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A novel medium-to-high strength aluminum alloy Al-6Mg-0.7Zr was prepared by using the rapid solidification-powder metallurgy process. The mechanical properties and microstructural changes of the annealed alloy were analyzed and studied through the methods，such as room temperature tensile testing，high temperature tensile testing，SEM and TEM. The results indicate that by using the rapid solidification-powder metallurgy method，the maximum solid solubility of Zr in the Al matrix is exceeded，and the successful preparation of a 0.7% Zr（mass fraction） alloy is achieved. The Al3Zr particles precipitated during the annealing process exhibit a coherent relationship with the matrix，contributing to the strength enhancement of the alloy. The nano-scale Al3Zr particles play a significant role in improving the strength of the Al-6Mg-0.7Zr alloy. At room temperature，the tensile strength of the Al-6Mg-0.7Zr alloy reaches 445 MPa，with the yield strength of 338 MPa and the post-fracture elongation of 20.10%. In addition，Al3Zr particles remain stable at high temperatures and exhibit significant hindrance to grain boundary migration and dislocation movement，thus providing the alloy with excellent high-temperature performance within temperatures below 200 ℃. At 150 ℃，the tensile strength of Al-6Mg-0.7Zr alloy reaches 320 MPa，the yield strength is 266 MPa and the elongation after fracture is 33.37%.

The coal base electrodes and efficient coal base loaded cobalt electrodes (Co-CE) were prepared by pyrolysis method of low rank coal united activation method of KOH in order to develop more pores structures. The morphology of electrodes were characterized by Scanning electron microscopy, meanwhile, the type of elements were detected by energy dispersive spectroscopy (EDS). The electrochemical performance of electrodes were tested by cyclic voltammetry and electrochemical impedance spectroscopy. The lamella structures and pores were observed in microtopography of electrodes and the cobalt were successfully loaded in Co-CE from the EDS analysis. The operating conditions of processing time, current density, electrolyte concentration, pH and initial phenol concentration on this electrochemical system in single factor experiment were separately explored, correspondingly, the value was 180 min, 40 mA·cm−2, 0.01 mol·L−1, 2, 100 mg·L−1, and the phenol removal rate (R) were at the range of 47.64–67.84%. In the optimization experiment of JMP design, the removal rate could reach at 83.47%. The response surface methodology was employed for optimizing operation conditions to improve R. And the prediction model obtained for the response can be represented as: R = 66.5275 + 6.7311X 1 – 5.4197X 4 – 5.2303X 5 + 4.9555X 1 2 – 12.5219X 2 2 – 6.2912X 4 2 + 16.0937X 5 2 + 2.4109X 2 X 4 – 7.910X 3 X 4 – 3.0123X 3 X 5 – 2.183X 4 X 5. The optimized conditions were pH 3, 100 mg·L−1 of phenol concentration, 0.1 mol/L of electrolyte concentration, 35 mA/cm2 of current density, and 180 min of processing time. Meanwhile, the predicted R was 90.86%, the actual R of three parallel experiments were 91.2%, 89.3%, 91.05%, which were well consistent with the predicted value. Additionally, the degradation mechanism was proposed as that the adsorption in pore structures synergy electrocatalytic effect of Co-CE. Micro-electric fields formed in pores and the transition metal catalysis accelerated the transformation of cathode hydrogen peroxide to generate hydroxyl radical (·OH). Furthermore, the ·OH were produced both by cathode and anode which promoted the degradation of phenol. This high catalytic activity and low cost Co-CE is a kind of prospective electrode for electrochemical degradation of phenolic wastewater.

Background Chronic non-communicable diseases have become the primary factor threatening human health at present. The strategy of "Healthy China 2030" in 2016 emphasized the comprehensive prevention and control of chronic diseases for the first time. In recent years, a growing number of general practitioners in China have been playing an important role as gatekeepers of health in community health care and chronic disease management. Primary care managers (PCMs) selected from experienced senior nurses who have received an appropriate training can form health management teams with general practitioners to improve clinical efficiency and management outcomes in chronic disease populations according to domestic and international literature. Objective To explore the feasibility and sustainability of collaborative outpatient chronic disease management model based on medical team consisting of general practitioners, internists and PCMs in secondary private general hospital. Methods General practitioners or internists were assigned as primary care physicians (PCPs) for patients with essential hypertension and/or type 2 diabetes mellitus (T2DM) , physician assistants or senior nurses in the corresponding departments were trained as PCMs, PCPs and PCMs form chronic disease management teams to collaborate on the health management of patients with chronic diseases. The control of blood pressure and/or glycated hemoglobin in the dynamic and fixed populations of patients from April 2020 to August 2021 was reviewed, two indexes for the quality control including the rates of poorly controlled blood pressure and blood glucose were used to assess the effectiveness of chronic disease management collaboratively conducted by the PCP-PCM team. A questionnaire was used to obtain feedback from PCP-PCM team members on chronic disease management efforts, and a net promoter score (NPS) questionnaire was used for the investigation of patients satisfaction on PCP. Results From April 2020 to August 2021, the rate of poorly controlled blood pressure in our hospital ranged from 18.34% to 20.82%, basically meeting the quality control target of no more than 20%; the rate of poorly controlled blood glucose ranged from 14.92% to 24.31%, with significant fluctuations, and did not meet the quality control target of no more than 20% in some months. The overall feedback from PCP-PCM team members on this chronic disease management model was very positive, but PCMs shared the concerns of excessive work load. The average NPS score for PCPs calculated from the results of 170 NPS questionnaires was 91, which was higher than than the average NPS department scores of general practitioners and internists in the same period (86 and 80, respectively) . Conclusion The chronic disease management model based on PCP-PCM team is effective in the improvement of clinical indicators in the hypertensive and diabetic patient populations, with positive feedbacks from PCPs, PCMs and patients suggesting feasibility of the chronic disease management model. However, the shortage of PCMs may have negative impact to the effectiveness of chronic disease management in long term, which is worthwhile to continue exploring in the sustainability of the model.

A three-degree-of-freedom fighter model is established to solve the maneuver decision problem in cooperative air combat, and an extended basic maneuver decision set is designed according to the pilot’s actual operation in air combat. The air combat situation assessment model is established by constructing angle, velocity, distance and altitude superiority functions. Based on group decision making theory, Cook-Seiford decision making algorithm is designed and applied to the research of multi-aircraft cooperative air combat maneuver decision making for the first time, which effectively optimizes the decision flow. Under the initial situation disadvantage, Cook-Seiford group decision algorithm, Copeland and Dodgson group decision algorithm are used to simulate adversarial air combat under typical air combat conditions of 2 versus 2 and 3 versus 2. The simulation results show that the cooperative maneuver decision algorithm based on Cook-Seiford group decision algorithm can effectively obtain obvious countermeasures advantage, which verifies the effectiveness and superiority of the algorithm designed in this paper, and provides a new idea for using the group decision algorithm to solve multi-aircraft cooperative air combat decision.

Powder adsorbent made by coal gasification slag (CGS) was used to adsorb pollutants from coking wastewater (CW). This study initially focused on the removal efficiency of volatile phenol, NH3–N, and chemical oxygen demand (COD) from CW. The removal rate of volatile phenol increased from 48.90% to 70.50% after acid precipitation of CW by 4.0 mL reagent of sulfuric acid (3.0 M) and optimization of adsorption process by central composite design-response surface methodology with optimized conditions. Volume ratio of liquid and solid adsorbent (V L/S) and pH were the significant factors in the adsorption process. Batch experiment improved the volatile phenol, NH3–N, and COD removal rate to 85.1%, 41.6%, and 77.3%, respectively. Multi-grade batch process in grade 3 made a further promotion of pollutants removal rate as 98.5%, 73.6%, and 80.5%, respectively. Scanning electron microscope-energy dispersive spectrum and Fourier-transform infrared spectrometer were used to confirm the adsorption effect. CGS-based adsorbent for CW treatment has potential advantages due to the features of good adsorption performance and low cost.

For precision agriculture, agricultural development planning, and regional food security, accurate crop mapping is essential. The GF-6 satellite offers novel approaches for crop identification. As Jinzhou, Hebei province, China, comprises a mix of grain and fruit trees, this research has selected the area for a case study. First, spectral, index, and texture features are combined in various classification feature schemes. Second, the random forest algorithm is employed to ascertain the significance of these features. Optimal features are then chosen to accurately identify winter wheat, summer maize, and pear orchards. Concurrently, land cover for wheat and maize seasons is mapped. The results demonstrate that: (1) multisource features considerably enhanced the accuracy of crop classification based on GF-6 WFV data. Among these, index features have the greatest contribution, followed by spectral features, while the contribution of texture features, is almost negligible. (2) The classification effect utilizing random forest feature optimization is superior, with an overall accuracy of 94% and a kappa coefficient of 0.91. The extracted land area for each scheme exhibits an accuracy of over 70% when compared to the statistical area. (3) The critical time phases for extracting winter wheat, summer maize, and pear orchards are identified as, in sequence, the leaf-out and jointing stage, the tasselling stage, the leaf-fall stage, fruit development, and the first flowering/full flowering stages. Based on GF-6 WFV data, these research findings will offer a technical reference for the identification of grain crops and the extraction of pear orchards. [ABSTRACT FROM AUTHOR]

Formaldehyde is widely utilized across various industries such as food, textiles, and leather, yet its presence raises significant health concerns due to its irritation and carcinogenicity. Numerous methods have been developed to probe it in solution and gas states. Among them, absorption spectroscopy-based methods commonly employed. However, these methods require complex preprocessing for colored solutions to avoid interference. In response to this challenge, a novel interference-resistant detection kit leveraging surface-enhanced Raman spectroscopy (SERS) coupled with reaction-induced self-amplification for rapid and direct detection of formaldehyde was developed. The kit needed only ten minutes and achieved a limit of detection lower than 10−4 μg/mL with a handheld Raman spectrometer. Thanks to the kit's reaction with formaldehyde, colloidal nanoparticles were linked to form "hot-spots" to further enhance the SERS signals. The signal demonstrated outstanding resistance to interference, including different dyes, ensuring a practical tool for direct detection of formaldehyde in complex solutions. [ABSTRACT FROM AUTHOR]

For the problem how to accurately and quickly locate targets in the current target tracking field, the core content of most popular trackers is to combine a kernel method to train a discriminative classifier to distinguish the target from the surrounding environment. For example, the kernel correlation filter algorithm (KCF) combines the Fourier transform with the kernel discriminant classifier to improve the speed of tracking, and the improved fuzzy kernel correlation filter (FKCF) algorithm introduced by Takagi-Sugeno-Kang fuzzy logic system (TSK-FLS) is used to improve the accuracy of tracking. Some improved KCF-based algorithms have proposed solutions to partial tracking problems. However, existing algorithms still have some room for improvement in improving accuracy. Aiming at this deficiency, based on FKCF, a new multi-fuzzy kernels correlation filter (MFKCF) is derived using multi-kernel fusion. MFKCF inherits the characteristics of high speed of KCF and high accuracy of FKCF and blurs the polynomial kernel and the Gaussian kernel. And it combines the fuzzified kernel function as a new target kernel function. Due to the above two improvements, the proposed algorithm is better than KCF and FKCF in the accuracy of tracking. KCF, FKCF and MFKCF are carried out on 30 randomly selected videos on 4 databases such as OTB50. The experimental results show that MFKCF performs well on the whole, accuracies of the MFKCF on 10 types of common attributes are all improved.

For the problem how to accurately and quickly distinguish the target from the background in the current target tracking field, the core task of most trackers is how to train a discriminant classifier to distinguish between the target and the surrounding environment. At present, the more advanced kernel correlation filter algorithm (KCF) and improved discriminant correlation filter (DCF) can combine the discriminant classifier with the Fourier transform to improve the tracking speed. Some KCF-based optimization algorithms provide solutions to partial tracking problems, such as the KCF algorithm for scale problems and the KCF algorithm for target disappearance. However, existing algorithms still have some room for improvement in improving accuracy. Aiming at this deficiency, a new fuzzy kernel correlation filter (FKCF) is derived using Takagi-Sugeno-Kang fuzzy logic system (TSK-FLS) based on kernel correlation filter. FKCF inherits the characteristics of high speed and small computational complexity of KFC, and further to improve the robustness, replacing the previous simple Gaussian mapping with the fuzzy membership function and introducing the consequent parameters of TSK-FLS in the process of kernel calculation. Thus, FKCF achieves better tracking accuracy than traditional KCF. Extensive experiments are carried out on 50 randomly selected videos on 4 databases such as OTB50. The experimental results show that accuracies of the FKCF on 10 types of common attributes are all improved compared with the traditional KCF.