It is crucial to consider structural design issues in Trombe wall (T-wall) buildings to promote more suitable indoor climates and thermal comfort standards. Therefore, the present study examined the impact of two different T-wall designs and six different roof types on the energy and operational efficiency of a building located in a low-temperature and high-humidity winter climate. Ansys-CFX 15.0 software was employed to simulate the thermal and fluid dynamics behavior of the T-wall system, and flow, thermal comfort, energy, and exergy analyses were conducted. Three-dimensional simulation results and the pertinent literature data showed a good level of agreement, and the accuracy of the model was ensured. Outcomes revealed an average air velocity variation of 0.186 m/s and maximum average indoor air temperature variation of 3.3 °C between the six roof geometries. The highest air speed (0.988 m/s) was recorded for the gambrel roof while the lowest one (0.802 m/s) was recorded for the typical flat roof. The shed roof right with a rounded T-wall was more comfortable for standing and sitting activity than the others for the two T-wall shapes, and, at Y = 0.6 m and Y = 1.1 m, the average predicted percentages of dissatisfied (PPD) values were 31 and 28%, respectively. Furthermore, it was determined in the study that solar radiation intensity and T-wall and roof geometries had a significant effect on energy and exergy efficiency, and high energy and exergy efficiencies were achieved at higher solar intensity values. The best energy and exergy efficiencies were obtained for the butterfly and shed roof configurations. This study can serve as a reference for the thermal environment design of buildings with T-walls.

Timely and accurate estimates of crop areas are critical to enhancing agriculture management and ensuring national food security. This study aims to combine remote-sensing data and an optimized spatial sampling scheme to improve the accuracy of crop area estimates and decrease the cost of crop surveys at a regional scale. This study focuses on winter wheat in Mengcheng County in Anhui Province, China. Advanced Land Observing Satellite (ALOS) Advanced Visible light and Near Infrared Radiometer (AVNIR)-2, and Landsat5 Thematic Mapper (TM) images from 2009 and 2010, respectively, are used to extract the winter wheat area and distribution. Additionally, a spatial sampling scheme was optimized by combining remotely sensed data, geographical information system (GIS), Geostatistics, and traditional sampling methods. The experimental results demonstrate that the variability in the proportion of winter wheat acreage in one sampling unit (PWS) increases with increasing sampling unit size. The PWS coefficient of variation (CV) varies from 32.75 to 43.46% among the eight sampling unit sizes. The spatial correlation thresholds of PWS increase with increasing sampling unit size. For small sampling unit sizes (500 m × 500 m–2000 m × 2000 m), the relative error and CV of the population extrapolation for the optimized sample layout are obviously lower than those of the simple random sampling method. For larger sampling unit sizes (2500 m × 2500 m–4000 m × 4000 m), the sample size is obviously lower for the optimized sample layout compared with that of the simple random sampling method, but there are no differences in the relative errors or CVs. By combining remote-sensing data and the optimized spatial sampling scheme, this research can improve the accuracy of crop area estimation at a regional scale.

Trustworthy technology for the monitoring of fugitive methane emissions is an indispensable component for low−carbon emission reduction and safe production in oil and gas infrastructure. A miniaturization and low-cost methane remote monitoring prototype based on near-infrared laser absorption spectroscopy is developed to retrieve the path−integral concentration by employing the harmonic conjoint analysis method for the backscattered echo signals from a distant non-cooperative target. A distorted harmonic template matching (DHTM) algorithm is proposed based on wavelength modulation spectroscopy with second harmonic normalized via the first harmonic (WMS−2f/1f) method, which suppresses the output concentration fluctuation caused by open path monitoring with non-cooperative target and avoids the issue of false alarms during the detection process without target gas. The reliability of the WMS−2f/1f−DHTM algorithm is verified by calibration and dynamic experiments. The results showed that this algorithm avoids the dilemma of false alarms in the absence of gas compared to the conventional WMS−2f/1f algorithm, while the root mean square error (RMSE) of the concentration inversion with a detection distance of 20 m is reduced by 57.6% compared to direct absorption spectroscopy (DAS) algorithm. And the minimum detection limit of system is 3.79 ppm·m. The methane telemetry sensor with the WMS−2f/1f−DHTM algorithm exhibits substantial application potential in carbon monitoring of oil and gas industry.