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Abstract Under the dual impacts of global climate change and human activities, how to accurately and quantitatively assess and identify changes in watershed runoff is crucial to the rational development and utilization of water resources. Different methods of identifying runoff change attribution are applicable to different environments. In this study, we used five different methods, including the double mass curve (DMC), the soil and water assessment tool (SWAT), and the three Budyko hypotheses based on Fu, Choudhury‒Yang, and Milly‒Zhang formulas to explore the impact of human activities and climate change on runoff change in the Jinghe River basin (JRB). The results show that annual runoff of the JRB decreased considerably, and human activities accounted for more than 90%. Specifically, the results of the SWAT model and the three Budyko hypotheses are highly consistent except for the DMC method, which is not recommended in basins with significant changes in factors other than precipitation. More importantly, the Budyko hypothesis based on the Milly‒Zhang formula uses the vegetation water utilization coefficient to consider the impact of vegetation change on runoff, which is most consistent with the simulation results of the SWAT model and is recommended for the attribution analysis of changes in the Loess Plateau represented by the JRB.

The article discusses the transformation processes that have taken place in the management of enterprises in extremely changing conditions of their functioning, which is war. For sustainability and development, these processes require, in particular, rapid adaptation, revision of the following: paradigms of management, security, personnel policy, information flows, business processes and business models; implementation of the principles of inclusiveness and accessibility, cost optimization; introduction of modern digital technologies; increase of productivity. The aim of the article is to substantiate and systematize the transformation processes of enterprise management during the wartime. To achieve this goal, the following tasks have been performed: the main problems in the management of enterprises that hinder their development in wartime have been systematized and characterized; the main blocks that unite the transformation processes in the management of enterprises during wartime are allocated on the basis of an analysis of the experience of various Ukrainian and international companies, this will allow other economic entities to lead their enterprises to sustainability, success, and efficiency. As result of the study, the authors determined that the main challenge in management in peacetime and in wartime is the rapid variability of factors of external influence, which leads to ultra-risky managerial decisions. During the war, it is difficult to foresee all sources of risks in management decisions that affect the business processes of the enterprise. At that, challenges appear quickly due to the variability of external factors, which leads to the rapid adaptation of managerial decisions and the need for their urgent solution. The loss of planning ability directly affects the decrease in the efficiency of the enterprise. Therefore, enterprises are trying to form certain patterns to stabilize their activities in extremely changing conditions of their functioning. Obviously, management has become the key instrument in this. The ability to make management decisions quickly, adapt to changing external factors, and implement high-quality communication with the team are crucial for success. Despite the lack of a one-size-fits-all approach, adhering to these core principles helps leaders navigate wartime and lead their businesses to sustainability, success, and efficiency.

Abstract Quantifying flood risk depends on accurate probability estimation, which is challenging due to non‐stationarity and the combined effects of multiple factors in a changing environment. The threat of compound flood risks may spread from coastal areas to inland basins, which have received less attention. In this study, a framework based on time‐varying copulas was introduced for the treatment of compound flood risk and bivariate design in non‐stationary environments. Archimedean copulas were developed to diagnose the non‐stationary trends of flood risk. Return periods, average annual reliabilities, and bivariate designs were estimated. Model uncertainty was analyzed by comparing the results for stationary and non‐stationary conditions. The case study investigated the extreme rainfall and water level series from the Qinhuai River Basin and the Yangtze River in China. The results showed that marginal distributions and correlations are non‐stationary in all bivariate combinations. Ignoring composite effects may lead to inappropriate quantification of flood risk. Excluding non‐stationarity may lead to risk over or underestimation. It showed the limitations of the 1‐day scale and quantified the uncertainty of non‐stationary models. This study provided a flood risk assessment framework in a changing environment and a risk‐based design technique, which is essential for climate change adaptation and water management.

Estimating the design flood under nonstationary conditions is challenging. In this study, a sample reconstruction approach was developed to transform a nonstationary series into a stationary one in a future time window (FTW). In this approach, the first-order moment (EFTW) of an extreme flood series in the FTW was used, and two possible methods of estimating EFTW values in terms of point values and confidence intervals were developed. Three schemes were proposed to analyze the uncertainty of design flood estimation in terms of sample representativeness, uncertainty from EFTW estimation, and both factors, respectively. To investigate the performance of the sample reconstruction approach, synthesis experiments were designed based on the annual peak series of the Little Sugar Creek in the United States. The results showed that the sample reconstruction approach performed well when the high-order moment of the series did not change significantly in the specified FTW. Otherwise, its performance deteriorated. In addition, the uncertainty of design flood estimation caused by sample representativeness was greater than that caused by EFTW estimation.

Phenology is a critical mirror reflecting vegetation growth and has a major impact on terrestrial ecosystems. The Loess Plateau (LP) is a paramount ecological zone in China that has experienced considerable vegetation changes. However, understanding the dynamics of vegetation phenology is limited by ambiguous vegetation interpretation and anthropogenic-induced forces. This study combined the multi-climatic and anthropogenic datasets to characterize the interactions between phenology shifts and environmental variables. The principal findings were as follows: (1) Phenological shifts exhibit spatial heterogeneity and an interannually increasing trend in greenness (R2 > 0.6, p < 0.05). Notably, SOS (the start of the growing season) advances while EOS (the end of the growing season) delays in both the southeastern and northwestern regions. (2) SOS and EOS, primarily in the range of 100–150 and 285–320 days, respectively. Phenological changes vary depending on vegetation types. The forest has an early SOS, within 80–112 days, and a delayed EOS, within 288–320 days. The SOS of shrub is mainly within 80–144 days. (3) EOS shows a strong response to the preseason of each climate variable. Precipitation (R = 0.76), soil moisture (R = −0.64), and temperature (R = 0.89) are the governing determinants in shaping vegetation phenology. In addition, agriculture and urbanization play a significant role in shaping the spatial variations of SOS. These findings provide a basis for a systematic understanding of the processes that affect vegetation growth, which is crucial for maintaining the health and sustainability of arid and semiarid ecosystems.

This study aims to investigate the transformation of organizational culture during year of the of the active phase of the russian-Ukrainian war that contributes to the survival of Bogdan Khmelnitsky Melitopol State Pedagogical University as an organization and is a factor in the transition from the survival to the phase of successful and dynamic development. Due to the war and the occupation of certain territories of Ukraine, the university was forced to relocate to the free territory in the city of Zaporizhzhia. It lost all its buildings and other material resources; around 25% of students, 25% of faculty, and 25% of funding. Despite the extreme conditions of the university’s functioning and the stress of teachers and students under constant attacks on civilians by the occupiers, the university has resumed its educational process and research. A formal sign of the success of the scientific work is the growth of the university’s position in the Consolidated ranking of Ukrainian universities. In the educational sphere, interdisciplinary academic programs have become a significant trend during the war. Innovations in management ensured that the university quickly adapted to the extreme environment. These innovations were primarily related to transitioning from a hierarchy to an adhocratic organizational culture. The main areas of changes in the university management were: renewal of the personal composition of the university management, reorganization of structural units, updating of the system of university regulations, and complete transition to electronic document management.

Climate change and anthropogenic interventions have obviously altered the eco-hydrological regimes. A quantitative evaluation and attribution of the eco-hydrological alterations are urgently required. In this study, we evaluated the various attributions of eco-hydrological regimes in the Weihe River Basin (WRB). Firstly, the trends and change-point analysis of hydrological elements were examined, and the natural streamflow was reproduced based on the variable infiltration capacity model. Then, the most ecologically relevant hydrological indicators (ERHIs) were selected and combined with the eco-deficit and eco-surplus indicators to assess the degree of eco-hydrological regime alterations. Finally, the relative contributions to eco-hydrological alterations were quantified using the ‘simulated–observed comparison’ method. The results showed that (1) the streamflow of the WRB exhibited significant decreasing trends (p < 0.01), and a significant change point (p < 0.01) of the streamflow series was identified in 1990. (2) Seven representative indicators of hydrological alteration were selected as ERHIs. (3) During the human-induced period (1991–2017), human activities were the dominant factors in the eco-hydrological alterations as well as the variations of the ERHI indexes and the eco-deficit and eco-surplus metrics. Overall, the proposed framework may improve the understanding of the driving forces of eco-hydrological regime alterations under a changing environment. HIGHLIGHTS The principal component analysis was used to remove redundancy and correlation among the indicators of hydrological alterations and determine the ecologically relevant hydrological indicators.; A combination of ERHIs and eco-metrics to assess the eco-hydrological regime alterations at different time scales is used.; The effects of climate change and anthropogenic activities on the eco-hydrological regime alterations were quantitatively distinguished.;

Climate change and human activities have a great impact on the environment and have challenged the assumption of the stability of the hydrological time series and the consistency of the observed data. In order to investigate the applicability of machine learning (ML)-based rainfall–runoff (RR) simulation methods under a changing environment scenario, several ML-based RR simulation models implemented in novel continuous and non-real-time correction manners were constructed. The proposed models incorporated categorical boosting (CatBoost), a multi-hidden-layer BP neural network (MBP), and a long short-term memory neural network (LSTM) as the input–output simulators. This study focused on the Dongwan catchment of the Yiluo River Basin to carry out daily RR simulations for the purpose of verifying the model’s applicability. Model performances were evaluated based on statistical indicators such as the deterministic coefficient, peak flow error, and runoff depth error. The research findings indicated that (1) ML-based RR simulation by using a consistency-disrupted dataset exhibited significant bias. During the validation phase for the three models, the R2 index decreased to around 0.6, and the peak flow error increased to over 20%. (2) Identifying data consistency transition points through data analysis and conducting staged RR simulations before and after the transition point can improve simulation accuracy. The R2 values for all three models during both the baseline and change periods were above 0.85, with peak flow and runoff depth errors of less than 20%. Among them, the CatBoost model demonstrated superior phased simulation accuracy and smoother simulation processes and closely matched the measured runoff processes across high, medium, and low water levels, with daily runoff simulation results surpassing those of the BP neural network and LSTM models. (3) When simulating the entire dataset without staged treatment, it is impossible to achieve good simulation results by adopting uniform extraction of the training samples. Under this scenario, the MBP exhibited the strongest generalization capability, highest prediction accuracy, better algorithm stability, and superior simulation accuracy compared to the CatBoost and LSTM simulators. This study offers new ideas and methods for enhancing the runoff simulation capabilities of machine learning models in changing environments.

Rapid socioeconomic development coupled with climate changes is likely to result in uncertainty in agricultural water use, making water resource management and regulation even more difficult. Traditional methods used in allocating and regulating water resources are unable to deal with the balance and optimize water supply and demand between different sectors. Achieving this goal requires a fundamental improvement in our current understanding of the hydrological cycle in farmland ecosystems, in particular, to what extent climate change and anthropogenic activities could reshape the hydrological processes such as precipitation, evapotranspiration, surface runoff, seepage and water consumption. Taking China as an example, this paper systematically analyzes the research progress made over the past few decades in the effect of global climate change on demand of agriculture for water, water supply to crop production, supply-demand balance, water recycling in different irrigation districts. We also analyze the areas that were poorly studied, and the perspectives for future research, especially the response of available water for agricultural use to environmental change at irrigation district scale. These include water supply to irrigation districts and their operations, crop water requirement, consolidating the study of the balance between water supply and demand, as well as theory and method for evaluating water resource bearing capacity in irrigation districts due to climate changes. We conclude that future study should address water cycle and transformation in irrigation districts, and mechanisms underlying the impact of water-saving irrigation technologies on the environment at regional scales.

Abstract In dynamic multi-objective optimization problems, the environmental parameters may change over time, which makes the Pareto fronts shifting. To address the issue, a common idea is to track the moving Pareto front once an environmental change occurs. However, it might be hard to obtain the Pareto optimal solutions if the environment changes rapidly. Moreover, it may be costly to implement a new solution. By contrast, robust Pareto optimization over time provides a novel framework to find the robust solutions whose performance is acceptable for more than one environment, which not only saves the computational costs for tracking solutions, but also minimizes the cost for switching solutions. However, neither of the above two approaches can balance between the quality of the obtained non-dominated solutions and the computation cost. To address this issue, environment-driven hybrid dynamic multi-objective evolutionary optimization method is proposed, aiming to fully use strengths of TMO and RPOOT under various characteristics of environmental changes. Two indexes, i.e., the frequency and intensity of environmental changes, are first defined. Then, a criterion is presented based on the characteristics of dynamic environments and the switching cost of solutions, to select an appropriate optimization method in a given environment. The experimental results on a set of dynamic benchmark functions indicate that the proposed hybrid dynamic multi-objective evolutionary optimization method can choose the most rational method that meets the requirements of decision makers, and balance the convergence and robustness of the obtained non-dominated solutions.

There are a lot of uncertainties in the water resources system, which makes the water allocation plan very risky. In order to analyze the risks of water resources allocation under uncertain conditions, a new methodology called the stochastic simulation-based risk assessment approach is developed in this paper. First, the main hydrological stochastic variable is fitted by a proper probability distribution. Second, suitable two-stage stochastic programming is constructed to obtain the expected benefit and optimized water allocation targets. Third, the Monte Carlo method is used to obtain a suitable stochastic sample of the hydrological variable. Fourth, a pre-allocated water optimization model is proposed to obtain optimized actual benefit. The methodology can give a way for risk analysis of water allocation plans obtained by uncertain optimization models, which provides reliable assistance to water managers in decision-making. The proposed methodology is applied to the Zhanghe Irrigation District and the risk of the water allocation plan obtained under the randomness of annual inflow is assessed. In addition, three different division methods of the annual inflow are applied in the first step, namely three levels, five levels and seven levels, respectively. From the results, the risk of the water allocation scheme obtained by the TSP model is 0.372–0.411 and decreases with the increase of the number of hydrological levels. Considering both the risk and model complexity, seven hydrological levels are recommended when using the TSP model to optimize water allocation under stochastic uncertainty. HIGHLIGHTS A stochastic simulation-based risk assessment method is proposed to analyze risks in water allocation plans obtained by uncertain models.; The suitable number of hydrological levels is revealed for using TSP model to optimize water allocation under stochastic uncertainty.;

The Jinsha River basin (JRB), known as China’s largest hydropower base, has been facing a surge in hydrological drought occurrences in the past several years. This study used the drought index model and soil and water assessment tool (SWAT) hydrological model to uncover the linkages between meteorological and hydrological drought using long-term datasets in the JRB. The results revealed that: (1) Over the past six decades, the JRB has experienced recurrent meteorological droughts, with the upper reaches being the most affected, accounting for a frequency of 17.5%. However, the frequency of drought in the middle and lower reaches has shown a marked increase in the last 15 years. (2) The frequency of hydrological drought in the JRB has been on the rise over the past six decades, with a particularly notable increase observed in the last two decades. Furthermore, a noticeable upward trend has been observed in the duration of these hydrological droughts. (3) The propagation durations from meteorological drought to hydrological drought exhibited noticeable seasonal differences in the JRB. The transmission duration during the flood season was shorter, whereas in the dry season, it was more protracted. Additionally, the connection between meteorological drought and hydrological drought demonstrates a weakening trend. The findings of this study hold significant implications for crafting an efficient reservoir dispatching strategy to safeguard the water security of the JRB.

Abstract Frequency‐dependent (FD) selection, whereby fitness and selection depend on the genetic or phenotypic composition of the population, arises in numerous ecological contexts (competition, mate choice, crypsis, mimicry, etc.) and can strongly impact evolutionary dynamics. In particular, negative frequency‐dependent selection (NFDS) is well known for its ability to potentially maintain stable polymorphisms, but it has also been invoked as a source of persistent, predictable frequency fluctuations. However, the conditions under which such fluctuations persist are not entirely clear. In particular, previous work rarely considered that FD is unlikely to be the sole driver of evolutionary dynamics when it occurs, because most environments are not static but instead change dynamically over time. Here, we investigate how FD interacts with a temporally fluctuating environment to shape the dynamics of population genetic change. We show that a simple metric introduced by Lewontin, the slope of frequency change against frequency near equilibrium, works as a key criterion for distinguishing microevolutionary outcomes, even in a changing environment. When this slope D is between 0 and –2 (consistent with the empirical examples we review), substantial fluctuations would not persist on their own in a large population occupying a constant environment, but they can still be maintained indefinitely as quasi‐cycles fueled by environmental noise or genetic drift. However, such moderate NFDS buffers and temporally shifts evolutionary responses to periodic environments (e.g., seasonality). Stronger FD, with slope D < –2, can produce self‐sustained cycles that may overwhelm responses to a changing environment, or even chaos that fundamentally limits predictability. This diversity of expected outcomes, together with the empirical evidence for both FD and environment‐dependent selection, suggests that the interplay of internal dynamics with external forcing should be investigated more systematically to reach a better understanding and prediction of evolution.

The purpose of this study is to examine how the changing Nigerian marketing environment affects consumer behavior with emphasis on the role of government and the criticisms that follow. A total of 323 questionnaires were distributed to the respondents cutting across consumers of various categories of products in the Nigerian manufacturing sector including employees and government officials of business regulatory agencies in the 36 States of the six geo-political zones of the Nigerian Federation including Abuja, the Federal Capital Territory. The study adopted simple random sampling technique to select the sample of the study. The study was analyzed using descriptive statistics, Chi-square and multiple linear regression analysis and qualitative descriptive method to help in achieving the research objectives. The study revealed that changes brought about by the environment are caused by several macro-environmental factors such as economic, technological, socio-cultural, legal/political and international factors which have significant effect on consumers’ buying characteristics (social, cultural, personal and psychological) which subsequently affect consumers’ satisfaction and purchase decision. Economic and legal/political variables were found to exhibit the most significant impact while the technological variable exhibits the least impact.

【Objective】 Water productivity in farmland is a comprehensive indicator describing the relationship between agricultural water use and agricultural production; it is the foundation for planning and managing water resources to improve irrigation water use efficiency. Over the past decades, how the water productivity responds to environmental change has become a new research topic. This paper reviews the latest development in this area and its perspective. 【Method】 The analysis is based on the relationship between biomass production and water flow in farmland. We review the current state based on the studies of how stomata controls photosynthesis and transpiration, as well as quantification of water cycling and water productivity in response to environment change. 【Conclusion】 We identify three barriers that need to be broken: ①The mechanisms underlying the integrated transpiration, soil water and groundwater. ②Developing water productivity models that integrate crop growth and water cycling. ③Develop models to simulate response of water productivity to climate change and implementation of water-saving agronomical practices. We also highlight the prospects for future study of water productivity in farmland in response to changing environments.

Evaluating the changes in runoff and analyzing its attribution under the changing environment is of great significance to water resources management. In this study, eight hydrological stations at the outlets of tributaries of the Upstream Yangtze River are selected. Based on the observed runoff data from 1951 to 2013, the spatial-temporal characteristics in runoff change are identified from time series analysis. Our results show that runoff in the Upstream Yangtze River decreases significantly with a rate of −7.6 km3 per ten years in general. The most significant declines in runoff are observed in the mainstream, Minjiang River, Tuojiang River, and Jialing River, while slight increase in runoff is found in the source area of the Yangtze River. Furthermore, the effects on runoff change from climate change and human activities are evaluated using the Soil and Water Assessment Tool (SWAT) and modified Fixing-Changing (MFC) method at multiple scales. Our results suggest that the main contributions to runoff change are from climate change variabilities (70%), land use/cover change (LUCC, 10%), and other human influence (20%). When examined at different spatial and temporal scales, climate change always appears to be the main cause of runoff change, although its contribution decreases over time. HIGHLIGHTS Runoff in the Upstream Yangtze River decreased significantly.; Runoff decline is attributed to climate change, LUCC and other human influence.; Climate change appears to be the main cause of runoff change at different spatial and temporal scales.; Contribution of climate change decreases over time.; Precipitation and temperature are the main meteorological causes of runoff change.;

The water resources system is in a vulnerable situation because of the influence of climate change and the changing environment. The adaptation mechanism was discussed through the analysis of the process of passive response, adaptation and even active adjustment of water resources system under changing environment. The adaptability of the water resources system can be described as natural resilience (NR) of natural system and artificial adaptation (AA) social (artificial) system. The natural resilience indexes were identified and analyzed from the aspects of water quantity, water quality and water ecology. The artificial adaptation indexes were identified and analyzed from the aspects of resource, eco-environment, socio-economic and technical factors. On this basis, the index system was constructed in accord with process mechanism of water resources system adaptability. Besides, to address the two-dimension factors of water resources system adaptability, a method of system analysis based on connection numbers–fuzzy risk matrix was proposed based on the theory of risk matrix. The synthesis interval [Apess, Aopt] of water resources system adaptability is obtained, by defining the pessimistic criterion when two-dimension factors meet the evaluation standard at the same time, and the optimistic criterion when either of the two-dimension factors meets evaluation standard. Finally, the case study in the Huaihe River basin in China was carried out. The results show that the adaptability level of water resources system in the Huaihe River basin expressed fluctuating uprising tendency in 2006–2015. The adaptability level is the lowest [1.856, 2.625] in 2009, the highest [2.500, 3.536] in 2015. HIGHLIGHTS First, the adaptation mechanism of water resources system under changing environment was discussed.; Second, the method of system analysis based on connection numbers–fuzzy risk matrix is proposed based on the theory of risk matrix.; Third, the synthesis interval [Apess, Aopt] of water resources system adaptability was obtained by defining pessimistic criterion and optimistic criterion.;

Understanding the genetic basis of how species respond to changing environments is essential to the conservation of species. However, the molecular mechanisms of adaptation remain largely unknown for long-lived tree species which always have large population sizes, long generation time, and extensive gene flow. Recent advances in landscape genomics can reveal the signals of adaptive selection linking genetic variations and landscape characteristics and therefore have created novel insights into tree conservation strategies. In this review article, we first summarized the methods of landscape genomics used in tree conservation and elucidated the advantages and disadvantages of these methods. We then highlighted the newly developed method “Risk of Non-adaptedness,” which can predict the genetic offset or genomic vulnerability of species via allele frequency change under multiple scenarios of climate change. Finally, we provided prospects concerning how our introduced approaches of landscape genomics can assist policymaking and improve the existing conservation strategies for tree species under the ongoing global changes.

The technology of sprinkler irrigation has a higher coefficient of land use compared to that of regular irrigation, due to the absence of irrigation erosion, the prevention of unnecessary absorption of water into the soil, the high degree of mechanization and automation of the irrigation process, the saving of equipment and fuel costs, the availability of irrigation in areas with uneven relief, the uniform moistening of the root layer. It has the advantage of achieving the same growth and development of the plant in all parts of the field. At the same time, there are opportunities to achieve savings as a result of giving mineral fertilizers in the form of suspension during irrigation. This article examines the distribution of water droplets on the field surface during sprinkler irrigation and the influence of wind speed on the intensity of irrigation. Water drop motion and wind motion were analyzed in one coordinate system. As a result, a mathematical model of the resistance coefficient of the environment and the movement of a water droplet in a variable environment was developed.

In addition to agriculture, the water–land nexus (WLN) also profoundly affects the sustainable development of industry and residents’ lives. However, little research has been designed to assess the water–land nexus from the perspective of industry development and people’s quality of life. In the current paper, Wi, a regional industrial water–land nexus matching index, and Wd, a matching index of the domestic water–land nexus, were proposed for evaluating the water–land nexus from the industry development and quality of life perspectives separately in the current paper. Furthermore, climate change and human activities have significant impacts on the water–land nexus. The WLNs were assessed spatially and temporally for the first time based on these two indexes in 128 municipalities in the Huang-Huai-Hai River Basin of China from 1951 to 2017 to analyze the impacts of the changing environment on them. The impact of changing environment was explored based on changes of some climate factors and land use. The value of Wi are higher in the eastern and southern cities than the western and northern cities, while Zhenjiang city in Jiangsu Province has the highest Wi. For Wd, there are two low Wd zones across the basin, while the minimum values occurred in Linxia Hui Autonomous Region (Wd = 35.34 mm). Wi and Wd in most cities in the basin showed a significant downward trend, and some cities in the southwest of the basin have the fastest-decreasing of Wd. Wt and Wa were also calculated to assess the total and agricultural water–land nexus separately based on existing research. The Wt for the Huang-Huai-Hai River Basin gradually increases from northwest to southeast, and its spatial distribution characteristics are similar to precipitation in the river basin. In addition, the government should simultaneously implement water transfer plans to reduce the agricultural water pressure in Ningxia and Gansu provinces. Dynamic driving factors of change of the four assessment indexes (Wt, Wa, Wi, Wd) are briefly analyzed in the end of the paper.

Abstract Cloud Computing enables providers to rent out space on their virtual and physical infrastructures. Denial of Service (DoS) attacks threaten the ability of the cloud to respond to clients requests, which results in considerable economic losses. The existing detection approaches are still not mature enough to satisfy a cloud-based detection systems requirements since they overlook the changing/dynamic environment, that characterises the cloud as a result of its inherent characteristics. Indeed, the patterns extracted and used by the existing detection models to identify attacks, are limited to the current VMs infrastructure but do not necessarily hold after performing new adjustments according to the pay-as-you-go business model. Therefore, the accuracy of detection will be negatively affected. Motivated by this fact, we present a new approach for detecting DoS attacks in a virtualized cloud under changing environment. The proposed model enables monitoring and quantifying the effect of resources adjustments on the collected data. This helps filter out the effect of adjustments from the collected data and thus enhance the detection accuracy in dynamic environments. Our solution correlates as well VMs application metrics with the actual resources load, which enables the hypervisor to distinguish between benignant high load and DoS attacks. It helps also the hypervisor identify the compromised VMs that try to needlessly consume more resources. Experimental results show that our model is able to enhance the detection accuracy under changing environments.

Diapause is a common dormancy strategy exhibited by many species of invertebrates and insects to temporarily avoid seasonally recurring unfavourable conditions for their development, most usually in winter. Less frequently, a prolonged diapause lasting two or more years is described in species living in unpredictable environments where it is adaptive, but with significant costs. In this paper we examine the occurrence of prolonged diapause in the lycaenid butterfly Tomares ballus. Pupae of this species undergo an obligate diapause from mid-May to late January the following year. However, during our rearing experiments (from 2009 to 2016) the emergence of adults occurred sequentially and a fraction of the pupae remained in diapause for up to seven years. The annual percentage emergence after the first year of diapause was 45.6%, and only barely exceeded 50.0% in 2015. Remarkably, 12 pupae (11.4% of the initial brood) remained in diapause in their eighth year. The negative exponential equation fitted to the emergence data suggests that further emergences may occur within the next five years. Therefore, the potential for successful prolonged diapause of T. ballus pupae may be more than 10 years. The adaptive value of this strategy is discussed in relation to the effects of adverse and unpredictable weather during the flight period of the butterfly, intra-guild competition, parasitoids and changes in habitat quality. We suggest that this strategy may also be exhibited by other species of Mediterranean lycaenids.

With the continuous and drastic changes due to the economic crisis, along with the increasing market demands, major reforms are initiated in the healthcare sector in order to improve the quality of healthcare and operational efficiency, while reducing costs and optimizing back-end operations. ERP systems have been the basic technological infrastructure to many sectors as well as healthcare. The main objective of this study is to discuss how the adoption of ERP systems in healthcare organizations improves their functionality, simplifies their business processes, assure the quality of care services and helps their management accounting and controlling. This study presents also the stages required for the implementation of ERP system in healthcare organizations. This study utilizes a literature review in order to reach the research conclusions. Specifically, through related case studies and research, it examines how ERP systems are used to evaluate the better functionality of the healthcare organizations, addressing in parallel important problems, and possible malfunctions. The implementation of ERP systems in healthcare organizations promises to evolve and align strictly to the organizations’ corporate objectives and high-levels of healthcare quality. In order to accomplish this goal, the right decisions should be made by the managers of the healthcare organization regarding the choice of the appropriate ERP system following its installation and its application. Limited research exists on the significance ERP systems implementation in healthcare organizations, while possible dysfunctions and challenges during its installation and implementation are recorded. Therefore, new evidence in the significance of ERP systems in healthcare organization is provided.

Climate change impacts crop production, pest and disease pressure, yield stability, and, therefore, food security. In order to understand how climate and atmospheric change factors affect trophic interactions in agriculture, we evaluated the combined effect of elevated carbon dioxide (CO2) and temperature on the interactions among wheat (Triticum aestivum L.), Barley yellow dwarf virus species PAV (BYDV-PAV) and its vector, the bird cherry-oat aphid (Rhopalosiphum padi L.). Plant traits and aphid biological parameters were examined under two climate and atmospheric scenarios, current (ambient CO2 and temperature = 400 ppm and 20 °C), and future predicted (elevated CO2 and temperature = 800 ppm and 22 °C), on non-infected and BYDV-PAV-infected plants. Our results show that combined elevated CO2 and temperature increased plant growth, biomass, and carbon to nitrogen (C:N) ratio, which in turn significantly decreased aphid fecundity and development time. However, virus infection reduced chlorophyll content, biomass, wheat growth and C:N ratio, significantly increased R. padi fecundity and development time. Regardless of virus infection, aphid growth rates remained unchanged under simulated future conditions. Therefore, as R. padi is currently a principal pest in temperate cereal crops worldwide, mainly due to its role as a plant virus vector, it will likely continue to have significant economic importance. Furthermore, an earlier and more distinct virus symptomatology was highlighted under the future predicted scenario, with consequences on virus transmission, disease epidemiology and, thus, wheat yield and quality. These research findings emphasize the complexity of plant–vector–virus interactions expected under future climate and their implications for plant disease and pest incidence in food crops.

Climate change and anthropogenic activities do collectively lead to an alteration of the flow regime, posing a great influence upon the structure and persistence of native biotic communities within river ecosystems. The range of variability approach (RVA) method is commonly used to evaluate the flow regime alteration. However, it was reported to underestimate the degree of flow regime potentially. In this study, two new assessment methods/metrics for evaluating the process behaviors of the flow regime are developed based on Euclidean distance and dynamic time warping (DTW) distance. They are then integrated with the metric of RVA, generating two composite metrics that represent both frequency and process changes of the flow regime. The new methods/metrics were applied to identify the flow regime alteration in a typical basin in the middle reaches of the Yellow River, China. The results show that the composite metrics consistently reveal a high alteration degree of flow regime in the basin. The decreased biological integrity of fish demonstrates the reasonability of the high-level overall alteration to some degree. The updated methods enable more scientific evaluation for the complex hydrologic alteration under a changing environment.

As an emerging and promising technique, device-free localization (DFL) estimates target positions by analyzing their shadowing effects. Most existing compressive sensing (CS)-based DFL methods use the changes of received signal strength (RSS) to approximate the shadowing effects. However, in changing environments, RSS readings are vulnerable to environmental dynamics. The deviation between runtime RSS variations and the data in a fixed dictionary can significantly deteriorate the performance of DFL. In this paper, we introduce ComDec, a novel CS-based DFL method using channel state information (CSI) to enhance localization accuracy and robustness. To exploit the channel diversity of CSI measurements, the DFL problem is formulated as a joint sparse recovery problem that recovers multiple sparse vectors with common support. To solve this problem, we develop a joint sparse recovery algorithm under the variational Bayesian inference framework. In this algorithm, dictionaries are parameterized based on the saddle surface model. To adapt to the environmental changes and different channel characteristics, dictionary parameters are modelled as tunable parameters. Simulation results verified the superior performance of ComDec as compared with other state-of-the-art CS-based DFL methods.

The skin, gills, and gut are the most extensively studied mucosal organs in fish. These mucosal structures provide the intimate interface between the internal and external milieus and serve as the indispensable first line of defense. They have highly diverse physiological functions. Their role in defense can be highlighted in three shared similarities: their microanatomical structures that serve as the physical barrier and hold the immune cells and the effector molecules; the mucus layer, also a physical barrier, contains an array of potent bioactive molecules; and the resident microbiota. Mucosal surfaces are responsive and plastic to the different changes in the aquatic environment. The direct interaction of the mucosa with the environment offers some important information on both the physiological status of the host and the conditions of the aquatic environment. Increasing attention has been directed to these features in the last year, particularly on how to improve the overall health of the fish through manipulation of mucosal functions and on how the changes in the mucosa, in response to varying environmental factors, can be harnessed to improve husbandry. In this short review, we highlight the current knowledge on how mucosal surfaces respond to various environmental factors relevant to aquaculture and how they may be exploited in fostering sustainable fish farming practices, especially in controlled aquaculture environments.

An agent-based computer simulation of death by inheritable mutations in a changing environment shows a maximal population, or avoids extinction, at some intermediate mutation rate of the individuals. Our results indicate that death seems needed to allow for evolution of the fittest, as required by a changing environment.Simulação computacional de agentes individuais que se reproduzem e morrem por acúmulo de mutações herdadas mostra um máximo da população ou evita extinção, para taxas de mutação intermediárias. Assim, as mortes parecem necessárias para a evolução dos mais adaptados a um ambiente mutante.

This paper establishes a water resources vulnerability framework based on sensitivity, natural resilience and artificial adaptation, through the analyses of the four states of the water system and its accompanying transformation processes. Furthermore, it proposes an analysis method for water resources vulnerability based on connection entropy, which extends the concept of contact entropy. An example is given of the water resources vulnerability in Anhui Province of China, which analysis illustrates that, overall, vulnerability levels fluctuated and showed apparent improvement trends from 2001 to 2015. Some suggestions are also provided for the improvement of the level of water resources vulnerability in Anhui Province, considering the viewpoint of the vulnerability index.

Under the combined impacts of climate change and human activities, a series of water issues, such as water shortages, have arisen all over the world. According to current studies in Science and Nature, water security has become a frontier critical topic. Water supply security (WSS), which is the state of water resources and their capacity and their capacity to meet the demand of water users by water supply systems, is an important part of water security. Currently, WSS is affected by the amount of water resources, water supply projects, water quality and water management. Water shortages have also led to water supply insecurity. WSS is now evaluated based on the balance of the supply and demand under a single water resources condition without considering the dynamics of the varying conditions of water resources each year. This paper developed an optimal allocation model for water resources that can realize the optimal allocation of regional water resources and comprehensively evaluate WSS. The objective of this model is to minimize the duration of water shortages in the long term, as characterized by the Water Supply Security Index (WSSI), which is the assessment value of WSS, a larger WSSI value indicates better results. In addition, the simulation results of the model can determine the change process and dynamic evolution of the WSS. Quanzhou, a city in China with serious water shortage problems, was selected as a case study. The allocation results of the current year and target year of planning demonstrated that the level of regional comprehensive WSS was significantly influenced by the capacity of water supply projects and the conditions of the natural water resources. The varying conditions of the water resources allocation results in the same year demonstrated that the allocation results and WSSI were significantly affected by reductions in precipitation, decreases in the water yield coefficient, and changes in the underlying surface.