Role of outdoor trees on pedestrian wind and thermal conditions around a pre-education building for sustainable energy management

Finding sustainable energy resources is essential to face the increasing energy demand.Trees are an important part of ancient architecture but are becoming rare in urban areas.Trees can control and tune the pedestrian-level wind velocity and thermal condition.In this study,a numerical investigation is employed to assess the role of trees planted in the windward direction of the building complex on the thermal and pedestrian wind velocity conditions around/inside a pre-education building located in the center of the complex.Compared to the previous studies(which considered only outside buildings),this work considers the effects of trees on microclimate change both inside/outside buildings.Effects of different parameters including the leaf area density and number of trees,number of rows,far-field velocity magnitude,and thermal condition around the main building are assessed.The results show that the flow velocity in the spacing between the first-row buildings is reduced by 30%-40% when the one-row trees with 2 m height are planted 15 m farther than the buildings.Furthermore,two rows of trees are more effective in higher velocities and reduce the maximum velocity by about 50%.The investigation shows that trees also could reduce the temperature by about 1℃around the building.

Smart Energy Management System Using Machine Learning

Energy management is an inspiring domain in developing of renewable energy sources.However,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and demand.The major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental effects.Modern energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user comfort.To address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve it.An inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy costs.Machine Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)network.The IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource management.In this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management problem.The proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

Reinforcement Learning-Based Energy Management for Hybrid Power Systems:State-of-the-Art Survey,Review,and Perspectives

The new energy vehicle plays a crucial role in green transportation,and the energy management strategy of hybrid power systems is essential for ensuring energy-efficient driving.This paper presents a state-of-the-art survey and review of reinforcement learning-based energy management strategies for hybrid power systems.Additionally,it envisions the outlook for autonomous intelligent hybrid electric vehicles,with reinforcement learning as the foundational technology.First of all,to provide a macro view of historical development,the brief history of deep learning,reinforcement learning,and deep reinforcement learning is presented in the form of a timeline.Then,the comprehensive survey and review are conducted by collecting papers from mainstream academic databases.Enumerating most of the contributions based on three main directions—algorithm innovation,powertrain innovation,and environment innovation—provides an objective review of the research status.Finally,to advance the application of reinforcement learning in autonomous intelligent hybrid electric vehicles,future research plans positioned as“Alpha HEV”are envisioned,integrating Autopilot and energy-saving control.

Participatory Technique Supporting Environmental Management: A Systematic and Bibliometric Review

The increasing need to manage natural resources sustainably, driven by population growth, requires the simultaneous use of Participatory Techniques (PT) and landscape planning for structured decision-making. We conducted a bibliometric and systematic review to provide an overview of PT usage, identifying evolution in scientific production. We considered the number of publications and citations, prominent journals, and highly cited articles on scientific papers published in the Web of Science database between 1993 and 2023. A total of 415 articles related to PT were identified. After content evaluation, 19 critical articles were selected that underpin the growing combined use of models and indices with PT, enhancing the robustness and credibility of decision-making processes.

Regenerative Braking Energy Recovery System of Metro Train Based on Dual-Mode Power Management

In order to fully utilize the regenerative braking energy of metro trains and stabilize the metro DC traction busbar voltage,a hybrid regenerative braking energy recovery system with a dual-mode power management strategy is proposed.Firstly,the construction of the hybrid regenerative braking energy recovery system is explained.Then,based on the power demand of low-voltage load in metro stations,a dual-mode power management strategy is proposed to allocate the reference power of each system according to the different working conditions,and the control methods of each system are set.Finally,the correctness and effectiveness of the dual-mode strategy are verified through simulation,and the proposed braking energy utilization scheme is compared with other singleform utilization schemes.The results illustrate that the hybrid system with the dual-mode strategy can effectively recycle the regenerative braking energy of metro train and inhibit the busbar voltage fluctuation;the proposed braking energy utilization scheme has certain advantages on energy recovery and DC bus voltage stabilization compared with other single-form schemes;the proposed power management strategy can correctly allocate the reference power of each system with a lower construction cost.

Reinforcement Learning Model for Energy System Management to Ensure Energy Efficiency and Comfort in Buildings

This article focuses on the challenges ofmodeling energy supply systems for buildings,encompassing both methods and tools for simulating thermal regimes and engineering systems within buildings.Enhancing the comfort of living or working in buildings often necessitates increased consumption of energy and material,such as for thermal upgrades,which consequently incurs additional economic costs.It is crucial to acknowledge that such improvements do not always lead to a decrease in total pollutant emissions,considering emissions across all stages of production and usage of energy and materials aimed at boosting energy efficiency and comfort in buildings.In addition,it explores the methods and mechanisms for modeling the operating modes of electric boilers used to collectively improve energy efficiency and indoor climatic conditions.Using the developed mathematical models,the study examines the dynamic states of building energy supply systems and provides recommendations for improving their efficiency.These dynamic models are executed in software environments such as MATLAB/Simscape and Python,where the component detailing schemes for various types of controllers are demonstrated.Additionally,controllers based on reinforcement learning(RL)displayed more adaptive load level management.These RL-based controllers can lower instantaneous power usage by up to 35%,reduce absolute deviations from a comfortable temperature nearly by half,and cut down energy consumption by approximately 1%while maintaining comfort.When the energy source produces a constant energy amount,the RL-based heat controllermore effectively maintains the temperature within the set range,preventing overheating.In conclusion,the introduced energydynamic building model and its software implementation offer a versatile tool for researchers,enabling the simulation of various energy supply systems to achieve optimal energy efficiency and indoor climate control in buildings.

Land Use and Forest Dynamics in the Context of Redd+: Pathway to Sustainable Environmental Management*

Reverting to nature as a major arsenals in a universal fight against Climate Change impact and loss of biodiversity, the United Nations Convention to Combat Desertification (UNCCD), views sustainable Land use and Forest (the main crux of the Glasgow declaration 2021) as the way to go. Forest conservation, protection and management in the context of REDD+ would guarantee sustainable ecosystem and mitigate climate change impacts. At National and subnational levels, the Nigerian REDD+ readiness scheme holds out hope for environmental sustainability. This study throws light into the historical background of trends in land use forest change in Nigeria, and places Nigeria on a “red” stage 3 (Low Forest Cover, High Deforestation Rate-LFHD) status while maintaining optimism that with REDD+ properly implemented in Nigeria, Stage 4: Low forest cover, Low Deforestation Rates (LFLD) and Stage 5: Low forest cover, Negative Deforestation Rates (LFND) can be achieved by 2030 and 2050 respectively, if the trio of reforestation, afforestation and natural restoration is practiced as a matter of national policy and subnational implementation within the context of REDD+. Four (4) broad drivers of deforestation and forest degradation were identified as direct, indirect, pre-disposing and planned /unplanned. The paper concludes that a viable pathway to sustainable environmental management is appropriate monitoring and evaluation of land use and forest dynamics in the context of REDD+.

Research on Smart Energy Monitoring and Management System Based on Digital Twin Technology

Smart energy monitoring and management system lays a foundation for the application and development of smart energy. However, in recent years, the work efficiency of smart energy development enterprises has generally been low, and there is an urgent need to improve the application efficiency, resilience and sustainability of smart energy monitoring and management system. Digital twin technology provides a data-centric solution to improve smart energy monitoring and management system, bringing an opportunity to transform passive infrastructure assets into data-centric systems. This paper expounds on the concept and key technologies of digital twin, and designs a smart energy monitoring and management system based on digital twin technology, which has dual significance for promoting the development of smart energy field and promoting the application of digital twin.

A Predictive Energy Management Strategies for Mining Dump Trucks

The plug-in hybrid vehicles(PHEV)technology can effectively address the issues of poor dynamics and higher energy consumption commonly found in traditional mining dump trucks.Meanwhile,plug-in hybrid electric trucks can achieve excellent fuel economy through efficient energy management strategies(EMS).Therefore,a series hybrid system is constructed based on a 100-ton mining dump truck in this paper.And inspired by the dynamic programming(DP)algorithm,a predictive equivalent consumption minimization strategy(P-ECMS)based on the DP optimization result is proposed.Based on the optimal control manifold and the SOC reference trajectory obtained by the DP algorithm,the P-ECMS strategy performs real-time stage parameter optimization to obtain the optimal equivalent factor(EF).Finally,applying the equivalent consumption minimization strategy(ECMS)realizes real-time control.The simulation results show that the equivalent fuel consumption of the P-ECMS strategy under the experimentally collected mining cycle conditions is 150.8 L/100 km,which is 10.9%less than that of the common CDCS strategy(169.3 L/100 km),and achieves 99.47%of the fuel saving effect of the DP strategy(150 L/100 km).

Enhanced Deep Reinforcement Learning Strategy for Energy Management in Plug-in Hybrid Electric Vehicles with Entropy Regularization and Prioritized Experience Replay

Plug-in Hybrid Electric Vehicles(PHEVs)represent an innovative breed of transportation,harnessing diverse power sources for enhanced performance.Energy management strategies(EMSs)that coordinate and control different energy sources is a critical component of PHEV control technology,directly impacting overall vehicle performance.This study proposes an improved deep reinforcement learning(DRL)-based EMSthat optimizes realtime energy allocation and coordinates the operation of multiple power sources.Conventional DRL algorithms struggle to effectively explore all possible state-action combinations within high-dimensional state and action spaces.They often fail to strike an optimal balance between exploration and exploitation,and their assumption of a static environment limits their ability to adapt to changing conditions.Moreover,these algorithms suffer from low sample efficiency.Collectively,these factors contribute to convergence difficulties,low learning efficiency,and instability.To address these challenges,the Deep Deterministic Policy Gradient(DDPG)algorithm is enhanced using entropy regularization and a summation tree-based Prioritized Experience Replay(PER)method,aiming to improve exploration performance and learning efficiency from experience samples.Additionally,the correspondingMarkovDecision Process(MDP)is established.Finally,an EMSbased on the improvedDRLmodel is presented.Comparative simulation experiments are conducted against rule-based,optimization-based,andDRL-based EMSs.The proposed strategy exhibitsminimal deviation fromthe optimal solution obtained by the dynamic programming(DP)strategy that requires global information.In the typical driving scenarios based onWorld Light Vehicle Test Cycle(WLTC)and New European Driving Cycle(NEDC),the proposed method achieved a fuel consumption of 2698.65 g and an Equivalent Fuel Consumption(EFC)of 2696.77 g.Compared to the DP strategy baseline,the proposed method improved the fuel efficiency variances(FEV)by 18.13%,15.1%,and 8.37%over the Deep QNetwork(DQN),Double DRL(DDRL),and original DDPG methods,respectively.The observational outcomes demonstrate that the proposed EMS based on improved DRL framework possesses good real-time performance,stability,and reliability,effectively optimizing vehicle economy and fuel consumption.

Imperative for long-term management and surveillance in Kawasaki disease

Kawasaki disease(KD)is a significant pediatric vasculitis known for its potential to cause severe coronary artery complications.Despite the effectiveness of initial treatments,such as intravenous immunoglobulin,KD patients can experience long-term cardiovascular issues,as evidenced by a recent case report of an adult who suffered a ST-segment elevation myocardial infarction due to previous KD in the World Journal of Clinical Cases.This editorial emphasizes the critical need for long-term management and regular surveillance to prevent such complications.By drawing on recent research and case studies,we advocate for a structured approach to follow-up care that includes routine cardiac evaluations and preventive measures.

Enhancing perianal disease management with integrated physical and psychological approaches

This article provides a comprehensive analysis of the study by Hou et al,focusing on the complex interplay between psychological and physical factors in the postoperative recovery(POR)of patients with perianal diseases.The study sheds light on how illness perception,anxiety,and depression significantly influence recovery outcomes.Hou et al developed a predictive model that demonstrated high accuracy in identifying patients at risk of poor recovery.The article explores the critical role of pre-operative psychological assessment,highlighting the need for mental health support and personalized recovery plans in enhancing POR quality.A multidisciplinary approach,integrating mental health professionals with surgeons,anesthesiologists,and other specialists,is emphasized to ensure comprehensive care for patients.The study’s findings serve as a call to integrate psychological care into surgical practice to optimize outcomes for patients with perianal diseases.

Revolutionizing diabetic retinopathy screening and management:The role of artificial intelligence and machine learning

Diabetic retinopathy(DR)remains a leading cause of vision impairment and blindness among individuals with diabetes,necessitating innovative approaches to screening and management.This editorial explores the transformative potential of artificial intelligence(AI)and machine learning(ML)in revolutionizing DR care.AI and ML technologies have demonstrated remarkable advancements in enhancing the accuracy,efficiency,and accessibility of DR screening,helping to overcome barriers to early detection.These technologies leverage vast datasets to identify patterns and predict disease progression with unprecedented precision,enabling clinicians to make more informed decisions.Furthermore,AI-driven solutions hold promise in personalizing management strategies for DR,incorpo-rating predictive analytics to tailor interventions and optimize treatment path-ways.By automating routine tasks,AI can reduce the burden on healthcare providers,allowing for a more focused allocation of resources towards complex patient care.This review aims to evaluate the current advancements and applic-ations of AI and ML in DR screening,and to discuss the potential of these techno-logies in developing personalized management strategies,ultimately aiming to improve patient outcomes and reduce the global burden of DR.The integration of AI and ML in DR care represents a paradigm shift,offering a glimpse into the future of ophthalmic healthcare.

Motivational interviewing in postoperative rehabilitation and chronic disease management: Current findings and future research directions

This editorial highlights a recently published study examining the effectiveness of music therapy combined with motivational interviewing(MI)in addressing an-xiety and depression among young and middle-aged patients following percuta-neous coronary intervention.It further explores existing evidence and potential future research directions for MI in postoperative rehabilitation and chronic disease management.MI aims to facilitate behavioral change and promote healthier lifestyles by fostering a trusting relationship with patients and enhan-cing intrinsic motivation.Research has demonstrated its effectiveness in posto-perative recovery for oncological surgery,stroke,organ transplants,and gastroin-testinal procedures,as well as in managing chronic conditions such as diabetes,obesity,and periodontal disease.The approach is patient-centered,adaptable,cost-effective,and easily replicable,though its limitations include reliance on the therapist’s expertise,variability in individual responses,and insufficient long-term follow-up studies.Future research could focus on developing individualized and precise intervention models,exploring applications in digital health management,and confirming long-term outcomes to provide more compre-hensive support for patient rehabilitation.

Association of the glycemic background patterns and the diabetes management efficacy in poorly controlled type 2 diabetes

BACKGROUND Inadequate glycemic control in patients with type 2 diabetes(T2DM)is a major public health problem and a significant risk factor for the progression of diabetic complications.AIM To evaluate the effects of intensive and supportive glycemic management strategies over a 12-month period in individuals with T2DM with glycated hemoglobin(HbA1c)≥10%and varying backgrounds of glycemic control.METHODS This prospective observational study investigated glycemic control in patients with poorly controlled T2DM over 12 months.Participants were categorized into four groups based on prior glycemic history:Newly diagnosed,previously well controlled with recent worsening,previously off-target but now worsening,and HbA1c consistently above 10%.HbA1c levels were monitored quarterly,and patients received medical,educational,and dietary support as needed.The analysis focused on the success rates of good glycemic control and the associated factors within each group.RESULTS The study showed significant improvements in HbA1c levels in all participants.The most significant improvement was observed in individuals newly diagnosed with diabetes:65%achieved an HbA1c target of≤7%.The results varied between participants with different glycemic control histories,followed by decreasing success rates:39%in participants with previously good glycemic control,21%in participants whose glycemic control had deteriorated compared to before,and only 10%in participants with persistently poor control,with mean HbA1c levels of 6.3%,7.7%,8.2%,and 9.7%,respectively.After one year,65.2%of the“newly diagnosed patients”,39.3%in the“previously controlled group”,21.9%in the“previously off-target but now worsened'”group and 10%in the“poorly controlled from the start”group had achieved HbA1c levels of 7 and below.CONCLUSION In poorly controlled diabetes,the rate at which treatment goals are achieved is associated with the glycemic background characteristics,emphasizing the need for tailored strategies.Therefore,different and comprehensive treatment approaches are needed for patients with persistent uncontrolled diabetes.

Solid Waste Management:A MADM Approach Using Fuzzy Parameterized Possibility Single-Valued Neutrosophic Hypersoft Expert Settings

The dramatic rise in the number of people living in cities has made many environmental and social problems worse.The search for a productive method for disposing of solid waste is the most notable of these problems.Many scholars have referred to it as a fuzzy multi-attribute or multi-criteria decision-making problem using various fuzzy set-like approaches because of the inclusion of criteria and anticipated ambiguity.The goal of the current study is to use an innovative methodology to address the expected uncertainties in the problem of solid waste site selection.The characteristics(or sub-attributes)that decision-makers select and the degree of approximation they accept for various options can both be indicators of these uncertainties.To tackle these problems,a novel mathematical structure known as the fuzzy parameterized possibility single valued neutrosophic hypersoft expert set(ρˆ-set),which is initially described,is integrated with a modified version of Sanchez’s method.Following this,an intelligent algorithm is suggested.The steps of the suggested algorithm are explained with an example that explains itself.The compatibility of solid waste management sites and systems is discussed,and rankings are established along with detailed justifications for their viability.This study’s strengths lie in its application of fuzzy parameterization and possibility grading to effectively handle the uncertainties embodied in the parameters’nature and alternative approximations,respectively.It uses specific mathematical formulations to compute the fuzzy parameterized degrees and possibility grades that are missing from the prior literature.It is simpler for the decisionmakers to look at each option separately because the decision is uncertain.Comparing the computed results,it is discovered that they are consistent and dependable because of their preferred properties.

Interest of D-dimer level,severity of COVID-19 and cost of management in Gabon

BACKGROUND Coronavirus disease 2019(COVID-19)is strongly associated with an increased risk of thrombotic events,including severe outcomes such as pulmonary embolism.Elevated D-dimer levels are a critical biomarker for assessing this risk.In Gabon,early implementation of anticoagulation therapy and D-dimer testing has been crucial in managing COVID-19.This study hypothesizes that elevated Ddimer levels are linked to increased COVID-19 severity.AIM To determine the impact of D-dimer levels on COVID-19 severity and their role in guiding clinical decisions.METHODS This retrospective study analyzed COVID-19 patients admitted to two hospitals in Gabon between March 2020 and December 2023.The study included patients with confirmed COVID-19 diagnoses and available D-dimer measurements at admission.Data on demographics,clinical outcomes,D-dimer levels,and healthcare costs were collected.COVID-19 severity was classified as non-severe(outpatients)or severe(inpatients).A multivariable logistic regression model was used to assess the relationship between D-dimer levels and disease severity,with adjusted odds ratios(OR)and 95%CI.RESULTS A total of 3004 patients were included,with a mean age of 50.17 years,and the majority were female(53.43%).Elevated D-dimer levels were found in 65.81%of patients,and 57.21%of these experienced severe COVID-19.Univariate analysis showed that patients with elevated D-dimer levels had 3.33 times higher odds of severe COVID-19(OR=3.33,95%CI:2.84-3.92,P<0.001),and this association remained significant in the multivariable analysis,adjusted for age,sex,and year of collection.The financial analysis revealed a substantial burden,particularly for uninsured patients.CONCLUSION D-dimer predicts COVID-19 severity and guides treatment,but the high cost of anticoagulant therapy highlights the need for policies ensuring affordable access in resource-limited settings like Gabon.

Analysis of Economic and Socio-Environmental Indicators for Energy, Materials and Water Management and Proposal of Technologies and Alternatives for Sustainable Construction in Housing

The work presents technologies of materials,energy and water management that can be used for sustainable buildings,reducing costs and environmental impacts.The aim was to encourage the reduction of energy consumption,adequate water management and more sustainable material choices in new or existing buildings.For this,a diagnosis of existing technologies and alternatives was carried out in the first stage of the work.The second stage consisted of analyzing among the technologies and alternatives diagnosed from the methodology which can be applied in a fictitious case study of housing,its implementation and maintenance and viability analyzing,finally,environmental indicators,social and economic.The results showed that the best evaluated technologies/alternatives were in Energy:ventilation and natural light;in Water Management:double-action sanitary basin,flow restrictors,aerators with constant flow,and minicistern systems;and in Materials:bamboo,wood,soil-cement brick,earth,steel frame and wood frame,aggregate with ash from rice husks,aggregate with ash from sugarcane bagasse,glass,phase change materials,aggregate with residues of construction and demolition,Portland cement and cement with blast furnace slag;which can be used in the civil construction sector,and provide socio-environmental and economic benefits,encouraging new studies and its use for public/private buildings,aid in the elaboration of public policies to reduce costs and improve the quality of buildings.

Application Model of Energy Management and Emissions Control to Limit Environmental Risks of Fossil Fuel

Around the world energy sustainability and environment protection face many challenges due to the continuously increasing population and energy demands, where the demanding rate of energy increases by at least 2.3% per year. According to statistical data, until 2035, fossil fuels still are the main source of energy consumption. Burning fossil fuels produces the greenhouse gas of carbon dioxide as a byproduct. CO2 emissions have a dangerous effect on both human health and the natural environmental balance. There are many types of clean energy like solar, biofuel, and wind energy. The major limitations of using these types, their availability depends on climate conditions and their production rate is inadequate for energy demand. For any country, energy resources’ availability and economic conditions imposed on projects’ prioritization. Application of energy management and emissions control techniques for industrial unit can limit fuel combustion environmental effects and transfer this fossil fuel to an eco-friendly type. In this work, we applied Green Energy Model GEM to the Hydrotreater Unit of the refinery, GEM is composed of four techniques. Which are Heat Exchangers Networks Synthesis [HENS], Fuel Switching, Thermal Insulation application, and Carbon Captures Storage [CCS]. Where they reduce energy consumption by the rate of 3% - 34% and control CO2 emissions by the rate of 26% - 90%. This model is a radical way to face climate change challenges and practical solutions for both energy and environmental crises.

ENERGY STATE APPROACH TO THE INTEGRATED FLIGHT PERFORMANCE MANAGEMENT OF COMMERCIAL AIRCRAFT

The integrated aircraft flight performance management techniques are discussed in this paper based on the point-mass energy state approximation principle. The flight performance optimization algorithms, developed with energy state approximation approach, are first introduced, the functionally integrated flight path/speed control system, so called total energy control system (TECS), is then discussed, and the guidance technique and algorithms, which relate the performance optimization results directly with the TECS, are analyzed and developed. Digital simulation results for a specific transport aircraft model demonstrate the satisfactory performances of the resulted flight performance management system.