Sustainable Development of Apparel Industry in Bangladesh: A Critical Review

Bangladesh’s apparel industry has become the country’s economic foundation. The textile and apparel employees especially over four million people. In this major industrial sector, it is critical to encourage sustainability. When the apparel market and corporations focus on environmentally friendly products, Bangladesh’s textile and apparel sectors remain far behind, putting the country at risk of losing market share. It is right of passage to implement techniques and a long-term strategy to sustainability. Bangladeshi apparel industries are currently facing significant issues in terms of labor conditions. In garment manufacturers, fires are a common occurrence. Thousands of workers have perished because of these dangers. Due to Bangladesh’s fire and safety difficulties, several foreign purchasers have already opted not to do business with the country again. Furthermore, workers receive the world’s lowest pay, which leaves them dissatisfied and frequently results in conflicts and violence during protests poor wages. This study is conveyed based on theoretical, analytical, and statistical aspects. The goal of this study is to represent the overall picture of sustainability in the apparel industry in Bangladesh. This study illustrates on using a life cycle approach to assessing manufactured products for environmental indicators to attain sustainability, fast fashion, government policy of sustainability, new method and material of garments and compare with the lifestyle of Europe against Bangladesh. This paper investigated Bangladesh’s garment industry’s working environment, fire, and safety hazards, and made suggestions for important environmental and sustainability activities. This study is helpful to all the people because sustainability is the main concern in a day

Traffic conflict techniques based on extreme value theories: Investigation of right-turn crash risks at signalised intersections

This study developed a comprehensive safety assessment framework for opposing-through crashes at signalised intersections by applying Traffic Conflict Techniques. The framework combines Artificial Intelligence-based analytics and Extreme Value Theory for microscopic investigation of opposing-through traffic conflicts. This investigation addresses the identification of suitable traffic conflict measures, time-varying crash risk heterogeneity, severity-based crash risk estimation, transferability, correlation between traffic conflict types, evaluation methodology for engineering treatments, and the application of crash risk estimates in real time. The outcomes of the study open avenues for integrating safety into the operational framework for signalised intersections

Estimating crash risk and injury severity considering multiple traffic conflict and crash types: A bivariate extreme value approach

Traffic conflicts are generally considered independent events in existing extreme value theory models to estimate the risk of total or single types of crashes. However, traffic events at a road entity are not necessarily independent interactions and can lead to multiple traffic conflicts with shared common unobserved factors. A comprehensive estimation of crash risks in a road entity needs to consider the correlation of potential traffic conflicts to avoid possible bias in prediction performance and the problem of undetected deficiencies. This study proposes a Bayesian non-stationary bivariate generalised extreme value modelling framework to estimate the severe and non-severe crash risks accounting for the correlation between right-turn and rear-end conflicts at signalised intersections. A deep neural network-based computer vision technique was applied to extract the traffic conflicts from 77 h of video recordings over two right-turn approaches at two signalised intersections in Cairns, Australia. Post encroachment time and modified time to collision were used to characterise right-turn and rear-end conflicts, respectively, while an expected post-collision velocity difference was combined with post encroachment time and modified time to collision for crash risk estimation by injury severity levels. Several covariates were used to address the time-varying heterogeneity of traffic conflict extremes and to estimate the differential crash risks at signal cycles. Results showed a significant correlation between right-turn and rear-end crashes at signal cycle levels, indicating the importance of accounting for the dependency among traffic conflict types. Overall, the bivariate models considering the correlation among traffic conflict types were found to understandably perform better than their univariate counterparts. This study provides a demonstration of a correlated crash risk modelling framework that addresses issues related to the suitable traffic conflict measures, time varying risks (non-stationarity), heterogeneity, and injury severity levels of different crash types.</p

Before-after safety evaluation of part-time protected right-turn signals: An extreme value theory approach by applying artificial intelligence-based video analytics

Extreme value theory models have opened doors for before-after safety evaluation of engineering treatments using traffic conflict techniques. Recent advancements in automated conflict extraction technologies have further expedited conflict-based safety evaluation as a potential alternative to traditional crash-based methods. However, the suitability of extreme value theory models in the before-after evaluation of engineering treatments needs to be rigorously tested. As such, this study proposes a traffic conflict-based before-after evaluation of a novel part-time protected right-turn signal strategy for right-turn or opposing-through crashes at signalised intersections. A part-time protected right-turn signal strategy refers to a signal arrangement where permissive and fully protected right-turn phasings are operated during peak and off-peak hours, respectively. A deep neural network-based computer vision technique was applied to extract the conflicts from a total of 654 h of video recordings (before period: 266 h and after period: 388 h) over seven treated approaches, and four matching control approaches at five signalised intersections in the city of Cairns, Australia. Using post encroachment time and post-collision velocity difference as traffic conflict measures, non-stationary bivariate generalised extreme value models were developed to estimate the severe and non-severe opposing-through crashes at signal cycle levels. The odds ratio analysis of model-predicted crash risks suggests that part-time protected right-turn signals reduce 67% and 81% of severe and non-severe opposing-through crashes at signalised intersections, respectively. Part-time protected right-turn signal strategy offers a good safety solution without precipitating need for capacity upgrades to accommodate queued right turners at signalised intersections.</p

A before-after evaluation of protected right-turn signal phasings by applying Empirical Bayes and Full Bayes approaches with heterogenous count data models

Right-turn crashes (or left-turn crashes for the US or similar countries) represent over 40% of signalized intersection crashes in Queensland, Australia. Protected right-turn phasings are a widely used countermeasure for right-turn crashes, but the research findings on their effects across different crash types and intersection types are not consistent. Methodologically, the Empirical Bayes and Full Bayes techniques are generally applied for before-after evaluations, but the inclusion of heterogeneous models within these techniques has not been considered much. Addressing these research gaps, the objective of this study is to evaluate the effectiveness of protected right-turn signal phasings at signalized intersections employing heterogeneous count data models with the Empirical Bayes and Full Bayes techniques. In particular, the Empirical Bayes approach based on random parameters Poisson-Gamma models (simulation-based Empirical Bayes), and the Full Bayes approach based on random parameters Poisson-Lognormal intervention models (simulation-based Full Bayes) are applied. A total of 69 Cross intersections (with ten treated sites) and 47 T intersections (with six treated sites) from Southeast Queensland in Australia were included in the analysis to estimate the effects of protected right-turn signal phasings on various crash types. Results show that the change of signal phasing from a permissive right-turn phasing to the protected right-turn phasing at cross and T intersections reduces about 87% and 91% of right-turn crashes, respectively. In addition, the effect of protected right-turn phasings on rear-end crashes was not significant. The heterogenous count data models significantly address extra Poisson variation, leading to efficient safety estimates in both simulation-based Empirical Bayes and simulation-based Full Bayes approaches. This study demonstrates the importance of accounting for unobserved heterogeneity for the before-after evaluation of engineering countermeasures

Before-after safety evaluation of part-time protected right-turn signals: an extreme value theory approach by applying artificial intelligence-based video analytics

Extreme value theory models have opened doors for before-after safety evaluation of engineering treatments using traffic conflict techniques. Recent advancements in automated conflict extraction technologies have further expedited conflict-based safety evaluation as a potential alternative to traditional crash-based methods. However, the suitability of extreme value theory models in the before-after evaluation of engineering treatments needs to be rigorously tested. As such, this study proposes a traffic conflict-based before-after evaluation of a novel part-time protected right-turn signal strategy for right-turn or opposing-through crashes at signalised intersections. A part-time protected right-turn signal strategy refers to a signal arrangement where permissive and fully protected right-turn phasings are operated during peak and off-peak hours, respectively. A deep neural network-based computer vision technique was applied to extract the conflicts from a total of 654 h of video recordings (before period: 266 h and after period: 388 h) over seven treated approaches, and four matching control approaches at five signalised intersections in the city of Cairns, Australia. Using post encroachment time and post-collision velocity difference as traffic conflict measures, non-stationary bivariate generalised extreme value models were developed to estimate the severe and non-severe opposing-through crashes at signal cycle levels. The odds ratio analysis of model-predicted crash risks suggests that part-time protected right-turn signals reduce 67% and 81% of severe and non-severe opposing-through crashes at signalised intersections, respectively. Part-time protected right-turn signal strategy offers a good safety solution without precipitating need for capacity upgrades to accommodate queued right turners at signalised intersections.</p

Co-exposure of chromium or cadmium and a low concentration of amoxicillin are responsible to emerge amoxicillin resistant Staphylococcus aureus

ABSTRACT: Background: Heavy metals and antimicrobials co-exist in many environmental settings. The co-exposure of heavy metals and antimicrobials can drive emergence of antimicrobial resistant (AMR) Enterobacteriaceae. We hypothesized that co-exposure to heavy metals and a low concentration of antibiotic might alter antimicrobial susceptibility patterns, which facilitate emergence of AMR Staphylococcus aureus. Methods: The growth kinetics of antimicrobial susceptible S. aureus was carried out in the presence of chromium or cadmium salt and a low concentration of antibiotics. Subsequently, the antimicrobial susceptibility pattern was determined by the Kirby-Bauer disc diffusion method. Moreover, the mRNA copy number was determined by reverse transcription polymerase chain reaction. Results: The antimicrobial susceptibility profile revealed that the zone of inhibition (ZOI) for ampicillin, amoxicillin, ciprofloxacin and doxycycline was significantly decreased in chromium pre-exposed S. aureus compared to unexposed bacteria, whereas cadmium pre-exposed bacteria only showed significant decreased in ZOI for amoxicillin. Moreover, the MIC of amoxicillin for S. aureus was increased by 8-fold in chromium and 32-fold in cadmium when bacteria were co-exposed with low concentrations of amoxicillin. The mRNA expression of femX, mepA and norA also significantly increased in S. aureus after exposure to chromium and a low concentration of amoxicillin. Conclusion: Cultivation of S. aureus at the minimum levels of chromium or cadmium and a low concentration of amoxicillin increased the inhibitory concentration of amoxicillin through inducing bacterial efflux pumps and antibiotic resistant genes. However, it is warranted to assess the whole transcriptome to find out all responsible factors behind this de novo amoxicillin resistant S. aureus