Stemming plugs are one of the widely used accessory in surface mining operations. Stemming plugs assist conventional stemming material in gas retention and help in better fragmentation and explosive utilization. Effective use of the stemming plugs results in economic benefits and enhance the efficacy of the project. Economic and productive viability of stemming plugs have been conducted in depth by different researchers. Addition of stemming plugs to a new system requires ergonomic challenges for operators conducting drilling and blasting operation. Induction of a newer product in already established system is subject to overall positive feedback. This work investigates ergonomics of three different stemming plugs introduced to a limestone quarry in Pakistan. The stemming plugs were evaluated based on extra time needed, workers feedback, failures during operation, recovery time after failure and number of extra equipment required to carry out the operation. Points based matrix was established with likeliness of each plug and based on overall scores stemming plug 1 was most acceptable followed by stemming plug 3. Stemming plug 2 was disliked by operation and did not reach the level of acceptability of operators. This work will help stemming plug making industry in adapting to best practices by incorporating ergonomics of plugs in designing. Literature shows no previous work on ergonomics of stemming plugs.

Marine mammal identification and classification for passive acoustic monitoring remain a challenging task. Mainly the interspecific and intraspecific variations in calls within species and among different individuals of single species make it more challenging. Varieties of species along with geographical diversity induce more complications towards an accurate analysis of marine mammal classification using acoustic signatures. Prior methods for classification focused on spectral features which result in increasing bias for contour base classifiers in automatic detection algorithms. In this study, acoustic marine mammal classification is performed through the fusion of 1D Local Binary Pattern (1D-LBP) and Mel Frequency Cepstral Coefficient (MFCC) based features. Multi-class Support Vector Machines (SVM) classifier is employed to identify different classes of mammal sounds. Classification of six species named Tursiops truncatus, Delphinus delphis, Peponocephala electra, Grampus griseus, Stenella longirostris, and Stenella attenuate are targeted in this research. The proposed model achieved 90.4% accuracy on 70–30% training testing and 89.6% on 5-fold cross-validation experiments.

Propofol infusion in anesthesia administration requires continual adjustment in the manual infusion system to regulate the hypnosis level. Hypnotic level is based on Bispectral Index Monitor (BIS) showing the cortical activity of the brain scaled between 0 to 100. The new challenging aspect of automation in anaesthesia is to estimate the concentration of hypnotic drugs in different compartments of the body including primary, rapid peripheral (muscle), slow peripheral (bones, fat) and effect site (brain) compartment based on Pharmacokinetics (PK) and Pharmacodynamics (PD) model. This paper aimed to regulate the hypnosis level with estimating the Propofol concentrations using a linear observer in feedback control strategy based on Integral Super-Twisting Sliding Mode Controller (ISTSMC). The drug concentration in plasma of the silico patients accurately estimated in nominal transient. The results show that tracking errors between the actual output in form of BIS level and linearized output nearly approaches to zero in the maintenance phase of anesthesia to ensure the controller response on sliding phase with optimum performances by achieving desired hypnotic level 50 on BIS. The robustness of control strategy is further ensured by adding measurement noise of electromagnetic environment of operation theatre distracting signal quality index of the output BIS level.