Every year, the world is producing around 100 million tons of waste glass (WG), the majority of them are going to landfills that create massive environmental problems. One approach to solve this ...problem is to transform waste glass into construction materials. Glass is recyclable; however, the melting temperature of the glass is highly dependent on its colour that requires sorting before recycling. To overcome this challenge, many researchers and end-users are using broken glass in concrete either as a binder or aggregates. While significant investigations have done in this area, however, the outcomes of these studies are scattered, and difficult to reach a firm conclusion about the effectiveness of WG in concrete. In this study, the roles of WG and its impact on microstructural and durability properties for both cement and geopolymer concrete are critically reviewed. This review reveals that the amorphous silica in WG effectively participate to the hydration and geopolymerization process and improve concrete microstructural properties. This behaviour of WG help to produce durable concrete against shrinkage, chemical attack, freeze-thaw action, electrical and thermal insulation properties. The optimum replacement volume of binders or natural aggregates and particle size of WG need to be selected carefully to minimise the possible alkali-silica reaction. This review discusses a wide range of parameters for durability properties and challenges associated with WG concrete, which provides necessary guidelines for best practice with future research directions.
The remote sensing hyperspectral images (HSIs) usually comprise many important information of the land covers capturing through a set of hundreds of narrow and contiguous spectral wavelength bands. ...Appropriate classification performance can only offer the required knowledge from these immense bands of HSI since the classification result is not reasonable using all the original features (bands) of the HSI. Although it is not easy to calculate the intrinsic features from the bands, band (dimensionality) reduction techniques through feature extraction and feature selection are usually applied to increase the classification result and to fix the curse of dimensionality problem. Though the Principal Component Analysis (PCA) has been commonly adopted for the feature reduction of HSI, it can often fail to extract the local useful characteristics of the HSI for effective classification as it considers the global statistics of the HSI. Consequently, Segmented-PCA (SPCA), Spectrally-Segmented-PCA (SSPCA), Folded-PCA (FPCA) and Superpixelwise PCA (SuperPCA) have been introduced for better feature extraction of HSI in diverse ways. In this paper, feature extraction through SPCA & FPCA and SSPCA & FPCA, termed as Segmented-FPCA (SFPCA) and Spectrally-Segmented-FPCA (SSFPCA) respectively, has further been improved through applying FPCA on the highly correlated or spectrally separated bands' segments of the HSI rather than not applying the FPCA on the entire dataset directly. The proposed methods are compared and analysed for a real mixed agricultural and an urban HSI classification using per-pixel SVM classifier. The experimental result shows that the classification performance using SSFPCA and SFPCA outperforms that of using conventional PCA, SPCA, SSPCA, FPCA, SuperPCA and using the entire original dataset without employing any feature reduction. Moreover, the proposed feature extraction methods provide the least memory and computation cost complexity.
•Review of the recent progress on the uses of waste tire in concrete.•Rubber can be used in concrete as replacement of aggregates, binders, and fibers.•Rubberized concrete leads to satisfactory ...mechanical and durability performances.•Finer rubber aggregates showing better performance than coarser ones.
Accumulation of waste is subsequently increased to hazardous levels. Tire waste is one of them that cause serious environmental issues because of the rapid rise in and numerous variations of modern developments worldwide. Thus, recycling waste tire rubber in the form of aggregates as supplementary construction material is advantageous. This paper reviews the source of waste tire rubbers and rubberized cementitious composites along with their material properties, usages, durability, and serviceability performances. This study also aims to provide a fundamental insight into the integrated applications of rubberized concrete (RuC) composite materials to improve construction methods, including applications to enhance environmental sustainability of concrete structures in the construction industry. Inclusion of recycled rubber aggregate (RA) lightens concrete, increases its fatigue life and toughness, advances its dynamic properties, and improves its ductility. Concrete with recycled RA performs well in hot and cold weather and achieved significant results under critical exposure and various loading conditions. Though RuC possesses low mechanical strength in general, specific treatment and additives inclusion can be a good solution to improve those properties reliably. Investigations of RuC as materials are available significantly, but researches on the structural members of RuC should be enriched.
Hyperspectral image (HSI) usually holds information of land cover classes as a set of many contiguous narrow spectral wavelength bands. For its efficient thematic mapping or classification, band ...(feature) reduction strategies through Feature Extraction (FE) and/or Feature Selection (FS) methods for finding the intrinsic bands' information are typically applied. Principal Component Analysis (PCA) is a frequently employed unsupervised linear FE method whereas cumulative-variance accumulation is used for selecting top features from PCA data. However, PCA can fail to extract intrinsic structure of HSI due to global variance consideration and domination by visible and near infrared bands while cumulative-variance accumulation has no capability to exploit non-linear relationships among the transformed features produced by PCA-based FE methods. Consequently, we propose an information theoretic normalized Mutual Information (nMI)-based minimum Redundancy Maximum Relevance (mRMR) non-linear measure to select the intrinsic features from the transformed space of our previously proposed Segmented-Folded-PCA (Seg-Fol-PCA) and Spectrally Segmented-Folded-PCA (SSeg-Fol-PCA) FE methods. We extensively analyse the effectiveness of the proposed unsupervised FE and supervised FS combinations Seg-Fol-PCA-mRMR and SSeg-Fol-PCA-mRMR with that of PCA-based existing linear and non-linear state-of-the-art methods. In addition, cumulative variance-based top features pick-up strategy is considered with all FE methods and Renyi quadratic entropy-based FS is used with Kernel Entropy Component Analysis (Ker-ECA). The experimental results illustrate that SSeg-Fol-PCA-mRMR and Seg-Fol-PCA-mRMR obtain highest classification result e.g. 95.39% and 95.03% respectively for agricultural Indian Pines HSI, and 96.58% and 95.30% respectively for urban Washington DC Mall HSI while the classification accuracies using all original features of the HSIs are 70.28% and 91.90% respectively. Moreover, the proposed SSeg-Fol-PCA-mRMR and Seg-Fol-PCA-mRMR outperform all investigated combinations of FE and FS using the real HSI datasets.
The hyperspectral remote sensing images (HSIs) are acquired to encompass the essential information of land objects through contiguous narrow spectral wavelength bands. The classification accuracy is ...not often satisfactory in a cost-effective way using the entire original HSI for practical applications. To enhance the classification result of HSIs the band reduction strategies are applied which can be divided into feature extraction and feature selection methods. PCA (Principal Component Analysis), a linear unsupervised statistical transformation, is frequently adopted for the extraction of features from HSIs. In this paper, PCA and SPCA (Segmented-PCA), SSPCA (Spectrally Segmented-PCA), FPCA (Folded-PCA) and MNF (Minimum Noise Fraction) as linear variants of PCA together with KPCA (Kernel-PCA) and KECA (kernel Entropy Component Analysis) as nonlinear variants of PCA have been investigated. The top transformed features were picked out using accumulation of variance for all other feature extraction methods except for MNF and KECA. MNF uses SNR (Signal-to-Noise Ratio) values and KECA employs Renyi quadratic entropy measurement for this purpose. The studied approaches are equated and analyzed for Indian Pine agricultural and urban Washington DC Mall HSI classification using SVM (Support Vector Machine) classifier. The experiment illustrates that the costly effective and improved classification performance of the feature extraction approaches over the performance using the entire original dataset. MNF offers the highest classification accuracy and FPCA offers the least space and time complexity with satisfactory classification result.
In the present era of the Internet of Things, wearable sensors have been receiving considerable attention owing to their great potential in a plethora of applications. Highly sensitive chemical type ...wearable sensors that can conformably adhere to the epidermis or textiles for monitoring personal microenvironment have gained incredible interest. Attributable to the large surface area and excellent mechanical, chemical, physical, thermal as well as biocompatible properties, nanomaterials have become a prominent building block to develop wearable sensors. In this review, recent progress in the development of nanomaterial enabled wearable chemical environmental sensors (WCESs) is presented by focusing on the chemistry‐based transduction principles. The developments in sensor structures, selection of materials, and fabrication methods are highlighted. The recent WCESs are summarized by grouping in three major types according to their transduction principles: electrical, photochemical, and electrochemical. In addition, sensors with multimodal sensing capability as well as sensors immobilized in wireless tags are summarized. Finally, issues, challenges, and future perspectives are discussed to develop next‐generation WCESs with long life, biocompatibility, self‐healing, and real‐time communication capabilities.
Owing to the outstanding chemical, mechanical, as well as thermal characteristics and large surface areas, wearable sensors comprising nanomaterial‐based substrates, electrodes, and active layers have emerged as a leading solution for the monitoring of one's ambient microenvironment. Several chemistry‐based transduction methods along with advanced fabrication techniques have the capability to produce environmental sensors with excellent sensitivity, selectivity, and responsivity.
Background:
Students are one of the most vulnerable groups to suicide. Before the COVID-19 pandemic, a Bangladeshi study was conducted assessing their suicide patterns regarding gender-based ...associations. But how has the pandemic changed the Bangladeshi students’ suicide patterns is not studied yet, which is investigated herein. Besides, for the first time, this study provides GIS-based distribution of suicide cases across the country’s administrative district.
Methods:
As Bangladesh has no suicide surveillance system, this study utilized media reporting suicide cases following the prior studies. A total of 127 students’ suicide cases from March 2020 to March 2021 were finally analyzed after eliminating the duplicate ones, and data were synthesized following the prior studies. Arc-GIS was also used to distribute the suicide cases across the administrative district.
Results:
Results revealed that female (72.4%; n = 92/127) was more prone to die by suicide than males. About 42.5% of the cases were aged between 14 and 18 years (mean age 16.44 ± 3.512 years). The most common method of suicide was hanging (79.5%; n = 101), whereas relationship complexities (15.7%), being emotional (12.6%), not getting the desired one (11%), conflict with a family member (9.4%), academic failure (9.4%), mental health problem (8.7%), sexual complexities (6.3%), scolded or forbidden by parents (3.9%) were the prominent suicide causalities. In respect to gender and suicide patterns, only the suicide stressor was significantly distributed, whereas the method of suicide was significantly associated with GIS-based distribution. However, a higher number of suicide cases was documented in the capital (i.e. Dhaka) and the northern region than in its surrounding districts.
Conclusions:
The findings reported herein are assumed to be helpful to identify the gender-based suicide patterns and suicide-prone regions in the time of the COVID-19 pandemic to initiate suicide prevention programs of the risky students.
•Identified the common mode of failures for FRP strengthen RC structures.•Accumulated theoretical models for different failures.•Suggestions provided to increase the acceptance of FRPs in ...strengthening RC structures.•Identified the research gaps for future investigations.
Structures are often subjected to extreme loading conditions that lead to their premature deterioration, and replacement of those structures before the end of their design lives is very expensive. The rehabilitation of deteriorated structures by using externally bonded fibre-reinforced polymer (FRP) composites is gaining popularity in the construction sector owing to its high strength, optimum durability and compatibility with concrete structures during application. This paper aims to review the current state-of-the-art on the performances, challenges and future opportunities of FRP-strengthened reinforced concrete (RC) structures under different loading scenarios. FRP strengthening leads to satisfactory performances under static, dynamic and extreme environmental conditions. Debonding and FRP rupture are the common types of failure observed, however, the failure mechanisms operating under the combined action of service loads and environmental exposures are still unclear. The acceptance and application of FRPs in strengthening RC structures will further increase upon developing techniques for utilising the full FRP strength, reducing the brittleness, risk of fires and accidental damage, minimising the energy consumption as well as carbon emission during production, and reducing the high initial cost. This paper also identifies the gaps in the present state of knowledge and the potential research directions for FRP-strengthened structures that lead to better understanding and establishment of design guidelines.
Lipid droplets (LDs) are storage organelles for neutral lipids which are critical for lipid homeostasis. Current knowledge of fungal LD biogenesis is largely limited to budding yeast, while LD ...regulation in multinucleated filamentous fungi which exhibit considerable metabolic activity remains unexplored. In this study, 19 LD-associated proteins were identified in the multinucleated species Aspergillus oryzae using a colocalization screening of a previously established enhanced green fluorescent protein (EGFP) fusion library. Functional screening identified 12 lipid droplet-regulating (LDR) proteins whose loss of function resulted in irregular LD biogenesis, particularly in terms of LD number and size. Bioinformatics analysis, targeted mutagenesis, and microscopy revealed four LDR proteins that localize to LD via the putative amphipathic helices (AHs). Further analysis revealed that LdrA with an Opi1 domain is essential for cytoplasmic and nuclear LD biogenesis involving a novel AH. Phylogenetic analysis demonstrated that the patterns of gene evolution were predominantly based on gene duplication. Our study identified a set of novel proteins involved in the regulation of LD biogenesis, providing unique molecular and evolutionary insights into fungal lipid storage.
Bangladesh positioned as third rice producing country in the world. In Bangladesh, regional growth and trend in rice production determinants, disparities and similarities of rice production ...environments are highly desirable. In this study, the secondary time series data of area, production, and yield of rice from 1969-70 to 2019-20 were used to investigate the growth and trend by periodic, regional, seasonal and total basis. Quality checking, trend fitting, and classification analysis were performed by the Durbin-Watson test, Exponential growth model, Cochrane-Orcutt iteration method and clustering method. The production contribution to the national rice production of Boro rice is increasing at 0.97% per year, where Aus and Aman season production contribution significantly decreased by 0.48% and 0.49% per year. Among the regions, Mymensingh, Rangpur, Bogura, Jashore, Rajshahi, and Chattogram contributed the most i.e., 13.9%, 9.8%, 8.6%, 8.6%, 8.2%, and 8.0%, respectively. Nationally, the area of Aus and Aman had a decreasing trend with a -3.63% and -0.16% per year, respectively. But, in the recent period (Period III) increasing trend was observed in the most regions. The Boro cultivation area is increasing with a rate of 3.57% per year during 1984-85 to 2019-20. High yielding variety adoption rate has increased over the period and in recent years it has found 72% for Aus, 73.5% for Aman, and 98.4% for Boro season. As a result, the yield of the Aus, Aman, and Boro seasons has been found increasing growth for most of the regions. We have identified different cluster regions in different seasons, indicating high dissimilarities among the rice production regions in Bangladesh. The region-wise actionable plan should be taken to rapidly adopt new varieties, management technologies and extension activities in lower contributor regions to improve productivity. Cluster-wise, policy strategies should be implemented for top and less contributor regions to ensure rice security of Bangladesh.
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