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.

A Discussion on Innovative Methods for Green Engineering Management in Construction Engineering

With the rapid development of society,the construction industry has made significant progress.However,as environmental issues intensify,many construction projects contribute to pollution.In this context,applying green project management methods in construction can enhance energy conservation and environmental protection,reduce energy consumption and costs,and minimize environmental damage,promoting balanced development between the environment and the economy.Therefore,integrating green construction concepts into current construction projects is essential.Based on project requirements and construction activities,effective green project management strategies should be developed to improve management standards and promote more efficient green construction.This paper analyzes green project management in construction engineering and offers recommendations for reference.

Construction Method for Performance Management Curriculum Content System Based on Knowledge Graph

Performance Management is the core course of human resource management major,but its knowledge points lack multi-dimensional correlations.There are problems such as scattered content and unclear system,and it is urgent to reconstruct the content system of the course.Knowledge graph technology can integrate massive and scattered information into an organic structure through semantic correlation and reasoning.The application of knowledge graph to education and teaching can promote scientific and personalized teaching evaluation and better realize individualized teaching.This paper systematically combs the knowledge points of Performance Management course and forms a comprehensive knowledge graph.The knowledge point is associated with specific questions to form the problem map of the course,and then the knowledge point is further associated with the ability target to form the ability map of the course.Then,the knowledge point is associated with teaching materials,question bank and expansion resources to form a systematic teaching database,thereby giving the method of building the content system of Performance Management course based on the knowledge map.This research can be further extended to other core management courses to realize the deep integration of knowledge graph and teaching.

Design and analysis of an advanced thermal management system for the solar close observations and proximity experiments spacecraft

In this paper,the mission and the thermal environment of the Solar Close Observations and Proximity Experiments(SCOPE)spacecraft are analyzed,and an advanced thermal management system(ATMS)is designed for it.The relationship and functions of the integrated database,the intelligent thermal control system and the efficient liquid cooling system in the ATMS are elaborated upon.For the complex thermal field regulation system and extreme space thermal environment,a modular simulation and thermal field planning method are proposed,and the feasibility of the planning algorithm is verified by numerical simulation.A solar array liquid cooling system is developed,and the system simulation results indicate that the temperatures of the solar arrays meet the requirements as the spacecraft flies by perihelion and aphelion.The advanced thermal management study supports the development of the SCOPE program and provides a reference for the thermal management in other deep-space exploration programs.

Analysis and management strategies for the low voltage problems of rural power grid based on the distribution network flow calculation method

For a long time, because of the lack of investment capital and enough attentions, the overall constructions of rural power grid were far behind than the urban power grid in Chongqing Jiangbei Power Company. The low voltage problems were highlighted in the rural power grid due to the characteristics of rural power grid. Using the distribution network flow calculation method, we evaluated the low voltage problems of the rural power grid which belongs to Chongqing Jiangbei Power Company. In addition, we collected the data of distribution transformers in electricity consumption peak period. Some practical management strategies were proposed by the analysis and evaluation of potential and appeared low voltage problems.

Adaptive battery thermal management systems in unsteady thermal application contexts

With the increasing attention paid to battery technology,the microscopic reaction mechanism and macroscopic heat transfer process of lithium-ion batteries have been further studied and understood from both academic and industrial perspectives.Temperature,as one of the key parameters in the physical fra mework of batteries,affects the performa nce of the multi-physical fields within the battery,a nd its effective control is crucial.Since the heat generation in the battery is determined by the real-time operating conditions,the battery temperature is essentially controlled by the real-time heat dissipation conditions provided by the battery thermal management system.Conventional battery thermal management systems have basic temperature control capabilities for most conventional application scenarios.However,with the current development of la rge-scale,integrated,and intelligent battery technology,the adva ncement of battery thermal management technology will pay more attention to the effective control of battery temperature under sophisticated situations,such as high power and widely varied operating conditions.In this context,this paper presents the latest advances and representative research related to battery thermal management system.Firstly,starting from battery thermal profile,the mechanism of battery heat generation is discussed in detail.Secondly,the static characteristics of the traditional battery thermal management system are summarized.Then,considering the dynamic requirements of battery heat dissipation under complex operating conditions,the concept of adaptive battery thermal management system is proposed based on specific research cases.Finally,the main challenges for battery thermal management system in practice are identified,and potential future developments to overcome these challenges are presented and discussed.

Time-Domain Higher-Order Boundary Element Method for Simulating High Forward-Speed Ship Motions in Waves

The hydrodynamic performance of a high forward-speed ship in obliquely propagating waves is numerically examined to assess both free motions and wave field in comparison with a low forward-speed ship.This numerical model is based on the time-domain potential flow theory and higher-order boundary element method,where an analytical expression is completely expanded to determine the base-unsteady coupling flow imposed on the moving condition of the ship.The ship in the numerical model may possess different advancing speeds,i.e.stationary,low speed,and high speed.The role of the water depth,wave height,wave period,and incident wave angle is analyzed by means of the accurate numerical model.It is found that the resonant motions of the high forward-speed ship are triggered by comparison with the stationary one.More specifically,a higher forward speed generates a V-shaped wave region with a larger elevation,which induces stronger resonant motions corresponding to larger wave periods.The shoaling effect is adverse to the motion of the low-speed ship,but is beneficial to the resonant motion of the high-speed ship.When waves obliquely propagate toward the ship,the V-shaped wave region would be broken due to the coupling effect between roll and pitch motions.It is also demonstrated that the maximum heave motion occurs in beam seas for stationary cases but occurs in head waves for high speeds.However,the variation of the pitch motion with period is hardly affected by wave incident angles.

A Novel Method for Linear Systems of Fractional Ordinary Differential Equations with Applications to Time-Fractional PDEs

This paper presents an efficient numerical technique for solving multi-term linear systems of fractional ordinary differential equations(FODEs)which have been widely used in modeling various phenomena in engineering and science.An approximate solution of the system is sought in the formof the finite series over the Müntz polynomials.By using the collocation procedure in the time interval,one gets the linear algebraic system for the coefficient of the expansion which can be easily solved numerically by a standard procedure.This technique also serves as the basis for solving the time-fractional partial differential equations(PDEs).The modified radial basis functions are used for spatial approximation of the solution.The collocation in the solution domain transforms the equation into a system of fractional ordinary differential equations similar to the one mentioned above.Several examples have verified the performance of the proposed novel technique with high accuracy and efficiency.

Highly Accurate Golden Section Search Algorithms and Fictitious Time Integration Method for Solving Nonlinear Eigenvalue Problems

This study sets up two new merit functions,which are minimized for the detection of real eigenvalue and complex eigenvalue to address nonlinear eigenvalue problems.For each eigen-parameter the vector variable is solved from a nonhomogeneous linear system obtained by reducing the number of eigen-equation one less,where one of the nonzero components of the eigenvector is normalized to the unit and moves the column containing that component to the right-hand side as a nonzero input vector.1D and 2D golden section search algorithms are employed to minimize the merit functions to locate real and complex eigenvalues.Simultaneously,the real and complex eigenvectors can be computed very accurately.A simpler approach to the nonlinear eigenvalue problems is proposed,which implements a normalization condition for the uniqueness of the eigenvector into the eigenequation directly.The real eigenvalues can be computed by the fictitious time integration method(FTIM),which saves computational costs compared to the one-dimensional golden section search algorithm(1D GSSA).The simpler method is also combined with the Newton iterationmethod,which is convergent very fast.All the proposed methods are easily programmed to compute the eigenvalue and eigenvector with high accuracy and efficiency.

Application of TOPSIS Method in Support of Decisions Made in Staff Management Issues

Evaluation model was proposed which refers to fuzzy formalism of the personnel management issues taking account their specific characteristics. Application of TOPSIS （technique for order Performance by similarity to ideal solution） method for evaluation and regulation of alternatives based on hierarchically structured criteria of qualitative character by multiple experts to intellectually support decisions made in staff management issues is reviewed in the article. Candidate selection experiment based on criteria system formed using TOPSIS method for evaluation of candidates during solution of hiring problems reviewed and obtained results were compared with results obtained using Matlab program package.

Research on fault time prediction method for high speed rail BTM unit based on multi method interactive validation

The Balise Transmission Module(BTM)unit of the on-board train control system is a crucial component.Due to its unique installation position and complex environment,this unit has a higher fault rate within the on-board train control system.To conduct fault prediction for the BTM unit based on actual fault data,this study proposes a prediction method combining reliability statistics and machine learning,and achieves the fusion of prediction results from different dimensions through multi-method interactive validation.Firstly,a method for predicting equipment fault time targeting batch equipment is introduced.This method utilizes reliability statistics to construct a model of the remaining faultless operating time distribution considering uncertainty,thereby predicting the remaining faultless operating probability of the BTM unit.Secondly,considering the complexity of the BTM unit’s fault mechanism,the small sample size of fault cases,and the potential presence of multiple fault features in fault text records,an individual-oriented fault prediction method based on Bayesian-optimized Gradient Boosting Regression Tree(Bayes-GBRT)is proposed.This method achieves better prediction results compared to linear regression algorithms and random forest regression algorithms,with an average absolute error of only 0.224 years for predicting the fault time of this type of equipment.Finally,a multi-method interactive validation approach is proposed,enabling the fusion and validation of multi-dimensional results.The results indicate that the predicted fault time and the actual fault time conform to a log-normal distribution,and the parameter estimation results are basically consistent,verifying the accuracy and effectiveness of the prediction results.The above research findings can provide technical support for the maintenance and modification of BTM units,effectively reducing maintenance costs and ensuring the safe operation of high-speed railway,thus having practical engineering value for preventive maintenance.

Vehicle Operating, Accident and User Time Costs in Pavement Management Systems： Approach for Portuguese Conditions

This paper presents a description and analysis of the most important models to predict each of the Road User Costs components （Vehicle Operating Costs, Accident Costs and Value of Time） and proposes a model for estimating RUC components suitable for the Portuguese road network. These results are part of a research which aimed to obtain a Road User Cost Model to be used as a tool in road management systems. This model is different from other models by the fact that it includes a simple formulation that allows calibration and calculation of cost parameters, for any year, in a simple and fast way, providing trustworthy results. The required data is already available in Portuguese institutions, allowing periodic revision of cost parameters to insure accuracy.

Traffic Incident Management Performance Measures: Ranking Agencies on Roadway Clearance Time

This study develops a procedure to rank agencies based on their incident responses using roadway clearance times for crashes. This analysis is not intended to grade agencies but to assist in identifying agencies requiring more training or resources for incident management. Previous NCHRP reports discussed usage of different factors including incident severity, roadway characteristics, number of lanes involved and time of incident separately for estimating the performance. However, it does not tell us how to incorporate all the factors at the same time. Thus, this study aims to account for multiple factors to ensure fair comparisons. This study used 149,174 crashes from Iowa that occurred from 2018 to 2021. A Tobit regression model was used to find the effect of different variables on roadway clearance time. Variables that cannot be controlled directly by agencies such as crash severity, roadway type, weather conditions, lighting conditions, etc., were included in the analysis as it helps to reduce bias in the ranking procedure. Then clearance time of each crash is normalized into a base condition using the regression coefficients. The normalization makes the process more efficient as the effect of uncontrollable factors has already been mitigated. Finally, the agencies were ranked by their average normalized roadway clearance time. This ranking process allows agencies to track their performance of previous crashes, can be used in identifying low performing agencies that could use additional resources and training, and can be used to identify high performing agencies to recognize for their efforts and performance.

Effects of Standing Time during Pretreatment on the Nitrite Concentration Detected by Spectrophotometric Method

Food safety problems caused by excessive nitrite addition have been frequently reported and the detection of nitrite in food is particularly important. The standing time during the pretreatment of primary sample has a great influence on the concentration of nitrite tested by spectrophotometric method. In this context, three kinds of food samples are prepared, including canned mustard, canned fish and home-made pickled water. A series of standing times are placed during the sample pretreatments and the corresponding nitrite contents in these samples are detected by spectrophotometric method based on N-ethylenediamine dihydrochloride. This study aims to find out a reasonable standing time during the pretreatment of food sample, providing influence factor for precise detection of nitrite.

The Application of Layered Progressive Teaching Method in Nursing Teaching of Health Management Centre

Objective:To explore the clinical effect of the layered progressive teaching method in the process of nursing teaching in the health management centre.Methods:100 nursing students in the health management centre of our hospital were randomly divided into two groups from April 2018 to April 2019,in which students of the control group were treated with routine teaching,while those of the experimental group were treated with layered progressive teaching.Then,the teaching effect of the two groups was compared and analysed.Results:The assessment results of nursing students in the two groups were compared,in which the theoretical knowledge scores and practical operation scores of nursing students in the experimental group were(94.34±2.33)and(90.45±2.20)respectively.By contrast,the score of students in the control group was lower and the difference between the two groups was not significant(P<0.50).The teaching effect of students in the experimental group is more significant.Conclusion:During the process of nursing teaching in the Health Management Centre,the progressive teaching method showed a significant clinical effect and could effectively enhance students’scores.Therefore,it is positively significant for clinical development.

AN ELEMENTARY ANALYSIS OF PROBLEMS IN APPLICATION OF SPACE METHOD IN STRATEGIC MANAGEMENT OF ENTERPRISES

Although it is still a new and developing subject, strategic management of enterprises, the importance of which will be understood by more and more enterprises as the reform and development is deepened, will surely be applied finally. This paper expounds how to apply the Delphi method to determine the essential elements in management and to evaluate enterprises in strategic position and action evaluation (SPACE) method of strategic management of enterprises. Meanwhile, its determination and evaluation are so realistic that they describe the strategic positions at which enterprises may be in the present competitive environment, and they establish a basis for enterprises to correctly choose their strategies of development.

Time Management of the Clinical Nurses at Public Hospital in Bangladesh

Nursing is a very intense and focused career, so picking the right time and its proper management can help to relieve personal stress, manage tasks properly and achieve patients and organizational outcome. Objective: This study aimed to explore perceived time management skill of the clinical nurses. Methods: This was a descriptive correlational study design using simple random sampling. A total of 77 clinical nurses working in Mugda Medical College Hospital, Dhaka, Bangladesh were selected as study participants. The data were collected through structure questionnaire which consisted of demographic questionnaire and five dimensions of Time Management Skill Questionnaire (TMSQ). Results: The overall mean score of time management skill was 2.63 (SD = 0.28) reflecting a medium level of nurses perceived time management skill. The results also revealed that nurses age (r = 0.39, p = 0.000), monthly income (r = 0.294, p = 0.017) and service experience (r = 0.248, p = 0.030) were statistically significant correlated with nurses’ perceived time management skill. Conclusion: The outcome of this research would add to develop a strategy for nurse administrators to take an initiative for how nurses could improve the time management and utilize their limited time effectively. It specifies that proper time management can influence to provide quality care and achieving organizational goal.

Forestry under Climate Change. Is Time a Tool for Sustainable Forest Management?

Changing climate conditions are known to influence forest tree growth response and the CO2 cycle. Dendroclimatological research has shown that the climate signal, species composition, and growth trends have changed in different types of forest ecosystems during the last century. Under current and demonstrated changes in climate variability at the geographic, regional, and local levels tree growth shows also variability and trends that can be non-stationary during time even at relatively short distance between sites. In forest planning and management, yield tables, site quality indices, age class, rate of growth, and spatial distribution are some of the most used tools and parameters. However, these methods do not involve climate variability during time although climate is the main driver in trends of forest and tree growth. Previous research warns about the risk that forest management under changing climatic conditions could amplify their negative effects. For example, changing climate conditions may impact on temperature and/or precipitation thresholds critical to forest tree growth. Forest biomass, resilience, and CO2 storage may be damaged unless forest planning and management implement the relationships between climate variability and trends of tree growth. A positive aspect is that, periods of favorable climate conditions may allow harvesting higher amount of wood mass and storing more CO2 than traditional planning methods. And, the average length of both favorable and adverse periods appears to occur within the validity period of a forest management plan. Here, we show a conceptual development to implement climate variability in forest management in the view of continuing the research.

Internet of Things Enabled Energy Aware Metaheuristic Clustering for Real Time Disaster Management

Wireless Sensor Networks(WSNs)are a major element of Internet of Things(IoT)networks which offer seamless sensing and wireless connectivity.Disaster management in smart cities can be considered as a safety critical application.Therefore,it becomes essential in ensuring network accessibility by improving the lifetime of IoT assisted WSN.Clustering and multihop routing are considered beneficial solutions to accomplish energy efficiency in IoT networks.This article designs an IoT enabled energy aware metaheuristic clustering with routing protocol for real time disaster management(EAMCR-RTDM).The proposed EAMCR-RTDM technique mainly intends to manage the energy utilization of nodes with the consideration of the features of the disaster region.To achieve this,EAMCR-RTDM technique primarily designs a yellow saddle goatfish based clustering(YSGF-C)technique to elect cluster heads(CHs)and organize clusters.In addition,enhanced cockroach swarm optimization(ECSO)based multihop routing(ECSO-MHR)approach was derived for optimal route selection.The YSGF-C and ECSO-MHR techniques compute fitness functions using different input variables for achieving improved energy efficiency and network lifetime.The design of YSGF-C and ECSO-MHR techniques for disaster management in IoT networks shows the novelty of the work.For examining the improved outcomes of the EAMCR-RTDM system,a wide range of simulations were performed and the extensive results are assessed in terms of different measures.The comparative outcomes highlighted the enhanced outcomes of the EAMCRRTDM algorithm over the existing approaches.

Effect of Climate Change on Water Resources and Water Management Practices： A Review of Research Methods and Findings with Special Reference to Australia

In this secondary research, published works on effect of climate change on water resources in other countries and in Australia were reviewed critically. Research question, objectives and assumptions were made to facilitate this study. First, methods used for such studies and their results at global level were reviewed. Then Australian specific methods of study and findings were reviewed. More commonly, both globally and in Australia, simulations using long-term real data on selected climatic scenarios of global climatic models are projected for long-term future trends. The validity and certainty of predicted occurrences depend upon the closeness of real time data with scenarios to which they are projected. Even with these limitations, projections of already rising temperatures and declining rainfall on surface water and ground water availabilities show gradual decline in water availability leading to water stress both for human communities and ecosystems The role of human-induced emissions in hastening the degradation process has also been investigated. Conserving all available water, practising efficient water consumption and prudent water policies only can provide some relief from what is inevitable.