Effective control strategy for building temperature regulation plays an important role in meeting peoples’ comfort demand. In this paper, the problem of temperature control is studied for two neighbouring rooms of a building using backstepping technique. The mathematical model of the considered building has strong coupled variables, which makes room temperature control more challenging. Firstly, in order to be convenient for controller design, the model is represented in state space form. Then, an appropriate controller is constructed using backstepping technique. Design parameters are introduced in controller design procedure. By choosing suitable design parameters, indoor temperature of the considered rooms can be regulated to desired value. Finally, simulation analysis and comparison are given including both cases in summer and winter, and simulation results show the effectiveness of the proposed controller.

High-temperature aerosols generated during industrial processes pose a significant threat to the operating environment and workers’ health. In this study, the dynamic behaviour of transiently generated aerosols during welding was simulated using the validated Euler–Lagrange approach. The maximum radial diffusion radius ( R ), horizontal dispersity ( D H ) and concentration decay rate loss coefficient ( ζ ) were calculated to quantify the spatiotemporal evolution of different particle size categories. Furthermore, the effect of Brownian forces on aerosols’ dispersion and distribution was systematically investigated. The results showed that welding aerosols larger than 0.2 μm exhibited favourable airflow following and their dispersion patterns tend to be consistent. Conversely, the migration paths of welding aerosols smaller than 0.2 μm deviated significantly from the airflow due to the molecular slip effect. For the 0.05 μm welding aerosol, the strong molecular slip effect resulted in its inability to effectively follow the hot air, showing a disordered dispersion pattern centred on the release source. In addition, Brownian force has a significant enhancement effect on the diffusion capability of 0.1 μm particles, leading to an augmentation in the R from 2 to 2.5 m, and an escalation in the D H from 42.4% to 70%.

The use of liquefied petroleum gas (LPG) for cooking is confronted with the major challenge of hazardous explosions caused by leakages. Previous efforts in solving the problem of gas leakages have been focused on reinforcing the strength of gas cylinders and the implementation of gas leakage detectors to alert the users of leakages promptly through warning alarms and short message service (SMS). These solutions fall short when there is nobody around to turn off the gas cylinder even when there is a detected leakage. In this study, we identified the need to automatically and immediately turn off the gas cylinder once a gas leakage is detected as a solution towards averting the hazardous explosion of cooking gas. A mechanical switch activated by a solenoid is connected to two MQ-5 gas sensors through a microcontroller in such a manner that the switch automatically disconnects the gas cylinder supply once a leakage is detected. The device also monitors temperature; triggers alarm and sends SMS to enrolled users upon detection of a leakage. The response and recovery time of the gas detector was evaluated to validate its reliability. The developed device is economically viable in low income countries of Sub-Saharan Africa for safety.

Thermal comfort in classrooms is mostly assessed in summer/hot and humid seasons; however, thermal perceptions across different climatic conditions have to be studied to undertake effective mitigation. In this study, thermal perception and occupant adaptive characteristics in classrooms of naturally ventilated school buildings were assessed during two different climatic conditions (i.e., hot-humid and monsoon seasons) in Sri Lanka. The assessments included: (1) measurements of weather parameters and (2) thermal sensation levels of students occupying those classrooms. About 879 and 852 responses were collected and assessed according to the ISO 7730 thermal sensation scale during hot-humid and monsoon seasons, respectively. Mean indoor temperatures were 30.5°C during the hot-humid season and 27.8°C during the monsoon season, while the corresponding mean relative humidity values were 75.8% and 79.8%, respectively. Findings revealed neutral temperatures of 28.5°C and 26.1°C, with comfort ranges of 25.9°C–31.0°C and 22.6°C–29.7°C, with 80% acceptability, were desired during hot-humid and monsoon seasons, respectively. The mean comfort temperatures forecasted for hot-humid and monsoon seasons using the Griffiths’ method were 28.9°C and 26.8°C, respectively. This study helps to align the design of classrooms in similar tropical climate conditions, considering the thermal comfort of occupants.

Current indoor fire detection systems often have limitations, including insufficient intelligence and slow response times. Recent studies have started to explore AI vision-based methods for real-time fire and smoke detection to replace traditional fire sensors. However, indoor environments present many interference factors, and few methods can effectively demonstrate superior fire and smoke detection performance under multiple simulated interference conditions. To meet the requirements of real-time, high accuracy and anti-interference, a 3D convolutional neural network (3D-CNN)-based real-time fire and smoke detection model has been developed via red-green-blue (RGB) and near-infrared (NIR) feature fusion. RGB and NIR images with complementary information were used as dual-stream inputs to the model. The feature extraction module was enhanced by 3D attention mechanisms to simultaneously capture dynamic features between frames and static features within frames of fire and smoke. A non-linear feature gated fusion module has been developed to solve the challenges of missing multi-modal features and insufficient fusion. To validate the engineering effectiveness of the model, a large-scale multi-modal dataset was constructed in real indoor building scenarios, and a real-time indoor fire and smoke detection system was also developed and deployed at the experimental site.

The building sector significantly contributes to global energy consumption and carbon emissions, making retrofitting a crucial strategy to reduce these impacts. Despite the availability of various retrofit technologies, identifying the most suitable ones for specific projects poses a major technical challenge. To address this, the current study has introduced a multi-stage, multi-objective optimization approach, integrating R, Energyplus and Excel for energy/thermal simulations, sensitivity analysis, optimization iterations and financial decision-making. Objective functions include thermal comfort, thermal energy demand, global cost, primary energy consumption and carbon emission equivalent. The functionality of the proposed framework was tested using the energy model of a residential reference building in Northern Cyprus. Cost-optimal recommendations included applying external wall thermal insulation, independently or with photovoltaics and energy-efficient air-conditioners, necessitating an initial investment of up to 46.40 €/m 2 . This yields potential benefits with up to 56.10% primary energy savings and a 35.17% reduction in global cost. Another option, replacing windows independently or with external shutters, applying thermal insulation and installing photovoltaics and energy-efficient air-conditioners, requires an initial investment of up to 536.70 €/m 2 , offering potential benefits with up to 58.52% primary energy savings. However, a substantial 89.49% increase in global costs is noted due to high initial investment costs.

To investigate cognitive changes under different workloads in thermoneutral environments, experiments were carried out on 15 participants. A 30-minute n -back task was utilized to produce three workloads. Three mental scales were completed to acquire the emotion, fatigue and mental workload of subjects. Meanwhile, the skin temperature, critical flicker frequency (CFF) was acquired, and the cognitive performance index (CPI) of n -back task was calculated. Results showed that the CPI was reduced significantly as the workloads were elevated from low to high level. Besides, under the high workload, the positive emotion was surprisingly increased ( p < 0.05), and participants reported lower fatigue ( p < 0.05). Furthermore, both local and mean skin temperature were negatively correlated with workloads, although under the high workload, the skin temperature was reduced significantly. Finally, the CFF was reduced significantly after low and moderate workload, but surprisingly it rose after high workload and was greater than when under lower workloads ( p < 0.05). Results of this study revealed that the cognitive performance was not reduced under high workload in a fixed time frame, which was in accordance with changes in emotion, fatigue, perceived workload, the skin temperature and CFF, implying that the high workload might have produced a compensatory effect on cognitive attenuation in this experimental frame.

The increasing frequency and severity of heat events driven by global warming highlight the need to assess the thermal resilience of buildings under normal operating conditions. The indoor overheating degree (IOD) is a widely adopted metric for quantifying thermal performance. However, its applicability to diverse building thermal resilience assessments is limited due to a lack of relevant data. To address this, this study introduced an improved IOD model that integrates weather conditions and building parameters for the evaluation of indoor thermal performance. The model’s effectiveness was demonstrated through a case study of a residential building in Guangzhou, China. In this study, the most influential factor on the IOD was the outdoor temperature, followed by solar radiation transmittance, the heat transfer coefficient of wall and window, lastly the indoor heat dissipation values. The results showed that by reducing the heat transfer coefficients of walls and windows by 80% and the solar radiation transmittance by 66.7% have yielded IOD reductions of 12.5%, 13.5% and 30.8%, respectively. This study presents an improved IOD model for assessing building thermal resilience, leveraging readily available climatic and building parameter data. It offers a scalable framework for supporting the design of adaptive strategies.

The global drive for net-zero carbon emissions has made zero-carbon buildings an essential goal, with modular construction emerging as a key strategy to achieve this. This research examines how modular construction can contribute to zero-carbon building objectives, specifically focusing on healthcare buildings. The Intensive Therapy Unit (ITU) modular building project at St George's University Hospital serves as a case study, assessing the carbon footprint across various lifecycle stages and evaluating its performance according to Building Research Establishment Environmental Assessment Method (BREEAM) standards. The study demonstrates that modular construction reduces carbon emissions through energy-efficient designs, the use of low-carbon materials, and optimized resource utilization in the production phase. Waste reduction during manufacturing, shortened assembly time, and the use of renewable energy further contribute to minimizing operational emissions. Additionally, the modular design facilitates future reuse and re-cycling of components during deconstruction. While high initial capital costs remain a challenge, the benefits of modular construction in terms of energy efficiency, sustainable material use, waste management and ecological considerations make it a promising solution. The research concludes with practical recommendations for policymakers, urban planners, and construction professionals to promote modular construction as a pathway to near-zero carbon building development.