The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often...The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often advanced one dimension—such as Internet of Things(IoT)-based data acquisition,Artificial Intelligence(AI)-driven analytics,or digital twin visualization—without fully integrating these strands into a single operational loop.As a result,many existing solutions encounter bottlenecks in responsiveness,interoperability,and scalability,while also leaving concerns about data privacy unresolved.This research introduces a hybrid AI–IoT–Digital Twin framework that combines continuous sensing,distributed intelligence,and simulation-based decision support.The design incorporates multi-source sensor data,lightweight edge inference through Convolutional Neural Networks(CNN)and Long ShortTerm Memory(LSTM)models,and federated learning enhanced with secure aggregation and differential privacy to maintain confidentiality.A digital twin layer extends these capabilities by simulating city assets such as traffic flows and water networks,generating what-if scenarios,and issuing actionable control signals.Complementary modules,including model compression and synchronization protocols,are embedded to ensure reliability in bandwidth-constrained and heterogeneous urban environments.The framework is validated in two urban domains:traffic management,where it adapts signal cycles based on real-time congestion patterns,and pipeline monitoring,where it anticipates leaks through pressure and vibration data.Experimental results show a 28%reduction in response time,a 35%decrease in maintenance costs,and a marked reduction in false positives relative to conventional baselines.The architecture also demonstrates stability across 50+edge devices under federated training and resilience to uneven node participation.The proposed system provides a scalable and privacy-aware foundation for predictive urban infrastructure management.By closing the loop between sensing,learning,and control,it reduces operator dependence,enhances resource efficiency,and supports transparent governance models for emerging smart cities.展开更多
Objective: to analyze the clinical effect of predictive nursing management in accompanying patients in department of hospital infection. Methods: a total of 90 infected patients who were admitted to our hospital in th...Objective: to analyze the clinical effect of predictive nursing management in accompanying patients in department of hospital infection. Methods: a total of 90 infected patients who were admitted to our hospital in the past year were randomly selected as the exploration target of this experiment. 90 patients were equally divided into the control group and the experimental group, 45 patients in each group. The patients in the control group used the common nursing method, while the patients in the experimental group used the common + predictive nursing method. After the same care cycle was performed, the attack rate of cross-infection and care satisfaction of the two groups were compared and analyzed. Results: the patients' satisfaction degree of nursing in experimental group was 24% higher than that in control group. The number of patients with cross-infection in the experimental group was 15 fewer than that in the control group. (P < 0.02) Discussion: applying predictive nursing management to the nursing work in the department of nosocomial infection can greatly reduce the probability of complications of patients and enhance the patients' nursing satisfaction. It has good efficacy and can be widely promoted in other departments of the hospital.展开更多
This paper evaluates accommodation of conflict management strategies and board performances in oil and gas sector.The study details the reflection of effectiveness,efficiency and productivity as the answer to th...This paper evaluates accommodation of conflict management strategies and board performances in oil and gas sector.The study details the reflection of effectiveness,efficiency and productivity as the answer to thorough efficiency in accommodation of conflict management in oil and gas sector,these parameters in the system express their efficacy on conflict management in these multinationals,this implies that for thorough efficiency,these variables must work simultaneously for effective and efficient in structural organization that can be a leading multinational sector in oil and gas environment.The study observed Linearized result from graphical representation explaining predominant lower efficiency and little higher efficiency in accommodation of conflict management in oil and gas companies.These experiences from the study monitor the system from generated simulation values that describe the growth rates in exponential phase of accommodation conflict strategic management.Despite exponential phase the results experienced lower parameters,when comparing on its variations showing its poor efficiency as observed in the study.Few periods observed higher effective accommodation on conflict strategic management.The developed model stimulation values were subjected to validation and both parameters generated favourable fits correlation,the study expressed the deficiency on accommodation of conflict management strategy thus developed models that can monitor the fluctuation and progressive state of accommodation on conflict management strategy,it defines the reflection of other parameters that express the behaviour of the system in terms of conceptual approach to monitor these type of strategic management in oil and gas companies.展开更多
The paper gives an overview on the need for smart coupling for battery management in grid integrated renewable energy system （RES）. Grid integrated photovoltaic （PV） battery system, as being popular and extensivel...The paper gives an overview on the need for smart coupling for battery management in grid integrated renewable energy system （RES）. Grid integrated photovoltaic （PV） battery system, as being popular and extensively used has been discussed in the paper. Smart coupling refers to intelligent grid integration such that it can foresee local network conditions and issue battery power flow management strategy accordingly to shave the peak PV and peak load. Therefore, a need for predictive energy management arises for smart integration to the grid and supervision of the power flow in accordance to the grid conditions. This is also a running project at the Institute of Energy Systems （INES）, Offenburg University of Applied Science, Germany since January, 2015. The paper should provide insights to the motivation, need and gives an outlook to the features of desired predictive energy management system （PEMS）.展开更多
https://www. sciencedirect. com/journal/energy-and-buildings/vol/346/suppl/C Volume346, 1November2025[OA](1)Towards energy flexible commercial buildings:Machine learning approaches,implementation aspects,and future re...https://www. sciencedirect. com/journal/energy-and-buildings/vol/346/suppl/C Volume346, 1November2025[OA](1)Towards energy flexible commercial buildings:Machine learning approaches,implementation aspects,and future research directions by M. M. A. L. N. Maheepala,Hangxin Li,Dilan Robert,et al,Article116170Abstract:Commercial buildings encounter considerable challenges in predicting and managing energy flexibility,arising from the complexity of their energy systems and the interdependencies among system components and building thermal mass. Nonetheless,the emergence of “smarter buildings” creates significant opportunities for applying machine learning(ML)techniques in energy flexibility.展开更多
We propose a hybrid and contingent approach to the management of digital projects,based on the assumption that there is not an absolute best way,but the approach should fit context and environment.The pillars of our c...We propose a hybrid and contingent approach to the management of digital projects,based on the assumption that there is not an absolute best way,but the approach should fit context and environment.The pillars of our contingent framework are Agile methodologies,predictive project management,and AI.The analysis of project management methodologies is based on a layered project model.Specifically,we identify 4 project concentric layers for both Agile and predictive project management.That layered model allows us to deploy different methodologies consistently with the distinct characteristics of each project.We describe the key characteristics of each approach and,also,we map Agile methodologies on the sociotechnical profile of projects.Finally,we summarize the impact of AI on predictive and hybrid projects.展开更多
This paper aims to answer how to use traffic information to design energy management strategies for fuel cell buses in a networked environment.For the buses entering the bus stops scenario,this paper proposes a hierar...This paper aims to answer how to use traffic information to design energy management strategies for fuel cell buses in a networked environment.For the buses entering the bus stops scenario,this paper proposes a hierarchical energy management strategy for fuel cell buses,which considers the traffic information near the bus stops.In the upper-level trajectory planning stage,the optimal SOC trajectory under various historical traffic conditions is solved through dynamic planning.The traffic information and the best SOC trajectory are mapped through BiLSTM,which can achieve fast,real-time long-term SOC reference.In the lower-level real-time predictive energy management strategy,the optimal SOC is used as the state reference to guide the predictive energy management of fuel cell buses when entering the bus stops.Simulation results show that compared with the strategy without SOC trajectory reference,the life cost of the proposed strategy is reduced by 13.8%,and the total cost is reduced by 3.61%.The SOC of the proposed strategy is closer to the DP optimal solution.展开更多
This study provides details of the energy management architecture used in the Goldwind microgrid test bed. A complete mathematical model, including all constraints and objectives, for microgrid operational management ...This study provides details of the energy management architecture used in the Goldwind microgrid test bed. A complete mathematical model, including all constraints and objectives, for microgrid operational management is first described using a modified prediction interval scheme. Forecasting results are then achieved every 10 min using the modified fuzzy prediction interval model, which is trained by particle swarm optimization.A scenario set is also generated using an unserved power profile and coverage grades of forecasting to compare the feasibility of the proposed method with that of the deterministic approach. The worst case operating points are achieved by the scenario with the maximum transaction cost. In summary, selection of the maximum transaction operating point from all the scenarios provides a cushion against uncertainties in renewable generation and load demand.展开更多
Aims In view of the growing interest in modelling the potential spread of invasive species,prediction of plant invasiveness on the basis of native range size holds considerable promise.Our objective was to use a simpl...Aims In view of the growing interest in modelling the potential spread of invasive species,prediction of plant invasiveness on the basis of native range size holds considerable promise.Our objective was to use a simple model to evaluate whether a wider native range predisposes plant species to become invasive in non-native regions and to easily identify potential invaders on this basis.The Kashmir Himalayan alien flora,of which a large proportion is native to Europe,was used to test this model.Methods The Kashmir Himalayan alien flora comprises 436 species of vascular plants at different stages of invasion.We focussed on plant species at two critical invasion stages(sensu Colautti and MacIsaac 2004),i.e.Stage II(species that are just at the earliest phase of introduction)and Stage V(species that are widespread and dominant in the invaded region and are thus considered invasive).We used the territorial distribution in Europe(number of countries)as a surrogate for the native range size of plants of European origin.Important Findings Using a subset of 88 species,for which information on the native European range was available,we showed that a large proportion(68%)of Stage II species growing in the Kashmir Valley had a relatively restricted European range(present in<20 countries);on the other hand,77%of Stage V species had an extensive native range(present in>20 countries).We consequently hypothesized that 14 Kashmir Himalayan Stage II species of European origin that are distributed in>20 European countries are at risk of becoming future invaders in Kashmir.On the other hand,those Kashmir Himalayan Stage II species of European origin distributed in<20 European countries are less likely to become invasive.Although this analysis is quite simple,the data suggest that a wider native range is a good predictor of plant invasiveness and could be used as a simple and low-cost early warning tool in predicting potential invasive species.展开更多
Safety performance functions（SPFs） are crucial to science-based road safety management.Success in developing and applying SPFs, apart data quality and availability, depends fundamentally on two key factors： the val...Safety performance functions（SPFs） are crucial to science-based road safety management.Success in developing and applying SPFs, apart data quality and availability, depends fundamentally on two key factors： the validity of the statistical inferences for the available data and on how well the data can be organized into distinct homogeneous entities. The latter aspect plays a key role in the identification and treatment of road sections or corridors with problems related to safety. Indeed, the segmentation of a road network could be especially critical in the development of SPFs that could be used in safety management for roadway types, such as motorways（freeways in North America）, which have a large number of variables that could result in very short segments if these are desired to be homogeneous. This consequence, from an analytical point of view, can be a problem when the location of crashes is not precise and when there is an overabundance of segments with zero crashes. Lengthening the segments for developing and applying SPFs can mitigate this problem, but at a sacrifice of homogeneity. This paper seeks to address this dilemma by investigating four approaches for segmentation for motorways, using sample data from Italy. The best results were obtained for the segmentation based on two curves and two tangents within a segment and with fixed length segments. The segmentation characterized by a constant value of all original variables inside each segment was the poorest approach by all measures.展开更多
基金The researchers would like to thank the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025)。
文摘The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often advanced one dimension—such as Internet of Things(IoT)-based data acquisition,Artificial Intelligence(AI)-driven analytics,or digital twin visualization—without fully integrating these strands into a single operational loop.As a result,many existing solutions encounter bottlenecks in responsiveness,interoperability,and scalability,while also leaving concerns about data privacy unresolved.This research introduces a hybrid AI–IoT–Digital Twin framework that combines continuous sensing,distributed intelligence,and simulation-based decision support.The design incorporates multi-source sensor data,lightweight edge inference through Convolutional Neural Networks(CNN)and Long ShortTerm Memory(LSTM)models,and federated learning enhanced with secure aggregation and differential privacy to maintain confidentiality.A digital twin layer extends these capabilities by simulating city assets such as traffic flows and water networks,generating what-if scenarios,and issuing actionable control signals.Complementary modules,including model compression and synchronization protocols,are embedded to ensure reliability in bandwidth-constrained and heterogeneous urban environments.The framework is validated in two urban domains:traffic management,where it adapts signal cycles based on real-time congestion patterns,and pipeline monitoring,where it anticipates leaks through pressure and vibration data.Experimental results show a 28%reduction in response time,a 35%decrease in maintenance costs,and a marked reduction in false positives relative to conventional baselines.The architecture also demonstrates stability across 50+edge devices under federated training and resilience to uneven node participation.The proposed system provides a scalable and privacy-aware foundation for predictive urban infrastructure management.By closing the loop between sensing,learning,and control,it reduces operator dependence,enhances resource efficiency,and supports transparent governance models for emerging smart cities.
文摘Objective: to analyze the clinical effect of predictive nursing management in accompanying patients in department of hospital infection. Methods: a total of 90 infected patients who were admitted to our hospital in the past year were randomly selected as the exploration target of this experiment. 90 patients were equally divided into the control group and the experimental group, 45 patients in each group. The patients in the control group used the common nursing method, while the patients in the experimental group used the common + predictive nursing method. After the same care cycle was performed, the attack rate of cross-infection and care satisfaction of the two groups were compared and analyzed. Results: the patients' satisfaction degree of nursing in experimental group was 24% higher than that in control group. The number of patients with cross-infection in the experimental group was 15 fewer than that in the control group. (P < 0.02) Discussion: applying predictive nursing management to the nursing work in the department of nosocomial infection can greatly reduce the probability of complications of patients and enhance the patients' nursing satisfaction. It has good efficacy and can be widely promoted in other departments of the hospital.
文摘This paper evaluates accommodation of conflict management strategies and board performances in oil and gas sector.The study details the reflection of effectiveness,efficiency and productivity as the answer to thorough efficiency in accommodation of conflict management in oil and gas sector,these parameters in the system express their efficacy on conflict management in these multinationals,this implies that for thorough efficiency,these variables must work simultaneously for effective and efficient in structural organization that can be a leading multinational sector in oil and gas environment.The study observed Linearized result from graphical representation explaining predominant lower efficiency and little higher efficiency in accommodation of conflict management in oil and gas companies.These experiences from the study monitor the system from generated simulation values that describe the growth rates in exponential phase of accommodation conflict strategic management.Despite exponential phase the results experienced lower parameters,when comparing on its variations showing its poor efficiency as observed in the study.Few periods observed higher effective accommodation on conflict strategic management.The developed model stimulation values were subjected to validation and both parameters generated favourable fits correlation,the study expressed the deficiency on accommodation of conflict management strategy thus developed models that can monitor the fluctuation and progressive state of accommodation on conflict management strategy,it defines the reflection of other parameters that express the behaviour of the system in terms of conceptual approach to monitor these type of strategic management in oil and gas companies.
基金supported by E-Werk Mittelbaden AG,Offenburg,Germany
文摘The paper gives an overview on the need for smart coupling for battery management in grid integrated renewable energy system （RES）. Grid integrated photovoltaic （PV） battery system, as being popular and extensively used has been discussed in the paper. Smart coupling refers to intelligent grid integration such that it can foresee local network conditions and issue battery power flow management strategy accordingly to shave the peak PV and peak load. Therefore, a need for predictive energy management arises for smart integration to the grid and supervision of the power flow in accordance to the grid conditions. This is also a running project at the Institute of Energy Systems （INES）, Offenburg University of Applied Science, Germany since January, 2015. The paper should provide insights to the motivation, need and gives an outlook to the features of desired predictive energy management system （PEMS）.
文摘https://www. sciencedirect. com/journal/energy-and-buildings/vol/346/suppl/C Volume346, 1November2025[OA](1)Towards energy flexible commercial buildings:Machine learning approaches,implementation aspects,and future research directions by M. M. A. L. N. Maheepala,Hangxin Li,Dilan Robert,et al,Article116170Abstract:Commercial buildings encounter considerable challenges in predicting and managing energy flexibility,arising from the complexity of their energy systems and the interdependencies among system components and building thermal mass. Nonetheless,the emergence of “smarter buildings” creates significant opportunities for applying machine learning(ML)techniques in energy flexibility.
文摘We propose a hybrid and contingent approach to the management of digital projects,based on the assumption that there is not an absolute best way,but the approach should fit context and environment.The pillars of our contingent framework are Agile methodologies,predictive project management,and AI.The analysis of project management methodologies is based on a layered project model.Specifically,we identify 4 project concentric layers for both Agile and predictive project management.That layered model allows us to deploy different methodologies consistently with the distinct characteristics of each project.We describe the key characteristics of each approach and,also,we map Agile methodologies on the sociotechnical profile of projects.Finally,we summarize the impact of AI on predictive and hybrid projects.
基金supported by the National Natural Science Foundation of China(Grand No.52202484)the Hebei Natural Science Foundation(Grand No.F2021203118)+1 种基金the Beijing Natural Science Foundation(Grand No.J210007)the Science and Technology Project of Hebei Education Department(Grand No.QN2022093).
文摘This paper aims to answer how to use traffic information to design energy management strategies for fuel cell buses in a networked environment.For the buses entering the bus stops scenario,this paper proposes a hierarchical energy management strategy for fuel cell buses,which considers the traffic information near the bus stops.In the upper-level trajectory planning stage,the optimal SOC trajectory under various historical traffic conditions is solved through dynamic planning.The traffic information and the best SOC trajectory are mapped through BiLSTM,which can achieve fast,real-time long-term SOC reference.In the lower-level real-time predictive energy management strategy,the optimal SOC is used as the state reference to guide the predictive energy management of fuel cell buses when entering the bus stops.Simulation results show that compared with the strategy without SOC trajectory reference,the life cost of the proposed strategy is reduced by 13.8%,and the total cost is reduced by 3.61%.The SOC of the proposed strategy is closer to the DP optimal solution.
文摘This study provides details of the energy management architecture used in the Goldwind microgrid test bed. A complete mathematical model, including all constraints and objectives, for microgrid operational management is first described using a modified prediction interval scheme. Forecasting results are then achieved every 10 min using the modified fuzzy prediction interval model, which is trained by particle swarm optimization.A scenario set is also generated using an unserved power profile and coverage grades of forecasting to compare the feasibility of the proposed method with that of the deterministic approach. The worst case operating points are achieved by the scenario with the maximum transaction cost. In summary, selection of the maximum transaction operating point from all the scenarios provides a cushion against uncertainties in renewable generation and load demand.
文摘Aims In view of the growing interest in modelling the potential spread of invasive species,prediction of plant invasiveness on the basis of native range size holds considerable promise.Our objective was to use a simple model to evaluate whether a wider native range predisposes plant species to become invasive in non-native regions and to easily identify potential invaders on this basis.The Kashmir Himalayan alien flora,of which a large proportion is native to Europe,was used to test this model.Methods The Kashmir Himalayan alien flora comprises 436 species of vascular plants at different stages of invasion.We focussed on plant species at two critical invasion stages(sensu Colautti and MacIsaac 2004),i.e.Stage II(species that are just at the earliest phase of introduction)and Stage V(species that are widespread and dominant in the invaded region and are thus considered invasive).We used the territorial distribution in Europe(number of countries)as a surrogate for the native range size of plants of European origin.Important Findings Using a subset of 88 species,for which information on the native European range was available,we showed that a large proportion(68%)of Stage II species growing in the Kashmir Valley had a relatively restricted European range(present in<20 countries);on the other hand,77%of Stage V species had an extensive native range(present in>20 countries).We consequently hypothesized that 14 Kashmir Himalayan Stage II species of European origin that are distributed in>20 European countries are at risk of becoming future invaders in Kashmir.On the other hand,those Kashmir Himalayan Stage II species of European origin distributed in<20 European countries are less likely to become invasive.Although this analysis is quite simple,the data suggest that a wider native range is a good predictor of plant invasiveness and could be used as a simple and low-cost early warning tool in predicting potential invasive species.
基金made possible by a Discovery Grant from the Natural Sciences and Engineering Research Council of Canada (NSERC)
文摘Safety performance functions（SPFs） are crucial to science-based road safety management.Success in developing and applying SPFs, apart data quality and availability, depends fundamentally on two key factors： the validity of the statistical inferences for the available data and on how well the data can be organized into distinct homogeneous entities. The latter aspect plays a key role in the identification and treatment of road sections or corridors with problems related to safety. Indeed, the segmentation of a road network could be especially critical in the development of SPFs that could be used in safety management for roadway types, such as motorways（freeways in North America）, which have a large number of variables that could result in very short segments if these are desired to be homogeneous. This consequence, from an analytical point of view, can be a problem when the location of crashes is not precise and when there is an overabundance of segments with zero crashes. Lengthening the segments for developing and applying SPFs can mitigate this problem, but at a sacrifice of homogeneity. This paper seeks to address this dilemma by investigating four approaches for segmentation for motorways, using sample data from Italy. The best results were obtained for the segmentation based on two curves and two tangents within a segment and with fixed length segments. The segmentation characterized by a constant value of all original variables inside each segment was the poorest approach by all measures.
