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The protozoan parasite Neospora caninum infects carnivores as definitive and a wide range of mammals as intermediate hosts. This parasite is regarded as an important cause of abortion in cattle worldwide, causing significant economic losses. Although there is serological evidence of infection in Old World camelids, the significance of N. caninum in these animal species is still poorly understood. The aim of this study was to use molecular and histological methods to detect N. caninum in the blood and tissues of 100 slaughtered one-humped camels (Camelus dromedarius) in Iran. For this, genomic DNA was extracted from blood, brain, portal lymph node and liver of the camels, and nested-PCR assay followed by sequencing were performed. Besides, paraffin-embedded tissue sections were stained with hematoxylin and eosin (H&E) and studied microscopically. In addition, immunohistochemical staining for N. caninum was attempted on brain samples with positive PCR results. All animals were tested for antibodies against N. caninum and Toxoplasma gondii by whole tachyzoite-agglutination tests. N. caninum DNA was detected in blood, brain, and portal lymph node, but not in the liver of two (2%) camels. Histopathological examination revealed cysts resembling N. caninum in brain samples of one of these camels; however, immunohistochemical staining for N. caninum and T. gondii did not allow a morphological identification. IgG antibodies to N. caninum and T. gondii were detected in 36% and 35% of the camels, respectively. This study provides the first insight into direct detection of N. caninum in C. dromedarius in Iran. Further molecular studies on aborted fetuses, stillborn animals and cases of perinatal mortality are needed to understand the possible involvement of N. caninum in cases of reproductive failure. As the definitive hosts of N. caninum are domestic and wild canids, producers should be advised to monitor and limit exposure of their camelids to these species and their feces.

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Permeability is an important parameter in the petrophysical study of a reservoir and serves as a key tool in the development of an oilfield. This is while its prediction, especially in carbonate reservoirs with their relatively lower levels of permeability compared to sandstone reservoirs, is a complicated task as it has larger contributions from heterogeneously distributed vugs and fractures. In this respect, the present research uses the data from two wells (well A for modeling and well B for assessing the generalizability of the developed models) drilled into a carbonate reservoir to estimate the permeability using composite formulations based on least square support vector machine (LSSVM) and multilayer extreme learning machine (MELM) coupled with the so-called cuckoo optimization algorithm (COA), particle swarm optimization (PSO), and genetic algorithm (GA). We further used simple forms of convolutional neural network (CNN) and LSSVM for the sake of comparison. To this end, firstly, the Tukey method was applied to identify and remove the outliers from modeling data. In the next step, the second version of the nondominated sorting genetic algorithm (NSGA-II) was applied to the training data (70% of the entire dataset, selected randomly) to select an optimal group of features that most affect the permeability. The results indicated that although including more input parameters in the modeling added to the resultant coefficient of determination (R2) while reducing the error successively, yet the slope of the latter reduction got much slow as the number of input parameters exceeded 4. In this respect, petrophysical logs of P-wave travel time, bulk density, neutron porosity, and formation resistivity were identified as the most effective parameters for estimating the permeability. Evaluation of the results of permeability modeling based on root-mean-square error (RMSE) and R2 shed light on the MELM-COA as the best-performing model in the training and testing stages, as indicated by (RMSE = 0.5600 mD, R2 = 0.9931) and (RMSE = 0.6019 mD, R2 = 0.9919), respectively. The generalizability assessment conducted on the prediction of permeability in well B confirmed the MELM-COA can provide reliable permeability predictions by achieving an RMSE of 0.9219 mD. Consequently, the mentioned methodology is strongly recommended for predicting the permeability with high accuracy in similar depth intervals at other wells in the same field should the required dataset be available.

Primary prevention of type 2 diabetes mellitus (T2DM) is possible in at-risk populations, and prevention programmes have been shown to be effective in real-world scenarios. Despite this evidence, diabetes prevalence has tripled in recent decades and is expected to reach 700 million patients by 2045, making it one of the leading causes of death globally. This review is aimed at identifying the issues and challenges in the primary prevention of T2DM.Scopus, Web of Science, PubMed and Ovid MEDLINE were systematically searched for published articles. Articles were screened based of inclusion and exclusion criteria. The inclusion criteria were: (1) published in 2010-2020, (2) full original article, (3) written in English, (4) qualitative, mixed-methods article, observational or interventional study. The exclusion criteria were: (1) animal study, (2) in vivo/in vitro study, (3) type 1 diabetes or gestational DM and (4) conference abstract, book chapter, report, and systematic review. Eligible articles were assessed using Mixed Methods Appraisal Tool (MMAT) by three assessors.A total of 11 articles were selected for qualitative synthesis from the initial 620 articles. The issues and challenges seen in T2DM primary prevention followed three themes: healthcare program (sub-themes: lack of resources, community partnership, participation, health literacy), health provider (sub-themes: lack of implementation, health care staff, collaboration, availability), individual (sub-themes: awareness, communication, misbehaviour, family conflict).Factors relating to healthcare programmes, health providers, and individual issues are the main challenges in T2DM primary prevention. By establishing sustainable preventative initiatives that address these issues and challenges in the primary prevention of T2DM, a reduction in T2DM prevalence could be achievable.

In this paper, we propose an optimal delay-independent robust controller based on the Lyapunov–Krakovskii theorem for the milling process as a time-delay system in the presence of varying axial depth of cut and different parametric uncertainty, such as stiffness, damping, etc. Milling is widely used in the manufacturing processes for the production of complex-shaped workpieces with high accuracy. However, chatter is a self-excited vibration that may cause adverse effects, such as tool damage, poor surface quality, excessive noise, etc. The dynamic model of the milling process is considered a two-dimensional time-delay system. A nonlinear programming with linear matrix inequality constraints is solved in order to obtain the controller gain where the objective function is corresponding to the norm-2 of controller gain, and the constraints guarantee robust stability. Using the semi-discretization method, stability lobes are shown in both uncontrolled and controlled plants to illustrate the improvement of the stable region via the proposed controller. Bifurcation phenomena have been improved with this controller by postponing the adverse effects to the higher values of the axial depth of cut, reducing the amplitude of limit cycles, and changing the type of bifurcation. Finally, we will compare the proposed controller with an intelligent controller in order to show the efficiency of the proposed method. It is shown that the proposed controller has improved the integral absolute error index by about 3.65 times compared with the intelligent controller.