Assessing the Impact of Dairy Policies on Farm-Level Profits in Dairy Farms in Bangladesh: Benchmarking for Rural Livelihoods Improvement Policy

Abstract: The objective of this study was to benchmark the set of dairy supporting policies on farm level profit in small-scale dairy farmers and also analyse how improved dairy support services and adoption of technologies enhance rural livelihoods. This study applies the International Farm Comparison Network (IFCN) method. The data were analysed by utilizing the extended version of TIPI-CAL (Technology Impact Policy Impact Calculations) model (TIPI-CAL software version 5.1). The improved dairy support services: marketing access (IM-MKS), veterinary services (IM-VHS), feeding and nutritional services (IM-FNS), community based fodder production system (CB-FPS), national breeding programme (NL-BRP) showed the highest impact on milk yield, entrepreneur's profit and household income in all three production systems compared with its base line farms. The extensive and traditional systems were responding more to the proposed policies to increase the entrepreneur's profits compared with intensive production systems. Adoption of policy increases the daily household income above the absolute poverty line (1US$/day). This study results could be useful for prioritizing the policies on delivery of support services and technology and are expected to be helpful as a benchmark to implement the 'draft policy proposal' by the Ministry of Fisheries and Livestock (MOFL) in Bangladesh

Pathogenicity of Aeromonas sobria to Thai silver barb (Barbodes gonionotus Bleeker) and its sensitivity to some antibiotic agents

Five isolates of Aeromonas sobria, collected from the diseased fish were selected for detection the pathogenicity following water-born infection method on silver barbs (Barbodes gonionotus) at the selected exposure dose 2.5x10⁸ CFU/ml which was standardized by preliminary test. In the experimental condition lesion and mortality were found in fishes. Among the isolate, Ass17 Ass19, Ass31 and Ass36 were successfully infected 20-60% fishes. Another isolate Ass20 was found non-pathogenic. Drug sensitivity test was performed by six antibiotics viz. Oxytetracycline, Oxolinic acid, Chloramphenicol, Stilphamethozazole, Streptomycin, Erythromycin. All the isolates showed variable reaction patterns to antibiotics. Most of the isolates were found sensitive to Oxytetracycline (OT), Oxolinic acid (OA) and Chloramphenicol (C) but resistance to Erythromycin and Sulphamethoxazole (SXT). Isolate Ass31 found resistant to Oxolinic acid

Study on the intestinal bacteria of Labeo rohita (Ham.)

The quantitative and qualitative aspects of intestinal bacteria of rohu fish (Labeo rohita) showed that total viable count of bacteria ranged from 9.9 x 106 to 1.4 x 107 CFU/g of intestine in different age groups of fish. The bacterial load was highest in the month of July and lowest in January. The genera of the isolates from intestine included Coryneform, Micrococcus, Flavobacterium, Cytophaga, Achromobacter, Aeromonas Enterobacteriaceae and Vibrio. Coryneform was the dominant group throughout the study period followed by Micrococcus and Enterobacteriaceae. Marked variations in the bacterial load and generic composition of intestinal bacteria were evident during the study period in different age groups of rohu fish

Effects of bio-slurry with chemical fertilizer on the performance of some high yielding varieties of boro rice (Oryza sativa L.)

Rice yield is greatly influenced by application of manures and fertilizer. Integrated use of organic manure and chemical fertilizer would be quite promising in providing better yield. To evaluate the effect of bio-slurry along with chemical fertilizer, a field experiment was conducted in the Agronomy Field Laboratory, Bangladesh Agricultural University, Mymensingh, Bangladesh. The experiment was comprised of four varieties of boro (dry season irrigated) rice viz., (i) BRRI dhan28, (ii) BRRI dhan29 (iii) Binadhan-8 (iv) Binadhan-10 and four fertilizer management viz., (i) control, (ii) recommended dose of inorganic fertilizer, (iii) bio-slurry@ 5 t ha-1 + inorganic fertilizer, (iv) farmers’ practice (average 15 farmers). The experiment was laid out in a randomized complete block design with three replications. It is evident that variety and fertilizer management had significant effect on effective tillers hill-1, number of grains panicle-1 which ultimately influenced grain yield. The highest grain yield was (6.03 t ha-1) in Binadhan-8 followed by Binadhan-10 and BRRI dhan29. The lowest grain yield was found from BRRI dhan28. In respect of fertilizer management, grain yield was highest (5.90 t ha-1) in bio-slurry @ 5 t/ha + inorganic fertilizer. The lowest grain yield was found from control. The combined effect of variety and fertilizer application showed that highest grain yield (6.10 t ha-1) was found from Binadhan-8 with bio-slurry @ 5 t ha-1 + inorganic fertilizer and the lowest grain yield (4.68 t ha-1) was found from BRRI dhan28 with farmers’ practice. Thus, the variety Binadhan-8 with application of bio-slurry @ 5 t ha-1 + inorganic fertilizer was superior for obtaining highest yield

Co-production of hydrogen and carbon nanofibers from methane decomposition over zeolite Y supported Ni catalysts

The objective of this paper is to study the influences of different operating conditions on the hydrogen formation and properties of accumulated carbon from methane decomposition using zeolite Y supported 15 and 30 Ni, respectively, at a temperature range between 500 and 650 degrees C in a pilot scale fixed bed reactor. The temperature ramp was showed a significant impact on the thermo-catalytic decomposition (TCD) of methane. An optimum temperature range of 550-600 degrees C were required to attain the maximum amount of methane conversion and revealed that at 550 and 600 degrees C, catalyst showed longer activity for the whole studied of experimental runs. Additionally, at 550 degrees C, the methane decomposition is two times longer for 30 Ni/Y zeolite than that for 15 Ni/Y zeolite catalyst, whereas it is almost three times higher at 500 degrees C. A maximum carbon yield of 614.25 and 157.54 g(c)/g(Ni) were reported after end of the complete reaction at 600 degrees C with 30 and 15 Ni/Y zeolite catalyst, respectively. From BET, TPD, and XRD analysis, we had reported that how the chemistry between the TCD of methane and metal content of the catalysts could significantly affect the hydrogen production as well as carbon nano-fibers. TEM analysis ensured that the produced carbon had fishbone type structures with a hollow core and grew from crystallites of Ni anchored on the external surface of the catalysts and irrespective of the metal loadings, the whisker types of nano filaments were formed as confirmed from FESEM analysis. Nevertheless, the effect of volume hourly space velocity (VHSV) on the methane conversion was also investigated and reported that the methane conversion increased as VHSV and nickel concentration in Ni-Y catalysts increased. Additionally, the initial methane decomposition rate increases with VHSV and it has reverse and non-linear relevancy to the weight of Ni/Y zeolite catalyst. (C) 2014 Elsevier Ltd. All rights reserved

Developing machine learning model to estimate the shear capacity for RC beams with stirrups using standard building codes

Shear failure in reinforced concrete (RC) beams with a brittle nature is a serious safety concern. Due to the inadequate description of the phenomenology of shear resistance (the shear behavior of RC beams), several of the existing shear design equations for RC beams with stirrups have high uncertainty. Therefore, the predicted models with higher accuracy and lower variability are critical for the shear design of RC beams with stirrups. To predict the ultimate shear strength of RC beams with stirrups, machine learning (ML)-based models are proposed in the present research. The models were created using a database of 201 experimental RC beams with stirrups gathered from earlier investigations for training and testing of the ML method, with 70% of the data being used for model training and the rest for testing. The performance of suggested models was evaluated using statistical comparisons between experimental results and state-of-the-art current shear design models (ACI 318–08, Canadian code, GB 510010–2010, NZS 3101, BNBC 2015). The suggested machine learning-based models are consistent with experimentally observed shear strength and current predictive models, but they are more accurate and impartial. To understand the model very well, sensitivity analysis is determining as input values for a specific variable affect the outcomes of a mathematical model. To compare the results with different machine learning models in training and testing R2 , RMSE and MSE are also established. Finally, proposed ML models such as gradient boost regressor and random forest give higher accuracy to evaluate the shear strength of the reinforcement concrete beam using stirrups.Md Nasir Uddin, Kequan Yu, Ling, zhi Li, Junhong Ye, T. Tafsirojjaman, Wael Alhadda

Contribution of machining to the fatigue behaviour of metal matrix composites (MMCs) of varying reinforcement size

The high cycle constant stress amplitude fatigue performance of metal matrix composite (MMC) components machined by a milling process was investigated in this study as a function of machining speed, feed rate and reinforcement particle size. The presence of reinforcement and particle size were found to be the most influential factors that affected the fatigue life. In contrast to this, the effect of feed and speed on tool-particle interaction, strain hardening and heat generation during milling of MMCs were balanced in such a way that the contributions of feed and speed on fatigue life were negligible. The interactions of different parameters contributed significantly to the fatigue life which indicated that the modelling of fatigue life based on these three parameters was relatively complex. The fatigue life of the machined MMC samples increased with decreasing particle size and increasing feed. However, the fatigue life was not influenced by speed variation. The presence of smaller or no particles induced a complete separation of failed samples, in contrast to that of specimens containing larger reinforcing particles where crack growth was arrested or deflected by the reinforcing particles

Autoantibodies against type I IFNs in patients with life-threatening COVID-19

Interindividual clinical variability in the course of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is vast. We report that at least 101 of 987 patients with life-threatening coronavirus disease 2019 (COVID-19) pneumonia had neutralizing immunoglobulin G (IgG) autoantibodies (auto-Abs) against interferon-w (IFN-w) (13 patients), against the 13 types of IFN-a (36), or against both (52) at the onset of critical disease; a few also had auto-Abs against the other three type I IFNs. The auto-Abs neutralize the ability of the corresponding type I IFNs to block SARS-CoV-2 infection in vitro. These auto-Abs were not found in 663 individuals with asymptomatic or mild SARS-CoV-2 infection and were present in only 4 of 1227 healthy individuals. Patients with auto-Abs were aged 25 to 87 years and 95 of the 101 were men. A B cell autoimmune phenocopy of inborn errors of type I IFN immunity accounts for life-threatening COVID-19 pneumonia in at least 2.6% of women and 12.5% of men

Anemia prevalence in women of reproductive age in low- and middle-income countries between 2000 and 2018

Anemia is a globally widespread condition in women and is associated with reduced economic productivity and increased mortality worldwide. Here we map annual 2000–2018 geospatial estimates of anemia prevalence in women of reproductive age (15–49 years) across 82 low- and middle-income countries (LMICs), stratify anemia by severity and aggregate results to policy-relevant administrative and national levels. Additionally, we provide subnational disparity analyses to provide a comprehensive overview of anemia prevalence inequalities within these countries and predict progress toward the World Health Organization’s Global Nutrition Target (WHO GNT) to reduce anemia by half by 2030. Our results demonstrate widespread moderate improvements in overall anemia prevalence but identify only three LMICs with a high probability of achieving the WHO GNT by 2030 at a national scale, and no LMIC is expected to achieve the target in all their subnational administrative units. Our maps show where large within-country disparities occur, as well as areas likely to fall short of the WHO GNT, offering precision public health tools so that adequate resource allocation and subsequent interventions can be targeted to the most vulnerable populations.Peer reviewe