During periods of global warming (GW), expected increases in urban temperatures can have adverse impacts on city climate and thermal discomfort due to combined urban and global warming effects. The different climates ...During periods of global warming (GW), expected increases in urban temperatures can have adverse impacts on city climate and thermal discomfort due to combined urban and global warming effects. The different climates in four cities in Israel are studied for the purpose of differentiating global vs. urban warming. Trends in urban and nearby rural areas were compared in order to estimate the urbanization effect on the local climate zones. Daily 06:00 and 15:00 Local Time (LT) temperatures for July 1980-2014 were investigated. The linear relationship between the urban warming and population growth observed in present climate data is assumed to continue into the near future. The Regional Climate Model (RegCM) temperature trends into the 21st century are assumed to represent primarily the GW because of the relatively coarse grid interval of 25 km. Hence, this study first differentiates between global and local warming past trends, and then uses this past result to make future projections that consider both factors. A unique feature of this study is the large climatic variety over Israel—a small country that encompasses no less than 5 different K?ppen climatic zones. The urban minus rural temperature (1980-2014) changes, ΔTu-r, show more intense warming in the afternoon in all 4 cities. For instance, in Jerusalem and Eilat, the ΔTu-r has increased by ~1.2°C. Following the RegCM predictions, by 2060 with “No population growth”, this temperature increase is expected to continue, by 1.114°C and 1.119°C, respectively. If, however, these cities grow rapidly, air temperature will increase by 2.937°C - 4.129°C and 2.778°C - 3.939°C, respectively.展开更多
This study investigates annual earnings analysis with ARIMA （Autoregressive Integrated Moving Average） for future earnings prediction. Earnings prediction is very important to be used in various aspect of decision m...This study investigates annual earnings analysis with ARIMA （Autoregressive Integrated Moving Average） for future earnings prediction. Earnings prediction is very important to be used in various aspect of decision making process, such as： investor, creditor, analyst, academicians, practitioners, etc.. Evidence supports the ARIMA model that it is more accurate. It also has a smaller size of error value.展开更多
Rapid regional population shifts and spatial polarization have heightened pressure on cultivated land—a critical resource demanding urgent attention amid ongoing urban-rural transition.This study selects Jiangsu prov...Rapid regional population shifts and spatial polarization have heightened pressure on cultivated land—a critical resource demanding urgent attention amid ongoing urban-rural transition.This study selects Jiangsu province,a national leader in both economic and agricultural development,as a case area to construct a multidimensional framework for assessing the recessive morphological characteristics of multifunctional cultivated land use.We examine temporal dynamics,spatial heterogeneity,and propose an integrated zoning strategy based on empirical analysis.The results reveal that:(1)The recessive morphology index shows a consistent upward trend,with structural breaks in 2007 and 2013,and a spatial shift from“higher in the east and lower in the west”to“higher in the south and lower in the north.”(2)Coordination among sub-dimensions of the index has steadily improved.(3)The index is expected to continue rising in the next decade,though at a slower pace.(4)To promote coordinated multidimensional land-use development,we recommend a policy framework that reinforces existing strengths,addresses weaknesses,and adapts zoning schemes to current spatial conditions.This research offers new insights into multifunctional cultivated land systems and underscores their role in enhancing human well-being,securing food supply,and supporting sustainable urban-rural integration.展开更多
Climate change is one of the major global challenges and it can have a significant influence on the behaviour and resilience of geotechnical structures.The changes in moisture content in soil lead to effective stress ...Climate change is one of the major global challenges and it can have a significant influence on the behaviour and resilience of geotechnical structures.The changes in moisture content in soil lead to effective stress changes and can be accompanied by significant volume changes in reactive/expansive soils.The volume change leads to ground movement and can exert additional stresses on structures founded on or within a shallow depth of such soils.Climate change is likely to amplify the ground movement potential and the associated problems are likely to worsen.The effect of atmospheric boundary interaction on soil behaviour has often been correlated to Thornthwaite moisture index(TMI).In this study,the long-term weather data and anticipated future projections for various emission scenarios were used to generate a series of TMI maps for Australia.The changes in TMI were then correlated to the depth of suction change(H s),an important input in ground movement calculation.Under all climate scenarios considered,reductions in TMI and increases in H s values were observed.A hypothetical design scenario of a footing on expansive soil under current and future climate is discussed.It is observed that a design that might be considered adequate under the current climate scenario,may fail under future scenarios and accommodations should be made in the design for such events.展开更多
In modern warfare,unpowered glide munitions,represented by JDAM,are widely used.Accurately predicting the future trajectory of such targets is crucial for intercepting them.This paper proposes a future point predictio...In modern warfare,unpowered glide munitions,represented by JDAM,are widely used.Accurately predicting the future trajectory of such targets is crucial for intercepting them.This paper proposes a future point prediction method for unpowered gliding targets based on attitude computation.By estimating the current state of the target,we derive the target’s attitude coordinate system.Subsequently,the paper analyzes the forces acting on the target and updates the state transition matrix,ultimately calculating the future position of the target.Experimental results show that,compared to traditional methods,this approach improves the accuracy of future point predictions by 9%to 45%.展开更多
The futures trading market is an important part of the financial markets and soybeans are one of the most strategically important crops in the world.How to predict soybean future price is a challenging topic being stu...The futures trading market is an important part of the financial markets and soybeans are one of the most strategically important crops in the world.How to predict soybean future price is a challenging topic being studied by many researchers.This paper proposes a novel hybrid soybean future price prediction model which includes two stages of data preprocessing and deep learning prediction.In the data preprocessing stage,futures price series are decomposed into subsequences using the ICEEMDAN(improved complete ensemble empirical mode decomposition with adaptive noise)method.The Lempel-Ziv complexity determination method was then used to identify and reconstruct high-frequency subsequences.Finally,the high frequency component is decomposed secondarily using variational mode decomposition optimized by beluga whale optimization algorithm.In the deep learning prediction stage,a deep extreme learning machine optimized by the sparrow search algorithm was used to obtain the prediction results of all subseries and reconstructs them to obtain the final soybean future price prediction results.Based on the experimental results of soybean future price markets in China,Italy,and the United States,it was found that the hybrid method proposed provides superior performance in terms of prediction accuracy and robustness.展开更多
This study explains the multi-decadal shoreline changes along the coast of Kanyakumari from 1980 to2020.The shorelines are extracted from the Landsat images to estimate the shoreline dynamics and future predictions us...This study explains the multi-decadal shoreline changes along the coast of Kanyakumari from 1980 to2020.The shorelines are extracted from the Landsat images to estimate the shoreline dynamics and future predictions using Digital Shoreline Analysis System(DSAS).By the estimation of End Point Rate(EPR)and Linear Regression Rate(LRR),it is quantified that the maximum erosion is 5.01 m/yr(EPR)and 6.13 m/yr(LRR)consistently with the maximum accretion of 3.77 m/yr(EPR)and 3.11 m/yr(LRR)along the entire coastal stretch of 77 km.The future shoreline predicted using the Kalman filter forecasted that Inayam,Periyakattuthurai and Kodimunai are highly prone to erosion with a shift of 170 m,157 m and 145 m by 2030 and 194 m,182 m and 165 m by 2040 towards the land.Also,the western coast is highly prone to erosion and it is predicted that certain villages are prone to loss of economy and livelihood.The outcome of this study may guide the coastal researchers to understand the evolution and decisionmakers to evolve with alternative sustainable management plans in the future.展开更多
In this work,we present a model that uses the fractional order Caputo derivative for the novel Coronavirus disease 2019(COVID-19)with different hospitalization strategies for severe and mild cases and incorporate an a...In this work,we present a model that uses the fractional order Caputo derivative for the novel Coronavirus disease 2019(COVID-19)with different hospitalization strategies for severe and mild cases and incorporate an awareness program.We generalize the SEIR model of the spread of COVID-19 with a private focus on the transmissibility of people who are aware of the disease and follow preventative health measures and people who are ignorant of the disease and do not follow preventive health measures.Moreover,individuals with severe,mild symptoms and asymptomatically infected are also considered.The basic reproduction number(R0)and local stability of the disease-free equilibrium(DFE)in terms of R0 are investigated.Also,the uniqueness and existence of the solution are studied.Numerical simulations are performed by using some real values of parameters.Furthermore,the immunization of a sample of aware susceptible individuals in the proposed model to forecast the effect of the vaccination is also considered.Also,an investigation of the effect of public awareness on transmission dynamics is one of our aim in this work.Finally,a prediction about the evolution of COVID-19 in 1000 days is given.For the qualitative theory of the existence of a solution,we use some tools of nonlinear analysis,including Lipschitz criteria.Also,for the numerical interpretation,we use the Adams-Moulton-Bashforth procedure.All the numerical results are presented graphically.展开更多
This research critically examines the alarming case of biodiversity loss in Gopalganj, Bangladesh, focusing on identifying the causes of this decline and assessing its long-term impact on ecosystems and communities. T...This research critically examines the alarming case of biodiversity loss in Gopalganj, Bangladesh, focusing on identifying the causes of this decline and assessing its long-term impact on ecosystems and communities. The main reason is anthropogenic activities, including land conversion, and infrastructure using a comprehensive approach. This research employs a combination of primary and secondary data analysis techniques, encompassing surveys, focus group discussions, interviews, and field surveys. Findings: A staggering biological decline in ethnic diversity seems predictions point in the direction of it is an alarming trend that will take place by 2054. At the same time, the study reveals a worrying decline in vegetation and a dramatic expansion of built-up areas. In light of these findings, this paper strongly emphasizes the urgent need for immediate and coordinated conservation efforts. The proposed measures include conservation and restoration of critical areas, strong measures to reduce greenhouse gas emissions, proactive climate adaptation planning, promotion of sustainable agricultural and forestry practices, and strong public awareness campaigns to emphasize the critical importance of biodiversity conservation. Collectively, these actions are pivotal in safeguarding Gopalganj’s rich biodiversity and ensuring a sustainable future for the region and the planet at large.展开更多
Predicting the future health state of a transformer can offer early warning of latent defects and faults within the transformer,thereby facilitating the formulation of power outage maintenance plans and power dispatch...Predicting the future health state of a transformer can offer early warning of latent defects and faults within the transformer,thereby facilitating the formulation of power outage maintenance plans and power dispatch strategies.However,existing prediction methods based on the structure of‘splicing prediction and diagnosis method’suffer from limitations such as inability to achieve global optimality,error accumulation,and low prediction accuracy.To fill this gap,a novel direct prediction method of a trans-former state based on knowledge and data fusion-driven model(K&DFDM)is pro-posed in this paper.Firstly,a state quantity data space is constructed to comprehensively reflect the changes in the health state of the transformer over time,encompassing online monitoring,offline testing,evaluation results,and actual operation data.After that,correlation knowledge between state quantities,fault diagnosis mechanism knowledge,current diagnosis experience knowledge,and uncertain fuzzy knowledge are extracted separately.The actual fault mechanism,existing expert experience,and other knowledge in the diagnosis process are quantified.Then,the attention model is sub-sequently optimised,leveraging quantitative knowledge to effectively constrain and guide the data prediction process.Incorporating fault diagnosis mechanism knowledge into the data prediction model enables the achievement of global optimisation in both diagnosis and prediction.The integration of traditional expert experience knowledge and the correlation knowledge between state quantities serves as constraints during the process of attaining the global optimum.The verification results,comprising 327 cases,demonstrate that K&DFDM effectively addresses the issue of error superposition encountered by existing state prediction methods,leading to a direct state prediction accuracy of 96.33%.展开更多
文摘During periods of global warming (GW), expected increases in urban temperatures can have adverse impacts on city climate and thermal discomfort due to combined urban and global warming effects. The different climates in four cities in Israel are studied for the purpose of differentiating global vs. urban warming. Trends in urban and nearby rural areas were compared in order to estimate the urbanization effect on the local climate zones. Daily 06:00 and 15:00 Local Time (LT) temperatures for July 1980-2014 were investigated. The linear relationship between the urban warming and population growth observed in present climate data is assumed to continue into the near future. The Regional Climate Model (RegCM) temperature trends into the 21st century are assumed to represent primarily the GW because of the relatively coarse grid interval of 25 km. Hence, this study first differentiates between global and local warming past trends, and then uses this past result to make future projections that consider both factors. A unique feature of this study is the large climatic variety over Israel—a small country that encompasses no less than 5 different K?ppen climatic zones. The urban minus rural temperature (1980-2014) changes, ΔTu-r, show more intense warming in the afternoon in all 4 cities. For instance, in Jerusalem and Eilat, the ΔTu-r has increased by ~1.2°C. Following the RegCM predictions, by 2060 with “No population growth”, this temperature increase is expected to continue, by 1.114°C and 1.119°C, respectively. If, however, these cities grow rapidly, air temperature will increase by 2.937°C - 4.129°C and 2.778°C - 3.939°C, respectively.
文摘This study investigates annual earnings analysis with ARIMA （Autoregressive Integrated Moving Average） for future earnings prediction. Earnings prediction is very important to be used in various aspect of decision making process, such as： investor, creditor, analyst, academicians, practitioners, etc.. Evidence supports the ARIMA model that it is more accurate. It also has a smaller size of error value.
基金National Natural Science Foundation of China,No.42101252。
文摘Rapid regional population shifts and spatial polarization have heightened pressure on cultivated land—a critical resource demanding urgent attention amid ongoing urban-rural transition.This study selects Jiangsu province,a national leader in both economic and agricultural development,as a case area to construct a multidimensional framework for assessing the recessive morphological characteristics of multifunctional cultivated land use.We examine temporal dynamics,spatial heterogeneity,and propose an integrated zoning strategy based on empirical analysis.The results reveal that:(1)The recessive morphology index shows a consistent upward trend,with structural breaks in 2007 and 2013,and a spatial shift from“higher in the east and lower in the west”to“higher in the south and lower in the north.”(2)Coordination among sub-dimensions of the index has steadily improved.(3)The index is expected to continue rising in the next decade,though at a slower pace.(4)To promote coordinated multidimensional land-use development,we recommend a policy framework that reinforces existing strengths,addresses weaknesses,and adapts zoning schemes to current spatial conditions.This research offers new insights into multifunctional cultivated land systems and underscores their role in enhancing human well-being,securing food supply,and supporting sustainable urban-rural integration.
基金supported by President’s Scholarships from the University of South Australia towards his PhD study。
文摘Climate change is one of the major global challenges and it can have a significant influence on the behaviour and resilience of geotechnical structures.The changes in moisture content in soil lead to effective stress changes and can be accompanied by significant volume changes in reactive/expansive soils.The volume change leads to ground movement and can exert additional stresses on structures founded on or within a shallow depth of such soils.Climate change is likely to amplify the ground movement potential and the associated problems are likely to worsen.The effect of atmospheric boundary interaction on soil behaviour has often been correlated to Thornthwaite moisture index(TMI).In this study,the long-term weather data and anticipated future projections for various emission scenarios were used to generate a series of TMI maps for Australia.The changes in TMI were then correlated to the depth of suction change(H s),an important input in ground movement calculation.Under all climate scenarios considered,reductions in TMI and increases in H s values were observed.A hypothetical design scenario of a footing on expansive soil under current and future climate is discussed.It is observed that a design that might be considered adequate under the current climate scenario,may fail under future scenarios and accommodations should be made in the design for such events.
文摘In modern warfare,unpowered glide munitions,represented by JDAM,are widely used.Accurately predicting the future trajectory of such targets is crucial for intercepting them.This paper proposes a future point prediction method for unpowered gliding targets based on attitude computation.By estimating the current state of the target,we derive the target’s attitude coordinate system.Subsequently,the paper analyzes the forces acting on the target and updates the state transition matrix,ultimately calculating the future position of the target.Experimental results show that,compared to traditional methods,this approach improves the accuracy of future point predictions by 9%to 45%.
基金fully supported by the National Natural Science Foundation of China(52072412)。
文摘The futures trading market is an important part of the financial markets and soybeans are one of the most strategically important crops in the world.How to predict soybean future price is a challenging topic being studied by many researchers.This paper proposes a novel hybrid soybean future price prediction model which includes two stages of data preprocessing and deep learning prediction.In the data preprocessing stage,futures price series are decomposed into subsequences using the ICEEMDAN(improved complete ensemble empirical mode decomposition with adaptive noise)method.The Lempel-Ziv complexity determination method was then used to identify and reconstruct high-frequency subsequences.Finally,the high frequency component is decomposed secondarily using variational mode decomposition optimized by beluga whale optimization algorithm.In the deep learning prediction stage,a deep extreme learning machine optimized by the sparrow search algorithm was used to obtain the prediction results of all subseries and reconstructs them to obtain the final soybean future price prediction results.Based on the experimental results of soybean future price markets in China,Italy,and the United States,it was found that the hybrid method proposed provides superior performance in terms of prediction accuracy and robustness.
文摘This study explains the multi-decadal shoreline changes along the coast of Kanyakumari from 1980 to2020.The shorelines are extracted from the Landsat images to estimate the shoreline dynamics and future predictions using Digital Shoreline Analysis System(DSAS).By the estimation of End Point Rate(EPR)and Linear Regression Rate(LRR),it is quantified that the maximum erosion is 5.01 m/yr(EPR)and 6.13 m/yr(LRR)consistently with the maximum accretion of 3.77 m/yr(EPR)and 3.11 m/yr(LRR)along the entire coastal stretch of 77 km.The future shoreline predicted using the Kalman filter forecasted that Inayam,Periyakattuthurai and Kodimunai are highly prone to erosion with a shift of 170 m,157 m and 145 m by 2030 and 194 m,182 m and 165 m by 2040 towards the land.Also,the western coast is highly prone to erosion and it is predicted that certain villages are prone to loss of economy and livelihood.The outcome of this study may guide the coastal researchers to understand the evolution and decisionmakers to evolve with alternative sustainable management plans in the future.
基金The authors Kamal Shah,and Thabet Abdeljawad would like to thank Prince Sultan University for paying the APC.
文摘In this work,we present a model that uses the fractional order Caputo derivative for the novel Coronavirus disease 2019(COVID-19)with different hospitalization strategies for severe and mild cases and incorporate an awareness program.We generalize the SEIR model of the spread of COVID-19 with a private focus on the transmissibility of people who are aware of the disease and follow preventative health measures and people who are ignorant of the disease and do not follow preventive health measures.Moreover,individuals with severe,mild symptoms and asymptomatically infected are also considered.The basic reproduction number(R0)and local stability of the disease-free equilibrium(DFE)in terms of R0 are investigated.Also,the uniqueness and existence of the solution are studied.Numerical simulations are performed by using some real values of parameters.Furthermore,the immunization of a sample of aware susceptible individuals in the proposed model to forecast the effect of the vaccination is also considered.Also,an investigation of the effect of public awareness on transmission dynamics is one of our aim in this work.Finally,a prediction about the evolution of COVID-19 in 1000 days is given.For the qualitative theory of the existence of a solution,we use some tools of nonlinear analysis,including Lipschitz criteria.Also,for the numerical interpretation,we use the Adams-Moulton-Bashforth procedure.All the numerical results are presented graphically.
文摘This research critically examines the alarming case of biodiversity loss in Gopalganj, Bangladesh, focusing on identifying the causes of this decline and assessing its long-term impact on ecosystems and communities. The main reason is anthropogenic activities, including land conversion, and infrastructure using a comprehensive approach. This research employs a combination of primary and secondary data analysis techniques, encompassing surveys, focus group discussions, interviews, and field surveys. Findings: A staggering biological decline in ethnic diversity seems predictions point in the direction of it is an alarming trend that will take place by 2054. At the same time, the study reveals a worrying decline in vegetation and a dramatic expansion of built-up areas. In light of these findings, this paper strongly emphasizes the urgent need for immediate and coordinated conservation efforts. The proposed measures include conservation and restoration of critical areas, strong measures to reduce greenhouse gas emissions, proactive climate adaptation planning, promotion of sustainable agricultural and forestry practices, and strong public awareness campaigns to emphasize the critical importance of biodiversity conservation. Collectively, these actions are pivotal in safeguarding Gopalganj’s rich biodiversity and ensuring a sustainable future for the region and the planet at large.
基金Research on Robust Decision and Full Stack Optimisation Techniques for Cloud Edge Intelligent Systems for Substation Inspection,Grant/Award Number:52550022001J。
文摘Predicting the future health state of a transformer can offer early warning of latent defects and faults within the transformer,thereby facilitating the formulation of power outage maintenance plans and power dispatch strategies.However,existing prediction methods based on the structure of‘splicing prediction and diagnosis method’suffer from limitations such as inability to achieve global optimality,error accumulation,and low prediction accuracy.To fill this gap,a novel direct prediction method of a trans-former state based on knowledge and data fusion-driven model(K&DFDM)is pro-posed in this paper.Firstly,a state quantity data space is constructed to comprehensively reflect the changes in the health state of the transformer over time,encompassing online monitoring,offline testing,evaluation results,and actual operation data.After that,correlation knowledge between state quantities,fault diagnosis mechanism knowledge,current diagnosis experience knowledge,and uncertain fuzzy knowledge are extracted separately.The actual fault mechanism,existing expert experience,and other knowledge in the diagnosis process are quantified.Then,the attention model is sub-sequently optimised,leveraging quantitative knowledge to effectively constrain and guide the data prediction process.Incorporating fault diagnosis mechanism knowledge into the data prediction model enables the achievement of global optimisation in both diagnosis and prediction.The integration of traditional expert experience knowledge and the correlation knowledge between state quantities serves as constraints during the process of attaining the global optimum.The verification results,comprising 327 cases,demonstrate that K&DFDM effectively addresses the issue of error superposition encountered by existing state prediction methods,leading to a direct state prediction accuracy of 96.33%.
