Suitable Actions Research Articles

A review of flood impact assessment approaches for underground infrastructures in urban areas: a focus on transport systems

ABSTRACT Flooding events can produce significant disturbances in underground transport systems within urban areas and lead to economic and socioenvironmental well-known consequences, which can be worsened by variations in the occurrence of weather and climate extremes. A better comprehension of these impacts and their conditions is consequently needed. Hence, this paper presents a state-of-the-art literature review on flood impact assessment in “metro” systems, analysing their purposes and their shortcomings. This document shows the adaptation measures dealing with specific classes of pluvial flood damages, besides identifying prospective paths towards the application of suitable actions facing actual and projected hazards in metro systems worldwide.

Bat Algorithm Based an Adaptive PID Controller Design for Buck Converter Model

The aim of this paper is to design a PID controller based on an on-line tuning bat optimization algorithm for the step-down DC/DC buck converter system which is used in the battery operation of the mobile applications. In this paper, the bat optimization algorithm has been utilized to obtain the optimal parameters of the PID controller as a simple and fast on-line tuning technique to get the best control action for the system. The simulation results using (Matlab Package) show the robustness and the effectiveness of the proposed control system in terms of obtaining a suitable voltage control action as a smooth and unsaturated state of the buck converter input voltage of ( ) volt that will stabilize the buck converter system performance. The simulation results show also that the proposed control system when compared with the other controllers results has the capability of minimizing the rising time to ( sec) and the settling time to ( sec) in the transient response and minimizing the voltage tracking error of the system output to ( ) volt at the steady state response. Furthermore, the number of fitness evaluations is decreased.

Energy saving in wastewater treatment plants: A methodology based on common key performance indicators for the evaluation of plant energy performance, classification and benchmarking

Wastewater treatment plants are a major energy user in the urban water cycle with an energy demand estimated in several countries in about the 1% of the overall national energy use. Nevertheless, the sector offers wide opportunities for energy saving and recovery to be fostered in order to reduce economic and environmental impacts. Nowadays, a consolidated procedure to assess energy performances in the wastewater sector is still not available. This work proposes a methodology for the evaluation, benchmarking and labelling of energy performances of wastewater treatment plants, based on easy-to-define data related to energy use and operative parameters. Statistical techniques are applied to validate input data and the step-by-step derived outcomes. Common key performance indicators were calculated and linearly aggregated in a global index able to convey the wastewater treatment plant energy performance. Such index allowed to carry out comparisons among energy consumptions supported by a dedicated built-up database collecting sectorial data, considered as a reference. A case study based on 10 selected wastewater treatment plants is proposed. The methodology led to derive a clear and affordable overview of the analysed plant energy performances. Moreover, the benchmarking analysis allowed to get, on the basis of the differences between the calculated global index values, a rough estimation of the potential energy gains achievable if suitable measures for ameliorating the plant energy efficiency performance are adopted. For three of the selected plants, the margins for energy savings resulted higher than 60%. The methodology suggests to focus on such cases by carrying out specific on-site inspection to detect energy use inefficiencies and define suitable actions addressed to save energy and enhance the system sustainability. In this view, the proposed procedure could also be applied with the aim of prioritizing actions to pursue energy savings among a large set of wastewater treatment plants.

Energy efficiency in small districts: Dynamic simulation and technoeconomic analysis

In European Union Countries, local authorities have developed their own Sustainable Energy Action Plan aiming at planning the urban strategies to achieve the European Union and national 2020 energy targets. This document is used for detecting and deploying the energy measures which each municipality should adopt in order to reduce the environmental impact of the municipality activities. Further efforts are expected to define the strategies for achieving 2050 goals. In this framework, this work deals with the dynamic estimation energy saving actions for small districts located in different Italian weather zones (Naples and Turin, located in South and North Italy, respectively). The data of the Sustainable Energy Action Plan were used to develop suitable dynamic models, implemented in TRNSYS environment, including different buildings of selected districts. In particular, all the buildings were modelled by means of the Type 56 of TRNSYS, coupled to the Google SketchUp TRNSYS3d plug-in. By means of Type 56, it is possible to take into account and simulate the buildings geometry, envelope, windows, lighting, machineries heat gains schedule as well as the buildings users’ occupation and activity. Given the loads, several energy efficient renovation alternatives based on the adoption of air-to-air conventional and carbon dioxide heat pumps, evacuated thermal collectors and photovoltaic panels, as well as the envelope renovation of all the public and private buildings of the district, are proposed and compared. Results, obtained by detailed dynamic energy and environmental analyses, can be used as guidelines for the local authorities to implement suitable energy efficiency actions of existing buildings in order to reduce both the thermal energy and carbon dioxide emissions due to the building space heating/cooling, domestic hot water and electricity. The proposed renewable layout for Naples case study achieves a primary energy savings of 58% with a payback period of 4 years. The renewable layout for Turin case study achieves a primary energy savings of 76% with a payback period of 10.6 years.

Performance evaluation of near real-time condition monitoring in haul trucks

Strategic maintenance plays a key role in ensuring high availability and utilization of the haul trucks, and as equipment began to grow more complex towards the end of the 20th century, there was a need for a proactive maintenance strategy, which led to the development of condition-based maintenance. Real-time condition monitoring (RTCM) is the ability to perform condition monitoring in real-time and has the ability to alert maintenance and operations of abnormal conditions. These alarms can be used as an indication leading to a problem, and if a suitable corrective action is initiated in time, it could result in significant savings of equipment downtime and repair costs. This study aims to compare some maintenance performance indicators prior to and after implementation of RTCM strategy at a mine site using some tests of statistical significance. The study also indicated the presence of seasonality in the data, and thus the data was deseasonalized and detrended prior to being subjected to the statistical tests. Finally, the results indicated that RTCM strategy has proven to be successful in improving the availability for some of the failure categories chosen in this study.

Time-frequency dependencies of financial and economic risks in South American countries

This study fills a gap in the relevant literature by exploring the time-frequency domain causal relationship between financial risk and economic risk for five South American countries, namely Argentina, Brazil, Colombia, Peru, and Venezuela, over the period from 1984Q1 – 2018Q4. To the best of our knowledge, the causal relationship between finance and economics has not been comprehensively explored for the case of South America within the framework of risk. Therefore, the empirical findings of this study are likely to shed light on and open a new debate regarding the nexus between financial and economic activities. Based on our aims, the time domain Toda-Yamamoto causality test is performed as a robustness check, and the wavelet coherence approach is implemented to explore the joint time-frequency domain causal relationship between financial and economic risk. The empirical findings reveal that: (1) there is a feedback causality between financial risk and economic risk in Venezuela; (2) there is a one-way causality that runs from economic risk to financial risk in Colombia and Peru, and; (3) financial risk significantly leads to changes in economic risk in Argentina and Brazil. The empirical results are crucial for policy decision making and can be used by both researchers and macro-economic policymakers to take suitable actions, if necessary, by implementing more appropriate or alternative economic and financial decisions.

Near horizon symmetries, emergence of Goldstone modes and thermality

For a long time it is believed that black hole horizon are thermal and quantum mechanical in nature. The microscopic origin of this thermality is the main question behind our present investigation, which reveals possible importance of near horizon symmetry. It is this symmetry which is assumed to be spontaneously broken by the background spacetime, generates the associated Goldstone modes. In this paper we construct a suitable classical action for those Goldstone modes, and show that all the momentum modes experience nearly the same inverted harmonic potential, leading to an instability. Thanks to the recent conjectures on the chaos and thermal quantum system, particularly in the context of an inverted harmonic oscillator system. Going into the quantum regime, the system of large number of Goldstone modes with the aforementioned instability is shown to be inherently thermal. Interestingly the temperature of the system also turns out to be proportional to that of the well known horizon temperature. Therefore, we hope our present study can illuminate an intimate connection between the horizon symmetries and the associated Goldstone modes as a possible mechanism of the microscopic origin of the horizon thermality.

A Decentralised Strategy for Heterogeneous AUV Missions via Goal Distribution and Temporal Planning

Heterogeneous multi-robot systems offer the potential to support complex missions, such as those needed for persistent autonomy in underwater domains. Such systems enable each robot to be optimised for specific tasks to better manage dynamic situations. In this context, temporal planning can generate plans to support the execution of multi-robot missions. However, the task distribution quality in the generated plans is often poor due to the strategies that existing planners employ to search for suitable actions, which do not tend to optimise task allocation. In this paper, we propose a new algorithm called the Decentralised Heterogeneous Robot Task Allocator (DHRTA) which enhances goal distribution by considering task spatial distribution, execution time, and the capabilities of the available robots. DHRTA is the first phase of our decentralised planning strategy which supports individual robot plan generation using temporal planners. Experiments illustrate the robustness of the approach and indicate improvements in plan quality by reducing the planning time, mission time and the rate of mission failures.

Internet of Things Analytics for Smart Home Applications

With advancement in smart home services on mobile and wearable devices, individual can smartly control his/her home appliances such as fan, refrigerator, TV, air conditioner, etc., in an efficient manner. Internets of Things (IoT) devices are extensively utilized to interchange the data between smart applications, mobiles, and wearables. IoT devices are responsible for monitoring and sensing the data about home appliances with the help of sensor nodes, the obtained data is then communicate to given high-end devices for taking the suitable action. The overall objective of this paper is to study the existing IoT analytics techniques which are used to build smart applications for homes. This paper also discusses the various challenges to design a suitable smart home using IoTs. Thereafter, a comparative analyzes are considered to evaluate the shortcomings of these techniques and various gaps are formulated in the existing techniques. Finally, a methdology has been devised which can overcome the shortcomings of existing models and help enhancing the functioning of human activity recognition in smart homes.

Design of a Low Latency and High Throughput Packet Classification Module on FPGA Platform

The Packet classification method plays a significant role in most of the Network systems. These systems categories the incoming packets in various flows and takes suitable action based on the requirements. If the size of the network is vast and complexity will arise to perform the different operations, which affects the network performance and other constraints also. So there is the demand for high-speed packet classifiers to reduce the network complexity and improve the network performance. In this article, The Bit vector Packet classifier (BV-PC) Module is designed to improve the network system performance and overcome the existing limitation of Packet classification approaches on FPGA. The BV-PC Module contains Packet generation Unit (PGU) to receive the valid incoming packets, Memory Unit (MU) to store valid packets, Header Extractor Unit (HEU) extracts the IP Header address information from the Valid packets, The BV-Based Source and Destination Address (BV-SA, BV-DA) unit receives the IP packet header Information and Process with BV based rule set and aggregates the BV-SA and BV-DA outputs, Priority Encoder encodes the Highest priority BV Rule for the generation of Classified output. The BV-PC utilizes <2% Chip area (slices), works at 509.38MHz, and consumed Less 0.103 W of total Power on Artix-7 FPGA. The BV-PC operates with a latency of 5 clock cycles and works at 815.03Mpps throughput. The BV-PC is compared with existing approaches and provides Better improvements in Hardware constraints.

Prediction of Soil Compression Index in Teziutlán, Mexico

One of the important soil materials for the evaluation of settlement due to consolidation of soil clay is the compression index (Cc). The assessment of its value through laboratory testing is usually time consuming and expensive. Hence to find the most appropriate model prediction, the paper is examined the compression index comparison with soil plasticity material (water content, liquid limit, plasticity index). The analysis is used by multiple regression analysis and some performance indicators such as Coefficient of Determination (R2), Index of Agreement (IA), Prediction Accuracy (PA), Normalized Absolute Error (NAE) and Root Mean Square Error (RMSE). The result identified the proposed model by Skempton’s, Sidharan, Nagaraj’s and Hough’s are suitable for the compression index (Cc) equation model. Another essential point, the research is found error measurement for soil plasticity index properties. Thus, this prediction model is useful to develop a preliminary assessment of the settlement of structures and carry out suitable action for landslides in Teziutlan, Mexico.

Challenges in customer due diligence for banks in the UAE

PurposeThe purpose of this paper is to unpack the customer due diligence (CDD) vulnerabilities and to examine and analyze the UAE specific dynamics that make the country exposed to these threats. This research also intends to put on the table suitable solutions and remedial action steps that the UAE government, regulators and financial institutions (FIs) can adopt.Design/methodology/approachThis study is qualitative in nature.FindingsDespite the impressive regulatory framework and the satisfactory practices by FIs, there still remains some UAE specific challenges that make it difficult to undertake CDD for certain customers. The challenges that were identified include difficulties in Arabic names, complications in identifying the beneficial owners, impediments in establishing the source of wealth/funds, concerns with politically exposed persons, the increasing cost of compliance that resulted in a pattern of de-risking within FIs.Research limitations/implicationsThe international bodies whose mandate is to formulate the necessary anti-money laundering and combating the financing of terrorism policies and regulations for global implementation together with Association of Certified Anti-Money Laundering Specialists (ACAMS) have published sufficient studies on CDD-related issues in the UAE. Yet on the other hand, very limited literature was found by independent scholars. This paper will, therefore, largely reference publications by Financial Action Task Force, the International Narcotics Control Strategy Report and ACAMS. It will also include works by respected law firms that have operations in the UAE, local publications, government documents, academic papers by the International Monetary Fund and the World Bank, legal journals and others.Originality/valueIllicit actors exploit the UAE’s relatively open business environment, a multitude of global banks and exchange houses and global transportation links to undertake illicit financial activity […] the UAE does not have any major anti-money laundering (AML) deficiencies. However, the monitoring of FIs for AML purposes, particularly in the area of CDD, could be improved. This paper unpacks the CDD vulnerabilities and analyzes the UAE specific dynamics that make the country exposed to these threats. This research also puts on the table suitable remedial action steps that the UAE government, regulators and FIs can adopt.

Hydrogeochemistry and evaluation of groundwater suitability for irrigation purpose in the Mornag region, northeastern Tunisia

In semiarid regions, salinization and anthropogenic contaminants are considered as the principal threat to groundwater resource availability. Consequently, the origin and processes of salinization should be identified, to take a sustainable measurement to mitigate these problems. In the present study, groundwater samples from 18 dug wells were collected during the sampling campaign carried out in May 2016 in order to identify the main natural and anthropogenic processes that control the quality of groundwater in the Mornag shallow aquifer (northeastern Tunisia) and to evaluate its suitability for agricultural irrigation purpose. This may create a clear database for decision maker to take suitable actions to sustainably manage the groundwater resources. The hydrogeochemical parameters (major ions concentration, pH, TDS, EC) were examined to assess the quality of groundwater and to identify the main mineralization processes. Permeability index (PI), percentage of sodium (%Na) and the ratio of sodium adsorption (SAR) were investigated to evaluate the groundwater suitability for irrigation. It has been demonstrated that the major ions abundance follows the order of sodium > calcium > magnesium > potassium and chloride > sulfate > bicarbonate > nitrate. Dissolution of evaporite minerals, such as halite, gypsum and anhydrite, and reverse ion exchange are the predominant processes of groundwater mineralization, generating the Cl–SO4–Ca and Na–Cl major water types. The calculated irrigation water quality index displayed that 55% of the Mornag groundwater samples are distinguished by elevated SAR and PI, suggesting their unsuitability for irrigation. So, the present study has provided a precious insight into the major geochemical processes occurring in the Mornag shallow groundwater and their suitability for irrigation which will support future management of these resources.

Classifying Obstacles and Exploiting Class Information for Humanoid Navigation Through Cluttered Environments

Humanoid robots are often supposed to share their workspace with humans and thus have to deal with objects used by humans in their everyday life. In this article, we present our novel approach to humanoid navigation through cluttered environments, which exploits knowledge about different obstacle classes to decide how to deal with obstacles and select appropriate robot actions. To classify objects from RGB images and decide whether an obstacle can be overcome by the robot with a corresponding action, e.g., by pushing or carrying it aside or stepping over or onto it, we train and exploit a convolutional neural network (CNN). Based on associated action costs, we compute a cost grid containing newly observed objects in addition to static obstacles on which a 2D path can be efficiently planned. This path encodes the necessary actions that need to be carried out by the robot to reach the goal. We implemented our framework in the Robot Operating System (ROS) and tested it in various scenarios with a Nao robot as well as in simulation with the REEM-C robot. As the experiments demonstrate, using our CNN, the robot can robustly classify the observed obstacles into the different classes and decide on suitable actions to find efficient solution paths. Our system finds paths also through regions where traditional motion planning methods are not able to calculate a solution or require substantially more time.

Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning

In modern manufacturing industry, dynamic scheduling methods are urgently needed with the sharp increase of uncertainty and complexity in production process. To this end, this paper addresses the dynamic flexible job shop scheduling problem (DFJSP) under new job insertions aiming at minimizing the total tardiness. Without lose of generality, the DFJSP can be modeled as a Markov decision process (MDP) where an intelligent agent should successively determine which operation to process next and which machine to assign it on according to the production status of current decision point, making it particularly feasible to be solved by reinforcement learning (RL) methods. In order to cope with continuous production states and learn the most suitable action (i.e. dispatching rule) at each rescheduling point, a deep Q-network (DQN) is developed to address this problem. Six composite dispatching rules are proposed to simultaneously select an operation and assign it on a feasible machine every time an operation is completed or a new job arrives. Seven generic state features are extracted to represent the production status at a rescheduling point. By taking the continuous state features as input to the DQN, the state–action value (Q-value) of each dispatching rule can be obtained. The proposed DQN is trained using deep Q-learning (DQL) enhanced by two improvements namely double DQN and soft target weight update. Moreover, a “softmax” action selection policy is utilized in real implementation of the trained DQN so as to promote the rules with higher Q-values while maintaining the policy entropy. Numerical experiments are conducted on a large number of instances with different production configurations. The results have confirmed both the superiority and generality of DQN compared to each composite rule, other well-known dispatching rules as well as the stand Q-learning-based agent.

Predicting the hydrological response of a forest after wildfire and soil treatments using an Artificial Neural Network

Accurate predictions of surface runoff and soil erosion after wildfire help land managers adopt the most suitable actions to mitigate post-fire land degradation and rehabilitation planning. The use of the Artificial Neural Networks (ANNs) is advisable as hydrological prediction tool, given their lower requirement of input information compared to the traditional hydrological models.This study proposes an ANN model, purposely prepared for forest areas of the semi-arid Mediterranean environments. The ANN hydrological prediction capability in non-burned, burned by wildfire, and burned and then treated soils has been verified at the plot scale in pine forests of South-Eastern Spain. Runoff and soil loss were much higher than non-burned soils (assumed as control), but mulch application was effective to control runoff and soil erosion in burned plots. Moreover, logging did not affect the hydrological response of these soils. The model gave very accurate runoff and erosion predictions in burned and non-burned soils as well as for all soil treatments (mulching and/or logging or not), with only one exception (that is, in the condition with the combination of treatments which gave the worst performance, burning, mulching and logging), as shown by the exceptionally high model efficiency and coefficients of determination. Although further experimental tests are needed to validate the ANN applicability to the burned forests of the semi-arid conditions and other ecosystems, the use of ANN can be suggested to landscape planners as decision support system for the integrated assessment and management of forests.

Field trials of an energy‐aware mission planner implemented on an autonomous surface vehicle

AbstractMission planning for autonomous marine vehicles is nontrivial due to the dynamic and uncertain nature of the marine environment. Communication can be low‐bandwidth and is not always guaranteed, so the operator must rely on the vehicles to adjust their plans according to the realized state of the environment. This paper presents the improvements made to an energy‐aware mission planner that allows it to generate and adjust plans for an autonomous surface vehicle (ASV) operating in an uncertain environment. The energy‐aware mission planning problem was redefined as a stochastic programming problem, and a two‐stage solver was developed to provide an initial plan for the ASV and then adjust it during run‐time according to predefined recourse actions. The mission planner and ASV were trialed in Lake Waverley, Tasmania. Adjusting the recourse action criteria demonstrated that the ASV could exhibit conservative or opportunistic behaviors according to the operator's preference of safety margin. In the pursuit of extending the planner's second‐stage so that it can predict a suitable recourse action ahead of time, a hybrid long short‐term memory energy forecaster was trained from the Waverley mission data. Comparison of the error between the forecaster and the test data shows that the forecaster has a reliable forecast horizon of about 10 s.

Responses of ecosystem services to natural and anthropogenic forcings: A spatial regression based assessment in the world's largest mangrove ecosystem

Most of the Earth's Ecosystem Services (ESs) have experienced a decreasing trend in the last few decades, primarily due to increasing human dominance in the natural environment. Identification and categorization of factors that affect the provision of ESs from global to local scales are challenging. This study makes an effort to identify the key driving factors and examine their effects on different ESs in the Sundarbans region, India. We carry out the analysis following five successive steps: (1) quantifying biophysical and economic values of ESs using three valuation approaches; (2) identifying six major driving forces on ESs; (3) categorizing principal data components with dimensionality reduction; (4) constructing multivariate regression models with variance partitioning; (5) implementing six spatial regression models to examine the causal effects of natural and anthropogenic forcings on ESs. Results show that climatic factors, biophysical factors, and environmental stressors significantly affect the ESs. Among the six driving factors, climate factors are highly associated with the ESs variation and explain the maximum model variances (R2=0.75-0.81). Socioeconomic (R2=0.44-0.66) and development (R2=27-0.44) factors have weak to moderate effects on the ESs. Furthermore, the joint effects of the driving factors are much higher than their individual effects. Among the six spatial regression models, Geographical Weighted Regression (GWR) performs the most accurately and explains the maximum model variances. The proposed hybrid valuation method aggregates biophysical and economic estimates of ESs and addresses methodological biases existing in the valuation process. The presented framework can be generalized and applied to other ecosystems at different scales. The outcome of this study could be a reference for decision-makers, planners, land administrators in formulating a suitable action plan and adopting relevant management practices to improve the overall socio-ecological status of the region.

A Flood Risk Management Program of Wadi Baysh Dam on the Downstream Area: An Integration of Hydrologic and Hydraulic Models, Jizan Region, KSA

For public safety, especially for people who dwell in the valley that is located downstream of a dam site, as well as the protection of economic and environmental resources, risk management programs are urgently required all over the world. Despite the high safety standards of dams because of improved engineering and excellent construction in recent times, a zero-risk guarantee is not possible, and accidents can happen, triggered by natural hazards, human actions, or just because the dam is aging. In addition to that is the impact of potential climate change, which may not have been taken into account in the original design. A flood risk management program, which is essential for protecting downstream dam areas, is required. Part of this program is to prepare an inundation map to simulate the impact of dam failure on the downstream areas. The Baysh dam has crucial importance both to protect the downstream areas against flooding, to provide drinking water to cities in the surrounding areas, and to use the excess water for irrigation of the agricultural areas located downstream of the dam. Recently, the Kingdom of Saudi Arabia (KSA) was affected by extraordinary rainstorm events causing many problems in many different areas. One of these events happened along the basin of the Baysh dam, which raised the alarm to the decision makers and to the public to take suitable action before dam failure occurs. The current study deals with a flood risk analysis of Wadi Baysh using an integration of hydrologic and hydraulic models. A detailed field investigation of the dam site and the downstream areas down to the Red Sea coast has been undertaken. Three scenarios were applied to check the dam and the reservoir functionality; the first scenario at 100- and 200-year return period rainfall events, the second scenario according to the Probable Maximum Precipitation (PMP), and the third scenario if the dam fails. Our findings indicated that the Baysh dam and reservoir at 100- and 200-year rainfall events are adequate, however, at the PMP the water will spill out from the spillway at ~8900 m3/s causing flooding to the downstream areas; thus, a well-designed channel along the downstream wadi portion up to the Red Sea coast is required. However, at dam failure, the inundation model indicated that a vast area of the section downstream of the dam will be utterly devastated, causing a significant loss of lives and destruction of urban areas and agricultural lands. Eventually, an effective warning system and flood hazard management system are imperative.

Optimal Execution Strategy for Large Orders in Big Data: Order Type using Q-learning Considerations

Investors who intend to execute large orders have to always a trade-off between price impact and opportunity cost in Big Data. In this study, reinforcement learning (Q-learning) is applied, due to its strength to support an agent, to make the best decision and take suitable action in a dynamic environment to achieve an optimal way to execute a large number of orders in a day trade. Through Q-learning algorithm, the agent has learned the kind of order yet by how much of it should be submitted in each step to achieve the optimum volume-weighted average price, as the objective function of learning in this study. Historical data of shares in Tehran Stock Exchange has been used to consider the possibility of order types to set the parameters of the simulated trading market, and the price impact on large orders has also been considered through this simulated Big Data to make it with higher accuracy. Results show that for a large order both on buy-side and sell-side separately, execution strategy adopting with multiple order types could be more appropriate compared with using a single order type of execution strategy. This case study suggests that passive order type leads the trader to achieve better results. Compared to the market, the optimal strategy has managed to reduce the volume weighted average price (transaction costs) by 0.95 percent on buy-side and increase it 1.31 percent on sell-side.