Royal Poinciana Biodiesel Blends with 1-Butanol as a Potential Alternative Fuel for Unmodified Research Engines.

This research work investigates the experimental work of a single-cylinder diesel engine operated with royal poinciana biodiesel blends with various proportions of 10, 20, and 30% volume with 1-butanol as an effective ignition-improving additive. The test blends were indicated as D90RP7B3 (90% diesel + 7% royal poinciana biodiesel + 3% butanol), D80RP14B6 (80% diesel + 14% royal poinciana biodiesel + 6% butanol), D70RP21B9 (70% diesel + 21% royal poinciana biodiesel + 9% butanol), and pure royal poinciana biodiesel (RP100) and diesel. The significant findings or results obtained during the experimentation are that BTE is suitable for blend D90RP7B3, and the least BSFC is found for blend D90RP7B3 in the 0.24 kg/kWh range. The inline cylinder pressures are found to be suitable for the blend D90RP7B3 in the range of 7 MPa; HRR is ideal for both the blends D90RP7B3 and D80RP14B6 in the range of 90 and 88 kJ; D90RP7B3 possesses adequate ignition delay at full load conditions 16° in crank angle advance; maximum A/F ratios are well suitable for the blend D90RP7B3 in the ratio 11:1 at higher loads. Volumetric efficiency is achieved well for all the blends and diesel; the emissions released from the royal poinciana blends, such as CO, CO2, HC, and NOX, were reduced by 14.12, 8.33, 11.1, and 18.8% compared to standard diesel. Hence, royal poinciana blends with 1-butanol can be considered the best fuels in the automobile sector.

CdIn2Se4@chitosan heterojunction nanocomposite with ultrahigh photocatalytic activity under sunlight driven photodegradation of organic pollutants.

This research focused on synthesizing a CdIn2Se4@Ch nanocomposite by doping CdIn2Se4 into chitosan using a photolysis assisted ultrasonic process. The aim was to enhance the photodegradation efficiency of ofloxacin and 2,4-dichlorophenoxyacetic acid under sunlight. The synthesized CdIn2Se4@Ch nanocomposite was investigated via different techniques, including XRD, XPS, FTIR, TEM, DSC, TGA, UV-Vis and PL. The study also investigated the influence of various reaction parameters, including the effects of inorganic and organic ions. The synthesized nanocomposite demonstrated exceptional efficiency, achieving 86 % and 95 % removal rates, with corresponding rate constants of 0.025 and 0.047 min-1. This performance surpasses that of CdIn2Se4 by approximately 1.35 and 2.25 times, respectively. The values of COD were decreased to 78 and 86 % for ofloxacin and 2,4-dichlorophenoxyacetic, while the TOC values decreased to 71 and 84 %, respectively, from their premier values. The improvement in performance is associated with the introduction of CdIn2Se4 into chitosan, resulting in the self-integration of Cd into the catalyst. This creates a localized accumulation point for electrons, enhancing the efficiency of charge separation and further reducing the surface charge of chitosan. Experimental evidence suggests that superoxide and hydroxyl radicals play a significant role in the photodegradation of pollutants. Additionally, the nanocomposite exhibits excellent stability and can be reused up to five times, indicating remarkable stability and reusability of the developed photocatalyst.

Antifungal plant flavonoids identified in silico with potential to control rice blast disease caused by Magnaporthe oryzae.

Rice blast disease, caused by the fungus Magnaporthe oryzae, poses a severe threat to rice production, particularly in Asia where rice is a staple food. Concerns over fungicide resistance and environmental impact have sparked interest in exploring natural fungicides as potential alternatives. This study aimed to identify highly potent natural fungicides against M. oryzae to combat rice blast disease, using advanced molecular dynamics techniques. Four key proteins (CATALASE PEROXIDASES 2, HYBRID PKS-NRPS SYNTHETASE TAS1, MANGANESE LIPOXYGENASE, and PRE-MRNA-SPLICING FACTOR CEF1) involved in M. oryzae's infection process were identified. A list of 30 plant metabolites with documented antifungal properties was compiled for evaluation as potential fungicides. Molecular docking studies revealed that 2-Coumaroylquinic acid, Myricetin, Rosmarinic Acid, and Quercetin exhibited superior binding affinities compared to reference fungicides (Azoxystrobin and Tricyclazole). High throughput molecular dynamics simulations were performed, analyzing parameters like RMSD, RMSF, Rg, SASA, hydrogen bonds, contact analysis, Gibbs free energy, and cluster analysis. The results revealed stable interactions between the selected metabolites and the target proteins, involving important hydrogen bonds and contacts. The SwissADME server analysis indicated that the metabolites possess fungicide properties, making them effective and safe fungicides with low toxicity to the environment and living beings. Additionally, bioactivity assays confirmed their biological activity as nuclear receptor ligands and enzyme inhibitors. Overall, this study offers valuable insights into potential natural fungicides for combating rice blast disease, with 2-Coumaroylquinic acid, Myricetin, Rosmarinic Acid, and Quercetin standing out as promising and environmentally friendly alternatives to conventional fungicides. These findings have significant implications for developing crop protection strategies and enhancing global food security, particularly in rice-dependent regions.

Enhancing Wear Resistance of UHMWPE Composites with Micro MoS2 and Nano Graphite: A Taguchi-DOE Approach.

This study presents an in-depth investigation into the wear characteristics of ultrahigh-molecular-weight polyethylene (UHMWPE) composites reinforced with microsized MoS2 and nanosized graphite particles. The objective is to enhance the wear resistance of the UHMWPE by examining the effects of various parameters and optimizing the wear performance. To achieve this goal, wet wear tests were conducted under controlled conditions, and the results were compared between composites with micro MoS2 and nano graphite reinforcements. The Taguchi method was employed to design the experiments (DOE) using an L9 orthogonal array. Four key parameters, namely, reinforcement percentage, load, speed, and track radius, were varied systematically to analyze their impact on wear characteristics, including wear rate, frictional forces, and the coefficient of friction (COF). The data obtained from the experiments were subjected to analysis of variance (ANOVA) to identify the significant factors affecting wear behavior. Subsequently, the optimal wear parameters were determined through regression analysis, allowing for the prediction of wear characteristics under the optimum conditions. This research not only provides insights into the comparative performance of micro MoS2 and nano graphite reinforcements in UHMWPE composites but also offers a comprehensive approach to optimizing wear resistance by employing advanced statistical and experimental techniques. The findings contribute to the development of more durable and wear-resistant materials with potential applications in various industries, such as those investigated in the study, which are commonly employed, such as automotive, aerospace, medical devices, or manufacturing.

Comparative simulation of nonlinear radiative nano casson and maxwell fluids with periodic magnetic force and sensitivity analysis.

This study investigated cyclic magneto-hydrodynamic radiative effects in Casson and Maxwell fluids, including nonlinear radiation and Arrhenius activation energy. It promotes non-Newtonian fluid use in diverse fields like industry, manufacturing, sciences, medicine, and engineering. Using boundary layer approximations, non-dimensional equations are formulated. For numerical solutions, widely recognized explicit finite difference method (EFDM) has been utilized. To ensure the robustness of EFDM results, stability and convergence tests are performed. Exploration involve a detailed sensitivity analysis by using RSM, offering a thorough understanding of influential parameters. These analyses explore complex interactions among physical parameters, affecting Nusselt number, skin friction, and Sherwood number. Maxwell fluid's velocity is more affected by periodic magnetic force than Casson fluid, during the presence of nonlinear radiation. Additionally, nonlinear thermal radiation has a greater impact on temperature and concentration profiles compared to linear radiation for both fluids. Moreover, Casson fluid has a stronger influence on the average heat transfer rate compared to Maxwell fluid with nonlinear thermal radiation which is 8.6 % greater than the Maxwell fluid. On the other hand, at constant thermal radiation (Ra), due to decrease of Brownian motion (Nb), the rate of heat transfer is reduced by 1.2 % and 0.3 % respectively for Maxwell and Casson fluid. Also, for thermophoresis parameter (Nt), this rate is reduced by 2 % and 1.6 % respectively. The investigation also revealed that the Ra exhibits a positive sensitivity towards average Nusselt number, while Nb and Nt are displayed a negative sensitivity.

Removal of heavy metal ions from wastewater using two-dimensional transition metal carbides.

Water is an indispensable material for human life. Unfortunately, the development of industrial activities has reduced the quality of water resources in the world. Meantime, heavy metals are an important factor in water pollution due to their toxicity. This study highlights the method for the capture of heavy metal ions from wastewater using the procedure of adsorption. The adsorption of toxic heavy metal ions (Pb2+, Hg2+, and Cd2+) on Ca2C as well as Cr2C carbide-nitride MXene monolayers is investigated using the density functional theory. We have carried out the optimization of the considered MXenes by nine DFT functionals: PBE, TPSS, BP86, B3LYP, TPSSh, PBE0, CAM-B3LYP, M11, and LC-WPBE. Our results have shown a good agreement with previously measured electronic properties of the Ca2C and Cr2C MXene layers and the PBE DFT method. The calculated cohesive energy for the Ca2C and Cr2C MXene monolayers are -4.12 eV and -4.20 eV, respectively, which are in agreement with the previous studies. The results reveal that the adsorbed heavy metal ions have a substantial effect on the electronic properties of the considered MXene monolayers. Besides, our calculations show that the metal/MXene structures with higher electron transport rates display higher binding energy as well as charge transfers between the metal and Ca2C and Cr2C layers. Time-dependent density functional analysis also displayed "ligand to metal charge transfer" excitations for the metal/MXene systems. The larger Ebin for the Pb@Ca2C as well as Pb@Cr2C are according to larger redshifts which are expected (Δλ = 45 nm and 71 nm, respectively). Our results might be helpful for future research toward the application of carbide-nitride MXene materials for removing wastewater pollutants.

Studies on the Removal of Malachite Green from Its Aqueous Solution Using Water-Insoluble ß-Cyclodextrin Polymers.

The rising global pollution of natural waters by dyes has brought to light the need for adaptable and efficient removal techniques. To create water-insoluble ß-cyclodextrin (ß-CD) polymers like CA/-CD, TA/-CD, and MA/-CD, several organic acids including citric acid (CA), tartaric acid (TA), and malic acid (MA) were cross-linked with ß-cyclodextrin in this study. The obtained polymers were characterized by different advanced analytical techniques such as FTIR, SEM, and UV-vis spectrophotometry. Malachite green dye was removed from aqueous solutions using the synthesized polymers by adsorption. The adsorption investigation was conducted under several conditions, including pH, adsorbent mass, dye concentration, temperature, contact time, adsorption isotherm, and kinetics. The adsorbent CA/ß-CD shows the highest adsorption of MG dye in all of the conditions because it contains a high number of carboxyl groups. The negatively charged carboxyl ions of CA/ß-CD attract the positively charged MG dye electrostatically and remove MG from aqueous media with an efficiency of 91%. As a result, the findings indicated that water-insoluble polymers based on ß-cyclodextrin are well-suited as inexpensive adsorbents to remove colors from aqueous media.

Mo3S13 Chalcogel: A High-Capacity Electrode for Conversion-Based Li-Ion Batteries.

Despite large theoretical energy densities, metal-sulfide electrodes for energy storage systems face several limitations that impact the practical realization. Here, we present the solution-processable, room temperature (RT) synthesis, local structures, and application of a sulfur-rich Mo3S13 chalcogel as a conversion-based electrode for lithium-sulfide batteries (LiSBs). The structure of the amorphous Mo3S13 chalcogel is derived through operando Raman spectroscopy, synchrotron X-ray pair distribution function (PDF), X-ray absorption near edge structure (XANES), and extended X-ray absorption fine structure (EXAFS) analysis, along with ab initio molecular dynamics (AIMD) simulations. A key feature of the three-dimensional (3D) network is the connection of Mo3S13 units through S-S bonds. Li/Mo3S13 half-cells deliver initial capacity of 1013 mAh g-1 during the first discharge. After the activation cycles, the capacity stabilizes and maintains 312 mAh g-1 at a C/3 rate after 140 cycles, demonstrating sustained performance over subsequent cycling. Such high-capacity and stability are attributed to the high density of (poly)sulfide bonds and the stable Mo-S coordination in Mo3S13 chalcogel. These findings showcase the potential of Mo3S13 chalcogels as metal-sulfide electrode materials for LiSBs.

A Retrospective Study on the Outcome of Coronavirus Disease 2019 (COVID-19) Patients Admitted to a District General Hospital and Predictors of High Mortality.

BACKGROUND: The clinical features and severity of coronavirus disease 2019 (COVID-19) vary between patients and countries. Patients with certain conditions are predisposed to poor outcomes compared with those without medical conditions, such as diabetes, dementia, and hypertension (HTN). METHODS: The aim of this retrospective study was to assess factors associated with higher mortality in patients with COVID-19 infections and to identify the reason for hospital admission in these patients. The study was performed on patients admitted between 1 and 31 March 2020. Data collection was done retrospectively from electronic medical records. RESULTS: There were 269 patient admissions during this period, of which 147 were included in this audit. The mean age of COVID-19-positive patients was 62.8 years and 65.9 years for COVID-19-negative patients during this period. Forty-seven patients requiring hospital admission were COVID-19 positive and 93 were COVID-19 negative. There were no COVID-19 swabs in the seven patients included in the audit. Approximately 50% of the COVID-19-positive patients presented with fever and shortness of breath (sob), followed by dyspnea and cough (seven patients). The most common comorbidity was HTN, followed by type 2 diabetes mellitus (T2DM) and ischemic heart disease (IHD). The survival rate was 72.3% in COVID-19-positive patients and 80% in COVID-19-negative patients. The average length of stay was 14.4 days for COVID-19-positive survivors compared to 7.8 days for COVID-19-negative survivors. Most patients who tested positive for COVID-19 infection received oseltamivir vaccination and antibiotics. The presence of HTN, diabetes mellitus (DM), age, and organ failure was associated with a high mortality risk. CONCLUSION: Our study supports the findings of previous studies that diabetes, HTN, coronary artery disease, old age, and organ failure were associated with high mortality in patients admitted to hospitals with COVID-19 infections.

State-transition dynamics of resting-state functional magnetic resonance imaging data: model comparison and test-to-retest analysis.

Electroencephalogram (EEG) microstate analysis entails finding dynamics of quasi-stable and generally recurrent discrete states in multichannel EEG time series data and relating properties of the estimated state-transition dynamics to observables such as cognition and behavior. While microstate analysis has been widely employed to analyze EEG data, its use remains less prevalent in functional magnetic resonance imaging (fMRI) data, largely due to the slower timescale of such data. In the present study, we extend various data clustering methods used in EEG microstate analysis to resting-state fMRI data from healthy humans to extract their state-transition dynamics. We show that the quality of clustering is on par with that for various microstate analyses of EEG data. We then develop a method for examining test-retest reliability of the discrete-state transition dynamics between fMRI sessions and show that the within-participant test-retest reliability is higher than between-participant test-retest reliability for different indices of state-transition dynamics, different networks, and different data sets. This result suggests that state-transition dynamics analysis of fMRI data could discriminate between different individuals and is a promising tool for performing fingerprinting analysis of individuals.

Did the COVID-19 pandemic affect levels of burnout, anxiety and depression among doctors and nurses in Bangladesh? A cross-sectional survey study.

INTRODUCTION: COVID-19 has caused severe disruption to clinical services in Bangladesh but the extent of this, and the impact on healthcare professionals is unclear. We aimed to assess the perceived levels of anxiety, depression and burnout among doctors and nurses during COVID-19 pandemic. METHODS: We undertook an online survey using RedCap, directed at doctors and nurses across four institutions in Bangladesh (The Sheikh Russel Gastro Liver Institute & Hospital (SRNGIH), Dhaka Medical College Hospital (DMCH), Mugda Medical College Hospital (MMCH) and M Abdur Rahim Medical College (MARMC) Hospital). We collected information on demographics, awareness of well-being services, COVID-19-related workload, as well as anxiety, depression and burnout using two validated questionnaires: the Hospital Anxiety and Depression Scale (HADS) and the Maslach Burnout Inventory (MBI). RESULTS: Of the 3000 participants approached, we received responses from 2705 (90.2%). There was a statistically significant difference in anxiety, depression and burnout scores across institutions (p<0.01). Anxiety, depression and burnout scores were statistically worse in COVID-19 active staff compared with those not working on COVID-19 activities (p<0.01 for HADS anxiety and depression and MBI emotional exhaustion (EE), depersonalisation (DP) and personal accomplishment (PA)). Over half of the participants exhibited some level of anxiety (SRNGIH: 52.2%; DMCH: 53.9%; MMCH: 61.3%; MARMC: 68%) with a high proportion experiencing depression (SRNGIH: 39.5%; DMCH: 38.7%; MMCH: 53.7%; MARMC: 41.1%). Although mean burnout scores were within the normal range for each institution, a high proportion of staff (almost 20% in some instances) were shown to be classified as experiencing burnout by their EE, DP and PA scores. CONCLUSION: We identified a high prevalence of perceived anxiety, depression and burnout among doctors and nurses during the COVID-19 pandemic. This was worse in staff engaged in COVID-19-related activities. These findings could help healthcare organisations to plan for future similar events.

Amorphous K-Co-Mo-Sx Chalcogel: A Synergy of Surface Sorption and Ion-Exchange.

Chalcogel represents a unique class of meso- to macroporous nanomaterials that offer applications in energy and environmental pursuits. Here, the synthesis of an ion-exchangeable amorphous chalcogel using a nominal composition of K2 CoMo2 S10 (KCMS) at room temperature is reported. Synchrotron X-ray pair distribution function (PDF), X-ray absorption near-edge structure (XANES), and extended X-ray absorption fine structure (EXAFS) reveal a plausible local structure of KCMS gel consisting of Mo5+ 2 and Mo4+ 3 clusters in the vicinity of di/polysulfides which are covalently linked by Co2+ ions. The ionically bound K+ ions remain in the percolating pores of the Co-Mo-S covalent network. XANES of Co K-edge shows multiple electronic transitions, including quadrupole (1s→3d), shakedown (1s→4p + MLCT), and dipole allowed 1s→4p transitions. Remarkably, despite a lack of regular channels as in some crystalline solids, the amorphous KCMS gel shows ion-exchange properties with UO2 2+ ions. Additionally, it also presents surface sorption via [S∙∙∙∙UO2 2+ ] covalent interactions. Overall, this study underscores the synthesis of quaternary chalcogels incorporating alkali metals and their potential to advance separation science for cations and oxo-cationic species by integrating a synergy of surface sorption and ion-exchange.

Person identification with arrhythmic ECG signals using deep convolution neural network.

Over the past decade, the use of biometrics in security systems and other applications has grown in popularity. ECG signals in particular are attracting increased attention due to their characteristics, which are required for a trustworthy identification system. The majority of ECG-based person identification systems are evaluated without considering the health-state of the individuals. Few person identification systems consider person-by-person health-state annotation. This paper proposes a person identification system considering the health-state annotated ECG signals where each person's beats overlap among variant arrhythmia classes. This overlapping between the normal class and other arrhythmia classes grants the ability to isolate normal beats in the train set from the Arrhythmic beats in the test set. Therefore, this paper investigates the effect of arrhythmic heartbeats on biometric recognition. An effective lightweight CNN based on depth-wise separable convolution (DWSC) is proposed to enhance the performance of person identification for several common arrhythmia types using the MITBIH dataset. The proposed methodology has been tested on nine arrhythmia types and presents how different types of arrhythmia affect ECG-based biometric systems differently. The experimental results show excellent recognition performance (99.28%) on normal heartbeats and (93.81%) on arrhythmic heartbeats, outperforming other models in terms of mean accuracy.

Beneficiation of Low-Grade Hematite Iron Ore Fines by Magnetizing Roasting and Magnetic Separation.

Present investigation includes the magnetizing roasting of low-grade iron ore fines followed by grinding and beneficiation using magnetic separation. The hematite iron ore used in the investigation contains 53.17% T Fe, 10.7% SiO2, and 4.5% Al2O3. Powdered bituminous coal of 210 µm size with an ash content of 12.5% and fixed carbon of 54.25% was used as reductant during magnetizing roasting. Optical microstructures have shown where iron and silicate minerals are found and how they are interconnected. Hematite is the most abundant material in the specimen and is found in fine- and medium-sized grains. Hematite emerged as the predominant iron-bearing mineral, accompanied by magnetite and goethite phases in smaller proportions according to XRD analyses. The primary gangue mineral identified by scanning electron microscopy is quartz, with gibbsite, feldspar, and pyrolusite present in lesser levels. The effects of iron/coal ratio, roasting time, and roasting temperature were considered as variable parameters. Hematite ore's magnetic characteristics were significantly impacted by magnetizing roasting. By selectively magnetizing roasting, hematite is transformed into magnetite. With an Fe grade of 65.25% at a recovery value of 72.5% in the concentrate, magnetic separation produced the greatest result for Fe. The performance of magnetization and therefore the magnetic separation process were shown to be significantly impacted by temperature, reductant %, and roasting duration in this investigation.

Exploring risk factors and molecular targets in leukemia patients with COVID-19: a bioinformatics analysis of differential gene expression.

The molecular mechanism of COVID-19's pathogenic effects in leukemia patients is still poorly known. Our study investigated the possible disease mechanism of COVID-19 and its associated risk factors in patients with leukemia utilizing differential gene expression analysis. We also employed network-based approaches to identify molecular targets that could potentially diagnose and treat COVID-19-infected leukemia patients. Our study demonstrated a shared set of 60 genes that are expressed differentially among patients with leukemia and COVID-19. Most of these genes are expressed in blood and bone marrow tissues and are predominantly implicated in the pathogenesis of different hematologic malignancies, increasingly imperiling COVID-19 morbidity and mortality among the affected patients. Additionally, we also found that COVID-19 may influence the expression of several cancer-associated genes in leukemia patients, such as CCR7, LEF1, and 13 candidate cancer-driver genes. Furthermore, our findings reveal that COVID-19 may predispose leukemia patients to altered blood homeostasis, increase the risk of COVID-19-related liver injury, and deteriorate leukemia-associated injury and patient prognosis. Our findings imply that molecular signatures, like transcription factors, proteins such as TOP21, and 25 different microRNAs, may be potential targets for diagnosing and treating COVID-19-infected leukemia patients. Nevertheless, additional experimental studies will contribute to further validating the study's findings.

Inclined Curved Crack Composite Beam: Experimental and Computational Analysis with Recycled Aluminum Composite Materials and Comparison with an Artificial Neural Network.

This research focuses on an inclined curved crack model for a recycled aluminum composite beam at various crack depths and locations. The inclined curved crack equation of the motion, by governing a free vibration curved beam with a different depth of crack, is solved computationally via the differential quadrature method (DQM) and experimentally; additionally, the result of the natural frequency has been compared with various depths of curvature. For the first four modes of cracked beams, the computational method's output is used to determine the natural frequencies associated with mode shapes. The outcomes of the computational results suggested a structural health monitoring system to detect deterioration in composite structures when modal parameters have changed. An experimental set of results was validated using MATLAB2019a, and the outcomes were compared with an artificial neural network.

Two-step evolution of HIV-1 budding system leading to pandemic in the human population.

The pandemic HIV-1, HIV-1 group M, emerged from a single spillover event of its ancestral lentivirus from a chimpanzee. During human-to-human spread worldwide, HIV-1 diversified into multiple subtypes. Here, our interdisciplinary investigation mainly sheds light on the evolutionary scenario of the viral budding system of HIV-1 subtype C (HIV-1C), a most successfully spread subtype. Of the two amino acid motifs for HIV-1 budding, the P(T/S)AP and YPxL motifs, HIV-1C loses the YPxL motif. Our data imply that HIV-1C might lose this motif to evade immune pressure. Additionally, the P(T/S)AP motif is duplicated dependently of the level of HIV-1 spread in the human population, and >20% of HIV-1C harbored the duplicated P(T/S)AP motif. We further show that the duplication of the P(T/S)AP motif is caused by the expansion of the CTG triplet repeat. Altogether, our results suggest that HIV-1 has experienced a two-step evolution of the viral budding process during human-to-human spread worldwide.

Morpho-molecular investigation of ectoparasitic infestation of companion animals in Sylhet city, Bangladesh.

Ticks (Ixodida) and Fleas (Siphonaptera) are considered among the most important arthropod of public health concern due to their ability to transmit vector-borne pathogens to humans. By sharing a common environment, vector-borne diseases constituted major setbacks to the development of a pet population in Bangladesh. This study aimed to determine companion animal-associated ticks and fleas based on morpho-molecular approaches. Between December 2021 and May 2022, 74 animals (62 cats and 12 dogs) were examined, of which 17 (27.4%) cats and 9 (75.0%) dogs had ectoparasitic infestations, with 35.1% overall prevalence. Morphometrical examination showed the ectoparasites in these animals were Ctenocephalides spp. (flea) and Riphicephalus spp. (tick). Genetic analysis using the mitochondrial markers i.e. Cytochrome c oxidase subunit 1 (cox1) revealed the presence of two flea species i.e., Ctenocephalides canis, Ctenocephalides felis, and one tick species Rhipicephalus sanguineus. Interviews of animal owners indicate that 35.14% of them had no concern about ectoparasitic infestation or ectoparasites-borne diseases. Our results indicated that fleas and ticks were the most common ectoparasites in companion animals of this area. The zoonotic nature of some ectoparasites can be regarded as a public health alert. The findings will assist epidemiologists and policymakers in offering customized guidance for upcoming monitoring and preventive tactics in this area.

Nutrient density of Bangladeshi foods and its application in planning diet for pregnant women.

Nutrient profiling is a method that classifies foods based on their nutrient content and identifies foods that are high in micronutrients both across and within food groups. This study aimed to identify foods that are rich sources of the seven micronutrients (iron, zinc, calcium, thiamine, riboflavin, vitamin A, and vitamin B12) of public health concern for the Bangladeshi population.. This study developed a metric termed "naturally nutrient-rich score 7 (NNR7)" specifically for third-trimester pregnant women to identify nutrient-dense foods. Further, it computed the nutrient adequacy score (NAS) of the top NNR7-scored foods for seven micronutrients to assess the extent (percent) to which foods can meet pregnant women's recommended dietary allowances (RDA). A linear programming technique was then used to construct a nutrient-adequate model diet for third-trimester pregnant women using the top ten NNR7-scored foods. According to the NNR7, food groups such as leafy vegetables, fish, meat, poultry and eggs, and vegetables are the richest sources of the problem micronutrients. Mutton liver (916.7%), soybean (39.3%), lamb liver (2160%) and duck liver (50.0%) were found to fulfill the highest percentage of the RDA of vitamin A, zinc, vitamin B12, and iron, respectively. In the formulated nutrient-adequate diets for pregnant women, rice, potato, brown wheat flour, and soya oil were universal to all three diets and Bengal gram, orange, Ganges River sprat, and duck liver were the most common ones. The study findings highlight the need for the consumption of foods such as leafy vegetables, fish, meat, poultry, eggs, pulses and vegetables to increase the intake of problematic micronutrients. Planning a nutrient-adequate diet for pregnant women using linear programming can be an alternative approach to optimize and shape food choices to meet their nutritional requirements.

Measuring 3D Video Quality of Experience (QoE) Using A Hybrid Metric Based on Spatial Resolution and Depth Cues.

A three-dimensional (3D) video is a special video representation with an artificial stereoscopic vision effect that increases the depth perception of the viewers. The quality of a 3D video is generally measured based on the similarity to stereoscopic vision obtained with the human vision system (HVS). The reason for the usage of these high-cost and time-consuming subjective tests is due to the lack of an objective video Quality of Experience (QoE) evaluation method that models the HVS. In this paper, we propose a hybrid 3D-video QoE evaluation method based on spatial resolution associated with depth cues (i.e., motion information, blurriness, retinal-image size, and convergence). The proposed method successfully models the HVS by considering the 3D video parameters that directly affect depth perception, which is the most important element of stereoscopic vision. Experimental results show that the measurement of the 3D-video QoE by the proposed hybrid method outperforms the widely used existing methods. It is also found that the proposed method has a high correlation with the HVS. Consequently, the results suggest that the proposed hybrid method can be conveniently utilized for the 3D-video QoE evaluation, especially in real-time applications.