Environmental factors are essential to effective mushroom cultivation, as controlled moisture, humidity, and temperature directly affect yield, quality, and growth. This study introduces the design and development of an Internet of Things (IoT)-based electronic management system for optimizing mushroom farming environments through real-time monitoring and automated control. The system continuously monitors temperature, soil moisture, and humidity, sending sensor data to the ThingSpeak platform to generate a visual representation for remote access and analysis, ensuring stable and optimal conditions. Automated sprinklers efficiently maintain humidity and moisture levels, minimizing water loss and distributing moisture evenly across the growing chamber. A prototype was developed to overcome the limitations of conventional mushroom production systems, which are often hindered by changing environmental conditions. Over a two-week evaluation, the controlled environment produced a significantly higher yield and cap diameter (mean = 352.5g and 17-20cm) compared to the non-controlled setup (136g and 7-14cm), with growth occurring 64% earlier. The post-evaluation survey indicated a weighted mean rating of 4.8 for functionality and 4.6 for effectiveness, with most aspects rated as "Very Highly Functional" and "Very Highly Effective". The results demonstrate that controlling uniform microclimate parameters leads to improved mushroom yield, quality, and labor reduction, making commercial mushroom cultivation more sustainable and profitable.

This paper presents a new mathematical feedback model to demonstrate how direct observations of the epidemiological compartments of population could be mapped to inputs, such that the social spread of the disease is asymptotically subdued. Details of the stabilization and robustness are included. This is a pivotal restructuring of modelling the control of corona virus from the current models in use world-wide which do not utilize feedback of functions of epidemiological compartments of population to construct the inputs. Although several vaccines have received Emergency Use Authorization (EUA) massive vaccination would take several years to reach herd immunity in most countries. Furthermore, the period of efficacy of the vaccination may be approximately one year only resulting in an unending vaccination. Even during the vaccination, there would be an urgent need to control the spread of the virus. When herd immunity is reached without feedback control and vaccination is discontinued, there could be new surges of the disease. These surges of disease could be prevented in appropriately designed stable feedback models. Moreover, extensive testing, contact tracing, and medical treatment of those found infected, must be maintained.

This paper presents a high-performance embedded micro-sensing and processing integrated non-intrusive load monitoring system for real-time appliance health assessment and remaining useful life (RUL) prediction, with the core innovation focused on the design and implementation of an STM32H7-based embedded micro-system that realizes seamless integration of high-frequency signal acquisition, edge-side real-time preprocessing, and intelligent multi-task analysis on a single chip. Utilizing an STM32H7 microcontroller, it captures high-frequency voltage and current signals to detect appliance degradation features. Time-frequency preprocessing with FFT and STFT improves signal representation, enabling better feature extraction. The proposed DRN-Transformer model integrates deep residual networks and Transformer attention mechanisms to extract both local and long-range temporal features. Tested on four common household appliances (refrigerator, washing machine, air conditioner, microwave oven), the system achieved over 92 % accuracy in health classification (up to 97.8 % for washing machines) and a low error in RUL estimation with an average RMSE of 3.2 months and an average MAPE of 8.5 %, outperforming traditional machine learning baselines (SVM/RF) by more than 40 % in health classification accuracy and surpassing mainstream deep learning models (LSTM/Transformer/LSTM-VMD) by 15-20 % in RUL prediction precision. Furthermore, the proposed embedded micro-sensing platform explicitly considers hardware-level constraints such as on-chip memory capacity, computational complexity, and real-time inference latency. By optimizing model architecture and adopting lightweight deployment strategies, the system achieves efficient edge-side intelligence while maintaining compatibility with resource-constrained microcontroller platforms. The system outperformed traditional baseline models and showed good generalization across different brands. With low latency and power consumption, it is well-suited for smart home applications.

This study examines how real estate development shapes urban resilience under uncertainty, using a balanced panel of 21 prefecture-level cities in Guangdong, China, over 2005–2024. Real estate market development (REM) and urban resilience (UR) are measured with entropy-weighted composite indices, and their relationship is examined through coupling coordination analysis, two-way fixed-effects models, a moderating-effect model, a Hansen panel threshold model, and a spatial Durbin model. Four findings emerge. First, the coupling coordination degree rose from 0.233 in 2005 to a peak of 0.397 in 2019 and fell back to 0.280 in 2024, remaining within the imbalanced range throughout, with the Pearl River Delta (0.364) clearly leading the rest of the province. Second, real estate expansion exhibits a significant negative average association with urban resilience (−0.145, p < 0.05). Third, this association is state-dependent: the REM × UNC interaction is significantly positive (+2.130, p < 0.001), and the threshold test identifies an uncertainty threshold of 0.074 below which real estate development weakens resilience (−0.153) and above which it strengthens it (+0.752). Fourth, the spatial Durbin model reveals negative strategic interaction (ρ = −0.974) and a negative spillover from neighbouring real estate expansion (−0.471, p < 0.05), with the moderating mechanism concentrated in the Pearl River Delta. The results imply that the resilience function of the real estate market is a direction-switching function of the uncertainty environment, calling for state-contingent regulation, regionally coordinated governance, and a repositioning of real estate from a growth instrument toward a resilience support system.