Deep-Ensemble Learning Method for Solar Resource Assessment of Complex Terrain Landscapes

As the global demand for renewable energy grows,solar energy is gaining attention as a clean,sustainable energy source.Accurate assessment of solar energy resources is crucial for the siting and design of photovoltaic power plants.This study proposes an integrated deep learning-based photovoltaic resource assessment method.Ensemble learning and deep learning methods are fused for photovoltaic resource assessment for the first time.The proposed method combines the random forest,gated recurrent unit,and long short-term memory to effectively improve the accuracy and reliability of photovoltaic resource assessment.The proposed method has strong adaptability and high accuracy even in the photovoltaic resource assessment of complex terrain and landscape.The experimental results show that the proposed method outperforms the comparison algorithm in all evaluation indexes,indicating that the proposed method has higher accuracy and reliability in photovoltaic resource assessment with improved generalization performance traditional single algorithm.

Machine Learning-Based Decision-Making Mechanism for Risk Assessment of Cardiovascular Disease

Cardiovascular disease(CVD)has gradually become one of the main causes of harm to the life and health of residents.Exploring the influencing factors and risk assessment methods of CVD has become a general trend.In this paper,a machine learning-based decision-making mechanism for risk assessment of CVD is designed.In this mechanism,the logistics regression analysismethod and factor analysismodel are used to select age,obesity degree,blood pressure,blood fat,blood sugar,smoking status,drinking status,and exercise status as the main pathogenic factors of CVD,and an index systemof risk assessment for CVD is established.Then,a two-stage model combining K-means cluster analysis and random forest(RF)is proposed to evaluate and predict the risk of CVD,and the predicted results are compared with the methods of Bayesian discrimination,K-means cluster analysis and RF.The results show that thepredictioneffect of theproposedtwo-stagemodel is better than that of the comparedmethods.Moreover,several suggestions for the government,the medical industry and the public are provided based on the research results.

Rapid detection and risk assessment of soil contamination at lead smelting site based on machine learning

A general prediction model for seven heavy metals was established using the heavy metal contents of 207soil samples measured by a portable X-ray fluorescence spectrometer(XRF)and six environmental factors as model correction coefficients.The eXtreme Gradient Boosting(XGBoost)model was used to fit the relationship between the content of heavy metals and environment characteristics to evaluate the soil ecological risk of the smelting site.The results demonstrated that the generalized prediction model developed for Pb,Cd,and As was highly accurate with fitted coefficients(R^(2))values of 0.911,0.950,and 0.835,respectively.Topsoil presented the highest ecological risk,and there existed high potential ecological risk at some positions with different depths due to high mobility of Cd.Generally,the application of machine learning significantly increased the accuracy of pXRF measurements,and identified key environmental factors.The adapted potential ecological risk assessment emphasized the need to focus on Pb,Cd,and As in future site remediation efforts.

Accuracy assessment of global vertical displacement loading tide models for the equatorial and Indian Ocean

The three-dimensional displacements caused by ocean loading effects are significant enough to impact spatial geodetic measurements on sub-daily or longer timescales,particularly in the vertical direction.Currently,most tide models incorporate the distribution of vertical displacement loading tides;however,their accuracy has not been assessed for the equatorial and Indian Ocean regions.Global Positioning System(GPS)observations provide high-precision data on sea-level changes,enabling the assessment of the accuracy and reliability of vertical displacement tide models.However,because the tidal period of the K_(2) constituent is almost identical to the orbital period of GPS constellations,the estimation of the K_(2) tidal constituent from GPS observations is not satisfactory.In this study,the principle of smoothness is employed to correct the systematic error in K_(2) estimates in GPS observations through quadratic fitting.Using the adjusted harmonic constants from 31 GPS stations for the equatorial and Indian Ocean,the accuracy of eight major constituents from five global vertical displacement tide models(FES2014,EOT11a,GOT4.10c,GOT4.8,and NAO.99b)is evaluated for the equatorial and Indian Ocean.The results indicate that the EOT11a and FES2014 models exhibit higher accuracy in the vertical displacement tide models for the equatorial and Indian Ocean,with root sum squares errors of 2.29 mm and 2.34 mm,res-pectively.Furthermore,a brief analysis of the vertical displacement tide distribution characteristics of the eight major constituents for the equatorial and Indian Ocean was conducted using the EOT11a model.

Assessment of Climate for Agricultural Suitability and Optimal Allocation of Agricultural Production in the Guanzhong Region, Shaanxi Province

Based on spatial climatic data of agriculture and the experiment data, the models of agro-ecological assessment of climate for agricultural suitability in this study were developed using the fuzzy mathematical method. Three coefficients, in- cluding the resource coefficient （Cr）, the efficiency coefficient （Ce）, and the utility co- efficient （K）, were used in the models, which were calculated based on temperature, moisture, and sunshine duration data of Guanzhong region, Shaanxi Province. The results indicated that resource coefficient was higher in west of the region than that in east, and higher in south （especially in the Central Shaanxi Plain） than that in the Weibei plateau. The value of Cr changed from 6.5 to 9.2 from north to plain area. Spatial change of efficiency coefficient was obvious, lower in the northeast than in the central plain, and the value of Ce changed from 2.3 to 6.5 from the northeast to the central plain. As for utility coefficient, it was lower in northeastern part of the Weibei plateau and in southern mountain areas than that in the central plain, showing significant latitudinal zonality. Furthermore, the value of K increased from 0.35 to 0.78 from northeast to the central plain, and decreased from 0.78 to 0.53 from the central plain to southern mountain areas. These indicated that climate resource in the central plain region was more abundant and potential, compared with other regions. GuanZhong region was classified into three larger agricultural zones and three small independent zones, according to agro-ecological assessment. Light, heat and water resources should be made use of in an efficient way in spatial allo- cation of agricultural production. For example, water facilities should also be im- proved in Weibei plateau region where highly-qualified fruit should be enhanced and fruit processing industrial chain should be shaped. Large-scale production area of wheat should be increased in central irrigation region and more vegetable bases should be developed around large and medium-scale cities. Thanks for outstanding water conservation function, the three-dimensional agriculture including medicine and other sideline production should be developed in Qinling Mountains and the special- ized commercial agriculture should be accelerated in independent small zones, ac- cording to local conditions. In the research, different crop varieties were developed in corresponding regions as per current eco-climatic conditions.

Performance in the Fundamentals of Laparoscopic Surgery: Does it reflect global rating scales in the Objective Structured Assessment of Technical Skills in porcine laparoscopic surgery?

Objective:To correlate the utility of the Fundamentals of Laparoscopic Surgery(FLS)manual skills program with the Objective Structured Assessment of Technical Skills(OSATS)global rating scale in evaluating operative performance.Methods:The Asian Urological Surgery Training and Educational Group(AUSTEG)Laparoscopic Upper Tract Surgery Course implemented and validated the FLS program for its usage in laparoscopic surgical training.Delegates’basic laparoscopic skills were assessed using three different training models(peg transfer,precision cutting,and intra-corporeal suturing).They also performed live porcine laparoscopic surgery at the same workshop.Live surgery skills were assessed by blinded faculty using the OSATS rating scale.Results:From March 2016 to March 2019,a total of 81 certified urologists participated in the course,with a median of 5 years of post-residency experience.Although differences in task time did not reach statistical significance,those with more surgical experience were visibly faster at completing the peg transfer and intra-corporeal suturing FLS tasks.However,they took longer to complete the precision cutting task than participants with less experience.Overall OSATS scores correlated weakly with all three FLS tasks(peg transfer time:r=0.331,r^(2)=0.110;precision cutting time:r=0.240,r^(2)=0.058;suturing with intracorporeal knot time:r=0.451,r^(2)=0.203).Conclusion:FLS task parameters did not correlate strongly with OSATS globing rating scale performance.Although FLS task models demonstrated strong validity,it is important to assimilate the inconsistencies when benchmarking technical proficiency against real-life operative competence,as evaluated by FLS and OSATS,respectively.

Assessment of Operational Performance in a Power Generation/Selling Integrated Company Using a Dynamic Proportional Adjustment Coefficient

Currently,the operational performance assessment system in the power market primarily focuses on power generation and electricity retail companies,lacking a system tailored to the operational characteristics of power generation/selling integrated companies.Therefore,this article proposes an assessment index system for assessing the operational performance of a power generation/selling integrated company,encompassing three dimensions:basic capacity,development potential,and external environment.A dynamic proportional adjustment coefficient is designed,along with a subjective and objective weighting model for assessment indexes based on a combined weightingmethod.Subsequently,the operational performance of an integrated company is assessed using extension theory.The results in the case study demonstrate the feasibility and effectiveness of the proposed dynamic proportional adjustment coefficient.

Assessment of Meteorological Threats to the Coordinated Search and Rescue of Unmanned/Manned Aircraft

The architecture and working principle of coordinated search and rescue system of unmanned/manned aircraft,which is composed of manned/unmanned aircraft and manned aircraft,were first introduced,and they can cooperate with each other to complete a search and rescue task.Secondly,a threat assessment method based on meteorological data was proposed,and potential meteorological threats,such as storms and rainfall,can be predicted by collecting and analyzing meteorological data.Finally,an experiment was carried out to evaluate the performance of the proposed method in different scenarios.The experimental results show that the coordinated search and rescue system of unmanned/manned aircraft can be used to effectively assess meteorological threats and provide accurate search and rescue guidance.

Transformation and assessment of Chinese pine pure plantations for soil and water conservation in western Liaoning Province,P. R. China

Western Liaoning Province is characterized by huge areas of lowly-efficient Chinese pine (Pinus tabulaeformis Carr.) pure plantations. How to transform these plantations has become an increasingly significant management problem. In this study, the authors summarized the approaches, which are based on close-to-nature silvicultural system, to transform the pure pine plantations. Native broadleaved trees were planted in three methods: 1) after strip clearcutting, 2) after patch clearcutting; 3) on the open forestland and the forest edge. The transformation targets and the selection of tree species were expatiated in this paper. The key techniques and their application conditions for each method were analyzed and discussed. Through investigation and contrastive analysis, the assessment was made to the stands transformed by strip method. Results showed that the mixed stands at 16 years after transformation had an obvious layered structure and the species richness of understorey vegetation increased by 23.5%–52.9%. Soil enzyme activities of urease, phosphatase and sucrase increased by 6%–142%, 46%–99% and 31%–200%, respetively. Moreover, the transformed stands could effectively control the occurrence of pine caterpillars in plantations. Consequently the transformations enhanced the function of soil and water conservation. Keywords Pinus tabulaeformis - Monoculture - Transformation - Principles and methods - Assessment CLC number S791.254 - S727.22 Document code A Foundation item: This research was supported by grants from the Chinese Academy of Sciences (KZCX3-SW-418), National Natural Science Foundation of China (30100144), and National Key Technologies R & D Program of China (96-007-01-06).Biography: ZENG De-hui (1965-), male, Ph.D. professor in the Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, P. R. ChinaResponsible editor: Zhu Hong

Risk assessment of oil and gas investment environment in countries along the Belt and Road Initiative

With the implementation of the Belt and Road Initiative, China is deepening its cooperation in oil and gas resources with countries along the Initiative. In order to better mitigate risks and enhance the safety of investments, it is of significant importance to research the oil and gas investment environment in these countries for China's overseas investment macro-layout. This paper proposes an indicator system including 27 indicators from 6 dimensions. On this basis, game theory models combined with global entropy method and analytic hierarchy process are applied to determine the combined weights, and the TOPSIS-GRA model is utilized to assess the risks of oil and gas investment in 76 countries along the Initiative from 2014 to 2021. Finally, the GM(1,1) model is employed to predict risk values for 2022-2025. In conclusion, oil and gas resources and political factors have the greatest impact on investment environment risk, and 12 countries with greater investment potential are selected through cluster analysis in conjunction with the predicted results. The research findings may provide scientific decisionmaking recommendations for the Chinese government and oil enterprises to strengthen oil and gas investment cooperation with countries along the Belt and Road Initiative.

Assessment of compressive strength of jet grouting by machine learning

Jet grouting is one of the most popular soil improvement techniques,but its design usually involves great uncertainties that can lead to economic cost overruns in construction projects.The high dispersion in the properties of the improved material leads to designers assuming a conservative,arbitrary and unjustified strength,which is even sometimes subjected to the results of the test fields.The present paper presents an approach for prediction of the uniaxial compressive strength(UCS)of jet grouting columns based on the analysis of several machine learning algorithms on a database of 854 results mainly collected from different research papers.The selected machine learning model(extremely randomized trees)relates the soil type and various parameters of the technique to the value of the compressive strength.Despite the complex mechanism that surrounds the jet grouting process,evidenced by the high dispersion and low correlation of the variables studied,the trained model allows to optimally predict the values of compressive strength with a significant improvement with respect to the existing works.Consequently,this work proposes for the first time a reliable and easily applicable approach for estimation of the compressive strength of jet grouting columns.

Assessment of the Benefits of Targeted Interventions for Pandemic Control in China Based on Machine Learning Method and Web Service for COVID-19 Policy Simulation

Taking the Chinese city of Xiamen as an example,simulation and quantitative analysis were performed on the transmissions of the Coronavirus Disease 2019(COVID-19)and the influence of intervention combinations to assist policymakers in the preparation of targeted response measures.A machine learning model was built to estimate the effectiveness of interventions and simulate transmission in different scenarios.The comparison was conducted between simulated and real cases in Xiamen.A web interface with adjustable parameters,including choice of intervention measures,intervention weights,vaccination,and viral variants,was designed for users to run the simulation.The total case number was set as the outcome.The cumulative number was 4,614,641 without restrictions and 78 under the strictest intervention set.Simulation with the parameters closest to the real situation of the Xiamen outbreak was performed to verify the accuracy and reliability of the model.The simulation model generated a duration of 52 days before the daily cases dropped to zero and the final cumulative case number of 200,which were 25 more days and 36 fewer cases than the real situation,respectively.Targeted interventions could benefit the prevention and control of COVID-19 outbreak while safeguarding public health and mitigating impacts on people’s livelihood.

Assessment of landscape ecology of agricultural protection forest system at Beizang Town, Daxing County, Beijing

Based on theories of protective forests and landscape ecology, the reasonableness of structures and patterns of shelterbelt system at Beizang Town, Daxing County, Beijing were analyzed and assessed from the two scales of forest belts and networks, by integrating uses of field investigation, GIS and RS techniques. Results showed that the existent main belt (3-12 m in width) was too narrow, while the assistant belt (3-27.1 m in width) was too wide; the species composition of the existent shelterbelts was single, and the structures and patterns of the shelterbelt system were unreasonable. It is suggested that the structure of the main and the assistant belts should be changed, such as increasing the width of main belts, decreasing the width of assistant belt, and planting more mixed species, and the pattern with arbores in the middle and shrubs in the sides of belts could be taken into account. For the landscape structure of forest network after regenerating or reconstruction, the grid number of closed network should be 13 per km2 and the minimum number of belts should be 34 per km2. This study also testified that integrating GIS and remote technique with landscape ecology could provide an effective method for reasonable reconstruction of the structures and patterns of shelterbelts system.

Assessment of China’s forest fi re occurrence with deep learning, geographic information and multisource data

Considerable economic losses and ecological damage can be caused by forest fi res,and compared to suppression,prevention is a much smarter strategy.Accordingly,this study focuses on developing a novel framework to assess forest fi re risks and policy decisions on forest fi re management in China.This framework integrated deep learning algorithms,geographic information,and multisource data.Compared to conventional approaches,our framework featured timesaving,easy implementation,and importantly,the use of deep learning that vividly integrates various factors from the environment and human activities.Information on 96,594 forest fi re points from 2001 to 2019 was collected on Moderate Resolution Imaging Spectroradiometer(MODIS)fi re hotspots from 2001 to 2019 from NASA’s Fire Information Resource Management System.The information was classifi ed into factors such as topography,climate,vegetation,and society.The prediction of forest fi re risk was generated using a fully connected network model,and spatial autocorrelation used to analyze the spatial aggregation correlation of active fi re hotspots in the whole area of China.The results show that high accuracy prediction of fi re risks was achieved(accuracy 87.4%,positive predictive value 87.1%,sensitivity 88.9%,area under curve(AUC)94.1%).Based on this,it was found that Chinese forest fi re risk shows signifi cant autocorrelation and agglomeration both in seasons and regions.For example,forest fi re risk usually raises dramatically in spring and winter,and decreases in autumn and summer.Compared to the national average,Yunnan Province,Guangdong Province,and the Greater Hinggan Mountains region of Heilongjiang Province have higher fi re risks.In contrast,a large region in central China has been recognized as having a long-term,low risk of forest fi res.All forest risks in each region were recorded into the database and could contribute to the forest fi re prevention.The successful assessment of forest fi re risks in this study provides a comprehensive knowledge of fi re risks in China over the last 20 years.Deep learning showed its advantage in integrating multiple factors in predicting forest fi re risks.This technical framework is expected to be a feasible evaluation tool for the occurrence of forest fi res in China.

Synthesis, Assessment of Biological Activity and Toxicity for N-(<i>β</i>-D-Glycopyranosyl)-Thiosemicarbazides

In the modern science, priority is given for the search of biological active compounds with specific properties. As a result of experimental data, it was found that in the reaction between N-(β-D-glycopyranosyl)-semicarbazide and the Lawesson reagent (2,4-bis(p-methoxyphenyl)-1,3-dithiadiphosphetane 2,4-disulfide) at the ratio 1:1 in pyridine when boiling under reflux in a water bath for 20 - 35 minutes, a new synthetic compound N-(β-D-glycopyranosyl)-thiosemicarbazide is formed. The individuality and structure of the target products were confirmed by 13C NMR spectroscopy, 1H NMR spectroscopy, IR spectroscopy, and elemental analysis. For the synthesized new compounds of N-(β-D-glycopyranosyl)-thiosemicarbazides, the probability of pharmacological and toxic effects were predicted by the computer method in silico. From the synthesized compounds N-(β-D-galactopyranosyl)-thiosemicarbazide, the probability of antibacterial (antibacterial) activity is predicted (Pa/Pi 0.544/0.013). The antibacterial activity of the compound (4) was confirmed in a test for salmonella infection of lambs, salmonellosis of calves, and colipathogenic E. coli serotypes. An experimental study by the in vitro method made it possible to conclude that the new synthetic compound N-(β-D-galactopyranosyl)-thiosemicarbazide in the studied concentrations has a pronounced bactericidal and bacteriostatic effect. The synthetic new compound N-(β-D-glyco- pyranosyl)-thiosemicarbazide is a promising compound for further study.

An Assessment of the Role of Art as a Resource for Learning and Development of the Child With Computer Applications

An assessment of the role of Art as a resource for learning and development of the child has become necessary now that education appears to be the yearning of all especially in the developing countries. This paper seeks to assess the role of Art in learning and development of a child. The paper stresses that the child needs Art to learn because children＇s attention is drawn first by pictures （Art works） before letters of Alphabet. According to the paper, children become more knowledgeable and creative as they participate in Art classes where they are given opportunities to express themselves while engaging in drawing and painting exercises. This leads to self-discovery and development in children. By this, entrepreneurial spirit is also imbibed. The paper also notes that children who would not ordinarily want to learn have found pleasure in learning with Art and computer. Here the use of cartoons and animations become appropriate in teaching the child. Children also become more adventurous and creative as they find pleasure in drawing with application software like Corel Draw, Microsoft Paint, and so on. Finally, the paper emphasizes the need for Art to be wholly infused in the curricular of schools especially the primaries. This will be a strong factor or agent of development in children. Here the creative and inventive spirit so developed and imbibed will lead to discoveries of future entrepreneurs, industrialists and technologists for national developments.

Postoperative accurate pain assessment of children and artificial intelligence: A medical hypothesis and planned study

Although the pediatric perioperative pain management has been improved in recent years,the valid and reliable pain assessment tool in perioperative period of children remains a challenging task.Pediatric perioperative pain management is intractable not only because children cannot express their emotions accurately and objectively due to their inability to describe physiological characteristics of feeling which are different from those of adults,but also because there is a lack of effective and specific assessment tool for children.In addition,exposure to repeated painful stimuli early in life is known to have short and long-term adverse sequelae.The short-term sequelae can induce a series of neurological,endocrine,cardiovascular system stress related to psychological trauma,while long-term sequelae may alter brain maturation process,which can lead to impair neurodevelopmental,behavioral,and cognitive function.Children’s facial expressions largely reflect the degree of pain,which has led to the developing of a number of pain scoring tools that will help improve the quality of pain mana-gement in children if they are continually studied in depth.The artificial inte-lligence(AI)technology represented by machine learning has reached an unprecedented level in image processing of deep facial models through deep convolutional neural networks,which can effectively identify and systematically analyze various subtle features of children’s facial expressions.Based on the construction of a large database of images of facial expressions in children with perioperative pain,this study proposes to develop and apply automatic facial pain expression recognition software using AI technology.The study aims to improve the postoperative pain management for pediatric population and the short-term and long-term quality of life for pediatric patients after operational event.

Assessment of glaucoma using extreme learning machine and fractal feature analysis

Dear Sir,Iam Dr.Kavitha S,from the Department of Electronics and Communication Engineering,Nandha Engineering College,Erode,Tamil Nadu,India.I write to present the detection of glaucoma using extreme learning machine(ELM)and fractal feature analysis.Glaucoma is the second most frequent cause of permanent blindness in industrial

Assessment of Satisfaction in the Reception and Emergency Department of a Health Center

Introduction The main objective of any healthcare establishment must be to ensure the quality of patient care and customer satisfaction. It is necessary to regularly assess patient satisfaction. The aim of this study was to assess the level of satisfaction of customers aged over 18 years attending the emergency department of the health center. Methodology This was a descriptive and analytical cross-sectional study of patients aged 18 years and over, who attended the Samu Municipal emergency department between 02 and 30 May 2023. The satisfaction index was determined using the adapted 2009 SAPHORA-MCO questionnaire and the Likert satisfaction scale. Results A total of 400 patients were surveyed. The average age was 35 years, with a standard deviation of 14.7. Of those surveyed, 51% were women, 87% were educated, 50% lived in Grand Yoff and 59.5% were unemployed. Satisfaction levels linked to perception of the cost of care (72%), waiting time (64.3%), information given to patients (69.1%) and pain management (74 .5%) are fair. On the other hand, the levels of satisfaction linked to administrative procedures (82.5%), staff attitudes towards patients (84%), staff availability (86.4%), patient privacy (89.2%), general atmosphere (87.2%), staff competence (87.3%), and the effectiveness of care (89.4%) were satisfactory. The average waiting time was 38 minutes. However, 32% of patients waited less than 30 minutes and 92% less than an hour. The satisfaction index linked to administration and reception was 72.9% and 79.85%, respectively. The satisfaction index linked to the administration and technical quality of care is equal to 85.8% and 83.7%, respectively. The overall satisfaction index is equal to 80.6%;the level of satisfaction of users of the health structure is satisfactory. Conclusion Patient satisfaction is an essential part of quality care. Patient satisfaction must be based on effective communication from the healthcare team and the creation of a patient-caregiver relationship.

Towards Automated Assessment of Learning Management Systems in Higher Education Institutions in Zambia

Zambia like any other country in most African regions is still grappling with the dynamics of harnessing technology for the betterment of Higher Education. The onset of the Covid 19 pandemic brought a test for the preparedness of the Zambian Higher Education Institutions (HEIs) in harnessing technology for pedagogical activities. As countries worldwide switched to electronic learning during the pandemic, the same could not be said for Zambian HEIs. Zambian HEIs struggled to conduct pedagogical activities on learning management platforms. This study investigated the factors affecting the implementation and assessment of learning Management systems in Zambia’s HEIs. With its focus on assessing: 1) system features, 2) compliance with regulatory standards, 3) quality of service and 4) technology acceptance as the four key assessment areas of an LMS, this article proposed a model for assessing learning management systems in Zambian HEIs. To test the proposed model, a software tool was also developed.