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In the petroleum industry, drilling optimizationinvolves the selection of operating conditions for achievingthe desired depth with the minimum expenditurewhile requirements of personal safety, environment protection,adequate information of penetrated formationsand productivity are fulfilled. Since drilling optimizationis highly dependent on the rate of penetration (ROP), estimationof this parameter is of great importance duringwell planning. In this research, a novel approach called‘optimized support vector regression’ is employed for makinga formulation between input variables and ROP. Algorithmsused for optimizing the support vector regressionare the genetic algorithm (GA) and the cuckoo search algorithm(CS). Optimization implementation improved thesupport vector regression performance by virtue of selectingproper values for its parameters. In order to evaluatethe ability of optimization algorithms in enhancing SVRperformance, their results were compared to the hybridof pattern search and grid search (HPG) which is conventionallyemployed for optimizing SVR. The results demonstratedthat the CS algorithm achieved further improvementon prediction accuracy of SVR compared to the GAand HPG as well. Moreover, the predictive model derivedfrom back propagation neural network (BPNN), which isthe traditional approach for estimating ROP, is selectedfor comparisons with CSSVR. The comparative results revealedthe superiority of CSSVR. This study inferred thatCSSVR is a viable option for precise estimation of ROP.

Owing to the correlation between acetone in human's exhaled breath (EB) and blood glucose, the development of EB acetone gas-sensing devices is important for early diagnosis of diabetes diseases. In this article, a noninvasive blood glucose detection device through acetone sensing in EB, based on an α-Fe 2 O 3 -multiwalled carbon nanotube (MWCNT) nanocomposite, was successfully developed. Different amounts of α-Fe 2 O 3 were added to the MWCNTs by a simple solution method. The optimized acetone gas sensor showed a response of 5.15 to 10 ppm acetone gas at 200 °C. Also, the fabricated sensor showed very good sensing properties even in an atmosphere with high relative humidity. Since the EB has high humidity, the proposed sensor is a promising device to exactly detect the amount of acetone in EB with high humidity. The sensor was powered by a 3200 mAh battery with the possibility of charging using mains electricity. To increase the reliability and calibration of the sensing device, a practical test was taken to detect acetone EB from 50 volunteers, and a deep learning algorithm (DLA) was used to detect the effect of various factors on the amount of acetone in each person's acetone EB. The proposed device with ±15 errors had almost 85% correct responses. Also, the proposed device had excellent response, short response time, good selectivity, and good repeatability, leading it to be a suitable candidate for noninvasive blood glucose sensing.

Transition metal dichalcogenides (TMDs) with a two-dimensional (2D) structure and semiconducting features are highly favorable for the production of NH 3 gas sensors. Among the TMD family, WS 2 , WSe 2 , MoS 2 , and MoSe 2 exhibit high conductivity and a high surface area, along with high availability, reasons for which they are favored in gas-sensing studies. In this review, we have discussed the structure, synthesis, and NH 3 sensing characteristics of pristine, decorated, doped, and composite-based WS 2 , WSe 2 , MoS 2 , and MoSe 2 gas sensors. Both experimental and theoretical studies are considered. Furthermore, both room temperature and higher temperature gas sensors are discussed. We also emphasized the gas-sensing mechanism. Thus, this review provides a reference for researchers working in the field of 2D TMD gas sensors., (© 2024 IOP Publishing Ltd.)