Uncertain Environmental Factors Research Articles

Projecting the future redistribution of Pinus koraiensis (Pinaceae: Pinoideae: Pinus) in China using machine learning

IntroductionAs an important coniferous tree in northeast China, Pinus koraiensis not only maintains the stability of the forest ecosystem at high latitudes but also plays a crucial role in regional socioeconomic development. With the intensification of climate change in recent years, the stability of P. koraiensis habitats is constantly disturbed by external uncertain environmental factors, which greatly affects the geographical distribution of P. koraiensis. However, its geographical distribution is still unclear, which greatly hinders further understanding of the ecological process of P. koraiensis. Consequently, it is particularly important to explore the potential distribution and migration of P. koraiensis during several critical periods.MethodsRandom forest (RF) was used to establish the redistribution of P. koraiensis.ResultsThe results showed that temperature seasonality and precipitation in the coldest quarter were the key factors limiting the current distribution of P. koraiensis. Currently, P. koraiensis is mainly distributed in the Lesser Khingan Mountains and Changbai Mountains, with a total suitable area of ~4.59 × 105 km2. In the past, the historical distribution of P. koraiensis during the LIG period was basically consistent with the current distribution range, but its distribution range was more complete. In the LGM period, the suitable distribution of P. koraiensis became fragmented, especially at the connection between the Lesser Khingan Mountains and the Changbai Mountains. Under future climate scenarios, the suitable distribution of P. koraiensis is projected to increase, while the highly suitable distribution will be reduced. The dramatically worrying change is that the suitable habitats of P. koraiensis are gradually breaking and separating in the junction zone between the Lesser Khingan Mountains and Changbai Mountains, which will cause the ecological corridor to break. The shifts in the distribution centroid indicated that the P. koraiensis population will migrate northward.DiscussionHowever, it remains to be verified whether long-distance migration can be achieved without human assistance. Our results can provide some solutions for protection and management strategies for P. koraiensis populations and the impact of climate change, shedding light on the effectiveness of management responses.

Study on predicting rolling contact fatigue of pitch bearing raceway in offshore wind turbine

Pitch bearing is the critical component that are widely used to ensure the operation of offshore wind turbine blades and enable efficient capture of wind energy. Uncertain environmental factors and extreme load conditions can induce early fatigue failure in pitch bearings, and lead to major accidents such as blade detachment. In this study, a damage coupling fatigue simulation model, considering residual stress (RS) and surface hardness effect, was constructed following the continuum damage mechanics (CDM) to investigate the rolling contact fatigue (RCF) of pitch bearing under ultimate loading conditions. Different ultimate loading conditions were calculated on an integrated load simulation platform established using Python script. The damage-coupled elastoplastic constitutive equation and damage-controlling equations considering RS and surface hardness were derived and programmed in the material user subroutine UMAT. The effects of ultimate loading conditions, RS, and surface hardness on RCF were studied. Results show that increasing the surface hardness or increasing the compressive RS enhances the anti-fatigue performance of pitch bearing.

A new energy vehicle supply chain member game based on triangular fuzzy numbers

The competition in the new energy vehicle industry has intensified with the rapid development of the industry. In order to create innovative products, many businesses are now seeking cooperation with their supply chain members. Previous research on the new energy vehicle supply chain has mainly focused on government policies, supply chain retailers and with consumer gaming issues. This manuscript examines the problem of cooperation decisions between members of the new energy vehicle supply chain, namely a battery manufacturer and vehicle producer. The benefits of the two members are analyzed by constructing two models, one with non-incentives and the other with government incentives. The model uses the triangular fuzzy number (TFN) instead of parameters in numerical calculations, taking complete account of the influence of uncertain environmental factors and using the triangular structured element method. The numerical examples result that government incentives positively promote cooperation between the two players, but the incentives should be as equal as possible. Finally, we aim to encourage supply chain members to cooperate and promote the development of the new energy vehicle industry. This study has positive implications for future supply chain member cooperation issues.

Multi-Sensor Scheduling Method Based on Joint Risk Assessment with Variable Weight.

In multi-sensor cooperative detection systems, to reduce target threat risk caused by attack tasks and target loss risk induced by uncertain environmental factors, this paper proposes a multi-sensor scheduling method based on joint risk assessment with variable weight. Firstly, considering the target state and prior expert experience of sensor scheduling, this paper gives a new scheme of target threat risk. Then, by combining the given target threat risk and the target loss risk, this paper constructs a joint risk model to meet the diversity of risk assessment. Secondly, a variable-weighted joint risk assessment model is given based on the adaptive weight of target loss risk and target threat risk, and the optimization problem of multi-sensor scheduling is described to minimize the multi-step prediction of the variable-weighted joint risk model. Finally, this paper relaxes above the non-convex optimization problem as a subconvex problem and designs the scheme of multi-sensor scheduling, improving the rapidity and optimization of the sensor scheduling solution. The simulation results show that the proposed method can adaptively schedule sensors and accurately track targets by using minimum sensor resources.

Multilingual Speech Emotion Research Based on Data Mining

In order to improve the effect of speech emotion research, this paper combines data mining technology to construct a speech emotion research model and establishes a state space model of speech emotion risk problem. Moreover, this paper uses fuzzy variables to describe the external uncertain environmental factors and intuitively shows the relationship between each technology, that is, clearly shows how actions and external environmental factors affect their action results, which is helpful in understanding the voice emotional risk problem from the root. In addition, this paper uses the basic knowledge of credibility theory to transform the model and finally transform the model into an optimal control problem. Through the speech emotion simulation experiment, it can be seen that the multilingual speech emotion analysis model based on data mining proposed in this paper has a good effect in speech emotion analysis.

A Practical Estimation Method of Inland Ship Speed Under Complex and Changeful Navigation Environment

The complexity and changefulness of inland navigation environment in space and time makes it hard to guarantee the applicability and accuracy of existing ship speed models. In this paper, a novel method for inland ship speed modelling under complex and changeful navigation environment is proposed. Firstly, an unsupervised machine learning algorithm, Density-Based Spatial Clustering of Application with Noise (DBSCAN), is utilized to cluster the environmental data including water level, water speed, wind speed and wind direction, to get the segment division information, which greatly helps reduce the influence of other uncertain environmental factors on the speed model. Then, Generalized Regression Neural Network (GRNN) is tailored and employed to build the ship speed estimation model with multiple input variables. Finally, a detailed case study of a ship sailing in the Yangtze River trunk line is conducted to validate the proposed methods. The results show that the ship speed model established based on machine learning methods works effectively in speed estimation and analysis. Moreover, compared with other regression methods and neural networks, the proposed GRNN model has the best performance in ship speed modelling.

Combustion of nitrate ester plasticized polyether propellants

Nitrate ester plasticized polyether (NEPE) is a kind of high-energy solid propellant that has both good mechanical properties and high specific impulse. However, its unique composition makes its combustion mechanism different from both double-base propellants and composite propellants. In order to study the combustion mechanism of NEPE propellants, we improved the free radical cracking model of previous research to make it capable of predicting the burning rate of NEPE propellants. To study the combustion characteristics and provide data support for the model, an experimental system was built and four kinds of NEPE propellants with different compositions and grain size distributions were tested. The results show that our modified model can reflect the combustion characteristics of NEPE propellants with an acceptable accuracy. The difference between the model and the experimental data is mainly caused by uncertain environmental factors and the ignorance of interactions between components. Both the experimental data and the results predicted by the model show that increasing the backpressure helps to increase the burning rate of NEPE propellants. Furthermore, the grain size of the oxidizer inside the NEPE propellant has a more severe impact on the burning rate but a lighter impact on the burning rate pressure exponent in comparison with the grain size of aluminum. For aluminum-free NEPE propellants, the reaction in the gas phase is dominant in the combustion process while adding aluminum into the propellant makes the solid phase dominant in the final stage. The combustion of fine aluminum particles near the burning surface generates heat feedback to the burning surface which evidently influences the surface temperature. However, the agglomeration of coarse aluminum particles has little effect on the burning surface temperature.

Enki

Data-driven Learning-enabled Systems are limited by the quality of available training data, particularly when trained offline. For systems that must operate in real-world environments, the space of possible conditions that can occur is vast and difficult to comprehensively predict at design time. Environmental uncertainty arises when run-time conditions diverge from design-time training conditions. To address this problem, automated methods can generate synthetic data to fill in gaps for training and test data coverage. We propose an evolution-based technique to assist developers with uncovering limitations in existing data when previously unseen environmental phenomena are introduced. This technique explores unique contexts for a given environmental condition, with an emphasis on diversity. Synthetic data generated by this technique may be used for two purposes: (1) to assess the robustness of a system to uncertain environmental factors and (2) to improve the system’s robustness. This technique is demonstrated to outperform random and greedy methods for multiple adverse environmental conditions applied to image-processing Deep Neural Networks.

Can environmental factors increase the risk of multiple sclerosis? A narrative review

Multiple sclerosis (MS) is one of the most common neurological disorders, which causes nontrauma in young adults in many countries. An important symptom of a disease is the presence of plaque in the brain or the spinal cord, which includes a demyelination region along with relative preservation of axons that may vary in acute and chronic stages. This review was conducted using related keywords through searching in scientific databases. Assessing the related papers showed that in general, MS is recognized as an autoimmune disease with genetic background caused by uncertain environmental factors. Epidemiological effects based on race, sex, geographical location are strongly linked to the frequency, types and severity of the disease. Vitamin D, infection, smoking and diet have been reported to be potential factors associated with MS. Due to the importance of these factors in MS disease, the control of them is potentially useful to prevent the disease and the rapid progression and exacerbation of its symptoms.

Direct Solution of Fuzzy Project Crashing Problem with Fuzzy Decision Variables using Tabu Search and Simulated Annealing Algorithms

Project management is a very important field employed for scheduling activities and monitoring the progress, in competitive and fluctuating environments. Project crashing analysis is concerned with shortening the project duration time by accelerating some of its activities at an additional cost. In reality, because of uncertain environment conditions there can be ambiguity in the parameters of the problem. The uncertainty in the parameters can be modeled via fuzzy set theory. Using fuzzy models give the chance of better project scheduling with more stability under uncertain environmental factors. In this study, a fuzzy project crashing problem with fuzzy decision variable - occurrence time of events - and fuzzy normal activity duration times is handled. The fuzzy project crashing problem is solved without any transformation process by employing a fuzzy ranking method and the tabu search and simulated annealing algorithms.

Fatigue Cracks Detection using PZT Transducers under the Influence of Uncertain Environmental Factors

Abstract This paper presents technique for qualitative assessment of fatigue crack growth monitoring, utilizing guided elastic waves generated by the sparse PZT piezoelectric transducers network in the pitch – catch configuration. Two Damage Indices (DIs) correlated with the total energy received by a given sensor are used to detect fatigue cracks and monitor their growth. The indices proposed carry marginal signal information content in order to decrease their sensitivity with respect to other undesired non-controllable factors which may distort the received signal. The reason for that is to limit the false calls ratio which besides the damage detection capability of a system, plays a crucial role in applications. However, even such simplified damage indices can alter over a long term, leading to the misclassification problem. Considering a single sensing path, it is very difficult to distinguish whether the resultant change of DIs is caused by a damage or due to decoherence of these DIs. Therefore, assessment approaches based on threshold levels fixed separately for DIs obtained on each of the sensing paths, would eventually lead to a false call. An alternative approach is to compare changes of DIs for all sensing paths. Developing damage distorts the signal only for the sensing paths in its proximity. In order to decrease the misclassification risk, a method of compensating such DIs drift is proposed. The main features and damage detection capabilities of this method will be demonstrated by conducting a laboratory fatigue test of an aircraft panel. The proposed approach has been verified on a real structure during fatigue test of a helicopter tail boom.

Presenting a Succession Management Model Based on Organizational Capabilities in Knowledge-Based Organizations (Case Study: Science and Technology Park of Tehran University and Science and Technology Park of Tarbiat Modares University

World in the present century is full of competition and uncertain environmental factors. The prerequisite of succeeding in this turbulent environment is to achieve numerous competitive advantages such as the ability of people and constant enhancement of quality and productivity level and this can be realized only with having much more intelligent, talented, capable and qualified individuals comparing to current managers who can direct organizations with their knowledge, expertise and experience and who can successfully deliver them to their destination. Succession planning management system with relying on organizational capabilities as a smart strategy for talent management can retain talents inside an organization and can assure the existence of the necessary and required skills for responding to quick environmental changes and can guarantee the future success of an organization. In the present study, with the title of "presenting a model for succession management based on organizational capabilities in knowledge-based organizations (case study: Science and Technology park of University of Tehran and Science and Technology park of Tarbiat Modares University" with the application of qualitative strategy of grounded theory, we have tried to study the current status of organizational capabilities and succession monument in Science and Technology park of University of Tehran and Science and Technology park of Tarbiat Modares University and to propose a comprehensive model for succession management based on organizational capabilities and proportional with the effective factors and internal and external characteristics of these two organizations. The approach used in this study is a mix approach and hence, for developing the comprehensive research model, qualitative and quantitative approaches (in combination with one another) were used. The present study in terms of methodology is a exploratory survey, causal, applied and fundamental study. Statistical population of the present study is consisted of academic experts and specialists in Science and Technology park of University of Tehran and Science and Technology Park of Tarbiat Modares University and data collection was continued until reaching a saturation point. For selecting sample volume, snow ball method was used and finally, the research model was developed based on the opinions of 27 experts. In the second stage, the presented research model was tested by using SPSS 20, Lisrel8.8 and smartPLS.03 and results indicated that the relationships and components of the proposed model are significant. DOI: 10.5901/mjss.2016.v7n2s2p199

Vehicle Stability Control Based on Generalized Predictive Contol

For the uncertain environmental factors existing in the vehicle systems, in this paper we established linear two degree-of-freedom vehicle dynamics state-space model which considering lateral interference, and designed the vehicle chassis coordination controller based on generalized predictive control, utilized combined steering and braking control to strengthen the vehicle yaw and lateral stability. Simulation and Analysis for the controller in MATLAB/Simulink environment were conducted, the results verified the feasibility and effectiveness of the control methods.

Solution of fuzzy multi-objective project crashing problem

Project management is a very important field employed for scheduling activities and monitoring the progress, in competitive and fluctuating environments. The feasible duration time required to perform a specific project is determined using critical path method. However, because of competitive priorities, time is important and the completion time of a project determined using critical path method should be reduced to meet a deadline requested. In this situation, project crashing problem arises. Project crashing analysis is concerned with shortening the project duration time by accelerating some of its activities at an additional cost. In general, the parameters of the problem are accepted as certain and the project crashing problems are solved using deterministic solution techniques. In reality, because of uncertain environment conditions, incomplete or unobtainable information, there can be ambiguity in the parameters of the problem. The uncertainty in the parameters can be modeled via fuzzy set theory. Using fuzzy models gives the chance of better project management decisions with more stability under uncertain environmental factors. In the literature, various authors solved different fuzzy versions of project management problems via transforming them into their crisp equivalents. In this study, a fuzzy multi-objective project crashing problem with fuzzy parameters is handled. The fuzzy project crashing problem is solved with a direct solution approach based on fuzzy ranking methods and the tabu search algorithm.

Analysing the effect of uncertain environmental factors on supplier-buyer strategic partnership (SBSP) by using structural equation model (SEM)

Supplier-buyer strategic partnerships (SBSP) are widely considered as an effective source of competitive advantage by firm’s operating in turbulent business environments. The objective of this research is to empirically investigate whether different dimensions of environmental uncertainty have different impacts on the SBSP. The uncertain environmental factors, namely business cost, labour availability, competitive hostility, government laws and regulations, political environment, dynamism in the market (volatility and complexity) impact SBSP including specific investments. The study is based on the reposes of 270 purchasing managers from 54 manufacturing and process industries from India (Mumbai region). A structural equation modelling was used to analyse the SBSP. The results suggest a statistically significant relationship between the selected uncertain business environmental factors and the SBSP. The primary contribution of this research lies in its ability to offer practically relevant insights to aid...

Relationship between five major phytophaga pests and their natural predatory enemies in pomegranate field

为统筹利用自然天敌和通过化学措施全年综合防治石榴园害虫, 对石榴园害虫及其天敌进行了系统调查, 明确了江淮丘陵区石榴园主要食叶类害虫为棉蚜、小绿叶蝉、黄刺蛾、扁刺蛾和小袋蛾。采用灰色关联度分析法、生态位分析法和空间格局聚集强度指数分析法分析了主要害虫与其捕食性天敌在数、时、空方面的关系, 综合排序得出: 棉蚜的主要捕食性天敌是异色瓢虫、中华草蛉和八斑球腹蛛, 小绿叶蝉的主要捕食性天敌是粽管巢蛛、三突花蟹蛛和草间小黑蛛, 黄刺蛾的主要捕食性天敌是粽管巢蛛、锥腹肖蛸和八斑球腹蛛, 扁刺蛾的主要捕食性天敌是锥腹肖蛸、八斑球腹蛛和粽管巢蛛, 小袋蛾的主要捕食性天敌是三突花蟹蛛、粽管巢蛛和异色瓢虫。棉蚜种群的聚集均数<i>l</i>值大于2, 其聚集是本身原因引起的; 而小绿叶蝉、黄刺蛾、扁刺蛾和小袋蛾及其捕食性天敌的<i>l</i>值小于2, 其聚集是环境中某些因子引起的。

Optimality of State-Dependent (s, S) Policies in Inventory Models with Markov-Modulated Demand and Lost Sales

Markov-modulated processes have been used in stochastic inventory models with setup costs for modeling demand under the influence of uncertain environmental factors, such as fluctuating economic and market conditions. The analyses of these models have been carried out in the literature only under the assumption that unsatisfied demand is fully backlogged. The lost sales situation occurs in many retail establishments such as department stores and supermarkets. We use the analysis of the Markovian demand model with backlogging to analyze the lost sales case; in particular, we establish the optimality of an (s, S)-type policy under fairly general conditions.

Infant feeding and autoimmune diabetes.

Type 1 (insulin-dependent) Diabetes Mellitus is a chronic autoimmune disorder of genetically susceptible hosts, caused by the interaction of uncertain environmental factors with the products of 20+ predisposing genes.1 Self-reactive T lymphocytes, targeting components of the pancreatic islets of Langerhans, accumulate in that locale early in life and begin a slow process, called “prediabetes”. Progressive prediabetes leads to the expansion of autoreactive T cells,2 with increasingly efficient destruction of insulin producing beta cells and their precursors.

OPTIMALITY OF STATE‐DEPENDENT (s, S) POLICIES IN INVENTORY MODELS WITH MARKOV‐MODULATED DEMAND AND LOST SALES

Markov‐modulated processes have been used in stochastic inventory models with setup costs for modeling demand under the influence of uncertain environmental factors, such as fluctuating economic and market conditions. The analyses of these models have been carried out in the literature only under the assumption that unsatisfied demand is fully backlogged. The lost sales situation occurs in many retail establishments such as department stores and supermarkets. We use the analysis of the Markovian demand model with backlogging to analyze the lost sales case; in particular, we establish the optimality of an ( s, S)‐type policy under fairly general conditions.

A framework for incorporating environmental factors into the utility planning process

A technique is presented for modeling and studying the effects of uncertain environmental factors in the electric utility planning process. This takes into account the traditional utility planning process and shows how it can be facilitated with the use of advanced decision analysis tools. The concept of influence diagram is introduced in the utility planning arena. Using the interdependent data analysis (IDA) technique, a method for providing the probability estimates necessary for the influence diagram is shown. The IDA technique allows the use of expert opinions and intuitive judgments to develop the necessary probability estimates. A sample case study is presented where the issue of emissions trading is incorporated. A base case coal and gas option is compared against a gas turbine alternative. Energy costs from these two options are compared by taking into account the capital, licensing, fuel, operation and maintenance, and emissions related costs.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>