Machine Learning-Enabled Smart Industrial Automation Systems Using Internet of Things.

Industrial automation uses robotics and software to operate equipment and procedures across industries. Many applications integrate IoT, machine learning, and other technologies to provide smart features that improve the user experience. The use of such technology offers businesses and people tremendous assistance in successfully achieving commercial and noncommercial requirements. Organizations are expected to automate industrial processes owing to the significant risk management and inefficiency of conventional processes. Hence, we developed an elaborative stepwise stacked artificial neural network (ESSANN) algorithm to greatly improve automation industries in controlling and monitoring the industrial environment. Initially, an industrial dataset provided by KLEEMANN Greece was used. The collected data were then preprocessed. Principal component analysis (PCA) was used to extract features, and feature selection was based on least absolute shrinkage and selection operator (LASSO). Subsequently, the ESSANN approach is proposed to improve automation industries. The performance of the proposed algorithm was also examined and compared with that of existing algorithms. The key factors compared with existing technologies are delay, network bandwidth, scalability, computation time, packet loss, operational cost, accuracy, precision, recall, and mean absolute error (MAE). Compared to traditional algorithms for industrial automation, our proposed techniques achieved high results, such as a delay of approximately 52%, network bandwidth accomplished at 97%, scalability attained at 96%, computation time acquired at 59 s, packet loss achieved at a minimum level of approximately 53%, an operational cost of approximately 59%, accuracy of 98%, precision of 98.95%, recall of 95.02%, and MAE of 80%. By analyzing the results, it can be seen that the proposed system was effectively implemented.

Detection of high levels of anti-α-galactosyl antibodies in sera of patients with Old World cutaneous leishmaniasis: a possible tool for diagnosis and biomarker for cure in an elimination setting.

In the Kingdom of Saudi Arabia (KSA), Old World cutaneous leishmaniasis (CL) is mainly caused by Leishmania major and Leishmania tropica parasites. Diagnosis of CL is predominately made by clinicians, who at times fail to detect the disease and are unable to identify parasite species. Here, we report the development of a chemiluminescent enzyme-linked immunosorbent assay (CL-ELISA) to measure the levels of anti-α-galactosyl antibodies in human sera. Using this assay, we have found that individuals infected with either Leishmania spp. had significantly elevated levels (up to 9-fold higher) of anti-α-Gal IgG compared to healthy control individuals. The assay sensitivity was 96% for L. major (95% CI; 94-98%) and 91% for L. tropica (95% CI; 86-98%) infections and therefore equivalent to restriction fragment length polymorphism-polymerase chain reaction analysis of parasite ITS1 gene. In addition, the assay had higher sensitivity than microscopy analysis, which only detected 68 and 45% of the L. major and L. tropica infections, respectively. Interestingly, up to 2 years following confirmed CL cure individuals had 28-fold higher levels of anti-α-Gal IgG compared to healthy volunteers. Monitoring levels of anti-α-Gal antibodies can be exploited as both a diagnostic tool and as a biomarker of cure of Old World CL in disease elimination settings.

Old World cutaneous leishmaniasis treatment response varies depending on parasite species, geographical location and development of secondary infection.

BACKGROUND: In the Kingdom of Saudi Arabia (KSA), Leishmania major and L. tropica are the main causative agents of Old World cutaneous leishmaniasis (CL). The national CL treatment regimen consists of topical 1% clotrimazole/2% fusidic acid cream followed by 1-2 courses of intralesional sodium stibogluconate (SSG); however, treatment efficacy is highly variable and the reasons for this are not well understood. In this study, we present a complete epidemiological map of CL and determined the efficacy of the standard CL treatment regime in several endemic regions of KSA. RESULTS: Overall, three quarters of patients in all CL-endemic areas studied responded satisfactorily to the current treatment regime, with the remaining requiring only an extra course of SSG. The majority of unresponsive cases were infected with L. tropica. Furthermore, the development of secondary infections (SI) around or within the CL lesion significantly favoured the treatment response of L. major patients but had no effect on L. tropica cases. CONCLUSIONS: The response of CL patients to a national treatment protocol appears to depend on several factors, including Leishmania parasite species, geographical location and occurrences of SI. Our findings suggest there is a need to implement alternative CL treatment protocols based on these parameters.

Isolation and genetic characterization of Rift Valley fever virus from Aedes vexans arabiensis, Kingdom of Saudi Arabia.

An outbreak of Rift Valley fever in the Kingdom of Saudi Arabia and Yemen in 2000 was the first recognized occurrence of the illness outside of Africa and Madagascar. An assessment of potential mosquito vectors in the region yielded an isolate from Aedes vexans arabiensis, most closely related to strains from Madagascar (1991) and Kenya (1997).