Risk factors associated with brucella seropositivity in sheep and goats in Duhok Province, Iraq

Sera from 432 small ruminants (335 sheep and 97 goats) from 72 farms in Duhok Province, northern Iraq, were collected to investigate risk factors associated with brucellosis seropositivity. Serum samples were tested using the Rose Bengal test (RBT) and an indirect enzyme-linked immunosorbent assay (iELISA). Using parallel interpretation, RBT and iELISA results showed that 31.7% (95% confidence interval (CI): 26.1, 36.3) of sheep and 34.0% (95% CI: 24.7, 44.3) of goats had antibodies against Brucella in the study area. A random-effects multivariable logistic regression model indicated that a higher chance of being seropositive (odds ratio (OR) = 1.7; 95% 1.4; 2.2) was associated with an increase in the age of animals. The odds of Brucella seropositivity in flocks where sheep and goats grazed together was 2.0 times higher (95% CI: 1.08; 3.9) compared to flocks where sheep and goats grazed separately. The odds of Brucella seropositivity in small ruminants was 2.2 higher (95% CI: 1.2; 4.3) for animals originating from farms with a history of goat abortion in the preceding 12 months. In contrast, for every 1000 Iraqi Dinars (~0.85 US Dollar) spent by the farmers on control of Brucella in their flocks, the odds of Brucella seropositivity decreased significantly (OR = 0.9, p-value = 0.021). The final model also indicated significant differences in Brucella seropositivity between the different districts of Duhok Province. This study provides a contribution to the epidemiology of brucellosis in small ruminants in northern Iraq

Deep learning for environmentally robust speech recognition

Deep learning is an emerging technology that is one of the most promising areas of artificial intelligence. Great strides have been made in recent years which resulted in increased efficiency with regards to many applications, including speech. Despite this, an environmentally Robust Speech Recognition system is still far from being achieved. In this article, an investigation of previous work has been conducted. The use of deep learning in speech recognition was analyzed and a proper deep learning architecture was identified. A method using convolutional neural network (CNN) is used with the aim of enhancing the performance of speech recognition systems (SRS). This study found that this CNN-based approach achieves a 94.32% validated accuracy

MXene Based Nanocomposites for Recent Solar Energy Technologies

This article discusses the design and preparation of a modified MXene-based nanocomposite for increasing the power conversion efficiency and long-term stability of perovskite solar cells. The MXene family of materials among 2D nanomaterials has shown considerable promise in enhancing solar cell performance because of their remarkable surface-enhanced characteristics. Firstly, there are a variety of approaches to making MXene-reinforced composites, from solution mixing to powder metallurgy. In addition, their outstanding features, including high electrical conductivity, Young’s modulus, and distinctive shape, make them very advantageous for composite synthesis. In contrast, its excellent chemical stability, electronic conductivity, tunable band gaps, and ion intercalation make it a promising contender for various applications. Photovoltaic devices, which turn sunlight into electricity, are an exciting new area of research for sustainable power. Based on an analysis of recent articles, the hydro-thermal method has been widely used for synthesizing MXene-based nano-composites because of the easiness of fabrication and low cost. Finally, we identify new perspectives for adjusting the performance of MXene for various nanocomposites by controlling the composition of the two-dimensional transition metal MXene phase

MXene-based novel nanocomposites doped SnO 2 for boosting the performance of perovskite solar cells

Since being first published in 2018, the use of two-dimensional MXene in solar cells has attracted significant interest. This study presents, for the first time, the synthesis of an efficient hybrid electrocatalyst in the form of a nanocomposite (MXene/CoS)-SnO2 designed to function as a high-performance electron transfer layer (ETL). The study can be divided into three distinct parts. The first part involves the synthesis of single-layer Ti3C2Tx MXene nanosheets, followed by the preparation of a CoS solution. Subsequently, in the second part, the fabrication of MXene/CoS heterostructure nanocomposites is carried out, and a comprehensive characterization is conducted to evaluate the physical, structural, and optical properties. In the third part, the attention is on the crucial characterizations of the novel nanocomposite-electron transport layer (ETL) solution, significantly contributing to the evolution of perovskite solar cells. Upon optimising the composition, an exceptional power conversion efficiency of more than 17.69% is attained from 13.81% of the control devices with fill factor (FF), short-circuit current density (Jsc), and open-circuit voltage (Voc) were 66.51%, 20.74 mA/cm2, and 1.282 V. Therefore, this PCE is 21.93% higher than the control device. The groundbreaking MXene/CoS (2 mg mL−1) strategy reported in this research represents a promising and innovative avenue for the realization of highly efficient perovskite solar cells

A mobile and automated walk-over-weighing system for a close and remote monitoring of liveweight in sheep

International audienceMonitoring bodyweight (BW) is a critical practice used for management purposes (e.g. assessing weight gain, body condition or establishing slaughtering schedules). Measuring BW indoors is relatively easy although time and labor consuming. However, recording BW outdoors may become difficult. The aim of this project was to trial an automated small ruminant weighing prototype using the remote weighing concept of walk-over-weighing (WoW), combined with radio-frequency identification and designed to be light, resistant, transportable and autonomous in energy. The BW is collected as the animal crosses freely over the WoW platform, strategically placed in an obligatory path combined with a small yard containing water and mineral salts as incentives. We studied the system's efficacy in a series of experiments under a range of sheep farming situations (i.e. indoor and outdoor). Time required for achieving individual voluntary passages, the number of daily visits and the pro- portion of biologically plausible BW records were analysed. The Lin’s concordance correlation coefficient (CCC) was used to establish the agreement between WoW records and the gold standard BW measurements (static weighing scale). Our results showed the feasibility of recording BW with free and voluntary passage of sheep with controlled sheepflow over the platform while preventing congestion. After 2–3 weeks of adaptation, 100% of animals crossed daily. Sheep misbehaviour (e.g. speed of passage) can result in spurious values and ccounted for many of the larger weight discrepancies. Once outliers were removed, the prediction accuracy of the system and the CCC ranged between 0.89 and 0.98, showing a substantial agreement between the two methods. Using this standalone WoW system, it was possible to record daily individual BW, which may contribute to save labor and time while providing timely information to improve productivity and animal welfare under varying farming conditions

Improved corrosion behavior of AZ31 alloy through ECAP processing

This study aims to establish the effects of equal channel angular pressing (ECAP) processing on the corrosion behavior and hardness values of the AZ31 Mg alloy. The AZ31 billets were processed through ECAP successfully at 250 °C and their microstructural evolution was studied using optical and field emission scanning electron microscopy. The corrosion resistance of the AZ31 alloy was studied before and after processing through ECAP. The homogeneity of the hardness distribution was studied using both sections cut parallel and perpendicular to the extrusion direction. ECAP processing resulted in highly deformed central regions with elon-gated grains aligned parallel to the extrusion direction, whereas the peripheral regions showed an ultra-fine-grain recrystallized structure. After processing, small ultra-fine secondary particles were found to be homogeneously dispersed alongside the grain boundaries of the α-Mg matrix. Regarding the corrosion properties, measurements showed that ECAP processing through 1-P and 2-Bc resulted in decreasing their corrosion rate to 67.7% and 78.3%, respectively, of their as-annealed counterpart’s. The corrosion resistance of the ECAPed Mg alloy increased with the number of processing passes. This was due to the refinement of the grain size of the α-Mg matrix and secondary phases till ultra-fine size, caused by the accumulation of strain during pro-cessing. On the other hand, ECAP processing through 2-Bc resulted in increasing the Vickers hardness values by 132% and 71.8% at the peripheral and central areas, respectively, compared to the as-annealed counterpart