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The purpose is to detect the nonlinearity wholesale rice price formation process in Italy in the 1995–2017 period.

A nonlinear smooth transition autoregressive (STAR)-type dynamics model is used.

Wholesale rice prices are significantly affected by variations in the international price of rice as well as variations in Arborio price.

The limitations include policy recommendations for the production and commercialization of rice in Italy.

Understanding rice pricing dynamics and nonlinearity behavior is pivotal for the survival of the entire European and Italian rice supply chain.

In the extant literature, no evidence exists on non-linearity of rice prices in Italy.

This paper studies the lateral stability regulation of intelligent electric vehicle (EV) based on model predictive control (MPC) algorithm.

Firstly, the bicycle model is adopted in the system modelling process. To improve the accuracy, the lateral stiffness of front and rear tire is estimated using the real-time yaw rate acceleration and lateral acceleration of the vehicle based on the vehicle dynamics. Then the constraint of input and output in the model predictive controller is designed. Soft constraints on the lateral speed of the vehicle are designed to guarantee the solved persistent feasibility and enforce the vehicle’s sideslip angle within a safety range.

The simulation results show that the proposed lateral stability controller based on the MPC algorithm can improve the handling and stability performance of the vehicle under complex working conditions.

The MPC schema and the objective function are established. The integrated active front steering/direct yaw moments control strategy is simultaneously adopted in the model. The vehicle’s sideslip angle is chosen as the constraint and is controlled in stable range. The online estimation of tire stiffness is performed. The vehicle’s lateral acceleration and the yaw rate acceleration are modelled into the two-degree-of-freedom equation to solve the tire cornering stiffness in real time. This can ensure the accuracy of model.

In this paper, a high-frequency radar test system was used to collect the data of clean ballast bed and fouled ballast bed of ballasted tracks, respectively, for a quantitative evaluation of the condition of railway ballast bed.

Based on original radar signals, the time–frequency characteristics of radar signals were analyzed, five ballast bed condition characteristic indexes were proposed, including the frequency domain integral area, scanning area, number of intersections with the time axis, number of time-domain inflection points and amplitude envelope obtained by Hilbert transform, and the effectiveness and sensitivity of the indexes were analyzed.

The thickness of ballast bed tested at the sleep bottom by high-frequency radar is up to 55 cm, which meets the requirements of ballast bed detection. Compared with clean ballast bed, the values of the five indexes of fouled ballast bed are larger, and the five indexes could effectively show the condition of the ballast bed. The computational efficiency of amplitude envelope obtained by Hilbert transform is 140 s·km⁻¹, and the computational efficiency of other indexes is 5 s·km⁻¹. The amplitude envelopes obtained by Hilbert transform in the subgrade sections and tunnel sections are the most sensitive, followed by scanning area. The number of intersections with the time axis in the bridge sections was the most sensitive, followed by the scanning area. The scanning area can adapt to different substructures such as subgrade, bridges and tunnels, with high comprehensive sensitivity.

The research can provide appropriate characteristic indexes from the high-frequency radar original signal to quantitatively evaluate ballast bed condition under different substructures.

Climate change poses diverse, often fundamental, challenges to livelihoods of island peoples. The purpose of this study is to demonstrate that these challenges must be better understood before effective and sustainable adaptation is possible.

Understanding past livelihood impacts from climate change can help design and operationalize future interventions. In addition, globalization has had uneven effects on island countries/jurisdictions, producing situations especially in archipelagoes where there are significant differences between core and peripheral communities. This approach overcomes the problems that have characterized many recent interventions for climate-change adaptation in island contexts which have resulted in uneven and at best only marginal livelihood improvements in preparedness for future climate change.

Island contexts have a range of unique vulnerability and resilience characteristics that help explain recent and proposed responses to climate change. These include the sensitivity of coastal fringes to climate-environmental changes: and in island societies, the comparatively high degrees of social coherence, closeness to nature and spirituality that are uncommon in western contexts.

Enhanced understanding of island environmental and social contexts, as well as insights from past climate impacts and peripherality, all contribute to more effective and sustainable future interventions for adaptation.

The need for more effective and sustainable adaptation in island contexts is becoming ever more exigent as the pace of twenty-first-century climate change increases.

This research analyses and evaluates the trends and perspectives of climate change in Colombia. This study aims to understand the main ideas and concepts of climate change in five regions of the country by analysing attitudes and values, information habits, institutionalism and the social appropriation of science and technology.

The research study involved a focus group technique. Ten focus groups in five regions of the country, including rural regions, were administered. The selection of cities and municipalities in this study took into account vulnerability scenarios based on the two criteria of temperature and precipitation for the 2011-2040 period.

The participants of the focus groups believe that climate change began 10 years ago and that human activities have caused climate change. The main effects of climate change are believed to be droughts and floods that have appeared in the past several years and have negatively impacted agricultural activities and the quality of life of the population. Moreover, the participants believe that it is important to design and apply adequate measures to adapt to and mitigate climate change.

This study makes an important contribution to the extant climate change literature by identifying and categorising the main ideas and knowledge on this issue from the perspective of the population in Colombia. In developing countries with high climate change vulnerability, it is especially important to analyse this issue to determine relevant official policy instruments that could promote adequate actions and instruments to prevent, adapt to and mitigate climate change.

The study aims to explore institutional adaptation for sustainable water resources management at the local level in the context of increasing climate-related challenges in Zimbabwe using the case of a semi-arid area in the mid-Zambezi Valley, north of the country.

Inspired by the critical institutionalism approach, the study uses qualitative methods (i.e. key informant interviews, semi-structured interviews, community workshops and documentary review) to understand the role of different formal and informal water-related institutions vis-à-vis responding to climate-related challenges in the case study area, and how the identified institutions can improve their efforts in the context of national water and environmental policy and regulation frameworks. Thematic analysis was used for data analysis.

The study found that climatic challenges in the case study area, as in most of rural Africa, have raised the stakes in local water management with respect to regulating access to and balancing competing interests in, and demands for, water. It ultimately argues for the embracing of complexity thinking and flexibility in local water management as well as clear coordination of institutions across scales in the face of increasing climate-related challenges.

The study adds to case studies and evidence-based analyses focused on institutional alternatives for climate adaptation vis-à-vis water resources management in water-stressed rural African communities.

The purpose of communicating with your esteemed magazine is your continuous support with Arab researchers and your sponsorship of research on the Arab world.

The research design was based on the APA style, and the methodology used in this research study is statistical and applied analysis to reach the desired results.

The results of this study provide an interesting perspective on the factors that influence changes in variables in Iraq: The prevailing patterns of temperature in Iraq are characterized by a clear pattern, and it is noticeable that they are linked to patterns of distance connection, but it is affected by some local influences, including the different terrain in Iraq. The highest values recorded by the correlation relationship were for the oscillation NAO and EA-WR with −0.812, −0.805, respectively. It is clear from the results that the correlation values are different between the northern and southern stations regions, and that the most influential appeared during the month of January, meaning that the effects of the remote correlation patterns are clearer during the winter season. Most of the correlation values of temperature were at the significance level of 0.5 and 0.1, in addition to that, the correlation was inverse, meaning that when the values of the distance correlation patterns rise, the temperature rates drop above the study stations and vice versa. The effect of SOI appeared during the months of December, April and May, and it recorded a strong correlation with the temperature variable. Most of the oscillations showed different effects with temperature, and it cannot be inferred that the effect is subjected and affected by the oscillation without reference to the extensions of pressure systems and their effects on the local conditions over Iraq.

There are no restrictions.

The topic of the research is one of the modern topics that researchers have not taken up in a large way, and the apparent thesis in the research is one of the effects of the newly observed climate changes with the aim of addressing and limiting the effects of these phenomena on the Arab world.

This paper reports a new technique for achieving optimized design for power system stabilizers. In any large scale interconnected systems, disturbances of small magnitudes are very common and low frequency oscillations pose a major problem. Hence small signal stability analysis is very important for analyzing system stability and performance. Power System Stabilizers (PSS) are used in these large interconnected systems for damping out low-frequency oscillations by providing auxiliary control signals to the generator excitation input. In this paper, collective decision optimization (CDO) algorithm, a meta-heuristic approach based on the decision making approach of human beings, has been applied for the optimal design of PSS. PSS parameters are tuned for the objective function, involving eigenvalues and damping ratios of the lightly damped electromechanical modes over a wide range of operating conditions. Also, optimal locations for PSS placement have been derived. Comparative study of the results obtained using CDO with those of grey wolf optimizer (GWO), differential Evolution (DE), Whale Optimization Algorithm (WOA) and crow search algorithm (CSA) methods, established the robustness of the algorithm in designing PSS under different operating conditions.

1. To improve the causality analysis performance, a novel causality detector based on time-delayed convergent cross mapping (TD-CCM) is proposed in this work. 2. Identify the root cause of plant-wide oscillations in process control system.

A novel causality analysis framework is proposed based on denoising and periodicity-removing TD-CCM (time-delayed convergent cross mapping). We first point out that noise and periodicity have adverse effects on causality detection. Then, the empirical mode decomposition (EMD) and detrended fluctuation analysis (FDA) are combined to achieve denoising. The periodicities are effectively removed through singular spectrum analysis (SSA). Following, the TD-CCM can accurately capture the causalities and locate the root cause by analyzing the filtered signals.

1. A novel causality detector based on denoising and periodicity-removing time-delayed convergent cross mapping (TD-CCM) is proposed. 2. Simulation studies show that the proposed method is able to improve the causality analysis performance. 3. Industrial case study shows the proposed method can be used to analyze the root cause of plant-wide oscillations in process control system.

1. A novel causality detector based on denoising and periodicity-removing time-delayed convergent cross mapping (TD-CCM) is proposed. 2. The influences of noise and periodicity on causality analysis are investigated. 3. Simulations and industrial case shows that the proposed method can improve the causality analysis performance and can be used to identify the root cause of plant-wide oscillations in process control system.