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The aim is to holistically assess the environmental performance of windows and analyse how their design and characteristics contribute to the overall performance of the building/space. This study focuses on the performance of windows in patient rooms hosting less mobile people.

This study investigates the life cycle environmental impacts of different glazing types, window frames and fire safety doors at the product level. This article also presents a building-integrated environmental analysis of patient rooms that considers the multiple functionalities of windows by incorporating dynamic energy analysis, comfort and daylighting performance with a life cycle assessment (LCA) study.

The results indicate that the amount of flat glass is the main contributor to the environmental impacts of the glazing units. As for the patient rooms, global warming shows the most significant contribution to the environmental costs, followed by human toxicity, particulate matter formation and eutrophication. The key drivers for these impacts are production processes and operational energy use. This study highlights the significance of evaluating a wide range of criteria for assessing the performance of windows.

An integrated assessment approach is used to investigate the influence of windows on environmental performance by considering the link between window/design parameters and their effects on energy use/costs, daylighting, comfort and environmental impacts. The embodied impacts of different building elements and the influence of various design parameters on environmental performance are assessed and compared. The environmental costs are expressed as an external environmental cost (euro).

The paper aims to investigate the comfort-related performances of an innovative solar shading solution based on a new composite patented material that consists of a cement-based matrix coupled with a stretchable three-dimensional textile. The paper’s aim is, through a performance-based generative design approach, to develop a high-performance static shading system able to guarantee adequate daylit spaces, a connection with the outdoors and a glare-free environment in the view of a holistic and occupant-centric daylight assessment.

The paper describes the design and simulation process of a complex static shading system for digital manufacturing purposes. Initially, the optical material properties were characterized to calibrate radiance-based simulations. The developed models were then implemented in a multi-objective genetic optimization algorithm to improve the shading geometries, and their performance was assessed and compared with traditional external louvres and overhangs.

The system developed demonstrates, for a reference office space located in Milan (Italy), the potential of increasing useful daylight illuminance by 35% with a reduced glare of up to 70%–80% while providing better uniformity and connection with the outdoors as a result of a topological optimization of the shape and position of the openings.

The paper presents the innovative nature of a new composite material that, coupled with the proposed performance-based optimization process, enables the fabrication of optimized shading/cladding surfaces with complex geometries whose formability does not require ad hoc formworks, making the process fast and economic.

This paper aims to give an oversight of what is being done by researchers in GIS and remote sensing (field) to explore minerals. The main objective of this review is to explore how GIS and remote sensing have been beneficial in identifying mineral deposits for easier and cost-effective mining.

The approach of this research used Web of Science to generate a database of published articles on the application of GIS and remote sensing techniques for mineral exploration. The literature was further digested, noting the main findings, adopted method, illustration and research scales.

When applied alone, each technique seems effective, but it is important to know that combining different methods is more effective in identifying ore deposits.

This paper also examined and provided possible solutions to both current and future perspective issues relating to the application of GIS and remote sensing to mineral exploration. The authors believe that the conclusions and recommendations drawn from case studies and literature review will be of great importance to geoscientists and policymakers.

This study aims to determine shoreline change statistics and net erosion and accretion, along the Kuakata Coast, a magnificent sea beach on Bangladesh’s southernmost point.

The research follows a three stages way to achieve the target. First, this study has used the geographic information system (GIS) and remote sensing (RS) to detect the temporal observation of shoreline change from the year 1991 to 2021 through satellite data. Then, the digital shoreline analysis system (DSAS) has also been explored. What is more, a prediction has been done for 2041 on shoreline shifting scenario. The shoreline displacement measurement was primarily separated into three analytical zones. Several statistical parameters, including Net Shoreline Movement (NSM), Shoreline Change Envelope (SCE), End Point Rate (EPR) and Linear Regression Rate (LRR) were calculated in the DSAS to quantify the rates of coastline movement with regard to erosion and deposition.

EPR and LRR techniques revealed that the coastline is undergoing a shift of landward (erosion) by a median rate of 3.15 m/yr and 3.17 m/yr, respectively, from 1991 to 2021, 2.85 km² of land was lost. Naval and climatic influences are the key reasons for this variation. This study identifies the locations of a significantly eroded zone in Kuakata from 1991 to 2021. It highlights the places that require special consideration while creating a zoning plan or other structural design.

This research demonstrates the spatio-temporal pattern of the shoreline location of the Kuakata beach, which would be advantageous for the region’s shore management and planning due to the impacts on the fishing industry, recreation and resource extraction. Moreover, the present research will be supportive of shoreline vulnerability. Hence, this study will suggest to the local coastal managers and decision-makers for particularizing the coastal management plans in Kuakata coast zone.

The purpose of this study is rangeland biomass estimation and its spatial–temporal dynamics. Remote sensing has been a significant method for estimating biomass in recent years. The connection between vegetation index and field biomass will be used to assign probabilities, but in some cases, it does not provide acceptable results because of soil background and geographical and temporal variability.

In this study, the normalized difference red-edge (NDRE) index was used to calculate the rangeland biomass in comparison to five vegetation indices. Field measurements of biomass of natural rangeland in the West of Iran were taken in 2015, 2018 and 2021, and SENTINEL-2 data were used for analysis.

The results indicated that the overall advantage of NDRE stems from the fact that it adjusts for changes in leaf water content while overcoming the detrimental effects of soil substrate heterogeneity, both of these factors have a significant impact on pasture biomass. These results suggest that an NDRE-based biomass estimation model might be useful for estimating and monitoring biomass in large rangelands with significant geographical and temporal variability.

Identifying the best vegetation index to establish a vegetation-based biomass regression model for rangelands in large areas with different climatic conditions, plant compositions and soil types is the overall aim of this study.

The purpose of this study is to develop a set of parameters universally acceptable for assessing design and construction strategies for reducing operational energy usage and its associated greenhouse gas (GHG) emission. Also, the parameters are intended to estimate the quantity of energy and its associated GHG emission reduction over the assessment period.

This study used five steps framework comprising definition of purpose, selecting the candidate parameters, criteria selection and description, selecting proposed parameters and defining the proposed parameters. The criteria used were the parameter’s prevalence, measurability, preference and feasibility toward adaptability to the relevant stakeholders.

This study consolidated 11 parameters. Seven cover designs and construction strategies comprising energy monitoring, natural lighting and ventilation design. Others are building thermal performance, efficient equipments, renewable energy and energy policy. The remaining four consider operational energy consumption, GHG emission quantification and their reduction over time.

Providing suitable indicators for assessing direct and indirect GHG emission with easily accessible data is essential for assessing built environment. The consolidated parameters can be used in developing rating systems, monitoring GHG inventories and activities of building related industries.

This study was conducted at the CEIES UTHM and used 11 existing rating systems open for research purposes, International Panel for Climate Change reports and GHG protocol report and guides and several other standards.

Images taken from construction site interiors often suffer from low illumination and poor natural colors, which restrict their application for high-level site management purposes. The state-of-the-art low-light image enhancement method provides promising image enhancement results. However, they generally require a longer execution time to complete the enhancement. This study aims to develop a refined image enhancement approach to improve execution efficiency and performance accuracy.

To develop the refined illumination enhancement algorithm named enhanced illumination quality (EIQ), a quadratic expression was first added to the initial illumination map. Subsequently, an adjusted weight matrix was added to improve the smoothness of the illumination map. A coordinated descent optimization algorithm was then applied to minimize the processing time. Gamma correction was also applied to further enhance the illumination map. Finally, a frame comparing and averaging method was used to identify interior site progress.

The proposed refined approach took around 4.36–4.52 s to achieve the expected results while outperforming the current low-light image enhancement method. EIQ demonstrated a lower lightness-order error and provided higher object resolution in enhanced images. EIQ also has a higher structural similarity index and peak-signal-to-noise ratio, which indicated better image reconstruction performance.

The proposed approach provides an alternative to shorten the execution time, improve equalization of the illumination map and provide a better image reconstruction. The approach could be applied to low-light video enhancement tasks and other dark or poor jobsite images for object detection processes.

The textile sector is moving towards new technologies, where the application of nanotechnology is offering fabrics with multifunctional properties making fabric odourless, hydrophobic, durable and self-cleaning. This aim of this research is to investigate self-cleaning ability of denim fabric with the application of zinc oxide nanoparticles (ZnO NPs) synthesized naturally. The primary focus of this investigation is achieving sustainability mark through green synthesis of ZnO NPs.

In this analysis, ZnO NPs being one of the metal oxides exhibiting self-cleaning, UV-protective and anti-microbial properties were synthesized naturally using Azadirachta Indica leaves. The prepared NPs were characterized by using X-ray diffraction and scanning electron microscopy analyses confirming their size and crystalline structure. Different formulations were investigated with varying concentration of zinc oxide and auxiliaries onto the denim fabric using pad-dry-cure application technique.

XRD analysis confirmed the successful green synthesis of ZnO NPs. SEM analysis revealed the homogeneous and hexagonal wurtzite NPs deposition on the denim fabric. It was ascertained that with 5% ZnO NPs and 7% Binder concentrations, the formulation resulted in a smooth and even layer on the denim fabric maintaining the appearance and feel at the same time offers appreciable grading (Grade 4) against the stringent stains of Ketchup, Coffee, Grape and Orange Juice with insignificant change in tensile strength.

In this study, self-cleaning attributes of denim fabric with zinc oxide nano formulations of different composition was studied to achieve promising functional properties in a single step not studied earlier.

In order to explore a new estimation approach of hyperspectral estimation, this paper aims to establish a hyperspectral estimation model of soil organic matter content with the principal gradient grey information based on the grey information theory.

Firstly, the estimation factors are selected by transforming the spectral data. The eigenvalue matrix of the modelling samples is converted into grey information matrix by using the method of increasing information and taking large, and the principal gradient grey information of modelling samples is calculated by using the method of pro-information interpolation and straight-line interpolation, respectively, and the hyperspectral estimation model of soil organic matter content is established. Then, the positive and inverse grey relational degree are used to identify the principal gradient information quantity of the test samples corresponding to the known patterns, and the cubic polynomial method is used to optimize the principal gradient information quantity for improving estimation accuracy. Finally, the established model is used to estimate the soil organic matter content of Zhangqiu and Jiyang District of Jinan City, Shandong Province.

The results show that the model has the higher estimation accuracy, among the average relative error of 23 test samples is 5.7524%, and the determination coefficient is 0.9002. Compared with the commonly used methods such as multiple linear regression, support vector machine and BP neural network, the hyperspectral estimation accuracy of soil organic matter content is significantly improved. The application example shows that the estimation model proposed in this paper is feasible and effective.

The estimation model in this paper not only fully excavates and utilizes the internal grey information of known samples with “insufficient and incomplete information”, but also effectively overcomes the randomness and grey uncertainty in the spectral estimation. The research results not only enrich the grey system theory and methods, but also provide a new approach for hyperspectral estimation of soil properties such as soil organic matter content, water content and so on.

The paper succeeds in realizing both a new hyperspectral estimation model of soil organic matter content based on the principal gradient grey information and effectively dealing with the randomness and grey uncertainty in spectral estimation.

This paper aims to determine the most practically applicable color-difference formula for yarn-dyed fabrics woven from warp and weft yarns in different color depths and to establish color-difference tolerance for perceptibility by evaluating yarn-dyed fabrics visually and instrumentally.

A total of 108 sample pairs were evaluated by a panel of 13 observers with perceptibility method under three typical light sources (A, D65 and cool white fluorescent). The data sets were statistically analyzed by the homogeneity of variance test (F-test), analysis of variance, standardized residual sum of squares and performance factor/3.

Light sources had a slight influence on the visual assessments of yarn-dyed fabrics. Among the eight color-difference formulae for measurements of yarn-dyed fabrics, CIEDE2000(2:1:1) outperformed all other tested formulae, and the color tolerance for the perceptibility of CIEDE2000(2:1:1) was 0.62. When the homochromy index (K) of warp and weft yarns of yarn-dyed fabric was lower than 1.25, the color difference based on ΔE*00(2:1:1) between the two samples was acceptable in terms of the color tolerance for perceptibility (i.e. 0.62).

The warp and weft yarns in different color depths could be woven in fabric with a relatively uniform color appearance.

This study could contribute to cost savings by reusing disqualified dyed yarns during the weaving manufacturing process.