Background: With the fast expansion of the social economy and construction industry, environmental pollution, ecological deterioration, and other challenges are becoming more and more serious. The construction industry is a significant contributor to environmental pollution, generating 40% of total solid waste in developed countries and accounting for 30% of global greenhouse gas emissions. Eco-friendly measures must be adopted to design and create environmentally responsible buildings that consume fewer resources throughout their existence. Green construction technologies may be used as an effective technique for implementing sustainability in the construction sector. This growing emphasis on carbon reduction may explain the heightened public awareness and interest in the development of low-carbon buildings. Objectives: The objective of this study is to investigate the benefits and application of green and low-carbon technology in developing countries such as China (Chongqing), where construction activities are primarily performed by Chinese companies and Chinese stakeholders. Our primary target is to find sustainable factors that encourages public to employ green and low-carbon building technologies. Considering above facts, the proposed study attempts to provide answers to the following questions: which enablers and challenges contribute to the adoption of green and low-carbon building technology; how to assess sustainable green and low-carbon building technology enablers under data uncertainty; how to find an interdependent relationship among the enablers. Methods: A systematic literature review (SLR) offers an overview of the research's scope. In total twenty-eight drivers and fifteen challenges of green and low-carbon building technology and sustainable construction were reported from 91 selected articles, and research questions were assessed and evaluated. Following that, primary data was collected through a two-step process involving a questionnaire survey (analysed using statistical software,) and factor analysis. The sample was collected from registered building companies listed in China's Ministry of Housing (Chongqing). The company's sample size in this study was 100 construction companies out of 400 chosen and 300 contacted persons, consists of professionals who are involve Engineers, truction as stakeholder, Project Managers, Architects, Civil Engineers and local literate people in these companies. Carbon emission values for each category of construction machinery were calculated using a standardized emission factor approach. Results: In total, 27 key enablers were identified through literature review and added to questionnaire to monitor the public response. The highest level of agreement, 40.96%, was recorded for "Optimization in energy and construction materials" (E18), indicating strong support for the incorporation and optimization of energy in construction materials. E18 reflects the most appropriate, sustainable, affordable, and trendy option to achieve green construction that offers little or no repercussions making it most voted enabler. Following E18, the factors representing the most valued enablers are "Enhance return on investment" (E25), "Improve safety and health" (E19), and "Environmentally friendly" (E17), with percentages of 39.36%, 39.10%, and 39.89%, respectively. The major challenges were mentioned as "affordability" and "lack of public demand" for green and smart buildings that produced same value of 38.30% highly agreed response. "Lack of knowledge and understanding" of green and low-carbon construction practices also marked as significant challenge with voted value 36.17% strongly agreed response, expressed a relatively low degree of disagreement. Mixed opinions were expressed regarding the "lack of client awareness". The most significant is Tire loader from Shovel and horizontal transport machinery carbon emitting 245.60 Kg/work, Self-raising tower crane from Hoisting and vertical lifting machinery emitting 201.32 Kg/work and grader from Compaction and pavement equipment with total emission 142.75 Kg/work. There is a scarcity of data on the carbon emissions of nonroad construction equipment and the relevant carbon emissions factors of the equipment. Conclusion: Government support, including building regulations, planning policies, and financial incentives such as subsidies and tax breaks was found to be a primary enabler, while limited stakeholder awareness and low market demand were major barriers. The study contributes new scientific knowledge by revealing nuanced interactions between enablers and challenges that previous research had not fully explored, particularly in the context of urban construction landscape of the study area.
Artificial intelligence driven educational technologies have rapidly transformed multiple domains, particularly primary education and language learning, where interactive systems such as augmented reality platforms and conversational agents enhance personalized instruction and learner engagement. These technologies enable adaptive feedback, real-time interaction tracking, and multimodal content delivery, creating new opportunities for improving English vocabulary acquisition. However, despite rapid technological achievements, structured integration of multi-source learning datasets remains limited, restricting comprehensive analysis of cognitive and behavioural learning outcomes. To solve this problem, this paper proposes a comprehensive dataset collation and harmonization framework that integrates three publicly available datasets: AR-based English vocabulary learning, student learning interaction logs, and chatbot-based English learning data. The methodology includes four stages. First, heterogeneous datasets are standardized through unified student identifiers, session mapping, and timestamp normalization. Secondly, vocabulary accuracy and response variables are cleaned and aligned across sources. Thirdly, engagement-related metrics such as time-on-task, dialogue turns, and interaction frequency are computed to capture behavioural dimensions. Finally, derived indicators, including accuracy rate, average response latency, and engagement index, are generated to enable multivariate statistical modelling. Experimental results indicate that the integrated dataset improves feature completeness by over 35
The isotope labeling method was adopted to reveal the mechanism by which exogenous xylose inhibited pyrazine compounds generation during thermal processing of Thr-ARP in this study. The [13C5]-D-xylose/Thr-ARP model reaction system was heated, the reaction products under 120 °C and an initial pH 7.5 were tracked and determined by UPLC-MS, the ion fragmentation mechanism indicated [13C5]-Thr-ARP was formed. It was proposed [13C5]-D-xylose trapped threonine regenerated from Thr-ARP and then underwent Amadori rearrangement to form [13C5]-Thr-ARP. Thr-ARP's regeneration delayed the release of regenerated threonine and partially hindered Strecker degradation to generate pyrazine compounds. In addition, the self-cleavage of exogenous xylose caused an increase in the concentrations of 1-deoxyxylosone and 3-deoxyxylosone. The organic acids generated from the cleavage of deoxyxylosones led to an accelerated decrease in pH, reducing regenerated threonine's nucleophilicity. The synergistic effect of the above two factors led to a reduction in the concentration and variety of pyrazine compounds produced by the thermal degradation of Thr-ARP.
The mechanical properties of fruit tissue play a crucial role in various food processing activities, including sorting, packaging, and transportation. Most traditional testing processes are destructive, time-consuming, and cannot capture the mechanical response under loading conditions. The present research proposes a parameter identification model that combines experimental analysis, finite element analysis, and a hybrid machine learning model to describe the viscoelastic behaviour of pear tissue. To obtain a rapid mapping from the intrinsic material constants to mechanical behavior, we develop a feasible surrogate architecture. This framework incorporates convolutional layers to extract spatial features, along with an attention-based bidirectional LSTM, which ensures a high-fidelity approximation of mechanical behavior. An enhanced genetic algorithm is suggested as a solution to the identification problem. The framework is tested through compression tests on Huangguan pears. The results indicate that the mean absolute percentage error of the surrogate model can reach a minimum of 0.05, and the force-displacement plots show an error of less than 0.08 compared to experimental data. The suggested technique minimizes the need for extensive physical testing while preserving predictive accuracy, providing a computationally efficient and experimentally economical solution for determining the material parameters of fruit tissues. It has the potential to assist in designing and optimizing food protection equipment for transporting fresh fruits, thereby reducing storage losses.
The management of municipal solid waste (MSW) in urban areas of developing nations poses significant challenges due to high waste generation. This study provided a comprehensive assessment of MSW generation, management practices, and their health impacts across nine districts in the Malakand division of Khyber Pakhtunkhwa, Pakistan, focusing on the composition and key components of MSW, such as plastic waste, household waste, and animal waste. A survey covering 4,431 households, 150 shops, 50 schools, and 30 hotels revealed notable district-level differences in waste composition and disposal methods. The results revealed that Upper Chitral generated the highest plastic waste, producing 420 kg/day of polythene bag waste and 270 kg/day of hard and soft plastic waste. Waste management varied across districts, with 95% of respondents in Buner selling plastic waste to collectors, compared to 93% in Malakand and 91% in Bajaur. Household waste was predominantly repurposed as animal feed, with Buner and Bajaur districts leading at 97% and 96%, respectively, while Shangla and Upper Chitral reported lower rates of 91% and 89%. Buner also recorded the highest animal waste generation at 575 kg/day. A correlation analysis indicated a significant relationship between plastic waste production and its use for heating and construction. The study emphasized the need for improved waste management strategies, including comprehensive policies, training for waste workers, and enhanced community participation in waste reduction initiatives.
