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Purpose The purpose of this paper is to formulate a benchmark to increase the tyre curing press production rate while minimizing tyre curing press downtime and maintenance cost with the help of a maintenance management technique based on overall equipment effectiveness (OEE). Design/methodology/approach The methodology is based on determining the OEE of tyre curing press before and after rectifying the causes of failures. The failure mode and effect analysis (FMEA) technique is used to find out the root causes of repetitive failures in tyre curing press by using the risk priority number. Findings A significant change in the value of OEE is observed after rectifying the repetitive failures, which were determined using the FMEA technique. Thus, it is concluded that the OEE and FMEA assist in improving the industrial performance and competitiveness of the production equipment studied. Research limitations/implications This study is limited to determining the OEE of single equipment only, not the whole production system. Manufacturing facilities are dependent on the operating environment; therefore a comparison of two different manufacturing plants based on the OEE value would not be justified. Practical implications This study can be applied in any tyre manufacturing industry in order to take competitive benefits, such as reduction in equipment downtime, increased production and reduction in maintenance cost. Originality/value The angle from which the paper approaches the bottleneck problem in a tyre production line is original for the studied company and shows positives results. It allows the company to apply the same approach in its other production equipment, lines and factories to achieve improvement in industrial performance and competitiveness.

Obese individuals seem to be at the highest risk of contracting COVID-19 infection. Furthermore, severity of morbidity and mortality rates are higher in the developed world as compared to the developing world. One probable reason for this difference could be the difference in living conditions and exposure to other infections. Secondly, the difference in food especially, alcohol use may have deteriorating effects superimposed with obesity. Our hypothesis suggests that a combination of alcohol consumption and obesity causes low immunity and makes the individual prone to develop ‘cytokine storm’ and ‘acute respiratory distress syndrome’; the hallmark of COVID-19 mortality and morbidity. Thus, we propose that reducing any one trigger can have a beneficial effect in combating the disease severity.

Supplying safe water to consumers is vital for protection of public health. With population of > 15 million, Karachi is the main economical hub of Pakistan. Lake Keenjhar serves as the main source of fresh water while Hub dam is the secondary water reservoir for Karachi. In this study, bacterial community of the drinking water supply system (DWSS) of Karachi was studied from source to tap using metagenomics approach. For this purpose, we collected 41 water samples from different areas of the city (n = 38) and water reservoirs (n = 3). 16S rDNA metagenomic sequencing of water samples revealed that 88% sequences were associated with Proteobacteria (52%), Planctomycetes (15%), Becteroidetes (12%), and Verrucomicrobia (6%). On the class level, α-proteobacteria (6-56%) were found to be the most abundant followed by β- (8-41%) and γ-proteobacteria (6-52%). On the genus level, substantial diversity was observed among the samples. Bacterial communities in water from Hub dam was found to be distantly related while among the residential towns, Lyari was highly distant from the others. Twenty-four bacterial genera were found to be exclusively present in residential area samples in comparison to the source waters which is suggestive of their resistance against treatment procedures and/or contamination. Metagenomic analysis revealed abundance of Pseudomonas, Legionella, Neisseria, Acinetobacter, Bosea, and Microcystis genera in residential areas water samples. The present metagenomic analysis of DWSS of Karachi has allowed the evaluation of bacterial communities in source water and the water being supplied to the city. Moreover, measurement of heavy metals in water samples from Karachi revealed arsenic concentration according to WHO standards which is in contrast of recent study which reported extensive arsenic contamination in aquifers in the Indus valley plain. To the best of our knowledge, this is the first metagenomic study of DWSS of Karachi.

Human Gait Recognition (HGR) is a biometric technique that has been utilized for security purposes for the last decade. The performance of gait recognition can be influenced by various factors such as wearing clothes, carrying a bag, and the walking surfaces. Furthermore, identification from differing views is a significant difficulty in HGR. Many techniques have been introduced in the literature for HGR using conventional and deep learning techniques. However, the traditional methods are not suitable for large datasets. Therefore, a new framework is proposed for human gait recognition using deep learning and best feature selection. The proposed framework includes data augmentation, feature extraction, feature selection, feature fusion, and classification. In the augmentation step, three flip operations were used. In the feature extraction step, two pre-trained models were employed, Inception-ResNet-V2 and NASNet Mobile. Both models were fine-tuned and trained using transfer learning on the CASIA B gait dataset. The features of the selected deep models were optimized using a modified three-step whale optimization algorithm and the best features were chosen. The selected best features were fused using the modified mean absolute deviation extended serial fusion (MDeSF) approach. Then, the final classification was performed using several classification algorithms. The experimental process was conducted on the entire CASIA B dataset and achieved an average accuracy of 89.0. Comparison with existing techniques showed an improvement in accuracy, recall rate, and computational time.

Fecal pollution, commonly detected in untreated or less treated sewage, is associated with health risks (e.g., waterborne diseases and antibiotic resistance dissemination), ecological issues (e.g., release of harmful gases in fecal sludge composting, proliferative bacterial/algal growth due to high nutrient loads) and economy losses (e.g., reduced aqua farm harvesting). Therefore, the discharge of untreated domestic sewage to the environment and its agricultural reuse are growing concerns. The goals of fecal pollution detection include fecal waste source tracking and identifying the presence of pathogens, therefore assessing potential health risks. This review summarizes available biological fecal indicators focusing on host specificity, degree of association with fecal pollution, environmental persistence, and quantification methods in fecal pollution assessment. The development of practical tools is a crucial requirement for the implementation of mitigation strategies that may help confine the types of host-specific pathogens and determine the source control point, such as sourcing fecal wastes from point sources and nonpoint sources. Emerging multidisciplinary bacterial enumeration platforms are also discussed, including individual working mechanisms, applications, advantages, and limitations.

We aimed to characterize the relationship of the oral microbiome with diabetes in Pakistan.Saliva samples were collected from diabetic patients (n = 49) and healthy individuals (n = 55).16S metagenomics saliva was carried out by NGS technology. We observed that the phylum Firmicutes (p-value = 0.024 at 95% confidence interval) was significantly more abundant among diabetic patients than among the controls. We found that the abundance of phylum Actinobacteria did not significantly vary among both groups in contrast to a similar report from the USA (Long et al., 2017). On genus level, acidogenic bacteria Prevotella (p-value = 0.024) and Leptotrichia (p-value = 1.5 x 10 -3 ); and aciduric bacteria Veillonella (p-value = 0.013) were found to be in higher abundance in diabetic patients. These bacteria are found in dental biofilm and involved in the metabolism of fermentable carbohydrates. Stratified analysis by gender revealed healthy and diabetic females to be more divergent. Abundance of Prevotella (p-value = 4.4 x 10-3) and Leptotrichia (p-value = 0.015) was significantly associated with male patients. A comparison of oral bacteriome between two groups revealed the dominance of acidogenic and aciduric bacteria in diabetics which suggested the involvement of these eubacteria in oral dysbacteriosis in diabetes mellitus.

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