%), crude fibre (0.32 ± 0.07 g/100 g), and ash (1.27 ± 0.09 g/100 g) were more abundant in Hylocereus costaricensis than in Hylocereus undatus and Hylocereus megalanthus. On the other hand, Hylocereus undatus had higher carbohydrate (17.02 ± 0.63 g/100 g) and energy (69.74 ± 2.44 kcal/100 g) contents. K (7.23 ± 0.35 mg/100 g), Ca (1.61 ± 0.13 mg/100 g), Fe (1.84 ± 0.05 mg/100 g), and Zn (0.37 ± 0.034 mg/100 g) are highly abundant in H. costaricensis. Additionally, Hylocereus costaricensis had the highest anthocyanin content (120.15 ± 3.29 mg/g FW) and total carotenoid content (72.51 ± 1.62 mg/g FW), along with the highest vitamin C content (8.92 ± 0.13 mg/g FW) and total soluble phenolic content (572.48 ± 20.77 mg/100 g). Its remarkable antioxidant activity was further highlighted by the lowest SC50 value (13.50 ± 0.4 mg/mL) for its DPPH radical scavenging capacity. The total soluble sugar content was highest in Hylocereus megalanthus (8.72 ± 0.30 g/100 g FW). Hierarchical clustering analysis revealed distinct trait and genotype associations; among the studied cultivars, Hylocereus costaricensis demonstrated superior performance across multiple traits. Correlation analysis indicated significant positive correlations among several traits, while principal component analysis highlighted the contribution of each trait to overall variance, with PC1 explaining 73.95% of the total variance. This study highlights the nutritional variations among dragon fruit cultivars, with Hylocereus costaricensis showing superior performance, guiding dietary planning and functional food development.

The article explores the multifaceted role of the neuropeptide oxytocin in human behavior and its connection to the oxytocin receptor (OXTR) gene. Oxytocin, produced in specific brain nuclei, is implicated in emotional, social, and maternal behaviors, stress reduction, uterine contraction during childbirth, and lactation. The OXTR gene, located on chromosome 3, encodes oxytocin receptors found in various body parts, including critical brain regions associated with social behaviors. The article delves into studies on rodents, revealing correlations between OXTR gene expression and pair bonding in the prefrontal cortex and social behavior regulation in the amygdala. The discussion extends to the impact of oxytocin on social support-seeking behavior, focusing on a specific genetic variation, rs53576. The article explores how this genetic variation influences empathy, stress reactivity, and susceptibility to disorders such as autism and social anxiety. Furthermore, the article examines structural and functional changes in the brain associated with OXTR gene variations. It discusses the role of DNA methylation in influencing oxytocin receptor availability, affecting social perception and responsiveness to negative stimuli. The article also highlights the oxytocinergic system's involvement in disorders such as autism and social anxiety, emphasizing the interplay between genetics and environmental factors. The article also touches on the potential therapeutic use of exogenous oxytocin in mitigating symptoms associated with these disorders. In summary, the article underscores the intricate relationship between oxytocin, the OXTR gene, and diverse aspects of human behavior, providing insights into social bonding, perception, and the development of behavioral disorders.

The increasing prevalence of diabetes necessitates the development of effective early detection methods to mitigate its health impacts. This paper investigates the impact of feature transformation and machine learning (ML) models on the early detection of diabetes using a binary tabular classification dataset. We explore three feature transformation techniques, no transformation, normalization, and min-max scaling, to assess their influence on the performance of various ML models. To comprehensively evaluate the effectiveness of these preprocessing techniques, we experimented with twelve different ML models, including both traditional algorithms and ensemble methods. A publicly available dataset has been used for this research, containing 768 samples and 8 features. To ensure their effectiveness, the models are assessed using several evaluation metrics, including accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Among the ML models, Light Gradient Boosting Machine (LGBM) achieved the highest accuracy of 82.91% when min-max scaling was applied to the data. Our results demonstrate the varying effectiveness of different combinations of feature transformation techniques and ML models in enhancing diabetes detection performance. Furthermore, it has been observed that the ensemble models generally achieved better performance than traditional ML models. These findings provide valuable insights for optimizing preprocessing and model selection strategies in the development of robust early diabetes detection systems.

Objectives: Lead (Pb), a toxic heavy metal, is a serious concern for poultry that negatively affects their productivity and health. To combat those issues efficiently, it is necessary to include feed supplements that have rich antioxidant properties for satisfactory health and productivity. Spirulina platensis (Sp), a microalgae, is a compound that provides several health benefits for humans and animals. This study explores that supplementation of Sp in diet as well as in water reduces the burden of Pb in different tissues, improves hematology, and improves the productive performance of advanced-age laying hens. Materials and methods: Forty birds were separated into four groups: the control (C), Spirulina (Sp), Pb, and (Pb + Sp) groups. The Pb group was given Pb acetate at a dose of 2 gm/l in water ad libitum for 4 weeks. Sp group was fed Sp at a dose of 4 gm/kg feed. The Pb + Sp group was given Pb and Sp as in the previous groups. Results: Productive performance and hematology such as hemoglobin (Hb), packed cell volume, red blood cell, mean corpuscular volume, mean corpuscular Hb (MCH) concentration, and MCH were significantly (p < 0.05) decreased in Pb-treated groups compared to controls. The distribution of Pb concentration was highest in the bones and lowest in the gizzard. However, Sp treatment significantly (p < 0.05) increased the productive performance and the hematological parameters. Moreover, Pb concentration in different organs significantly decreased in the group treated with Sp. Conclusion: This study indicates that Sp can possibly be used as a natural and powerful dietary additive to mitigate heavy metal intoxication in chickens, thereby being efficient and effective for production. [J Adv Vet Anim Res 2023; 10(4.000): 794-800]

In the rapidly evolving domain of e-commerce, analyzing customer feedback through reviews is crucial, particularly for understanding and enhancing consumer experience in the Bangladeshi market. Our comprehensive dataset, derived from two Bangladeshi e-commerce platforms, Daraz and Pickaboo, features a diverse collection of reviews in both Bengali and English, covering a broad range of products. These reviews are not only rich in linguistic variety but also encapsulate a spectrum of emotions, some even conveyed through emojis, offering a deep dive into consumer sentiment. Expert annotators have meticulously examined and categorized each review, classifying emotions into five distinct types - Happiness, Sadness, Fear, Anger, and Love - and sentiments into Positive (Happiness, Love) and Negative (Sadness, Anger, Fear) categories. This level of detailed annotation enables precise assessments of customer emotions and preferences, which are essential for evaluating and improving existing product offerings. Moreover, the insights gleaned from this dataset are invaluable for guiding future product development and uncovering new opportunities in the dynamic Bangladeshi market. Ultimately, this dataset not only serves as a significant resource for sentiment analysis using natural language processing (NLP) techniques but also contributes valuable insights into the unique consumer behavior patterns in Bangladesh, enriching the NLP community's understanding of diverse market dynamics.

Fortification of edible oil with retinyl palmitate (vitamin A) is a significant advancement to fulfill the nutritional deficiency of major population. The growing interest in the enrichment of foods warrants a simple and accurate method to analyze fortified products. This study developed an easy and reliable strategy based on normal-phase HPLC analysis to determine the edible oil’s retinyl palmitate. We also validated the method according to the International Conference on Harmonization (ICH) guidelines. The analysis was performed with a DB silica column (250 cm × 4.6 mm; 5 μm), with a mobile phase composed of n-heptane and isopropyl alcohol at a ratio of 75:25 v/v and a flow rate of 1.0 ml/min. The detection was performed at a wavelength of 326 nm, and the retention time of vitamin A was around 8 min, with a total run time of 10 min. The calibration plot gave a linear relationship (R2 = 0.9998) over the concentration range of 3.93–63 μg/ml. The LOD and LOQ were 0.029 and 0.096 μg/ml, respectively. The accuracy of the proposed method was determined by recovery studies and was found with a mean recovery of >95%. RSD% of the determination of precision was

This article introduces a novel plasmonic magnetic field sensor (MFS) that utilizes a Metal-Insulator-Metal (MIM) waveguide configuration with a W-shaped cavity filled with magnetic fluid (MF). The MFS's unique design combines the advantages of plasmonic sensing, offering a promising solution for the detection of magnetic field strength. It operates based on the inherent properties of surface plasmon polaritons and the magneto-optical properties of MF, resulting in a shift in resonant wavelength. The performance of the proposed MFS has been investigated through numerical calculation employing the finite element method (FEM). Remarkably, the MFS exhibits a maximum magnetic field sensitivity of 49.11 pm/Oe, covering a detection range from 33 Oe to 200 Oe. The recorded figure of merit (FOM) and Q-factor of the MFS are 18.39 and 18.4 respectively, attesting to its high performance and reliability. This innovation has the potential to revolutionize fields such as navigation, medical diagnostics, and robotics technologies by seamlessly integrating optical sensing into traditional devices. The proposed sensor's excellent performance, compact size, and cost-effectiveness position it as a promising technology for widespread adoption, contributing to advancements in magnetic field sensing across scientific, industrial, and technological domains.

Numerous studies have used the Drude model to optimize silver-based Metal-Insulator-Metal plasmonic sensors for some limited sensing applications; however, Lab-on-a-chip sensing for cell protein concentration measurement still needs to be accomplished. Furthermore, little study has been conducted on sensing for the salinity measurement of seawater, Bovine serum albumin concentration measurement, human tissue categorization, mitigating the oxidation issue of these sensors, and nanofabrication challenges. In this article, a Metal-Insulator-Metal based oxidation free material (gold modeled with Drude-Lorentz model) built round-edged hexagonal plasmonic refractive index sensor with nanorods embedded both in the straight waveguide and resonator has been investigated using the Finite element method (FEM) for all of the above sensing applications. This sensor achieves 19.05 nm/g/100mL and 1476.6 nm/ppm sensitivity for cell protein concentration and salinity measurement. The sensor’s max sensitivity and Sensing Resolution are 9231.7 nm/RIU and 1.083∗10−7. This sensor has also been employed as an electromagnetic interference-free temperature sensor using toluene, achieving 5.16 nm/°C sensitivity. Thus, this label-free and low-footprint sensor can be employed to study the salinity of seawater, which is linked with global warming and the earth’s hydrologic cycle, study protein concentration for nanomedicine applications, and Lab-on-chip sensing as an oxidation-free alternative to silver-based sensors.

In this article, a novel surface plasmon polariton-based plasmonic refractive index sensor is proposed and numerically investigated. It consists of a structure of an octagonal cavity resonator inside a square disk along with silver nanodots and two slits which are coupled to a straight metal–insulator–metal (MIM) waveguide arrangement. The finite element method (FEM) has been employed to perform a numerical analysis of the proposed structure in the near-IR (Infrared) and mid-IR spectrum. The sensor detects changes in refractive index in terms of changes in resonant wavelength which can be measured using an optical spectrum analyzer. The recorded maximum sensitivity is 2527.6 nm per refractive index unit (RIU) with a sensing resolution of 3.956×10−7 RIU. The proposed sensor has been further numerically analyzed, and its ability to measure the quality of honey samples and detect lactose concentration in a solution has been verified.

In this article, an optical refractive index (RI) nanosensor has been presented. The layout is arranged in a metal–insulator-metal (MIM) waveguide configuration with gold being the plasmonic metal. The waveguides are coupled with quintuple grids resonant cavity where the grids are uneven in length. The transmission spectral profile of the schematic is numerically analyzed employing the two-dimensional Finite Element Method (FEM) within the mid-infrared region (MIR). Moreover, the key structural parameters are optimized individually to realize the maximum performance of the schematic. The most optimal structure displays a high sensitivity of 4737.87 nm/RIU and a FOM of 9.37. Furthermore, the structure can be suitably used in applications such as sensing organic gas vapors and temperature. Due to the practicality of the applications and the simplicity of the schematic along with the higher longevity provided by gold, this device can replace complex structures effectively.

In this paper, a plasmonic refractive index sensor consisting of a Metal-Insulator-Metal (MIM) waveguide is proposed introducing a hexagonal ring resonator coupled with two waveguides for the sensing purpose of the refractive index with fabrication plan. The simulation results obtained from the analysis, electromagnetic field and transmission spectra have been observed. We have demonstrated the change in transmittance if specific physical parameters are changed in the design. By tuning the device, the sensitivity of the structure can go up to 2950 nm/RIU and FoM up to 43.055. Furthermore, the base material is changed to doped silicon to make the device CMOS-compatible.

This article presents a novel refractive index nanosensor based on a metal–insulator–metal (MIM) waveguide that incorporates a silver nanorod embedded in a rectangular resonator with two baffles. The suggested sensor has great potential in the medical field for early disease diagnosis and research, as it can detect and identify many biological components by using the interdependence between the refractive index and the resulting change in the resonant wavelength. To perform the numerical calculation, the finite element method has been utilized. The maximum sensitivity of the sensor is recorded as 2963.73 nm per refractive index unit (RIU), with a figure of merit (FOM) and sensing resolution of 25.1 and 3.37×10−7 RIU, respectively. The high sensitivity and precision of the sensor make it capable of detecting glucose concentration and distinguishing between different degrees of diabetes. Additionally, the sensor can measure hemoglobin concentration, which is useful in classifying anemia into different categories. This information can be a vital factor in identifying the severity level of anemia and taking appropriate measures. The sensor’s high performance facilitates the differentiation of healthy cells from cancerous ones, making it an essential tool for the early detection of cancer. Furthermore, the sensor has the potential to identify biological components in the brain and blood, which may pave the way for future research and development in the field of medicine.

Globalization has resulted in an increasing worldwide movement of humans and animals as well as changes in the external environment, temperature, and social interactions, resulting in the accelerated emergence of new diseases in humans and dogs, such as Onchocerca lupi-caused ocular onchocerciasis. Presently, zoonotic onchocerciasis affects 15.5 million people globally; however, the lack of effective anthelmintics and vaccinations highlighted the disease as a global public health concern. Therefore, the current study offers computational methods for anticipating and creating a multi-epitope subunit vaccine targeting Onchocerca lupi by utilizing suitable linkers and adjuvants to combine CTL, HTL, and BCL epitopes. Three different vaccines (V1, V2, and V3) were designed using three distinct adjuvants and then the vaccine constructs antigenicity, solubility, and non-allergenicity were determined. Then, the physicochemical characteristics of the constructed vaccine candidates were analyzed, and also the tertiary structure of the vaccines was anticipated, which was later verified and refined. Molecular docking and molecular dynamics simulation results showed that the V1 vaccine constructs expected affinity and binding free energy for the dimeric state of human Toll-like receptor-4 (hTLR-4) were promising. These results indicate that the suggested vaccine candidate could probably enhance the immune response to Onchocerca lupi infection. The results should be confirmed in experimental studies.

Genetic variations are a crucial source of germplasm heterogeneity, as they contribute to the development of new traits for plant breeding by offering an allele resource. Gamma rays have been widely used as a physical agent to produce mutations in plants, and their mutagenic effect has attracted much attention. Nonetheless, few studies have examined the whole mutation spectrum in large-scale phenotypic evaluations. To comprehensively investigate the mutagenic effects of gamma irradiation on lentils, biological consequences on the M1 generation and substantial phenotypic screening on the M2 generation were undertaken. Additionally, the study followed the selected mutants into the M3 generation to evaluate the agronomic traits of interest for crop improvement. Seeds of lentil variety Moitree were irradiated with a range of acute gamma irradiation doses (0, 100, 150, 200, 250, 300, and 350 Gy) to induce unique genetic variability. This research focused on determining the GR50 value while considering seedling parameters and examining the status of pollen fertility while comparing the effects of the gamma irradiation dosages. The GR50 value was determined to be 217.2 Gy using the seedling parameters. Pollens from untreated seed-grown plants were approximately 85% fertile, but those treated with the maximum dosage (350 Gy) were approximately 28% fertile. Numerous chlorophyll and morphological mutants were produced in the M2 generation, with the 300 Gy -treated seeds being the most abundant, followed by the 250 Gy -treated seeds. This demonstrated that an appropriate dosage of gamma rays was advantageous when seeking to generate elite germplasm resources for one or multiple traits. Selected mutants in the M3 generation showed improved agronomic traits, including plant height, root length, number of pods per plant, and yield per plant. These investigations will contribute to a comprehensive understanding of the mutagenic effects and actions of gamma rays, providing a basis for the selection and design of suitable mutagens. This will facilitate the development of more controlled mutagenesis protocols for plant breeding and help guide future research directions for crop improvement using radiation-induced mutation breeding techniques.

Background: With its rapid spread, the coronavirus disease 2019 (COVID-19) pandemic had a detrimental effect on students’ psychological well-being, depression, and behavioral changes due to indefinite educational leaves, lockdowns, restricted outdoor activities, and excess use of social media. This study aims to assess the relationship of social media exposure with the psychological well-being, depression, and behavioral changes of Bangladeshi university students. Methods: A web-based cross-sectional survey was carried out on 530 students from June 17 to July 10, 2020, to evaluate psychological well-being, depression, behavioral changes, and social media exposure via self-reported measures. Results: The prevalence of factors were as follows: poor psychological well-being was 24.9%; moderate to severe depression was 56.6%; severe behavioral changes was 32.1%; and of moderate to severe addiction to social media exposure was 38.3%. All factors were positively associated with social media exposure. Multivariate logistic regression showed that the addiction of participants to social media was 7.488 times higher with severe behavioral changes (OR: 7.488; 95% CI 4.708–11.909), 2.299 times higher with poor psychological functioning (OR: 2.299; 95% CI 1.421–3.721), 30.54 times higher with severe depressed (OR: 30.54; 95% CI 15.0–62.177) than that of individuals without such symptoms. Conclusions: The above findings imply that the government needs to pay greater attention to improve the overall situation of Bangladeshi university students.

Photonic integrated circuits (PICs) suffer from birefringence due to high‐index contrast. Polarisation handling devices improve the performance of the PICs by reducing the polarisation‐dependent dispersion and loss. Furthermore, there is a growing interest in building polarisation division multiplexed transceivers using PICs, which require polarisation management. The authors provide an overview of the recent work on developing polarisation handling devices such as polarisation beam splitters and polarisers in indium phosphide and silicon‐on‐insulator platforms for optical communications and sensing applications. These devices expand the PICs library of polarisation handling devices and can be used to design more complex circuits with advanced or new functionalities.

In the scenario of global climate change, understanding how plants respond to drought is critical for developing future crops that face restricted water resources. This present study focuses on the role of WRKY transcription factors on drought tolerance in tomato, Solanum lycopersicum L., which is a significant vegetable crop. WRKY transcription factors are a group of proteins that regulate a wild range of growth and developmental processes in plants such as seed germination and dormancy and the stress response. These transcription factors are defined by the presence of a DNA-binding domain, namely, the WRKY domain. It is well-known that WRKY transcription factors can interact with a variety of proteins and therefore control downstream activities. It aims to simulate the effect of curcumin, a bioactive compound with regulatory capacity, on the protein–protein interaction events by WRKY transcription factors with an emphasis on drought stress. It was found that curcumin binds to WRKY with an energy of −11.43 kcal/mol with inhibitory concentration (Ki) 0.12 mM and has the potential to improve fruit quality and reinforce drought tolerance of S. lycopersicum, according to the results based on bioinformatics tools. The root means square deviation (RMSD) of the C-α, the backbone of 2AYD with ligand coupled complex, displayed a very stable structure with just a little variation of 1.89 Å. MD simulation trajectory of Cα atoms of 2AYD bound to Curcumin revealed more un-ordered orientation in PC1 and PC10 modes and more toward negative correlation from the initial 400 frames during PCA. Establishing the binding energies of the ligand–target interaction is essential in order to characterize the compound’s binding affinity to the drought transcription factor. We think we have identified a phyto-agent called curcumin that has the potential to enhance the drought tolerance. Compared to the part of the mismatch repair-base technique that can be used to fix drought related genes, curcumin performed better in a drop-in crop yield over time, and it was suggested that curcumin is a potential candidate factor for improving drought tolerance in tomatoes, and it needs future validation by experiments in laboratory and field.

This article presents an ultra-compact refractive index sensor based on titanium nitride (TiN), a well-known alternative plasmonic material. The designed sensor consists of a straight waveguide, coupled with a groove and a Square-Ring Resonator (SRR). In addition, nanosized TiN dots are embedded in the waveguide and groove to enhance sensing performance. The spectral characteristics of the proposed sensor are numerically analyzed using the Finite Element Method (FEM). After extensive simulations, the most optimal structure provides a maximum sensitivity of 1074.88 nm/RIU and a Figure of Merit (FOM) of 32.4 and proves to be tolerant against probable real-life manufacturing defects. Furthermore, the incorporation of TiN equips the illustrated sensor with numerous advantages, for example, immunity to oxidation, optical tunability, capability to endure high-temperature, and compatibility with the standard Complementary Metal Oxide Semiconductor (CMOS) fabrication process. The major pitfall of traditional plasmonic metals is that the metals lack all these qualities, limiting real-life implementations of plasmonic devices. Therefore, the proposed model is superior to the conventional Metal–Insulator–Metal (MIM) setup and is expected to facilitate the development of practical, robust sensors.

In this paper, a refractive index sensor based on three circular rings coupled with a straight metal-insulator-metal (MIM) waveguide is presented. At near-infrared (NIR) spectrum, the spectral characteristics of the sensor are extensively analyzed using the finite element method (FEM). Moreover, the refractive index sensing property is comprehensively investigated by altering the design parameters. The results show a maximum sensitivity of 3573.3 nm/RIU with a figure of merit of 21.9, which is suitable for gas-sensing applications. The proposed sensor has also displayed prominent tolerance against manufacturing defects. Furthermore, the sensor proves to be highly sensitive for the label-free detection of blood components and DNA hybridization.

A highly sensitive sensor focused on two Metal-Insulator-Metal (MIM) waveguides and three quadrilateral cavities sandwiched perpendicularly in between the MIM waveguides is proposed. Fano resonance induced by the coherent superposition of the narrow band spectral response and broadband spectral response, excites the structural transmission characteristics. The Finite Element Method (FEM) is used to numerically investigate the transmission characteristics and sensitivity of the refractive index sensor for different configurations. The linear relationship between resonant wavelength and refractive index is used to sense the materials. By optimizing the structural parameters, a numerical evaluation of the refractive index sensitivity (S) up to 1556 nm/RIU, and associate figure of merit (FOM) of 14.83 is recorded. The device is also explored as a temperature sensor with 0.61 nm/°C sensitivity. Since ethanol is used as a sensing medium, the operating range of the sensor is between −114.3 °C and 78 °C, melting and boiling temperature of ethanol. To detect human blood groups using the proposed sensor, a mathematical model is developed, which shows impressive performance for the detection of human blood groups very precisely. Compact size, ultrafine sensitivity, and sharp Fano peak profile make the proposed sensor an ideal candidate for on-chip sensors.

Cell wall biogenesis protein phosphatases play important roles in various cellular processes in fungi. However, their functions in the widely distributed mycoparasitic fungus Clonostachys rosea remain unclear, as do their potential for controlling plant fungal diseases. Herein, the function of cell wall biogenesis protein phosphatase CrSsd1 in C. rosea 67-1 was investigated using gene disruption and complementation approaches. The gene-deficient mutant ΔCrSsd1 exhibited much lower conidiation, hyphal growth, mycoparasitic ability, and biocontrol efficacy than the wild-type (WT) strain, and it was more sensitive to sorbitol and Congo red. The results indicate that CrSsd1 is involved in fungal conidiation, osmotic stress adaptation, cell wall integrity, and mycoparasitism in C. rosea.

Clonostachys rosea is an important mycoparasite, with great potential for controlling numerous plant fungal diseases. Understanding the mechanisms and modes of action will assist the development and application of this biocontrol fungus. In this study, the highly efficient C. rosea 67-1 strain was marked with the green fluorescent protein (GFP), and the transformant possessed the same biological characteristics as the wild-type strain. Fungal interactions with Botrytis cinerea during co-culture and encounter on tomato leaves were assessed by fluorescence confocal and electron microscopy. The results indicated that once the two fungi met, the hyphae of C. rosea grew alongside those of B. cinerea, then attached tightly to the host and developed special structures, via which the biocontrol fungus penetrated the host and absorbed nutrients, eventually disintegrating the cells of the pathogen. Mycoparasitism to B. cinerea was also observed on tomato leaves, suggesting that C. rosea can colonize on plants and act following the invasion of the pathogenic fungus.

The study was conducted in the 20 upazila of 10 selected major potato growing districts of Bangladesh during the period from December 2014 to February 2015 to find out the present status and diversity of insect pests of potato, their risks and management options. The data were collected through interview of 60 field level officers of DAE including one UAO, one AEO and one SAAO. The data were analyzed using computer program SPSS 20 version. BARI Alu-7 (Diamant) variety and BARI Alu-8 (Cardinal) variety of potato were the most popular potato varieties used by the farmers for cultivation. The major sources of potato seeds used by the farmers for cultivation were the own produced seeds, BADC seeds, seed traders/dealers and neighboring farmers. Major problems faced during potato cultivation were diseases, insect pest attack and weed attack. The BARI Alu-7 (Diamant) and BARI Alu-8 were most susceptible potato varieties to insect pests and diseases, whereas the Lal-pakhri was the least susceptible to pests. Mostly (98.50%) the potato was infested in the field by cutworm, aphid, potato tuber worm, leaf miner, mole cricket, field cricket and leaf hoppers. Among these insect pests, cutworm and aphid were identified as major pests and caused damage with high and moderate infestation intensity, respectively. Others were identified as minor insect pests of potato caused damage with low infestation intensity. Mostly (73.50%) the potato tuber worm attacked potato tubers in storage and designated as a major insect pest of potato in storage and damaged potato with high infestation intensity. Currently cutworm and aphid were more damaging insect pest of potato in field condition than previous infestation, and potato tuber worm was more damaging insect pest in storage. Other important control options were application of flood irrigation and hand picking particularly for controlling cutworm, use of granular insecticides in furrows during planting of seed tubers. Most (95%) of the farmers received assistance and advices for controlling insect pests of potato from pesticide dealers. Other sources of services were DAE officials, neighboring farmers and NGO officials. [Fundam Appl Agric 2018; 3(3.000): 559-564]

This article is based on a study of the morphological changes of Dhaka City, the capital of Bangladesh. The main objective of the research is to study the transformation of urban morphology in Dhaka City from 1947 to 2007. Three sample wards (18, 19 and 72) of Dhaka City Corporation are strategically selected as the study areas. Ward 72 has an indigenous type of organic settlement, whereas ward 19 is a planned area, and ward 18 represents a mixed (both planned and informal) type of settlement. In this research, the transformation of urban settlement pattern is examined through space syntax. The results show that the organic settlements (ward 72) are highly integrated both in terms of the local and global syntactic measures (lowest standard deviation for local and global integration, with the highest intelligibility values), and are more connectivity. The scenario is opposite in the case of planned settlements. The characteristics of mixed areas (ward 18) lie in between the organic and planned settlements. Therefore, in summary, it can be stated that the integration, connectivity and intelligibility measures of Dhaka City are found to be high, medium and low for the indigenous, mixed and planned settlement types; respectively.