Status of sustainable manufacturing practices: literature review and trends of triple bottom-line-based sustainability assessment methodologies.

Due to significant requirement of energy, water, material, and other resources, the manufacturing industries significantly impact environmental, economic, and social dimensions of sustainability (triple bottom-line). In response, today's research is focused on finding solution towards sustainable manufacturing. In this regard, sustainability assessment is an essential strategy. In the past, a variety of tools was developed to evaluate the environmental dimension. Because of this fact, previous review studies were grounded mostly on tools for green manufacturing. Unlike previous review articles, this study was aimed to review and analyze the emerging sustainability assessment methodologies (published from 2010 to 2020) for manufacturing while considering the triple bottom-line concept of sustainability. In this way, the paper presents a decade review on this topic, starting from 2010 as the guidelines for the social dimension became available in 2009. This paper has analyzed various methods and explored recent progress patterns. First, this study critically reviewed the methods and then analyzed their different integrating tools, sustainability dimensions, nature of indicators, difficulty levels, assessment boundaries, etc. The review showed that life cycle assessment and analytic hierarchy process-based approaches were most commonly used as integrating tools. Comparatively, still, environmental dimension was more commonly considered than economic and social dimensions by most of the reviewed methods. From indicators' viewpoint, most of the studied tools were based on limited number of indicators, having no relative weights and validation from the experts. To overcome these challenges, future research directions were outlined to make these methods more inclusive and reliable. Along with putting more focus on economic and social dimensions, there is a need to employ weighted, validated, and applicable indicators in sustainability assessment methods for manufacturing.

Re-evaluating the merits of decentralization as a core strategy for effective delivery of drug-resistant tuberculosis care in Pakistan.

Decentralized, person-centred models of care delivery for drug-resistant tuberculosis (DR-TB) continue to be under-resourced in high-burden TB countries. The implementation of such models-made increasingly urgent by the COVID-19 pandemic-are key to addressing gaps in DR-TB care. We abstracted data of rifampicin-resistant (RR)/multidrug-resistant tuberculosis (MDR-TB) patients initiated on treatment at 11 facilities between 2010 and 2017 in Sindh and Balochistan provinces of Pakistan. We analysed trends in treatment outcomes relating to programme expansion to peri-urban and rural areas and estimated driving distance from patient residence to treatment facility. Among the 5586 RR/MDR-TB patients in the analysis, overall treatment success decreased from 82% to 66% between 2010 and 2017, as the programme expanded. The adjusted risk ratio for unfavourable outcomes was 1.013 (95% confidence interval 1.005-1.021) for every 20 km of driving distance. Our analysis suggests that expanding DR-TB care to centralized hubs added to increased unfavourable outcomes for people accessing care in peri-urban and rural districts. We propose that as enrolments increase, expanding DR-TB services close to or within affected communities is essential.

Elucidation of larvicidal potential of metallic and environment friendly food-grade nanostructures against Aedes albopictus.

To combat health challenges associated with mosquito-borne diseases, the larvicidal activity of metallic nanoparticles, food-grade polymeric nano-capsules and insecticides was investigated against larvae of Aedes albopictus as an effective alternate control approach. The Ae. albopictus was identified using sequencing and phylogenetic analyses of COXI, CYTB and ITS2 genes. The characterization of synthesized nanostructures was performed through Zetasizer, UV-VIS spectroscopy, atomic force microscopy, scanning electron microscopy and energy dispersive X-ray spectroscopy. The mosquito larvae were exposed to varying concentration of nanostructures and insecticides, and their percentage mortality was evaluated at different time intervals of 24 h and 48 h exposure. The highest efficacy was observed in zinc oxide nanoparticles (ZnO-NPs) and polymeric nanocapsules FG-Cur E-III (LC50 = 0.24 mg/L, LC90 = 0.6 mg/L) and (LC50 = 3.8 mg/L, LC90 = 9.33 mg/L), respectively, after 24 h; while (LC50 = 0.18 mg/L, LC90 = 0.43 mg/L) and (LC50 = 1.95 mg/L, LC90 = 6.46 mg/L), respectively, after 48 h against fourth instar larvae of Ae. albopictus. Ag, CuO, NiTiO3 and CoTiO3 nanoparticles evaluated in this study also showed promising larvicidal activity. Although ZnO-NPs proved to be effective larvicides, their possible toxicity (producing ROS species) can limit their use. The curcumin nanostructures (FG-Cur E-III) stabilized by food-grade materials are thought to exert their larvicidal activity by binding to sterol carrier protein-2, and depriving the larvae from the essential dietary cholesterol, and bears effective larvicidal potential as safe alternative for chemical larvicides, due to their environment friendly, food-grade and easy biodegradability.

Eye and Voice-Controlled Human Machine Interface System for Wheelchairs Using Image Gradient Approach.

Rehabilitative mobility aids are being used extensively for physically impaired people. Efforts are being made to develop human machine interfaces (HMIs), manipulating the biosignals to better control the electromechanical mobility aids, especially the wheelchairs. Creating precise control commands such as move forward, left, right, backward and stop, via biosignals, in an appropriate HMI is the actual challenge, as the people with a high level of disability (quadriplegia and paralysis, etc.) are unable to drive conventional wheelchairs. Therefore, a novel system driven by optical signals addressing the needs of such a physically impaired population is introduced in this paper. The present system is divided into two parts: the first part comprises of detection of eyeball movements together with the processing of the optical signal, and the second part encompasses the mechanical assembly module, i.e., control of the wheelchair through motor driving circuitry. A web camera is used to capture real-time images. The processor used is Raspberry-Pi with Linux operating system. In order to make the system more congenial and reliable, the voice-controlled mode is incorporated in the wheelchair. To appraise the system's performance, a basic wheelchair skill test (WST) is carried out. Basic skills like movement on plain and rough surfaces in forward, reverse direction and turning capability were analyzed for easier comparison with other existing wheelchair setups on the bases of controlling mechanisms, compatibility, design models, and usability in diverse conditions. System successfully operates with average response time of 3 s for eye and 3.4 s for voice control mode.

Enhanced Accuracy for Multiclass Mental Workload Detection Using Long Short-Term Memory for Brain-Computer Interface.

Cognitive workload is one of the widely invoked human factors in the areas of human-machine interaction (HMI) and neuroergonomics. The precise assessment of cognitive and mental workload (MWL) is vital and requires accurate neuroimaging to monitor and evaluate the cognitive states of the brain. In this study, we have decoded four classes of MWL using long short-term memory (LSTM) with 89.31% average accuracy for brain-computer interface (BCI). The brain activity signals are acquired using functional near-infrared spectroscopy (fNIRS) from the prefrontal cortex (PFC) region of the brain. We performed a supervised MWL experimentation with four varying MWL levels on 15 participants (both male and female) and 10 trials of each MWL per participant. Real-time four-level MWL states are assessed using fNIRS system, and initial classification is performed using three strong machine learning (ML) techniques, support vector machine (SVM), k-nearest neighbor (k-NN), and artificial neural network (ANN) with obtained average accuracies of 54.33, 54.31, and 69.36%, respectively. In this study, novel deep learning (DL) frameworks are proposed, which utilizes convolutional neural network (CNN) and LSTM with 87.45 and 89.31% average accuracies, respectively, to solve high-dimensional four-level cognitive states classification problem. Statistical analysis, t-test, and one-way F-test (ANOVA) are also performed on accuracies obtained through ML and DL algorithms. Results show that the proposed DL (LSTM and CNN) algorithms significantly improve classification performance as compared with ML (SVM, ANN, and k-NN) algorithms.

Factors associated with month 2 smear non-conversion among Category 1 tuberculosis patients in Karachi, Pakistan.

Predictors of smear non-conversion at baseline can help identify cases at risk for failure of tuberculosis treatment. Retrospective data for smear-positive Category 1 patients in Karachi, Pakistan, was analyzed. Predictors of sputum conversion were determined using multiple logistic regression with sputum conversion as outcome variable and patient demographics, baseline weight, baseline sputum smear grade, case-finding approach as explanatory variables. Age ≥35 years, baseline sputum grade of 3+ were significantly associated with predicting sputum smear positivity at month 2 of treatment. Monitoring compliance to TB treatment should be considered amongst older patients and those with a high sputum grade at baseline.

Sugarcane Mosaic Virus-Based Gene Silencing in Nicotiana benthamiana.

Background: Potyvirus-based virus-induced gene silencing (VIGS) is used for knocking down the expression of a target gene in numerous plant species. Sugarcane mosaic virus (SCMV) is a monopartite, positive single strand RNA virus. Objectives: pBINTRA6 vector was modifi ed by inserting a gene segment of SCMV in place of Tobacco rattle virus (TRV) genome part 1 (TRV1 or RNA1) and the two nonstructural proteins of TRV2(RNA2). Materials and Methods: SCMV construct was inoculated into 3-4 weeks Nicotiana benthamiana plant leaves either by using a needleless syringe or applying pricking with a toothpick. Results: The construct (SCMV-RNA2) successfully induced post-transcriptional gene silencing (PTGS) of the target genes GFP and ChlI through agroinoculation proving that SCMV is a substitute of the RNA1, which plays a pivotal role in the systemic gene silencing. 2-3-weeks of post inoculation, target genes' silencing was observed in the newly developed noninoculated leaves. Conclusions: The newly developed construct expresses the knocked down of the endogenous as well as exogenous genes and only four weeks are required for the transient expression of the gene silencing based on SCMV-VIGS system.