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To ensure the stable operation of ironmaking process and the quality and output of sinter, the multi-objective optimization of sintering machine batching process was carried out.

The purpose of this study is to establish a multi-objective optimization model with iron taste content and batch cost as targets, constrained by field process requirements and sinter quality standards, and to propose an improved balance optimizer algorithm (LILCEO) based on a lens imaging anti-learning mechanism and a population redundancy error correction mechanism. In this method, the lens imaging inverse learning strategy is introduced to initialize the population, improve the population diversity in the early iteration period, avoid falling into local optimal in the late iteration period and improve the population redundancy error correction mechanism to accelerate the convergence rate in the early iteration period.

By selecting nine standard test functions of BT series for simulation experiments, and comparing with NSGA-?, MOEAD, EO, LMOCSO, NMPSO and other mainstream optimization algorithms, the experimental results verify the superior performance of the improved algorithm. The results show that the algorithm can effectively reduce the cost of sintering ingredients while ensuring the iron taste of sinter, which is of great significance for the comprehensive utilization and quality assurance of sinter iron ore resources.

An optimization model with dual objectives of TFe content and raw material cost was developed taking into account the chemical composition and quality indicators required by the blast furnace as well as factors such as raw material inventory and cost constraints. This model was used to adjust and optimize the sintering raw material ratio. Addressing the limitations of existing optimization algorithms for sintering raw materials including low convergence accuracy slow speed limited initial solution production and difficulty in practical application we proposed the LILCEO algorithm. Comparative tests with NSGA-III MOEAD EO LMOCSO and NMPSO algorithms demonstrated the superiority of the proposed algorithm. Practical applications showed that the proposed method effectively overcomes many limitations of the current manual raw material ratio model providing scientific and stable decision-making guidance for sintering production operations.

This study aims to investigate how digital technology influences the happiness of villagers in traditional ethnic minority communities, with Waipula Village as a focal case study. Recognized as a forerunner in achieving the United Nations Sustainable Development Goals, Waipula Village exemplifies how digital innovation can transform rural communities.

Using an exploratory case study approach, the research reveals that digital technology enhances villagers’ happiness by improving market access, mitigating geographical limitations and fostering entrepreneurship and innovation.

In addition, digital technology has led to new consumption patterns and cultural values, significantly contributing to the village’s sustainable development and overall well-being.

This analysis expands the understanding of the role of digital technology in ethnic minority villages and offers valuable insights for rural revitalization strategies, highlighting its importance in enhancing happiness and preserving cultural heritage.

Accurate and rapid tracking and counting of building materials are crucial in managing on-site construction processes and evaluating their progress. Such processes are typically conducted by visual inspection, making them time-consuming and error prone. This paper aims to propose a video-based deep-learning approach to the automated detection and counting of building materials.

A framework for accurately counting building materials at indoor construction sites with low light levels was developed using state-of-the-art deep learning methods. An existing object-detection model, the You Only Look Once version 4 (YOLO v4) algorithm, was adapted to achieve rapid convergence and accurate detection of materials and site operatives. Then, DenseNet was deployed to recognise these objects. Finally, a material-counting module based on morphology operations and the Hough transform was applied to automatically count stacks of building materials.

The proposed approach was tested by counting site operatives and stacks of elevated floor tiles in video footage from a real indoor construction site. The proposed YOLO v4 object-detection system provided higher average accuracy within a shorter time than the traditional YOLO v4 approach.

The proposed framework makes it feasible to separately monitor stockpiled, installed and waste materials in low-light construction environments. The improved YOLO v4 detection method is superior to the current YOLO v4 approach and advances the existing object detection algorithm. This framework can potentially reduce the time required to track construction progress and count materials, thereby increasing the efficiency of work-in-progress evaluation. It also exhibits great potential for developing a more reliable system for monitoring construction materials and activities.

The purpose of this paper is to examine the use of phragmites australis ash (PAA) in cementitious systems to achieve sustainable construction.

In this paper, the properties of mortar containing PAA as partial cement replacement are determined. The PAA is produced through slow burning in a closed system to minimize the CO₂ emission. A total of four mortar mixes are prepared with PAA replacement levels ranging from 0% to 30% by weight. The water to binder and the proportions of binder to sand are 0.55 and 1:3 by weight, respectively. The properties tested are density, compressive strength, flexural strength, ultrasonic pulse velocity, water absorption by total immersion and capillary rise. Testing is conducted at 1, 7, 28 and 90 days.

While there is a decrease in strength as the amount of PAA increases, there is strong indication of pozzolanic reaction in the presence of PAA. This is in agreement with the results reported by Salvo et al. (2015), where they found noticeable pozzolanic activities in the presence of straw ash, which is rich in SiO₂ and relatively high K₂O content. At 90 days of curing, there is a decrease of 5% in compressive strength at 10% PAA replacement. However, at 20% and 30% replacement, the reduction in compressive strength is 23% and 32%, respectively. The trend in flexural strength and ultrasonic pulse velocity is similar to that in compressive strength. The water absorption by total immersion and capillary rise tends to increase with increasing amounts of PAA in the mix. There seems to be a linear relationship between water absorption and compressive strength at each curing age.

The Phragmites australis plant used in this investigation is obtained from one location and this present a limitation as the type of soil may change the properties. Also one method of slow burning is used. Different burning methods may alter the composition of the PAA.

This outcome of this research will contribute towards sustainable development as it will make use of the waste generated, reduce the amount of energy-intensive cement used in construction and help generate local employment in the area where the Phragmites australis plant grows.

To the best knowledge of the authors, the ash from the Phragmites australis plant has not been used in cementitious system and this research can be considered original as it examines the properties of mortar containing PAA. Also, the process of burning in a closed system using this material.

The purpose of this study is to examine the probable role of geographic information systems (GIS) in sustainable tourism, rural community-based natural resource management (CBNRM) and inclusive community development and participation in Sub-Saharan Africa, Africa naturally and many rural areas elsewhere abroad.

The study uses narrative literature and document reviews to assess African and global environmental and rural tourism resource management procedures. The data analysis was done manually from the narrative and general literature reviews of the older and latest research. It links CBNRM, GIS and conjoining tools to sustainable tourism, public leadership, subsistence and local community empowerment applications.

This examination displays a possible association between tourism and rural and agricultural enterprises that GIS, its associative procedures and tools, and the concept of CBNRM can strengthen while enhancing public leadership and sustainability and spurring livelihoods, especially in remote areas. Therefore, it underscores the need for a reputable and myriad tourism strategy to develop and empower the relevant environs in many rural and marginalized areas within the continent.

Numerous remote rural neighborhoods in Sub-Saharan Africa, southern Africa and Africa usually live in low-income areas with meager socioeconomic programs. However, such localities thrive on natural biodiversity, including tourism destination sites. Information systems and information technology, such as GIS and remote sensing, with sustainable tourism, CBNRM and inclusive public leadership can synergize local community development schemes within their settings.

Rapid industrialization and construction generate substantial concrete waste, leading to significant environmental issues. Nearly 10 billion metric tonnes of concrete waste are produced globally per year. In addition, concrete also accelerates the consumption of natural resources, leading to the depletion of these natural resources. Therefore, this study uses artificial intelligence (AI) to examine the utilization of recycled concrete aggregate (RCA) in concrete.

An extensive database of 583 data points are collected from the literature for predictive modeling. Four machine learning algorithms, namely artificial neural network (ANN), random forest (RF), ridge regression (RR) and least adjacent shrinkage and selection operator (LASSO) regression (LR), in predicting simultaneously concrete compressive and tensile strength were evaluated. The dataset contains 10 independent variables and two dependent variables. Statistical parameters, including coefficient of determination (R²), mean square error (MSE), mean absolute error (MAE) and root mean square error (RMSE), were employed to assess the accuracy of the algorithms. In addition, K-fold cross-validation was employed to validate the obtained results, and SHapley Additive exPlanations (SHAP) analysis was applied to identify the most sensitive parameters out of the 10 input parameters.

The results indicate that the RF prediction model performance is better and more satisfactory than other algorithms. Furthermore, the ANN algorithm ranks as the second most accurate algorithm. However, RR and LR exhibit poor findings with low accuracy. K-fold cross-validation was successfully applied to validate the obtained results and SHAP analysis indicates that cement content and recycled aggregate percentages are the effective input parameter. Therefore, special attention should be given to sensitive parameters to enhance the concrete performance.

This study uniquely applies AI to optimize the use of RCA in concrete production. By evaluating four machine learning algorithms, ANN, RF, RR and LR on a comprehensive dataset, this study identities the most effective predictive models for concrete compressive and tensile strength. The use of SHAP analysis to determine key input parameters and K-fold cross-validation for result validation adds to the study robustness. The findings highlight the superior performance of the RF model and provide actionable insights into enhancing concrete performance with RCA, contributing to sustainable construction practice.