Explore the vast range of titles available.

A large input delay, parametric uncertainties, matched disturbances and mismatched disturbances exist extensively in variable air volume air-conditioning systems, which can deteriorate the control performance of the room temperature and even destabilize the system. To address this problem, an adaptive-gain command filter control framework for the room temperature of variable air volume air-conditioning systems is exploited. Through skillfully designing an auxiliary system, both the filtered error and the input delay can be compensated concurrently, which can attenuate the effect of the filtered error and the input delay on the control performance of the room temperature. Then, a smooth nonlinear term with an adjusted gain is introduced into the control framework to compensate for parametric uncertainties, matched disturbances and mismatched disturbances, which relieves the conservatism of the controller gain selection. With the help of the Lyapunov theory, both the boundedness of all the system signals and the asymptotic tracking performance for the room temperature can be assured with the presented controller. Finally, the contrastive simulation results demonstrate the validity of the developed method.

This paper introduces a novel cold storage: phase-temperature storage, which is characterized by its distinctive coupling jacket structure that connects the sub-storehouse units to the main storehouse. This innovative design facilitates heat transfer while effectively inhibiting mass transfer. Experimental results indicate that polyethylene film, with a thermal conductivity of 0.42 W/m·K, is a more suitable material for constructing sub-storehouses. Enhancing the surface area of the sub-storehouse and increasing convective wind speed are identified as key factors for improving convective heat transfer within the sub-storehouse. Moreover, the optimized design ensures a more uniform temperature distribution inside the sub-storehouse. In contrast to conventional cold storage, the defrosting unit in phase-temperature storage consumes only 5.72 units of energy under equivalent conditions, compared to 154.02 units for conventional cold storage. This demonstrates that the energy expenditure during the defrosting process of phase temperature storage is less than 4% of that required by conventional cold storage, achieving an energy savings rate exceeding 96%. Under identical circumstances, conventional cold storage consumes a total of 36.359 units of electrical energy for defrosting, with 34.231 units being released as defrosting waste heat into the cold storage environment, resulting in a loss rate of approximately 94.13%. Based on apple preservation experiments, phase-temperature storage exhibited significantly superior performance compared to conventional cold storage in terms of apple respiratory peak, weight loss rate, hardness, and TSS content, with respective values of 17.05 CO2 mg·kg−1·h−1, 2.89%, 9.29 N, and 16.3%. In contrast, the conventional cold storage group recorded values of 18.15 CO2 mg·kg−1·h−1, 5.16%, 8.42 N, and 14.9%. These results highlight the exceptional freshness-retention capabilities of phase-temperature storage, underscoring its considerable potential for application in storage systems.

Variations in running conditions cause fluctuation in the temperature field of precision machine tools, which inevitably results in thermal errors. To meet the demands of dynamic and time-varying temperature control capability, an active temperature control (ATC) method based on time grating principle is proposed, and the ATC system is developed. The ATC system contains main-loop and sub-loops. The oil target temperature in the sub-loop is determined according to the running parameters and the matching principle of the generalized heat generation–dissipation power. In accordance with the time grating principle, dynamic and differential oil temperature control of each sub-loop is achieved via the inlet time regulation of high-temperature (H-t) or low-temperature (L-t) oil in the main-loop. The main-loop H-t and L-t oil target temperatures are determined by the target range of the sub-loop temperature. The dynamic distribution of the refrigeration capacity and proportional heating mode is adopted to control the temperatures of H-t and L-t oil. By focusing on the feed system of precision machine tool, we carry out both dynamic simulation study and verification experiments, and the results show that the ATC method and system can effectively regulate the temperature field of precision machine tools, thus improving the thermal accuracy of the precision machine tool.

Background The study focuses on enhancing the effectiveness of precision agriculture through the application of deep learning technologies. Precision agriculture, which aims to optimize farming practices by monitoring and adjusting various factors influencing crop growth, can greatly benefit from artificial intelligence (AI) methods like deep learning. The Agro Deep Learning Framework (ADLF) was developed to tackle critical issues in crop cultivation by processing vast datasets. These datasets include variables such as soil moisture, temperature, and humidity, all of which are essential to understanding and predicting crop behavior. By leveraging deep learning models, the framework seeks to improve decision-making processes, detect potential crop problems early, and boost agricultural productivity. Results The study found that the Agro Deep Learning Framework (ADLF) achieved an accuracy of 85.41%, precision of 84.87%, recall of 84.24%, and an F1-Score of 88.91%, indicating strong predictive capabilities for improving crop management. The false negative rate was 91.17% and the false positive rate was 89.82%, highlighting the framework's ability to correctly detect issues while minimizing errors. These results suggest that ADLF can significantly enhance decision-making in precision agriculture, leading to improved crop yield and reduced agricultural losses. Conclusions The ADLF can significantly improve precision agriculture by leveraging deep learning to process complex datasets and provide valuable insights into crop management. The framework allows farmers to detect issues early, optimize resource use, and improve yields. The study demonstrates that AI-driven agriculture has the potential to revolutionize farming, making it more efficient and sustainable. Future research could focus on further refining the model and exploring its applicability across different types of crops and farming environments.

Accurately measuring the cutting temperature in micro cutting zone is crucial for characterizing and optimizing the cutting status during ultra-precision machining. This work proposes an innovative method for self-sensing of cutting temperature using a locally boron-doped diamond tool. A longitudinal layered deposition synthesis methodology, instead of the traditional growth method under high temperature and high pressure conditions (HTHP), was developed to enable the fabrication of the locally boron-doped diamond tool. The doping contents, lattice integrity, and electrical properties of the diamond were characterized. Owing to the inherently low thermal capacity and quick carrier migration induced by the thin-layer structure for sensing temperature, the diamond tool has the advantages of rapid response and enhanced sensitivity, compared with traditional cutting temperature measurement technologies. An insulated diamond tool edge without boron doping enables to accurately measure cutting temperature for various conductive materials in ultra-precision cutting processes. The locally boron-doped diamond tool was employed for in-process monitoring of the temperature in micro cutting zone during ultra-precision machining processes. The experimental results demonstrated the capabilities of in-process cutting temperature monitoring of conductive materials using the diamond tool, as well as the high-sensitivity identification of micro/nano morphologies and defects on machined surface based on the measured temperature. It provides a potential approach for advanced status analysis and diagnosis in the process of ultra-precision machining.

Meteorological data are essential in precision agriculture in the Canadian Prairies and are often associated with spatiotemporal discontinuity and scarcity. Reanalysis data products aim to address this challenge and have recently gained popularity. The European Centre for Medium-Range Weather Forecasts’ ERA5, and its high-resolution land component, ERA5-Land, are two reanalysis datasets that provide hourly estimates of many climate variables globally. This paper focuses on evaluating the performance of ERA5 and ERA5-Land over the Canadian prairies, utilizing data from 109 weather stations situated in southern Manitoba, Canada. Various variables are investigated at daily, monthly, and annual aggregation periods, including air temperature, ground temperature, soil water content, wind speed, precipitation, and evaporation. The datasets are evaluated regarding seasonal bias and spatial distribution of errors over the study area. Regression parameters are also presented to address the biases. Among the investigated variables, air temperature and wind speed exhibit the lowest errors. The evaluation further reveals an overall tendency to overpredict ground temperatures and precipitation while underpredicting evaporation. Based on these findings, the ERA5 and ERA5-Land datasets hold significant potential in applications such as climate-smart agriculture, energy demand analysis, assessing renewable energy resources, and facilitating sustainable urban development.

Over the past four decades the dietary needs of the global population have been elevated, with increased consumption of animal products predominately due to the advancing economies of South America and Asia. As a result, livestock production systems have expanded in size, with considerable changes to the animals’ management. As grazing animals are commonly grown in herds, economic and labour constraints limit the ability of the producer to individually assess every animal. Precision Livestock Farming refers to the real-time continuous monitoring and control systems using sensors and computer algorithms for early problem detection, while simultaneously increasing producer awareness concerning individual animal needs. These technologies include automatic weighing systems, Radio Frequency Identification (RFID) sensors for individual animal detection and behaviour monitoring, body temperature monitoring, geographic information systems (GIS) for pasture evaluation and optimization, unmanned aerial vehicles (UAVs) for herd management, and virtual fencing for herd and grazing management. Although some commercial products are available, mainly for cattle, the adoption of these systems is limited due to economic and cultural constraints and poor technological infrastructure. This review presents and discusses PLF applications and systems for grazing animals and proposes future research and strategies to improve PLF adoption and utilization in today’s extensive livestock systems.

The success of weed control is critical for our food security. Non-chemical weed control is a promising technique in sustainable agriculture to ensure the food security. In this review, multiple directed energy weed control methods are reviewed with a specific focus on laser and optical radiation weed control. The mechanisms of the weed control in terms of adverse ablation, radiation thermal effects, and molecular-level damages are systematically reviewed. In particular, the underlying mathematical models determining the dose and response relationship of the weed control are also analyzed for a rigorous study of the physical law of the control process. Challenges of applying the techniques into practice are also illustrated to guide practical weed control applications.

Grinding is an ultra-precision machining technology. The grinding force and grinding heat emerge as pivotal physical parameters. Excessive grinding temperature can engender unwarranted thermal damage to the processed material. In cup grinding wheel face grinding, employing a singular abrasive grain discrete heat source method enables a more precise establishment of the face grinding temperature field. Cross tracks of abrasive exist widely in cup grinding wheel, and the influence of cross-point temperature should be considered in order to accurately establish the grinding temperature field model. Thus, a single-grain discrete point heat source superposition temperature field analytical model was established. Through trochoid feed scratch experiments, the variation law of thermal effect of cross-points under different cutting depth is verified. The experimental findings reveal conspicuous changes in cutting force and cutting heat at the entry and exit positions of the scratch intersection region. Moreover, the abrasive grain scratch sustains more severe damage compared to other regions. The energy change caused by the impact effect is the key factor leading to the temperature change at the intersection. The energy lost at the entrance of the intersection position is close to the energy of the impact effect. With the increase of the cutting depth, the ratio of the two tends to converge towards 1, ranging from 0.868 to 0.932 to 0.965. The error between the theoretical model and experimental verification is less than 5%, indicating the single-particle discrete heat source superposition temperature field model can well characterize the grinding surface temperature field caused by cross-point effect, which lays a foundation for the grinding heat theory based on trochoid model.

Ultra-precision machining represents a key technology for manufacturing optical components in medical, aerospace and automotive industry. Dedicated single crystal diamond tools enable the production of innovative optical surfaces and components with high dimensional accuracies and low surface roughness values in a wide range of airborne sensing and imaging applications concerning space telescopes, fast steering mirrors, laser communication and high-energy laser systems. Despite the high mechanical hardness of single crystal diamonds, temperature-induced wear of the diamond tools occurs during the process. In order to increase the economic efficiency of ultra-precision turning, the characterisation and interpretation of cutting temperatures are of utmost importance. According to the state-of-the-art, there are no precise methods for online temperature monitoring during the process at the cutting edge with regard to the requirements for resolution accuracy, response time and accessibility to the cutting edge. For this purpose, a dedicated cutting edge temperature measurement system based on ion-implanted boron-doped single crystal diamonds as a highly sensitive temperature sensor for ultra-precision turning was developed. To enable highly sensitive temperature measurements, ion implantation was used for partial and specific boron doping close to the cutting edge of single crystal diamond tools. Within the investigations, a resolution accuracy of 0.29 °C ≤  a R  ≤ 0.39 °C could be proven for the developed cutting edge temperature measurement system. In addition, a total measurement uncertainty of u M  = 0.098 °C was determined for the sensor accuracy  a S in the investigated process area. For a rake angle range of 0° ≤ γ 0  ≤ −30°, reaction times of 420 ms ≤  t R  ≤ 440 ms were further determined. Using the developed cutting edge temperature measurement system enables a holistic view of the temperature development during ultra-precision machining, whereby a correlation between the measured cutting temperatures and the chip formation mechanisms depending on the applied process parameters could be identified. Within the investigations, the highest measured temperatures of ϑ M  = 50.18 °C and simulated maximum temperatures of ϑ S,max  = 183.12 °C were determined at a cutting speed of v c  = 350 m/min, a cutting depth of a p  = 35 µm as well as a feed of f  = 35 µm using a rake angle of γ 0  = −30°. The most uniform chips with the smoothest surfaces were identified within the chip analysis using a cutting speed of v c  = 50 m/min, a cutting depth of a p  = 5 µm and a feed of f  = 35 µm with a measured temperature of ϑ M  = 21.30 °C and a simulated temperature of ϑ S  = 38.47 °C in the examined finishing area. According to the results, it was also shown that the cutting edge temperature measurement system with ion-implanted diamonds can be used for both electrically conductive and non-conductive materials. This provides the fundamentals for further research works to identify the complex temperature-induced wear behaviour of single crystal diamonds in ultra-precision turning and serves as the basis for self-optimising and self-learning ultra-precision machine tools.