This study explores fault-tolerant consensus in leader-following heterogeneous multi-agent systems, focusing on actuator failures in uncrewed aerial vehicles (UAVs) and uncrewed ground vehicles (UGVs). An optimization-based fault-tolerant consensus algorithm is proposed. The algorithm utilizes the Euler-Lagrange formula to ensure system consistency under actuator failures, with the Lyapunov stability theory proving the asymptotic stability of the consistency error. The algorithm is applied to heterogeneous multi-agent systems of UAVs and UGVs to derive optimal fault-tolerant consensus control laws for each vehicle type. Simulation experiments give evidence for the feasibility of the proposed control strategy.

In this study the effects of three different main preparatory tillage operations [ploughing at 0.4 m (P40) and 0.20 m (P20) depth and minimum tillage at 0.20 m depth (MT) each of them carried out at two different soil water contents (WC) [low, 58% (LH) and high, 80% (HH) of field capacity] were investigated. The results obtained in this research show high values of soil strength in term of Penetration resistance (CI) and shear strength (SS) particularly in deeper soil layers at lower water content. Fossil-fuel energy requirements both for P40 LH and P20 LH were 25 and 35% higher with respect to the HH treatments and tractor slip were very high (P40 LH = 32.4%) with respect to the P40 HH treatment (16%). Therefore soil water content had significantly influenced tractor performance during soil ploughing, particularly at 0.40 m depth while MT was not influenced at all. A significant correlation between grain yield and soil penetration resistance was found highlighting how soil strength may be good indicator of its productivity. Obtained results during these field tests allowed considering MT and P20 treatments more suitable for this type of soil in climate change scenarios.

Oilseed plants of the Brassicaceae plant family are cultivated for food, feed and industrial purposes on large-scale in Europe. This review gives an overview of current market-oriented applications of new genomic techniques (NGTs) in relevant Brassicaceae oilseed crops based on a literature survey. In this respect, changes in oil quality, yield, growth and resistance to biotic and abiotic stress are goals in oilseed rape (B. napus) and camelina (C. sativa). Environmental risk assessment scenarios are developed for specific NGT applications in Brassicaceae oilseed crops with either a changed oil composition or with fitness related traits. In case of a changed oil composition an increase or decrease of polyunsaturated fatty acids (PUFA) demonstrates risks for health and survival of pollinators. In case of fitness related traits other risks were identified, i. e. an increased risk of spread and persistence of NGT plants. Furthermore, there are indications for potential disturbance of interactions with the environment, involving signalling pathways and reaction to stress conditions. It is shown that for a comprehensive risk assessment the technological potential of NGTs, the plants’ biology and the scale of releases have to be considered in combination. Therefore, the release of NGT plants into the environment for agricultural purposes will require risk assessment and monitoring of single traits as well as of combinatorial and long-term cumulative effects. In addition, risk management should develop concepts and measures to control and potentially restrict the scale of releases. This is especially relevant for NGT Brassicaceae in Europe which is a centre of diversity of this plant family.

This article provides a literature review on tubal carcinoma to offer an updated insight into its preventative strategies, diagnosis, treatment, and oncological surveillance. In addition to the search string utilized, the authors' focus extended to key scientific studies, consensus statements, guidelines, and relevant case reports essential for the proper clinical management of the disease, providing a methodologically well-structured literature review combined with the practical expertise in the oncological field. The article also includes two emblematic clinical cases that emphasize the importance of understanding the physiopathology and the current state of the art in the anatomopathological advancements of tubal/ovarian/peritoneal carcinoma, often assimilated into a single clinical entity.

Vibration signal analysis is regarded as a fundamental approach in diagnosing faults in rolling bearings, and recent advancements have shown notable progress in this domain. However, the presence of substantial background noise often results in the masking of these fault signals, posing a significant challenge for researchers. In response, an Adaptive Denoising Autoencoder (ADAE) approach is proposed in this paper. The data representations are learned by the encoder through convolutional layers, while the data reconstruction is performed by the decoder using deconvolutional layers. Both the encoder and decoder incorporate adaptive shrinkage units to simulate denoising functions, effectively removing interfering information while preserving sensitive fault features. Additionally, dropout regularization is applied to sparsify the network and prevent overfitting, thereby enhancing the overall expressive power of the model. To further enhance ADAE's noise resistance, shortcut connections are added. Evaluation using publicly available datasets under scenarios with known and unknown noise demonstrates that ADAE effectively enhances the signal-to-noise ratio in strongly noisy backgrounds, facilitating accurate diagnosis of faults in rolling bearings.

This work presents a Fault-Tolerant Model Predictive Control (FTMPC) algorithm applied to a simulation model for sewer networks. The control system will aim to prevent contaminated water from escaping the network, especially during periods of heavy rainfall, that can cause combined sewer overflows (CSO), while maintaining the flow of water towards the wastewater treatment plant (WWTP) as close as possible to its nominal value. The main purpose of the work is to preserve the operation of the predictive controller as much as possible, in accordance with its operational objectives, when there may be anomalies affecting the elements of the control system, mainly sensors and actuators. Comparing the results obtained considering various types of faults, with situations of normal controlled operation and with the behavior of the sewer network when no control is applied, will allow some conclusions to be drawn at the end.

The Mara River basin (MRB) has a world-famous ecosystem, but the vegetation has been damaged due to economic development in recent years, and there is little known about the area that will experience severe vegetation damage in the future. Based on the vegetation vulnerability system, principal component analysis, and three CMIP6 scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5), vegetation vulnerability was calculated in the base period (2010-2019), near-term period (2020-2059) and long-term period (2060-2099) in the MRB. The spatial cluster of vegetation vulnerability was revealed by spatial correlation analysis, and the transition of vegetation vulnerability of different periods was analyzed by stochastic matrix. The results showed that in all periods, the vulnerability showed a high-high cluster in the east, and a low-low cluster midstream and downstream. From the stochastic matrix, the area of high vulnerability increased the least under the SSP1-2.6, while it increased the most under the SSP5-8.5. The vegetation vulnerability upstream increased the most from the base period to the near-term period and long-term period in the MRB. By comparing the vegetation vulnerability under different scenarios, and pointing out the areas with the highest vulnerability increase, this study can better provide comprehensive decision-making for vegetation protection in the MRB.

The understanding of spatiotemporal variations in reference evapotranspiration (ETo) and its long-term trends is of paramount importance for water cycle studies, modeling, and water resource management, especially in the context of climate change. Therefore, the primary aim of this study is to critically evaluate the performance of various CMIP5 global climate models in simulating the Penman-Monteith reference evapotranspiration and its associated climate variables (maximum and minimum air temperature, incident solar radiation, relative humidity, and wind speed). This evaluation is based on data from nine climate models and 33 automatic meteorological stations (EMA) in the state of Mato Grosso, spanning the period 2007-2020. The statistical metrics used for evaluation include Bias, root mean square error, and Pearson and Spearman correlation coefficients. The projections of the most accurate model were then used to analyze the spatial and temporal changes and trends in ETo under the RCP 2.6, 4.5, and RCP 8.5 scenarios from 2007 to 2100. The HadGEM2-ES model projections indicate static averages similar to current conditions until the end of the century in the RCP 2.6 scenario. However, in the RCP 4.5 and 8.5 scenarios, there is a continuous increase in ETo, with the most significant increase occurring during the dry period (May to September). The areas of the Amazon biome in the north of Mato Grosso exhibit the largest increases in ETo when comparing the observed (2007-2020) and projected (2020-2100) averages. The trend analysis reveals significant changes in ETo and its variables across the state of Mato Grosso in the RCP 4.5 and 8.5 scenarios. In the RCP 2.6 scenario, significant trends in ETo are observed only in the northern Amazon areas. Despite not being observed in all AWSs, the trend analysis of the observed data demonstrates more intense changes in ETo and the existence of the evapotranspiration paradox with an increase in the Cerrado areas and reductions in the Pantanal and southern Amazon areas.

(1) Background: The aim of the work was to characterize the climatic evolution and change based on the Multi Criteria Classification through the dynamics of bioclimatic indices in viticulture across Italy and its regional administrative boundaries, focusing on latitudes and elevations. (2) Methods: The impact of climate change on viticulture in Italy was analysed and spatialized with reference to historical data from 1991 to 2021 and to the Future Scenarios up to 2080 assumed by SSP2-4.5 and SSP5-8.5, taking into account 13 GCMs. (3) Results: The bioclimatic indices have all shown a significant trend in the historical period, with an increase in temperature and a decrease in precipitation, reflecting their effects on the entire Italian territory with respect to HI, up to 44°N for CI and up to 46°N for DI, regardless of altitude. The Future Scenarios highlighted a shift towards the warmer classes of the two temperature-based indices (HI and CI) for both SSPs, especially for altitudes up to 900 m a.s.l.. The DI-based classification based on DI remained relatively stable in Italy over time, although DI values will become increasingly negative in the near future. (4) Conclusions: The climate in Italy is warming, especially in the south and in the coastal regions. By 2080, more areas will be “Very Hot” with “Warm Nights.” Drought will also increase and have a negative impact on viticulture. The importance of higher latitudes and altitudes in mitigating the effects of climate change justifies the continuing trend towards the relocation of vineyards as a medium-term solution and as an alternative to targeted cultivation methods that must be adopted in the short-term to safeguard the suitability of an area to quality viticulture.

This study aimed to learn disturbance information to effectively control multiple small unmanned surface vehicles (USVs) for marine research purposes at sea. The learned disturbance information was used to investigate the control of multiple USV swarms based on the leading–following formation technique. Numerous studies have already been conducted on swarm control using drones in the air; however, there has been a paucity of studies on swarm control in the sea. Contrary to swarm control in aviation, the fluid density of the sea is high, and the environment is constantly changing; thus, a robot system would be significantly affected by disturbances. This ultimately influences the maintenance of swarm formations and the accomplishment of missions [1,2]. Therefore, it is difficult to control numerous moving objects while maintaining a formation at sea by using the existing aerial drone swarm control technology. To solve this problem, we learned a long short-term memory (LSTM) model with time-series marine environmental data to predict maritime disturbances. Thereafter, the model was applied to a mission-based position control algorithm of each USV constituting the swarm to design a marine unmanned floating vessel swarm control system that can be applied to various marine environments. Specifically, to effectively control numerous small USVs for marine research purposes, ocean currents are learned using the LSTM algorithm, and the predicted ocean currents are used to generate a swarm USV control system for USV formations. In this study, a control system model of several small USVs equipped with two rear thrusters and a front lateral thruster was designed. The LSTM algorithm was learned using the previous ocean current data, and the velocity of the following ocean currents was predicted. The predictions were then used as a system disturbance to determine the thrust of the controller. To measure ocean currents in the sea when each USV moves, the velocity, azimuth, and position (latitude, longitude) data from the GPS mounted on the USV were used to measure the velocity and direction of the hull's movement. Further, the flow rate was measured using a flow rate sensor on a small USV. The USV movement and position were controlled using an artificial neural network-PID (ANN-PID) controller. This study entailed a comparative analysis of the designed USV model results and those generated by the simulator, including the behavior control rule of the USV swarm and the path of the actual USV swarm at sea. It was verified that the effectiveness of the USV mathematical model and behavior control rules. By comparing the movement path of the swarm USV with and without the disturbance learning algorithm and ANN-PID control algorithm applied to the designed simulator, the position error and maintenance performance of the swarm formation were analyzed, and the application results were compared.

Classifying network flow subsequences in an Industrial IoT gateway is an effective method for identifying Industrial IoT faults. This paper proposes a subsequence classification algorithm SSGBUL-IKNN based on unsupervised learning encoding to address the problem of low classification accuracy. Firstly, we built a network module based on unsupervised learning to encode the flow sequence of the Industrial IoT gateway. In the encoding module, a subsequence calibration algorithm controls the increasing error during the coding process. Then, the specific fault type is obtained through an improved KNN classification algorithm based on the indication distance source from the integrated coding sequences converted by multi-dimensional network flow sequences. The test result on three Industrial IoT datasets of a sewage treatment plant shows that the accuracy of SSGBUL-IKNN in this paper exceeds 92%, significantly higher than the algorithm DTW and TSF. It reaches the classification requirements of Industrial IoT fault diagnosis.

This study introduces a novel algorithm tailored for the precise detection of lower outliers in univariate datasets, particularly suited for scenarios with a single cluster. The approach leverages a combination of transformative techniques and advanced filtration methods to efficiently segregate anomalies from normal values. Notably, the algorithm emphasizes high precision, ensuring minimal false positives, and requires only a few parameters for configuration. Its unsupervised nature enables robust anomaly filtering without the need for extensive manual intervention. To validate its efficacy, the algorithm is rigorously tested using real-world data obtained from photovoltaic (PV) module strings with similar DC capacities, containing various anomalies. Results demonstrate the algorithm's capability to accurately identify lower outliers while maintaining computational efficiency and reliability in practical applications.

The relentless advancement and integration of mechanization and intelligence within the coal mining industry, combined with the increasing depths of mining operations, necessitate significantly higher safety standards. This necessity arises due to the increasingly complex geological and coal seam conditions encountered as mining operations delve deeper into the earth. Traditionally, 3D seismic exploration technology has been fundamental in ensuring both operational efficiency and safety in the coal mining sector. However, despite its critical role, this technology still has limitations in detecting and characterizing small-scale geological structures which can pose substantial risks if not accurately identified and mitigated. To address these challenges, this paper introduces a sparsity-constrained adaptive hard thresholding algorithm designed to tackle the spectral inversion optimization problem with an norm constraint for the enhanced retrieval of reflection coefficients, thereby establishing a comprehensive workflow for enhancing seismic data resolution through compressed sensing spectral inversion. Real data validation has demonstrated that this method significantly enhances the resolution of seismic data while maintaining the amplitude integrity and fidelity of the original signals. This improvement allows for a more detailed and accurate characterization of small-scale geological features, which are critical in identifying and mitigating potential hazards within coal mining operations, thus offering robust support for the implementation of more effective safety protocols and disaster prevention strategies in coal mines.

Species distribution models (SDMs) are usually used to predict current species’ geographic distributions and to forecast the impact of climate change, with different aims, such as conservation, and biodiversity management. SDMs use has been increasing in the last decades, however, they are vulnerable to parametrization and data quality input. Thus, inappropriate input can lead to potential unreliability in results. In this context, the most used data and methodologies in SDM, and putative deviations from the consensual best practices, were identified, by analysing recent literature (2018 to 2022). Results show that the parameters presented more consistently are the chosen algorithm (MaxEnt was used in 98% of the studies), the accuracy measures, and the time windows. Many papers fail to specify other parameters, limiting the reproducibility of the studies. Some papers also fail to provide information about the target species: only a fraction of the species' range is considered, or no justification for including specific variables in the model is provided. These options can decrease reliability in predictions under future scenarios since data provided to the model is inaccurate from the start or there is insufficient information for outputs discussion.

The review aims to assess fifteen (15) academic literature sources, highlighting the application of machine learning algorithms in the maintenance operations of optical fiber networks. It exhibits the collection of data using PRISMA methodology—Preferred Reporting Item for Systems Review and Meta-Analyses. The application, results, and performance metrics are discussed based on the collected observations, computations, and statistics in the studies, which revealed records of high accuracy degrees ranging from 86% to 98% on average and quality ML models including Neural Networks (NNs), Support Vector Machines (SVMs), and LSTM, as well as deep learning models that disclosed effective results of determining challenges and problems within the optical fiber lines. The review mainly centralized on superior machine learning technologies that surpass traditional techniques in fault detection and localization for improved optical fiber networks’ operations while providing insights into the limitations and challenges encountered in real-world applications of these models, offering a comprehensive perspective on the optical fiber network’s domain.

This research paper examines a cosmological model in flat space-time via $f(R,G)$ gravity where $R$ and $G$ are respectively the Ricci scalar and Gauss-Bonnet invariant. Our model assumes that $f(R,G)$ is an exponential function of $G$ combined with a linear combination of $R$. We scrutinize the observational limitations under a power law cosmology that relies on two parameters - $H_0$, the Hubble constant, and $q$, the deceleration parameter, utilizing the 57-point $H(z)$ data, 8-point BAO data, 1048-point Pantheon data, joint data of $H(z)$ + Pantheon, and joint data of $H(z)$ + BAO + Pantheon. The outcomes for $H_0$ and $q$ are realistic within observational ranges. We have also addressed Energy Conditions, $Om(z)$ analysis and cosmographical parameters like Jerk, Lerk and Snap. Our estimate of $H_0$ is remarkably consistent with various recent Planck Collaboration studies that utilize the $\Lambda$CDM model. According to our study, the power law cosmology within the context of $f(R,G)$ gravity provides the most comprehensive explanation for the important aspects of cosmic evolution.

The Circular Bioeconomy (CBE) combines the concepts of bioeconomy and circular economy. As an alternative concept to the current fossil-based, linear economy, it de-scribes an economy based on the efficient valorization of biomass. It is regional in nature and aims to improve sustainability. An analysis of the transition process, by identifying its success criteria and assessing its impacts through the modelling of technology-specific scenarios is necessary to ensure that CBE concepts are sustainable. However, a compre-hensive consideration of regional influences on both is lacking. Based on extensive literature research and an expert survey, we (i) present a compre-hensive catalog of CBE success criteria and discuss their region-specific character and (ii) develop a methodology based on evaluation matrices that enable to match CBE technolo-gies with regions. The matrices support the evaluation of technological and regional characteristics influencing the successful CBE implementation. The results show that the success criteria "biomass resources", "technological", and "social" are perceived as highly important, and that most of the success criteria are both region- and technology-specific, highlighting the relevance of developing matrices to match them. We describe such matrices indicatively for the two broadest and most im-portant success criteria clusters “social acceptance” and “biomass supply chain”. With this, we substantiate the regional nature of CBE and raise the awareness on the importance of considering regional conditions in CBE transition processes. Furthermore, we provide practical guidance on how regional conditions can be reflected in the selection of technologies, e.g. in regional CBE technology scenarios.

The valorization of aquaculture/fisheries processing by-products, as well as unavoidable/unwanted catches and discards in Greece is currently an underutilized activity despite the fact that there are several best practices in Northern Europe and overseas. One of the main challenges is to determine whether the available quantities for processing are sufficient to warrant valorization of discards and fish side streams. This is the first attempt to systematically record and analyze the available quantities of fish by-products and discards in Greece in a spatial and temporal way in an effort to create a national exploitation Master Plan for the valorization of this unavoidable and unwanted biomass. A thorough survey conducted within the VIOAXIOPIO project unveiled a substantial biomass of around 19,000 tonnes annually that could be harnessed for valorization. Furthermore, the production of various High Added Value Biomolecules (HAVB) was investigated, and experimental trials were conducted to assess the potential yields and the collected data were used to formulate four valorization scenarios.

Single-phase-to-ground fault in non-effectively grounded systems poses serious public safety concern. Along with increasing deployment of low-voltage (LV) sensors, new opportunities and ways are created for solving this traditional problem in distribution systems. A new approach, identifying primary single-phase-to-ground line fault and locating it through measurement data from smart sensors deployed in the secondary side of distribution transformers, is proposed in this paper. When a fault occurs in primary distribution feeder, the transient aerial mode voltage (AMV) of each LV smart sensor is calculated. The fault line can be identified by an abrupt change value of AMV at LV side. Next, by finding the position of the maximum value of this AMV from LV sensors on the fault line, the fault location can be pinpointed to two neighboring distribution transformers. The proposed method has been proven to be efficient and reliable by simulation studies.

The Sanaga River Valley is a deep-seated structure of about 220 km in width, bounded on its northern and southern sides by 025-0550 fault zones. Its internal structure has been very little described before this study. It is characterized by the presence of surface features where a thick stratum of shale and argillaceous sandstones have been documented. The use of electrical resistivity tomography for near-surface imaging and hydrogeological prospecting within shale succession is widespread. However, several methods of spatial modeling of resistivity contours are generally proposed for the interpretation of inverse resistivity model sections. To determine the most appropriate method for interpreting resistivity tomographies in the context of subsoil marked by significant heterogeneity, logarithmic contour intervals, and linear contour interval processing were implemented on data collected over five areas. The data consists of outcrops data, cuttings samples from groundwater boreholes, and resistivity sections ranging in length from 240 to 1000 m. Well-control of the inverse resistivity model sections derived from both processes shows that the linear contour intervals modeling gives a more realistic picture of the reservoir size of the wa-ter-bearing sand in both shale and argillaceous sandstone succession. The sedimentary architecture over Sanaga fault breccia is marked by vertical intrusion anomalies up to 80 m high. The shape of these anomalies, as well as the very low resistivity values that characterize them, suggests that they could be related to mud intrusion.

Properties of time domain parameters of the vibration signal have been extensively studied for the fault diagnosis of rolling element bearings (REB). Parameters like kurtosis and Envelope Harmonic-to-Noise Ratio are most widely applied in this field and some important progress has been made. However, since only one-sided information is contained in these parameters respectively, problems still exist in practice when the signals collected are of complicated structure and/or contaminated by strong background noises. A new parameter, named Shock pulse index (SPI), is proposed in this paper. It integrates the mutual advantage of both parameters above and can help effectively identify fault related impulse components under the interference of strong background noises, unrelated harmonic components and random impulses. The SPI optimizes the parameters of Maximum Correlated Kurtosis Deconvolution (MCKD), which is used to filter the signals under consideration. Finally, the interested transient information contained in the filtered signal can be highlighted through demodulation with Teager Energy Operator (TEO). Fault related impulse components can therefore be extracted accurately. Simulations and experiment analyses verify the effectiveness and correctness of the SPI.

The Eastern Kunlun Fault Zone (EKFZ) is located in a seismic blank area. This study investigates the hydrogeochemical characteristics of varied-temperature springs in the EKFZ to understand the relationship between fractures, fluids and earthquakes. It aids in establishing their induction mechanisms and assists in monitoring the earthquake precursors of the EKFZ. In this study, the main elements, trace elements, hydrogen and oxygen isotopes and strontium isotopes of 23 varied-temperature springs in the EKFZ were analyzed. The results reveal the source of groundwater recharge, hydrochemical types, mineral saturation, and for a select few springs indicative of geothermal activity, collected geothermal reservoir temperatures. The study also explores the relationship between earthquakes and springs, identifying sensitive chemical indicators. Based on continuous monitoring at Maqu Station, we found that before the occurrence of the Maerkang Ms 6.0 earthquake, the concentration of Cl− rose by 2.9 times, and 46 days before the Gande earthquake (Ms = 4.7), the concentrations of Na+ and SO42− significantly increased. Finally, a conceptual model of the hydrogeochemistry of springs is presented to describe the groundwater circulation in the study area. This research is of great significance for the analysis of fluid geochemistry in this area and for assessing the seismic hazards of the EKFZ.

incipient fault diagnosis is particularly important in process industrial systems, as its early detection helps to prevent major accidents. Against this background, this study proposes a combined method of Mixed Kernel Principal Components Analysis and Dynamic Canonical Correlation Analysis (MK-DCCA). The robust generalization performance of this approach is demonstrated through experimental validation on a randomly generated dataset. Furthermore, Comparative experiments were conducted on a CSTR Simulink model, comparing the MK-DCCA method with DCCA and DCVA methods, demonstrating its excellent detection performance for incipient fault in nonlinear and dynamic system. Meanwhile, fault identification experiments were conducted, validating the high accuracy of fault identification method based on contribution. The experimental findings demonstrate that the method possesses a certain industrial significance and academic relevance.

In situations of uncertainty, scenarios serve as input for evidence-based decision making. However, past experience shows that not all scenarios are treated equally, and we hypothise that only those based ion a world view shared by decision makers are perceived as credible and receive full attention. While intuitively plausible, this hypothesis has not been analysed by quantitative correlation analyses, so instead of drawing on quantitative data the paper analyses the archetypical scenarios developed in the ALARM project to substantiate the plausibility by a comparative analysis of world views, value systems and policy orientations. Shock scenarios are identified as a means to explore the possibility space of future developments beyond the linear developments models and most scenario storylines suggest. The analysis shows that the typical scenarios are based on mutually exclusive assumptions. In conclusion, a comparison of storyline and empirical data can reveal misperceptions, policy failures and the need to rethink world views as a necessary step to open up to new challenges. Deeply held beliefs will make this a transition unlikely to happen without severs crises, if not dedicated efforts to reveal the role of world views for scenarios and policies are undertaken.

Tomato leaf curl disease (TLCD) is the most common viral disease in the tomato plant. It is caused by begomoviruses, which are viruses that cause plant development to be slowed. Many of the traditional disease management methods are still in use. They are, however, ineffective and out of date. Modern biotechnology is being used to detect illness in tomato plants as early as possible, thus reducing damage to the plants. Through genetic engineering, the spread of viruses may be controlled or prevented entirely. Here reviewed many methods for decreasing or eliminating the viral influence on crop growth through biotechnology and genomics. We also investigated the possibility of genetic engineering to reduce or remove the virus TLCD impact on tomato crop development.

Background: Learning arrangements in health care profession education are increasingly taking place in digital environments. VR in nursing education, as one of the digital elements, is the subject of controversial debate. On the one hand it supports the authenticity of case studies by adding realistic perspectives and information. On the other hand, the costs of developing and maintaining software and hardware hinder long-term implementation. Methods: In this paper, we describe our findings and insights from two different research projects focused on the incorporation of digital tools, particularly virtual reality (VR), into nursing education. Based on the German context, our aim is to promote the adoption of innovative digital methods in nursing education and to offer invaluable experiences from the field. Results: Starting with a brief recapitulation of the projects, we then elucidate the pedagogical strategies for embedding VR-driven scenarios in nursing education. Based on our experiences during the projects we identify various positive aspects, such as change of perspective and simulating acute situations. Key findings: Although potential drawbacks remain, we advocate long-term implementation and specific use of VR at the interface between theory and practice. Nevertheless it is crucial to establish regular evaluation observing the value of digitalisation, especially VR; for nursing education.

Intelligent fault diagnosis techniques have replaced the time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning model can improve the accuracy of intelligent fault diagnosis with the help of its multilayer nonlinear mapping ability. This paper has proposed a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in first convolutional layer for extracting feature and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform state of the art DNN model which is based on frequency features under different working load and noisy environment.

This article provides a detailed analysis of non-invasive techniques for prediction and diagnosis of faults in internal combustion engines, focusing on the application of the Proknow-C and Methodi Ordinatio systematic review methods. Initially, the relevance of these techniques in promoting energy sustainability and mitigating greenhouse gas emissions is discussed, aligning with the Sustainable Development Goals (SDGs) of Agenda 2030 and the Paris Agreement. The systematic review conducted in the subsequent sections offers a comprehensive mapping of the state-of-the-art, highlighting the effectiveness of combining these methods in categorizing and systematizing relevant scientific literature. The results reveal significant advancements in the use of artificial intelligence (AI) and digital signal processors (DSP) to enhance fault diagnosis, as well as underscore the crucial role of non-invasive techniques in minimizing interference in monitored systems. Finally, concluding remarks point towards future research directions, emphasizing the need to develop digital twins for internal combustion engines and identify gaps for further improvements in fault diagnosis and prediction techniques.

Brushless Synchronous Machines (BSM) are replacing conventional synchronous machines with static excitation in generation facilities due to the absence of sparking and lower maintenance. However, this excitation system makes challenging measuring electric parameters in the rotor. This fact highly difficult the detection of ground faults, which are the most common type of electrical fault in electric machines. In this paper, a ground fault detection method for BSM is proposed. It is based on an inductive AC/DC rotating current sensor installed in the shaft. In case of a ground fault in the rotating parts of the BSM, a fault current will flow through the rotor’s sensor, inducing voltage in its stator. Analyzing the frequency components of the induced voltage the detection of a ground fault in the rotating elements is possible. The ground faults detection method proposed covers the whole rotor and discern between DC and AC sides. To corroborate the detection method, experimental tests have been performed using a prototype of this sensor connected to laboratory synchronous machines achieving satisfactory results.

It is necessary to manage the power consumption and use fault-tolerance techniques to meet high reliability in Real-Time Embedded Systems, . Hence, in recent years, this is the main reason for providing an ideal approach to decrease power consumption through the worst-case behavior of instantaneous power management (peak-power management), while observing the Thermal Design Power (TDP). Although the multi-core chips in real-time embedded systems provide great opportunity for implementation of Two-phase Triple modular redundancy to meet high reliability, the energy overhead management of concurrently executing tasks is one of the challenges facing designers. In this article, we propose a scheme named Permanent Fault Aware, Two-Phase Peak-Power Management (PFA-TP3M), for scheduling real-time tasks in multi-core systems to eliminate peak power overlap from concurrent running tasks to maintain peak power consumption at the chip TDP. and through mapping different parts of each task on separate cores, prevents consecutive conflicts on cores, which have faced permanent faults to design the fault-tolerant system. Our simulation results show that our proposed scheme provides up to 700x fault tolerance increase (630x on average) compared to the state-of-the-art power management algorithm, an achievement costs up to 14%. (average 11%) increase the length of the task graph compared to it.

In the last two centuries, land use change (LUC) has been the most important direct changes driver for terrestrial ecosystems. To contrast the consequent ecosystems degradation, forward-looking spatial policies and target landscape and land-use planning processes, promoting a sustainable land use change, are needed. The present paper proposes a framework of action including different landscape planning and ecological approaches: from the spatial modelling to recognize the LUC and build different scenarios, to the ecosystem services (ESs) assessment to evaluate the possible environmental impacts. Three different scenarios were built: Trend, No-Tillage and Energy crops. The Sediment Delivery Ratio and Carbon Storage and Sequestration ESs were assessed and compared for each scenario. The aim of the paper is to support decision-makers and local communities into the landscape planning process. Results show that a regional development in line with past trend could lead to further land degradation. Instead, the two scenarios proposed in compliance with EU policies, could bring benefits only if related to moderate LUCs and respecting the naturally grass-vegetated land. From the local to global scale, a guided and shared LUC management allows implementing sustainable development, basing on a deep knowledge of physical-environmental but also social and economic issues.

High efficiency and safety are critical factors in ensuring optimal performance and reliability of systems and equipment across various industries. Fault Monitoring (FM) techniques play a pivotal role in this regard by continuously monitoring system performance and identifying the presence of faults or abnormalities. However, traditional FM methods face limitations in fully capturing the complex interactions within a system and providing real-time monitoring capabilities. To overcome these challenges, Digital Twin (DT) technology has emerged as a promising solution to enhance existing FM practices. By creating a virtual replica or digital copy of a physical equipment or system, DT offers the potential to revolutionize fault monitoring approaches. This paper aims to explore and discuss the diverse range of predictive methods utilized in DT and their implementations in FM across industries. Furthermore, it will showcase successful implementations of DT in FM across a wide array of industries, including manufacturing, energy, transportation, and healthcare The utilization of DT in FM enables a comprehensive understanding of system behavior and performance by leveraging real-time data, advanced analytics, and machine learning algorithms. By integrating physical and virtual components, DT facilitates the monitoring and prediction of faults, providing valuable insights into the system’s health and enabling proactive maintenance and decision-making.

The population’s mobility in urban areas is a necessary variable in the modeling of risk scenarios caused by atmospheric contamination. The inclusion of this concept makes static models more dynamic while considering people within a city to be an entity with complex mobility processes. We propose a conceptual and methodological tool to make the representation of the social, economic and territorial components, as well as the patterns in the population´s mobility to delimitate risk areas for human health by exposure of contaminants. In the volatile organic compounds (VOC), benzene, ethylbenzene, toluene and xylene (BTEX) are amongst the most dominant substances in fugitive vapor emissions in gas stations (GS). In urban areas, the exposure to BTEX by residential proximity and proximity to other facilities, which cause intra-urban agglomeration, can impact and affect human health. This model seeks to facilitate the focalization, identification and prioritization of risk areas by BTEX environmental contamination. This article goes beyond de conceptual framework. It suggests methodological and instrumental aspects to be applied in other cities. The government agencies must consider these results when establishing rules, permissions and procedures to reduce environmental pollution for managing the risk in a complex urban environment.

This work examines drought and wet events based on Standardized Precipitation-Evapotranspiration Index (SPEI) over Kenya from 1981 to 2016. Spatiotemporal analysis of dry and wet events is conducted for 3 and 12-month SPEI. The drought incidences were observed during the period 1984, 1987, 2000, 2006, 2009, 2015, and 2016 for SPEI-3 whilst the SPEI-12 demonstrated the manifestation of drought during the year 2000 and 2006. SPEI clearly shows that the wettest period, 1997 and 1998 that coincide with the El Nino event in both time steps. SPEI -3 shows a reduction in moderate drought events while severe and extreme cases were on increase towards the end of the twentieth century. Conversely, SPEI-12 depicts an overall increase in severe drought occurrence over the study location with observed intensity of -1.54 and cumulative frequency of 64 months during the study period. The trend of wet events is upwards in the western and central highlands while the rest of the regions show increase in dry events during the study period. Moreover, moderate dry/wet events predominate whilst extreme events occur least frequent across all grid cells. It is apparent that the study area experiences mild extreme dry events in both categories although moderately severe dry events dominate most parts of the study area. High intensity and frequency of drought is noted in SPEI-3 while least occurrences of extreme events are recorded in SPEI-12. Although drought event prevails across the study area, there is evidence of extreme flood conditions over the recent decades. These findings form a good basis for next step of research that will look at projection of droughts over the study area based on regional climate models.

Despite the extensive literature on missing data theory and cautionary articles emphasizing the importance of realistic analysis for healthcare data, a critical gap persists in incorporating domain knowledge into missing data problem formulation, assumption specification, and method development. In this paper, we highlight the gap particularly for observational data from healthcare facilities. We address this gap by identifying ten fundamental missingness scenarios arising during data measurement, recording, and pre-processing in observational health data, influenced by physicians, patients, healthcare facilities, and data scientists. We analyze the effect of scenarios on estimand formulation, missing data identification, estimation, and sensitivity analysis. To emphasize how domain-informed analysis can improve method reliability, we conduct simulation studies under the influence of various missingness scenarios. We compare the results of three common methods in medical data analysis (complete-case analysis, Missforest imputation, and inverse probability weighting estimation) for two estimands (variable mean estimation and classification accuracy). We advocate for our analysis approach as a reference for the analysis of observational health data. Furthermore, we posit that the proposed analysis framework is applicable to other medical domains, including medical wearable data analysis.

This paper presents an efficient topology optimization approach considering fail-safe requirements in the framework of the solid isotropic material with penalization method. Existing methods either are computationally extremely expensive or do not consider fail-safe requirements in the optimization. The current approach not only obtains the fail-safe optimization structure, but also significantly improves the computational efficiency of fail-safe topology optimization. In this method, the worst-case compliance is considered to minimize as the optimization objective, using the Kreisselmeier–Steinhauser function to approximate the non-differentiable max-operator. The sensitivity is derived by the adjoint method, and a general fail-safe optimality criteria method is developed to update design variables. The damage scenarios greatly reduces in the process of failsafe strategy optimization by employing the density-based filter method. Finally, the effectiveness and computational efficiency of this method for fail-safe optimization is verified by several numerical examples.

Sea level rise due to global warming is a continuing and, disappointingly, accelerating process, which has already affected and will further impact coastal lowlands and the social and economic activities in these areas. Delos Island, situated in the middle of the Cyclades in the Aegean Sea, was considered the most sacred of all islands in ancient Greek culture and a trading hub for the entire eastern Mediterranean. Uninhabited since the 7th century AD, and consistently the focus of research and touristic attention, the island is designated as an archaeological site and inscribed on the Unesco World Heritage List. Previous studies on the relative sea level (rsl) changes suggest a steadily rsl rise during the last 6,300 years, starting from a sea level at -4.80±0.20m in the Late Neolithic. The seafront of the ancient city of Delos is subject to the effects of the rsl rise, which have caused significant coastline retreat and exposure to the northerly winds and waves, whereas parts of the coastal lowland where the remains of the ancient city lie are inundated, forming extended wetlands. The future impacts of rsl rise on the seafront of ancient Delos are illustrated on very high-resolution digital surface models, evaluating both the flooding risk under different climatic projections, as provided by the IPCC AR6 report, and the ongoing land subsidence, as recorded by GNSS data. An rsl rise ranging from 87cm (SSP1-2.6 scenario) to 148cm (SSP5-8.5 scenario) is anticipated by 2150, requiring both resilience strategies and adaptation solutions as well as mitigation policies to cope with the effects of climate change.

Ambrosia trifida is a kind of malignant invasive plant, which has very high reproductive and en-vironmental adaptability. Through strong resource acquisition ability and allelopathy, it could inhibit the growth and reproduction of surrounding plants, destroy the stability of an invasive ecosystem. It is very important to predict the change of suitable distribution area of Ambrosia trifida with climate change before implementing scientific control measures. Based on 106 Ambrosia trifida distribution data and 14 environmental data, the optimal parameter combination (RM = 0.1, FC = LQ) was obtained by using the MaxEnt model optimized by Kuenm package, the potential suit-able areas of Ambrosia trifida in Northeast China under three different climate scenarios (RCP2.6, RCP4.5, RCP8.5) with different emission intensities in the future (2050,2070) were predicted. The changes of Ambrosia trifida suitable area in Northeast China under three climate scenarios were compared, and the relationship between the change of suitable area and emission intensity was analyzed. In general, the suitable area of Ambrosia trifida in Northeast China will expand gradually in the future, and the area of its highly suitable area will also increase with the increasing emission intensity, which is unfavorable to the control of Ambrosia trifida.

This paper describes the proposal and development of a proof of concept (PoC) for a data logger system designed for operation and fault detection in bench testing of combustion engines. The main objective was to develop a data logger system that collects data on vibration, sound, temperature, and CO2 levels to identify ignition and combustion faults, thereby contributing to reducing greenhouse gas emissions (GHG). The paper presents (i) the proposal and development and (ii) the open-source code: proof of concept for a data logger system focused on detecting faults and monitoring the operational condition of benchtop combustion engines. The results indicate the feasibility of the PoC for data acquisition and dataset creation for Research and Development purposes.

When an aircraft malfunctions, quickly and accurately identifying the faulty unit is essential for ensuring normal operation. Unfortunately, maintenance engineers often struggle to acquire the necessary fault-related knowledge due to poor management and utilization of aircraft fault documents. To address this issue, we introduce knowledge graph technology into the field of aircraft fault diagnosis, exploring its construction and application for effective knowledge management. Our work starts by analyzing the critical knowledge elements required for aircraft fault diagnosis and designing a schema layer for fault knowledge graphs. We then we then combine deep learning and heuristic rules to extract fault knowledge from both structured and unstructured data, enabling the construction of aircraft fault knowledge graphs. Finally, we develop a fault question-answering system based on fault knowledge graphs that can accurately give solutions to questions posed by maintenance engineers. Our practice demonstrates that knowledge graphs provide an effective means of managing aircraft fault knowledge, assisting engineers in locating fault reasons accurately and quickly.

The fault diagnosis of safety critical systems such as wind turbine installations includes extremely challenging aspects that motivate the research issues considered in this paper. In fact, the prompt detection and the reliable diagnosis of faults in their earlier occurrence represent the key point especially for offshore installations. For these plants, operation and maintenance procedures in harsh environments would inevitably increase the cost of the energy production. Therefore, this work investigates fault diagnosis solutions that are considered in a viable way and used as advanced techniques for condition monitoring of dynamic processes. To this end, the work proposes the design of fault diagnosis strategies that exploit the estimation of the fault by means of data--driven approaches. This solution leads to the development of effective methods allowing the management of partially unknown information of the system dynamics, while coping with measurement errors, the model--reality mismatch and other disturbance effects. In mode detail, the proposed data--driven methodologies exploit fuzzy systems and neural networks in order to estimate the nonlinear dynamic relations between the input and output measurements of the considered process and the faults. To this end, the fuzzy and neural network structures are integrated with auto--regressive with exogenous input descriptions, thus making them able to approximate unknown nonlinear dynamic functions with arbitrary degree of accuracy. Once these models are estimated from the input and output data measurement acquired from the considered dynamic process, the capabilities of their fault diagnosis capabilities are validated by using a high--fidelity benchmark that simulates the healthy and the faulty behaviour of a wind turbine system. Moreover, at this stage the benchmark is also useful to analyse the robustness and the reliability characteristics of the developed tools in the presence of model--reality mismatch and modelling error effects featured by the wind turbine simulator. On the other hand, a hardware--in--the--loop tool is finally implemented for testing and comparing the performance of the developed fault diagnosis strategies in a more realistic environment and with respect to different fault diagnosis approaches.

We analyze the Covid-19 mortality scenario in India and compare it with those in other large-population regions such as Asia-excluding-China, Africa, European Union, South America, and USA. We compare existing fatality data and offer an interpretation for low fatality based on immunity due to endemic malaria and TB. We identify the hot climate in the past summer as a possible cause for low death count in southern-hemisphere countries without endemic malaria and TB. We also make India-specific observations for easing the lockdown and estimations for the time required to attain herd immunity. Whatever optimism we present should be viewed as a guarded optimism. There should not be room for complacency.

Dynamic virtual testing by Hardware in the Loop (HIL) real time simulation systems is common to minimize physical tests to validate and verify control algorithms, logics and software, under normal and emergency conditions. The accurate but costly HIL models setup activities could have an extended use to simulate faulty conditions that are seldom observable in the real system, yet they must be detected in advance and recognized. Data driven diagnostic methods have reached a mature development, in various sectors, proving effective in early detecting slowly varying deviations from nominal patterns of signals, even without a priori knowledge of their nature and behavior. These methods rely on vast amount of data logged from real systems that are ever and ever richer of monitoring telemetries that are worth being exploited. However, due to the usually reliable and safe systems being managed, it is often difficult to collect enough data on anomalous situations to validate fault detection and isolation and limit false positives. HIL model-based simulations allow generating and observing operational domains or transients rarely observed in real operations. This allows identifying the signals that are most correlated to faults and that could be selected to setup data-based diagnostic models of parts or subsystems, which are more suitable than the whole HIL model for online diagnostics, as well as to identify the features and trends that are most symptomatic of anomalous conditions.

The paradigm shift has revealed a need among industrial designers to have entrepreneurial capabilities. Being the most effective modality to exercise discipline today. The implementation of this approach requires reviewing the discursive scaffolding that the design has been using to set immovable high standards of complexity and quality, reducing the field of professional action and limiting the intervention alternatives. This article considers the dynamics and trends in people's lifestyles, explores possible strategies and specific content for the entrepreneurial training required by design.

Portugal, in line with the European Union, is aiming for carbon neutrality by 2050 (Net 1 Zero), which implies a transition to sustainable energy sources. Climate change is all too evident, extreme weather periods are occurring in a cyclical manner, with greater brevity to such an extent that the grid operator must deal with production scenarios where it can no longer rely on hydroelectric production given the recurring drought situation. This situation increases dependence on thermal production using natural gas and imports. This has significant economic implications. Portugal has exploited its onshore wind potential, reaching an installed capacity of 5.671 MW by 2022. However, the expansion of onshore wind energy is limited to reinforcing existing infrastructure. To overcome these challenges, it is necessary to expand the exploitation of offshore wind potential that is already underway. This article proposes the location of offshore wind production platforms along the Portuguese coast. This allows for an analysis of offshore production and its optimization according to the minimum cost per MWh in the face of extreme scenarios, i.e., in periods of extreme drought where hydroelectric production capacity is practically non-existent. The model is fed by using market price indications and the amount of energy needed for the following day. Using forecast data, the model adapts offshore wind production for the following day according to the minimization of the average market price. The results of the simulations allow us to conclude that despite the high cost of offshore technology (in deep waters), in extreme climate scenarios, it enables cost reduction and a clear decrease in imports.

In this paper we investigate how the Reionization process is affected by early galaxy formation in different cosmological scenarios. We use a semi-analytic model with suppressed initial power spectra to obtain the UV Luminosity Function in thermal Warm Dark Matter and sterile neutrino cosmologies. We retrace the ionization history of intergalactic medium with hot stellar emission only, exploiting fixed and variable photons escape fraction models ( fesc). For each cosmology, we find an upper limit to fixed fesc, which guarantees the completion of the process at z <6.7. The analysis is tested with two limit hypothesis on high-z ionized hydrogen volume fraction, comparing our predictions with observational results.

This study develops a progressive optimal fault-tolerant control method based on insufficient fault information. By combining passive and active fault-tolerant control manners during the process of fault diagnosis, insufficient fault information is fully used, and optimal fault-tolerant control effect is achieved. In addition, the fault-tolerant control method based on guaranteed robust cost control is introduced. The proposed progressive optimal fault-tolerant control method considers two aspects. First, as the amount of fault information continually increases, the performance index of the progressive optimal fault-tolerant controller improves. Second, at each moment, based on the corresponding insufficient fault information and prior knowledge, optimal fault-tolerant control is achieved according to current fault information. The process of progressive optimal fault-tolerant control converges to active fault-tolerant control when the fault is completely identified and the optimal fault-tolerant controller is no longer reconfigured until no more useful fault information can be provided. Furthermore, a progressive optimal fault-tolerant control algorithm based on the grid segmentation in the parameter uncertainty domain and the selection of different auxiliary center points is introduced. The simulation results verify the feasibility of the proposed algorithm and the validity of the proposed theory.

This work aims to develop an integrated fault location and restoration approach for microgrids (MGs). This work contains two parts. Part I presents the fault location algorithm, and Part II shows the restoration algorithm. The proposed algorithms are implemented by particle swarm optimization (PSO). The fault location algorithm is based on network connection matrices, which are the modifications of bus-injection to branch-current and branch-current to bus-voltage (BCBV) matrices, to form the new system topology. The backward/forward sweep approach is used for the prefault power flow analysis. After the occurrence of fault, the voltage variation at each bus is calculated by using the Zbus modification algorithm to modify Zbus. Subsequently, the voltage error matrix is computed to search for the fault section by using PSO. After the allocation of the fault section, the multi-objective function is implemented by PSO for optimal restoration with its constraints. Finally, the IEEE 37-bus test system connected to distributed generations is utilized as the sample system for a series simulation and analysis. The outcomes demonstrated that the proposed optimal algorithm can effectively solve the fault location and restoration problem in MGs.

This paper describes a novel thruster fault-tolerant control system (FTC) for open-frame remotely operated vehicles (ROVs). The proposed FTC consists of two subsystems: a model-free thruster fault detection and isolation subsystem (FDI) and a fault accommodation subsystem (FA). The FDI subsystem employs fault detection units (FDUs), associated with each thruster, to monitor their state. The robust, reliable and adaptive FDUs use a model-free pattern recognition neural network (PRNN) to detect internal and external faulty states of the thrusters in real time. The FA subsystem combines information provided by the FDI subsystem with predefined, user-configurable actions to accommodate partial and total faults and to perform an appropriate control reallocation. Software-level actions include penalisation of faulty thrusters in solution of control allocation problem and reallocation of control energy among the operable thrusters. Hardware-level actions include power isolation of faulty thrusters (total faults only) such that the entire ROV power system is not compromised. The proposed FTC system is implemented as a LabVIEW virtual instrument (VI) and evaluated in virtual (simulated) and real-world environments. The proposed FTC module can be used for open frame ROVs with up to 12 thrusters: eight horizontal thrusters configured in two horizontal layers of four thrusters each, and four vertical thrusters configured in one vertical layer. Results from both environments show that the ROV control system, enhanced with the FDI and FA subsystems, is capable of maintaining full 6 DOF control of ROV in the presence of up to 6 simultaneous total faults in the thrusters. With the FDI and FA subsystems in place the control energy distribution of the healthy thrusters is optimised so that the ROV can still operate in difficult conditions under fault scenarios.

Distribution systems can form islands when faults occur. Each island represents a subsection with variable boundaries subject to the location of a fault(s) in the system. A subsection with variable boundaries is referred to as island in this paper. For operation in autonomous mode, it is imperative to detect the island configurations and stabilize these subsections. This paper presents a novel scheme for the detection of islanding boundaries and stabilizing the system during autonomous operation. In the first stage, a boundary detection method is proposed to detect the configuration of the island. In the second stage a dynamic voltage sensitivity factor (DVSF) is proposed to assess the dynamic performance of the system. In the third stage, a wide area load shedding program is adopted based on DVSF to shed the load in weak busbars and stabilize the system. The proposed scheme is validated and tested on a generic 18-bus system using a combination of EMTDC/PSCAD and MATLAB softwares.

Coastal zones experience increasing climatic and human pressures, which lead to growing risks and tipping points (TPs) under future scenarios and natural resource scarcity. To avoid crossing TPs with irreversible coastal losses, this paper proposes the development of adaptation pathways based on advanced coastal oceanography and engineering knowledge that enables an objective assessment of evolving coastal risks. These pathways feature sequential interventions, combining conventional engineering works with nature-based solutions (NbS), steered by simulations and observations as a function of available coastal resources (space and sediment) and risks for infrastructure and socioeconomic assets. Such an adaptation has been developed for urban and peri-urban Mediterranean beaches, considering conventional coastal engineering together with NbS. Both types of interventions are assessed in terms of key physical variables, which serve to evaluate performance and estimate TPs. This analysis supports the new coastal protection and management plan promoted by the local government and the coastal adaptation plan of the central government. The approach and results illustrate the potential of adaptation pathways for beach sustainability, enhancing the compatibility between short/long-term coastal protection objectives under present/future climate and management scenarios. The adaptation pathways development underpins an increasing stakeholder cooperation to achieve shared decisions for coastal sustainability.

Factory safety inspections are crucial for ensuring safe production. However, manual inspections present issues such as low efficiency and high workload. Inspection robots provide an efficient and reliable solution for completing patrol tasks. The development of robot localization and path planning technologies provides guarantees for factory inspection robots to autonomously complete inspection tasks in complex environments. This paper studies mapping and localization, as well as path planning methods for robots in order to meet the application requirements of factory inspections. Two SLAM application systems based on multiple-line laser radar and vision are designed for different scenarios in consideration of the limitations of cameras and laser sensors in terms of their own characteristics and applicability in different environments. To address the issue of low efficiency in inspection tasks, a hybrid path planning algorithm that integrates the A-star algorithm and time elasticity band algorithm is proposed. This algorithm effectively solves the problem of path planning in complex environments that is prone to falling into local optimal solutions, thereby improving the inspection efficiency of robots. Experimental tests show that the designed SLAM and path planning methods can meet the needs of robot inspection in complex scenes and have good reliability and stability. The code used in this article is open source and can be accessed at https://github.com/Mxiii99/RSPP_CS.git.

The International Maritime Organization (IMO) is enforcing increasingly stringent regulations on ship carbon emissions, The International Maritime Organization (IMO) has been implementing progressively strict regulations on ship carbon emissions, leading to the adoption of the virtual arrival (VA) method by many vessels to reduce their carbon footprint. However, the effectiveness of the traditional VA method often varies in busy ports with complex traffic organization scenarios. To address this, our study presents a novel, comprehensive model that integrates vessel scheduling with the VA approach. This model is designed to achieve a dual objective: reducing carbon emissions through Virtual Arrival and simultaneously minimizing vessel waiting times. In addition to these goals, it incorporates essential aspects of safety, efficiency, and fairness in port management, utilizing the NSGA-2 algorithm to find optimal solutions. This model has been tested and validated through a case study at Ningbo-Zhoushan port, employing its dataset. The results demonstrate that our innovative model and algorithm significantly outperform traditional scheduling methods, such as First-Come-First-Serve (FCFS) and Virtual-Arrival Last-Serve (VALS), particularly in terms of operational efficiency and reduction in vessel carbon emissions.

This paper presents a scenario evolution model for maritime accidents using Bayesian networks (BN) to predict the most likely causes of specific types of maritime incidents. The BN nodes encompass accident type, life loss contingency, accident severity, the quarter and time period of the accident, and the type and gross tonnage of the ships involved. We analyzed 5,660 global maritime accidents from 2005 to 2020. Using Netica software, we constructed a Tree Augmented Network (TAN) model, accounting for interdependencies among risk influencing factors. We validated the results through sensitivity analysis and historical accident records. Forward causal inference and reverse diagnostic inference were then performed on each node variable to investigate the accident development trend and evolution process under predetermined conditions. The findings indicate that the model can effectively predict the likelihood of various accident scenarios under specific conditions, as well as the extrapolation of accident consequences. Forward causal reasoning reveals that general cargo ships with a gross tonnage of 1-18,500 t are most likely to experience collision, grounding, and stranding accidents in the first quarter. Reverse diagnostic reasoning indicates that during early morning hours, container ships, general cargo ships, and chemical ships with a tonnage of 1-18,500 t are less likely to involve life loss in the event of collision accidents.

Nowadays, hydropower is the principal renewable; however, climate change increases extreme events such as floods, droughts, erosion, and sedimentation of rivers that produce uncertainty in hydroelectric generation. Thus, this document aims to analyze the climate change projections in the hydropower systems of Ecuador based on data from 14 projects studying the scenarios ac-cording to the Shared Socioeconomic Pathways from the Intergovernmental Panel on Climate Change. The study period starts from 2010 to 2020 with historical data, collects the tendency, defines a database year, and then projects the scenarios to 2050. The quantitative methodology uses a statistic on Ecuador's hydropower obtained inflow time series to calculate the deviation over the last years and develop a model to simulate future generation. The results show that the hydropower in Ecuador is expected to decrease considerably through 2050 due to meteorological changes. In this calculation of the Shared Socioeconomic Pathways, the selected scenarios show a reduction in SSP5 of 11.5%, SP2 of 16.2%, and SSP4 of 18.2% to 2050, concluding that the oppor-tunities for hydroelectric production facing climate change are variable, but the challenges are broad. In Ecuador, the projections of hydropower plants represent a sensitive issue of their re-ductions, especially knowing that the country had an energy grid in 2020 that depended on 87% of hydroelectric production.

The RMA11 water quality model, which has been previously calibrated and validated, was coupled with the RMA10 3D hydrodynamic model to evaluate sanitation scenarios in Buenaventura Bay. The bay receives direct discharge of untreated wastewater from 500,000 people through 695 outlets along the coast. Five different effluent reduction scenarios were proposed and compared based on fecal coliform concentration as an indicator. The areas where values exceed the standard for primary contact were also used to evaluate the scenarios. The model results indicate poor water quality in the bay and how that the situation will become even more severe if immediate action is not taken. The proposed reduction in discharges in stages will have temporarily more severe effects than the current situation, but these will end when the treatment plant becomes operational. However, even with the plant in operation, complete sanitation of the bay cannot be achieved, and further measures must be considered.

Quantum computers are machines that are designed to use quantum mechanics in order to improve upon classical computers by running quantum algorithms. One of the main applications of quantum computing is solving optimization problems. For addressing optimization problems, we can use linear programming. Linear programming is a method to obtain the best possible outcome in a special case of mathematical programming. Application areas of this problem consist of resource allocation, production scheduling, parameter estimation, etc. In our study, we look at quantum speedup ratios of HHL Algorithm for different scenarios of linear programming. In a first scenario we look quantum speedup ratio (S(N)) as a function of phase transition and the ratio (κ) between the greatest and smallest eigenvalues of the matrix in linear equation system. As a second scenario, we investigate the changes in S(N) as a function of κ and s, which is the coefficient for defining the matrix as s-sparse.

It is known that the source of a tectonic earthquake in the framework of the theory of elasticity and viscoelasticity is considered as a displacement along a certain fault surface. Usually, when describing the source, the geometry of the fault surface is simplified to a flat rectangular area. The displacement vector is assumed to be constant. In this paper, we propose a model of an earthquake source in the form of a displacement with a constant vector along a stochastic surface. A number of standard assumptions were made when modeling. We take into account only the elastic properties of the medium. We consider the Earth’s crust as a half-space and assume that the medium is homogeneous and isotropic. For the mathematical description of the earthquake source, we use the classical force equivalent of displacement along the fault. This is the distribution of double pairs of forces. The field of displacements under the action of body forces is found through a combination of Mindlin nuclei of strain. The paper presents solutions for strike-slip fault. To obtain a stochastic fault surface, we propose to introduce a random deformation of a rectangular flat surface. The paper presents the results of a computational experiment comparing the levels and regions of relative deformations of the Earth’s crust in the case of displacement along a flat rupture surface and along a stochastic one. In the case of a stochastic surface, the regions of relative deformations become asymmetric. The developed model will help to indirectly take into account various mechanical properties of the Earth’s crust when modeling deformations caused by earthquakes.

A detection system, modeled in a graph, uses "detectors" on a subset of vertices to uniquely identify an "intruder" at any vertex. We consider two types of detection systems: open-locating-dominating (OLD) sets and identifying codes (ICs). An OLD set gives each vertex a unique, non-empty open neighborhood of detectors, while an IC provides a unique, non-empty closed neighborhood of detectors. We explore their fault-tolerant variants: redundant OLD (RED:OLD) sets and redundant ICs (RED:ICs), which ensure that removing/disabling at most one detector guarantees the properties of OLD sets and ICs, respectively. This paper focuses on constructing optimal RED:OLD sets and RED:ICs on the infinite king's grid, and presents the proof for the bounds on their minimum densities; [3/10, 1/3] for RED:OLD sets and [3/11, 1/3] for RED:ICs.

A new method for short circuit fault location is proposed based on instantaneous signal measurement and its derivatives, and is based on the retardation phenomena. The difference between the times in which a signal is registered in two detectors is used to locate the fault. Although a description of faults in terms of a lumped circuit is useful for elucidating the methods for detecting the fault. This description will not suffice to describe the fault signal propagation hence a distributed models is needed which is given in terms of the telegraph equations. Those equations are used to derive a transmission line transfer function, and an exact analytical description of the short circuit signal propagating in the transmission line is obtained. The analytical solution was verified both by numerical simulations and experimentally.

New developments in broadcast technology, such as the ad hoc wireless network and the detection devices that work, enable daily connections to the Internet, commonly referred to as the Internet of Things (IoT). IoT saw this as a support for the development of creative and contextual services and applications. The service can respond to the user's environment and preferences. Finite state machines are recently making waves when the issue of self-controlling systems come into the picture of designs. This research focuses on the design of a fault tolerant system that controls random failures impressed on the network. The method comprises of diodes, Resistors, Optocouplers, ESP8266 WI-FI Module Webpage with button, and two Arduinos which in effect will determine iteratively who to handle a failure at hand modelled as when the button on the webpage is pressed. In this work, a Heartbeat Detection and Monitoring System will be made using Arduino that detect the electrical state using the Pulse Sensor and show the readings in Beats Per Minute on the LED connected a microcontroller. It sends the texts to the server using the Wi-Fi module ESP8266 to signals can be monitored from the monitoring zone via the internet. This is reflected by the declaration of a master state by either of two Arduinos whenever the push button is pressed as observed on the codes. The result obtained from the research shows apparently, the state of the fault tolerance system none power deny of heartbeat condition connected to the LED.

Pre-existing fracture and secondary cracks in rock mass are formed by natural power, such as magma condensed to igneous rocks and tectonic movement. The orientation and inclination of these fractures obey certain laws relating to the stress, temperature, minerals, water and so on. Therefore, cracks react differently under the same external loading on the condition of various inclination, fissure apertures, stiffness and joint roughness. To simulate the crack propagation, experiments on hollow cylinder cut by one oblique interface mimicking single fracture accumulated numerous data discovering the failure criterion in accordance with the Mohr-Coulomb criterion. And theory on the Terzaghi’s effective principle take an essential role in controlling the behavior of triggering fault. This paper introduced a series of oblique plane cutting the cylinder regarded as fractures at different inclination to concentrate on how the fracture characteristics effect the stress and strain distribution inside the specimen, especially, the relationship between displacement and water head. The key point of this numerical simulation is coupling the solid phase and the fluid phase, specifically, the mechanic and seepage field. According to the statics, curves referring to deformation and water head could be described as increasing lines. Besides, simulation on coupling solid phase and fluid phase can supply crucial evaluation on activating existing fault, and thus predicting induced seismicity in reservoirs or estimating damage in shale gas exploration.

The Doubly-Fed Induction Generator (DFIG) has significant features in comparison with Fixed Speed Wind Turbine (FSWT), which has popularized its application in power system. Due to partial rated back-to-back converters in the DFIG, Fault Ride-Through (FRT) capability improvement is one of the great subjects regarding new grid code requirements. To enhance the FRT capability of the DFIG, many studies have been carried out. Fault current limiting devices as one of the techniques are utilized to limit the current level and protect switches of the back-to-back converter from over-current damage. In this paper, a review is done based on fault current limiting characteristic of the proposed fault current limiting devices Therefore, Fault Current Limiters (FCLs) and Series Dynamic Braking Resistors (SDBRs) are mainly taken into account. Operation of all configurations including their advantages and disadvantages is explained. Impedance type and the fault current limiting devices’ location are two important factors, which significantly affect the DFIG behaviour in the fault condition. These two factors are basically studied by the simulation and their effects on the key parameters of the DFIG are investigated. Finally, future works in respect to the FCL application in the FRT improvement of the DFIG have also been discussed.

Keywords: domain adversarial; channel reconstruction; transfer learning; fault diagnosis

The objective was to estimate the monetary loss of potato producers up to the year 2100 as a result of temperature and precipitation impacts, taking into account the A2 and B2 scenarios of the IPCC (Intergovernmental Panel on Climate Change). The Pooled Production Panel Model was used, whose database was prepared taking into account climatic variables (temperature and precipitation) and agricultural variables (production, harvested area, farm-gate price) for the period 1996 - 2020, which form the independent variables of the study. The estimations used 60 observations and a total of 38 estimations were run in the econometric software EViews8, of which Equation 05 of the Production Pooled Panel Model was chosen as the best. The model obtained used temperature and precipitation forecasts from Brazil's INPE (National Institute for Space Research), validated for the study area. The results indicate a concave function between potato production (t/ha), temperature and precipitation. Finally, based on the A2 climate scenario, which is the most pessimistic and using the period 2021 - 2100, a loss for potato producers of approximately 8'927,521.48 million soles was estimated.

Social networks have become the scenario with the greatest potential for the circulation of disinformation, hence there is a growing interest in understanding how this type of information is spread, especially in relation to the mechanisms used by disinformation agents such as bots, trolls, among others. In this scenario, the potential of bots to facilitate the spread of disinformation is recognised, however, the analysis of how they do this is still in its initial stages. Taking into consideration what was previously stated, this paper aimed to model and simulate scenarios of disinformation propagation in social networks caused by bots based on the dynamics of this mechanism documented in the literature. For achieving the purpose, System dynamics was used as the main modelling technique. The results present a mathematical model, as far as disinformation by this mechanism is concerned, and the simulations carried out against the increase in the rate of activation and deactivation of bots. Thus, the preponderant role of social networks in controlling disinformation through this mechanism, and the potential of bots to affect citizens, is recognised.

Followed by the aspect of respect for rights 20, the social and environmental issue has a score of 19.9, the dialogue issue is 19. In short, the proximity plan indicates that the policies used regarding the issue of generating employment and economic growth (Empresa Mantaro Perú SAC), continue to be the strategic variables in the first instance, but they are far from the social actors (rural communities of Aco, Vico, Cruz Pampa and others) and environmental (Junín environmental dialogue table) and Ombudsman's Office (rights) that are currently closer together. The actor from the Archbishop of Huancayo referred to communication is one of the driving variables of power. The proximity plan shows us that the policies used regarding the issue of generating employment and economic growth (Empresa Mantaro Perú SAC), continue to be the strategic variables in the first instance, but that they are far from the social actors (Rural Communities of Aco , Vico, Cruz Pampa and others) and environmental (Junín Environmental Dialogue Table) and Ombudsman's Office (rights) that are currently closer together. The actor from the Archbishop of Huancayo referred to communication is one of the driving variables of power. The proximity plan shows us that the policies used regarding the issue of generating employment and economic growth (Empresa Mantaro Perú SAC), continue to be the strategic variables in the first instance, but that they are far from the social actors (Rural Communities of Aco , Vico, Cruz Pampa and others) and environmental (Junín Environmental Dialogue Table) and Ombudsman's Office (rights) that are currently closer together. The actor from the Archbishop of Huancayo referred to communication is one of the driving variables of power. but they are far from the social actors (rural communities of Aco, Vico, Cruz Pampa and others) and environmental (Junín Environmental Dialogue Table) and the Ombudsman's Office (rights) that are closer together today. The actor from the Archbishopric of Huancayo referred to communication, is within the driving variables of power. but they are far from the social actors (rural communities of Aco, Vico, Cruz Pampa and others) and environmental (Junín Environmental Dialogue Table) and the Ombudsman's Office (rights) that are currently closer together. The actor of the Archbishopric of Huancayo referred to communication, is within the driving variables of power.

The service conditions of underground coal mine equipment are poor, and it is difficult to accurately extract the fault characteristics of rolling bearings. In order to better improve the accuracy of fault identification of rolling bearings, a fault detection method based on multiscale permutation entropy and SOA-SVM is proposed. First, the whale optimization algorithm is used to select the modal analysis number K and the penalty factor α of the variational mode decomposition algorithm. Then, the vibration signal of rolling bearings is dissolved according to the optimized variational mode decomposition algorithm, and the multi-scale permutation entropy of the main intrinsic mode function is calculated. Finally, the feature values of the matrix are entered into the SVM algorithm optimized by the seagull optimization algorithm to obtain the classification result. The experimental results based on the published rolling bearing datasets of Western Reserve University show that the identification success rate of the proposed method can reach 98.75%. The fault detection of the rolling bearings can be completed accurately and efficiently.

This paper studies the integrated design problems of control and guidance with parameter uncertainties, target disturbances, input constraints and actuator faults. Firstly, based on the integrated design idea of the missile guidance and control, the auxiliary variable is used to establish and transform it into a cascade system with input constraints, actuator faults and disturbances of unknown upper bounds. Secondly, the adaptive anti-saturation dynamic surface fault-tolerant controller is designed by using the back-stepping method, adaptive control, auxiliary system and tracking differentiator. By introducing the tracking differentiator and tangent barrier Lyapunov function, the computational explosion problem in the traditional back-stepping method is avoided and the angle of attack can be guaranteed in prospective range, respectively. Finally, the theoretical proof of the designed control strategy is given to ensure that the states of the closed-loop system are bounded. At the same time, the digital simulation of the maneuvering target of different maneuvering forms is carried out, which further illustrate the effectiveness and robustness of the designed control schemes.

Building a new power plant to address the growing demand for power due to population concentration in the metropolitan area is one of the world's major concerns. However, since a large power plant can not be located around the city due to burden of economic cost, building power plant outside metropolitan and cities is necessary. Therefore, new power generation facilities are promoting policies to provide distributed generator(DG) with a small capacity relatively near the metropolitan. When the DG (photovoltaic, wind farm, etc.) is connected with the grid using medium voltage direct current (MVDC) system, voltage sourced converter(VSC) should supply reactive power to the grid, because of fault ride through(FRT) operation in grid fault. If the voltage drop is severe, the converter should be disconnected from the grid immediately without supplying the reactive power, resulting in a considerable economic loss. In general, superconducting fault current limiter(SFCL) is introduced as a measure to enhance FRT capability. In this paper, we use trigger type SFCL which protects superconducting element and reduces low voltage. On the other hand, the active power unbalance of the DC-link and the DC voltage rise due to the reactive power supply of the grid-side converter. The rise of the DC voltage causes the P (active power), Q (reactive power) control of the converter to deviate, causing malfunction and damage of the DC equipment. Therefore, the rise of the DC voltage must be prevented. In this paper, we consider the suppression the DC voltage rising caused by the FRT operation through the active power tracking control (APTC).

Coseismic landslides have the potential to cause catastrophic disasters. Thus, it is of crucial importance to conduct a comprehensive regional assessment of susceptibility to coseismic landslides. This study rigorously interprets 13,759 coseismic landslides triggered by the 2022 Luding earthquake within the seismic zone. Employing the Newmark method, we systematically assess the susceptibility to coseismic landslides through the application of six distinct displacement regression models. The efficacy of these models is validated against the actual landslide inventory using the area under the curve. A hazard map of coseismic landslides is generated based on the displacement regression model with the highest degree of fit. The results show that Moxi Town, Detuo Town, the flanks of the Daduhe River, Wandonghe River, Hailuogou River, and Yanzigou River are high susceptibility areas for coseismic landslides. The study explores factors influencing model fit, revealing that the inclusion of both epicentral distance and the distance to seismogenic fault and in displacement prediction enhances model performance. Nevertheless, in close proximity to fault zones, the distance to seismogenic fault exerts a more significant influence on the spatial distribution density of coseismic landslides compared to the epicentral distance. Conversely, in regions situated further from fault zones, the epicentral distance has a greater impact on the spatial distribution density of coseismic landslides compared to the distance to seismogenic fault. These findings contribute to a nuanced understanding of coseismic landslide susceptibility and offer valuable insights for future Newmark method-based coseismic landslide displacement calculations.

Driver’s distractions are more prone to occur with automated driving vehicles, which might turn into cancelling some of their advantages. Traffic reports, naturalistic observations and in-lab tests are carried out to determine the influence of driver’s distractions in car crashes and to develop technology to detect and prevent them from happening. One of the best strategies to cope with this problem is education. In this work we propose a simulation environment to design laboratory experiments to study driver’s distractions as well as simple games to train drivers to take over the control of the vehicle when required. The result framework enables drivers to experience driving highly automated cars in their neighborhoods or in places of their choice with different levels of events to check their ability in taking over the control when required.

We have modelled the energy consumption of prototype and real buildings under present and future climatic conditions with the EnergyPlus model to develop a better understanding of the relationship between changing climate conditions and energy demand. We have produced detailed meteorological information with 50 meters of spatial resolution through dynamical downscaling process combining regional, urban and computational fluid dynamics models which include the effects of the buildings on urban wind patterns. The city of Madrid has been chosen for our experiment. The impact on energy demand and their respective economic cost are calculated for year 2100 versus 2011 based on two IPCC climate scenarios, RCP 4.5 (stabilization of emissions) and RCP 8.5 (not reduction of emissions). Findings show that climate change will have a significant impact on the energy demand for buildings. Space heating demand will be increased by the RCP 4.5 and cooling demand will be increased for the RCP 8.5 in the analysed buildings.

A numerical approach for generating a limited number of water demand scenarios and estimating their occurrence probabilities in a Water Distribution Network (WDN) is proposed. This approach makes use of the demand scaling laws in order to consider the natural variability and spatial correlation of nodal consumptions. The scaling laws are employed to determine the statistics of nodal consumption as a function of the number of users and the main statistical features of the unitary user's demand. Besides, consumption at each node is considered to follow a Gamma probability distribution. A high number of groups of cross-correlated demands, i.e., scenarios, for the entire network were generated using Latin Hypercube Sampling (LHS) and the numerical procedure proposed by Iman and Conover. The Kantorovich distance is used to reduce the number of scenarios and estimate their corresponding probabilities, while keeping the statistical information on nodal consumptions. By hydraulic simulation, the whole number of generated demand scenarios was used to obtain a corresponding number of pressure scenarios on which the same reduction procedure was applied. The probabilities of the reduced scenarios of pressure were compared with the corresponding probabilities of demand.

Software fault prediction is the very consequent research topic for software quality assurance. Data driven approaches provide robust mechanisms to deal with software fault prediction. However, the prediction performance of the model highly depends on the quality of dataset. Many software datasets suffers from the problem of class imbalance. In this regard, under-sampling is a popular data pre-processing method in dealing with class imbalance problem, Easy Ensemble (EE) present a robust approach to achieve a high classification rate and address the biasness towards majority class samples. However, imbalance class is not the only issue that harms performance of classifiers. Some noisy examples and irrelevant features may additionally reduce the rate of predictive accuracy of the classifier. In this paper, we proposed two-stage data pre-processing which incorporates feature selection and a new Rough set Easy Ensemble scheme. In feature selection stage, we eliminate the irrelevant features by feature ranking algorithm. In the second stage of a new Rough set Easy Ensemble by incorporating Rough K nearest neighbor rule filter (RK) afore executing Easy Ensemble (EE), named RKEE for short. RK can remove noisy examples from both minority and majority class. Experimental evaluation on real-world software projects, such as NASA and Eclipse dataset, is performed in order to demonstrate the effectiveness of our proposed approach. Furthermore, this paper comprehensively investigates the influencing factor in our approach. Such as, the impact of Rough set theory on noise-filter, the relationship between model performance and imbalance ratio etc. comprehensive experiments indicate that the proposed approach shows outstanding performance with significance in terms of area-under-the-curve (AUC).

Multi-microgrid has many new characteristics, such as bi-directional power flows, flexible operation modes and variable fault currents with different control strategy of inverter interfaced distributed generations (IIDGs). All these featuring aspects pose challenges to multi-microgrid protection. In this paper, current and voltage characteristics of different feeders are analyzed when fault occurs in different positions of multi-microgrid. Based on the voltage and current distribution characteristics of the line parameters, a new protection scheme for the internal fault of multi-microgrid is proposed, which takes the change of phase difference and amplitude of measured bus admittance as the criterion. This scheme with high sensitivity and reliability, has a simple principle and is easy to be adjusted. PSCAD/EMTDC is used in simulation analysis, and simulation results have verified the correctness and effectiveness of the protection scheme.

In recent years, Synthetic Aperture Radar (SAR) has received attention in the Automatic Target Recognition (ATR) field due to its ability to acquire images both day and night, combined with the superior resolution offered by the latest sensors. However, despite advances, there remains a deficiency in dataset quality and quantity. This has necessitated the use of simulated data to overcome challenges associated with obtaining large quantities of high-quality images of targets and their subsequent labeling. In this study, we trained a Convolutional Neural Network (CNN) using a simulated dataset, subsequently employing it to classify real images from the MSTAR dataset. Traditional training methodologies often struggle with generalization when confronted with domain disparities between simulated and real data. To mitigate this, we exploited Margin Disparity Discrepancy (MDD), a domain adaptation technique not previously explored in SAR context. Findings from this study showcased a 13% increase in classification accuracy and improved generalization using MDD. Through Explainable AI (XAI) techniques such as t-SNE and other metrics like Kullback Leibler Divergence (KLD), we revealed a more distinct alignment of feature spaces between the two domains after domain adaptation, emphasizing MDD’s role in enhancing model accuracy and generalization.

Disassembly is a pivotal technology to enable the circularity of electric vehicle batteries by applying circular economy strategies that extend the life cycle of their components, like remanufacturing and repurposing, or by using efficient recycling to reintegrate the gained materials in producing new battery systems. This paper aims to develop a multi-method self-configuring simulation model for investigating disassembly scenarios while considering the battery design as well as the configuration and layout of the disassembly station. We demonstrate the developed model in a case study using a battery of Mercedes-Benz and the automated disassembly station of the project DeMoBat at Fraunhofer IPA. Furthermore, we introduce two disassembly scenarios, the component-oriented and the accessibility-oriented disassembly, and compare them using the simulation model to determine several indicators, such as the frequency of tool change, the number and distribution of robot routes, the tool utilization, and the disassembly time.

This paper proposes a model to evaluate business projects to get into an incubator, allowing to rank them in order of selection priority. The model combines the Momentum method to build prospective scenarios and the Multiple Criteria Decision Making (MCDM) method TOPSIS-2N to rank the alternatives. Six business projects were evaluated to be incubated. The Momentum method made it possible for us to create an initial core of criteria for the evaluation of incubation projects. The TOPSIS-2N method supported the decision to choose the company to be incubated by ranking the alternatives in order of relevance. Our evaluation model has improved the existing models used by incubators. This model can be used and / or adapted by any incubator to evaluate the business projects to be incubated. The set of criteria for the evaluation of incubation projects is original and the use of prospective scenarios with a MCDM method to evaluate companies to be incubated does not exist in the literature.

This article aims to serve as a guide for the construction of supply chain simulation models designed with a lean approach, using Promodel software. To achieve this, a supply chain was designed for a fictitious company located in the City of Celaya, Guanajuato and a set of suppliers located in different cities within the same State. It was used as a google tool to define the distances between each of the companies. As a final result, a representative model of a supply chain was obtained, as well as a methodology that allows the construction of lean supply chains regardless of the number of companies that comprise it. The effect of the variability in the delivery times between suppliers was incorporated into the simulation model, as well as an equation that calculates the pollution emissions of the vehicles that integrate the network that moves the products between the companies. With this work it is possible to represent networks of supply chains of real world companies, where the variability and contamination factor is included, to facilitate the decision making regarding the number of vehicles, inventory levels, quantities to be shipped, frequency in the shipments, etc. with the purpose of contaminating as little as possible and at the same time preventing interruptions in the supply chain using the least amount of resources possible.

Contrapose the highly integrated, multiple types of faults and complex working conditions of aircraft Electro Hydrostatic Actuator (EHA), to effectively identify its typical faults, we propose a fault diagnosis method based on the fusion convolutional neural networks (FCNN). First, the aircraft EHA fault data is encoded by GADF to obtain the fault feature images. Then we build an FCNN model that integrates the 1DCNN and 2DCNN, where the original 1D fault data is the input of the 1DCNN model, and the feature images obtained by GADF transformation are used as the input of 2DCNN. Multiple convolution and pooling operations are performed on each of these inputs to extract the features, next these feature vectors are spliced in the convergence layer, and the fully connected layers and the Softmax layers are finally used to attain the classification of aircraft EHA faults. Furthermore, the multi-strategy hybrid particle swarm optimization (MSPSO) algorithm is applied to optimize the FCNN to obtain a better combination of FCNN hyperparameters; MSPSO incorporates various strategies, including an initialization strategy based on homogenization and randomization, and an adaptive inertia weighting strategy, etc. The experimental result indicates that the FCNN model optimized by MSPSO achieves an accuracy of 96.86% for identifying typical faults of the aircraft EHA, respectively higher than the 1DCNN and the 2DCNN about 16.5% and 5.7%. Additionally, the FCNN model improved by MSPSO has a higher accuracy rate when compared to PSO.

Simulation tools capturing the interactions of communication and electrical system operation represent a powerful support for fully assessing the potential benefits and impacts of ICT in future smart power distribution network. A strong interest is upon the possibility of exploiting the last generation communication systems for supporting the transition of distribution network towards a smart grid scenario. Having in mind the above, the authors propose a numerical co-simulation tool useful to thoroughly understand the impact of the communication networks on the performance of whole power system dynamics. The co-simulation tool has been purposely developed to simulate the highly time-critical smart grid application of fault management and network reconfiguration and permits reproducing and evaluating the behavior of the public mobile telecommunication system 4G Long Term Evolution (LTE), as communication technology for smart grid applications. Results of the paper demonstrates that LTE provides good performances for supporting the data communication required to perform fault location, extinction and a subsequent network reconfiguration in smart power distribution networks.

The Huichang fault Basin in Jiangxi Province exhibits high terrestrial heat flow values and a dense concentration of hot springs. However, existing research is limited to individual geothermal fields without a comprehensive basin-wide analysis of geothermal water origins. This study investigates the source of geothermal water across five representative geothermal fields using hydrochemical components and isotopic analyses. The findings indicate: (1) The hydrochemical type transitions from bicarbonate sulphate-sodium-calcium in the north to bicarbonate sodium-calcium in the south; (2) Major chemical components of the geothermal water derive from evaporative salt rocks (Cretaceous sand conglomerate cover) and siliceous formations (granite/metamorphic rock basement); (3) The recharge elevation for geothermal water is approximately 1 km, sourced from adjacent hills. The central basin's geothermal water has an apparent age of about 26,000 years, with δ¹³C indicating participation in deep circulation; (4) Thermal reservoir temperatures range from 74 to 113℃, with the lowest temperatures at the basin's edges (Anzishan and Shangjin geothermal fields) and higher temperatures towards the center, suggesting increased warming and a circulation depth of 1.5 to 2.5 km; (5) Simulation results of reverse water-rock interaction indicate that mineral precipitation-dissolution processes are governed by the basin's main thermal control structures. The geothermal fields are predominantly influenced by reduction alterations associated with granite and are closely linked to paleo-geothermal waters in the enclosed basin, with the formation of these fields heavily reliant on the heat production or conduction from radioactive elements in granite.

Beginning with navigation system design, this paper presents a comprehensive strategy for enhancing the search and rescue capabilities of agile mobile robots. Towards this, the autonomous ground vehicle (AGV) utilizes surface classification to determine and prioritize the terrain it is traversing. Our developed system design incorporates real-time terrain data with task objectives at a high level, ensuring that the robot can effectively navigate complex and ever-changing environments. This design, in conjunction with the introduction of a novel lightweight surface classification model, forms the basis of our adaptive terrain perception and decision-making systems, enabling robots such as Jackal to adapt rapidly and make the decisions necessary to complete the task. Subsequently, we exhaustively validated these systems through a series of extensive experiments in a variety of terrains, including normal and mixed terrains, demonstrating their robustness and efficacy in real-life situations.

Address parsing is a crucial task in natural language processing, particularly for Chinese addresses. The complex structure and semantic features of Chinese addresses present challenges due to their inherent ambiguity. Additionally, different task scenarios require varying levels of granularity in address components, further complicating the parsing process. To address these challenges and adapt to low-resource environments, we propose CapICL, a novel Chinese address parsing model based on the In-Context Learning (ICL) framework. CapICL leverages a sequence generator, regular expression matching, BERT semantic similarity computation, and GPT modeling to enhance parsing accuracy by incorporating contextual information. We construct the sequence generator using a small annotated dataset, capturing distribution patterns and boundary features of address types to model address structure and semantics, mitigating interference from unnecessary variations. We introduce the REB-KNN algorithm, which selects similar samples for ICL-based parsing using regular expression matching and BERT semantic similarity computation. The selected samples, raw text, and explanatory text are combined to form prompts, and inputted into the GPT model for prediction and address parsing. Experimental results demonstrate significant achievements of CapICL in low-resource environments, reducing dependency on annotated data and computational resources. Our model's effectiveness, adaptability, and broad application potential are validated, showcasing its positive impact in natural language processing and geographical information systems.

The article evaluates the use of machine learning methods to predict valve plate failures in hydraulic pumps. To conduct this assessment, a laboratory setup was developed that allowed the creation of a training dataset containing both the normal operating state of the pump and the operating state with three different valve plate damages. Five different predictive models were evaluated, including three composite models based on decision trees: Random Forest, Gradient-Boosted Trees, and Rotation Forest, as well as the kNN algorithm and a neural network. The analysis showed that neural networks achieved the highest prediction accuracy, enabling a prediction accuracy level of 89%. The kNN algorithm ranked second, and tree-based algorithms performed 4% worse than the neural network. Additionally, the attribute importance analysis revealed that leak flow, pressure output together with output flow, are the most important parameters for accurate prediction.

In this article the problem of detection and isolation (FDI) in wheeled mobile robot is 1 treated. first of all we consider a master-slave robot system in which the master robot has to follow a 2 desired trajectory given by a well-determined control law, then the slave is controlled by the master. 3 However in real environment there is no ideality because faults can appear in the master or slave 4 or even the both and affect the system and may lead to unacceptable results so to ensure a well 5 functioning an extended kalman filter is tuned to diagnosis eventual faults and to generate residuals 6 in order to detect and isolate them. The performance of an estimator based on an EKF is simulated 7 based MATLAB Simulink simulations.

To realize the diagnosis of compound faults in gearboxes at different speeds, an "end-to-end" intelligent diagnosis method based on a deep neural network is proposed, named efficiency channel attention-capsule network (ECA-CN). First, the process uses a deep convolutional neural network to extract fault features from the collected raw vibration signals, then embeds the efficient channel attention module to filter important fault features, then uses the capsule network to vectorize the feature space information, and finally calculates the correlation between different levels of capsules by the dynamic routing algorithm to achieve accurate gearbox compound fault diagnosis. The effectiveness of the proposed ECA-CN fault diagnosis method was verified by the composite fault dataset of the 2009 PHM Challenge gearbox, with an average accuracy of 99.63% and a standard deviation of 0.22%. In the comparison experiments with the traditional fault diagnosis method, the average accuracy of the ECA-CN method was improved by 4.62%, and the standard deviation was reduced by 0.58%. The experimental results show that ECA-CN has a more competitive diagnostic performance.

Microgrids can be islanded to improve the reliability of the electrical energy supply during contingencies such as faults. It is still unclear what stability phenomena can occur in microgrids without inertia during and immediately after the fault-initiated islanding transient, and how the stability should be evaluated. To allow robust planning and operation, a stability analysis methodology should be able to take into account the inherent uncertainty of load and renewable energy sources. Therefore, this paper proposes a probabilistic stability analysis methodology for fault-initiated islanding of microgrids based on a thorough analysis of the root causes of instability during islanding transients. To perform stability analysis within reasonable time and allow control actions during operation, dynamical microgrid models in the dq reference frame are described and validated by comparing time-domain simulations results to previously validated component-based models. The stability analysis methodology is demonstrated in a case study of a generic microgrid. The instability phenomena and the dq microgrid models are successfully validated, and the results indicate that the stability analysis methodology can assist the microgrid operator to improve fault-initiated islanding capability in order to improve reliability of supply.

In the past years, various intelligent machine learning and deep learning algorithms have been developed and widely applied for gearbox fault detection and diagnosis. However, the real-time application of these intelligent algorithms has been limited, mainly due to the fact that the model developed using data from one machine or one operating condition has serious diagnosis performance degradation when applied to another machine or the same machine with a different operating condition. The reason for poor model generalization is the distribution discrepancy between the training and testing data. This paper proposes to address this issue using a deep learning based cross domain adaptation approach for gearbox fault diagnosis. Labelled data from training dataset and unlabeled data from testing dataset is used to achieve the cross-domain adaptation task. A deep convolutional neural network (CNN) is used as the main architecture. Maximum mean discrepancy is used as a measure to minimize the distribution distance between the labelled training data and unlabeled testing data. The study proposes to reduce the discrepancy between the two domains in multiple layers of the designed CNN to adapt the learned representations from the training data to be applied in the testing data. The proposed approach is evaluated using experimental data from a gearbox under significant speed variation and multiple health conditions. An appropriate benchmarking with both traditional machine learning methods and other domain adaptation methods demonstrates the superiority of the proposed method.

Primary importance is devoted to Fault Detection and Diagnosis (FDI) of electrical machine and drive systems in modern industrial automation. The widespread use of Machine Learning techniques has made it possible to replace traditional motor fault detection techniques with more efficient solutions that are capable of early fault recognition by using large amounts of sensory data. However, the detection of concurrent failures is still a challenge in the presence of disturbing noises or when the multiple faults cause overlapping features. The contribution of this work is to propose a novel methodology using multi-label classification method for simultaneously diagnosing multiple faults and evaluating the fault severity under noisy conditions. Performance of various multi-label classification models are compared. Current and vibration signals are acquired under normal and fault conditions. The applicability of the proposed method is experimentally validated under diverse fault conditions such as unbalance and misalignment.

Machine fault diagnosis systems need to collect and transmit dynamic monitoring signals, like vibration and current signals, at high-speed. However, industrial wireless sensor networks (IWSNs) and Industrial Internet of Things (IIoT) are generally based on low-speed wireless protocols, such as ZigBee and IEEE802.15.4. To address this tension when implementing machine fault diagnosis applications in IIoT, this paper proposes a novel IWSN with on-sensor data processing. On-sensor wavelet transforms using four popular mother wavelets are explored for fault feature extraction, while an on-sensor support vector machine classifier is investigated for fault diagnosis. The effectiveness of the presented approach is evaluated by a set of experiments using motor bearing vibration data. The experimental results show that compared with raw data transmission, the proposed on-sensor fault diagnosis method can reduce the payload transmission data by 99.95%, and reduce the node energy consumption by about 10%, while the fault diagnosis accuracy of the proposed approach reaches 98%.

The fault diagnosis of wind farms has been proven to be a challenging task and motivates the research activities carried out through this work. Therefore, this paper deals with the fault diagnosis of a wind park benchmark model, and it considers viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, noise, uncertainty and disturbances. In particular, the proposed data-driven solutions rely on fuzzy models and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive with exogenous input models, as they can represent the dynamic evolution of the system along time. The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model, that simulates the normal and the faulty behaviour of a wind farm installation. The achieved performances are also compared with those of a model-based approach relying on nonlinear differential geometry tools. Finally, a Monte-Carlo analysis validates the robustness and the reliability of the proposed solutions against typical parameter uncertainties and disturbances.

Malaria is a constant reminder of the climate change impacts on health. Many studies have investigated the influence of climatic parameters on the of malaria transmission. Climate conditions can modulate malaria transmission through increased temperature, which reduces the duration of the parasite's reproductive cycle inside the mosquito. The intensity and frequency of the rainfall modulate the development of the mosquito population. In this study, the Liverpool Malaria Model (LMM) is used to simulate the spatio-temporal variation of the malaria incidence in Senegal. The simulations are based on the WATCH Forcing Data applied to ERA-Interim data (WFDEI) used as a point of reference, and biased-corrected CMIP6 models, separating historical and projections for 3 Shared Socio-economic Pathways scenarios (SSP126, SSP245 and SSP585). Our results highlight a strong increase in temperatures, especially towards eastern Senegal under the SSP245 but mainly the SSP585 scenarios. The ability of the LMM model to simulate the seasonality of malaria incidence is assessed. The model reveals a period of high malaria transmission between September and November with a maximum reached in October. Results indicate a decrease in malaria incidence in certain regions of the country for the far future and for the extreme scenario. This study is importance for the planning, prioritization, and implementation of control activities in Senegal.

Accessing energy in the world is crucial nowadays. Energy is an essential factor to achieve other SDGs including SDG7. However, the determination and evaluation of the relationship between different energy scenarios to achieve SDG 7 and other SDGs have not done yet. This paper seeks to fill this gap by investigating how energy seniors can contribute to achieving SDG 7 and other SDGs. Web of Science, ScienceDirect and Scopus databases were utilized for conduction a systematic review. A total finial 25 from 249 papers were filtered from 2015 to December 2020 via inclusion and exclusion criteria. This review involves six seniors of energy which primary linked to achieving the SDG 7 and other SDGs: modern energy 20% (n = 5/25), energy access 16% (n = 4/20), energy efficiency 8% (n= 2/16), renewable energy 28% (n= 7/14), energy services 8% (n= 2/7), and miscellaneous energy 20% (n= 5/5). This systematic review explores the opportunities, constraints and limitations, recommendations, and new directions. The results show that different energy scenarios contribute to achieving mainly (SDG7) and other SDGs. The outcomes from this systematic review provide a sense of direction for future researchers for future studies in this domain.

The Philippines is both a biodiversity hotspot and a megadiverse country. The country also has experienced one of the highest rates of deforestation in Southeast Asia and is among the first countries to introduce a massive reforestation program to address the country’s rapid biodiversity and forest loss. Drawing upon an empirical study from the Leyte island and other relevant case studies from the Philippines, in this chapter, we demonstrate that recovering secondary forests following shifting cultivation, locally known as kaingin have the high potentials for biodiversity and carbon co-benefits. Based on our empirical study, we also found that secondary forest regrowing after kaingin use can potentially be used as a cost-effective reforestation measure with multiple benefits to people and the environment in upland areas of the Philippines. We also discuss measures that are essential for such programs to be successful.

A key component for the proper functioning, availability, and reliability of power grids is the power trans- former. Although these are very reliable assets, the early detection of incipient degradation mechanisms are very important to prevent failures that may shorten their lifetime. In this work a review and comparative analysis, of classical Machine Learning algorithms (such as single and ensemble classification algorithms) and two automatic machine learning classifiers, are presented for the fault diagnosis of power transformers. The goal is to determine whether fully automated ML approaches are worse or better than traditional ML frameworks that require a human in the loop (such as a data scientist) to identify transformer faults from oil-dissolved gases data. The methodology uses a DB compiled from published and proprietary transformer fault samples. Faults data is obtained from the literature, the Duval pentagon method, and user-expert knowledge. The parameters from, either single or ensemble classifiers, were optimized through standard machine learning procedures. The results showed that the best performing algorithm is a robust automatic machine learning classifiers model followed by classical algorithms such as neural networks and stacking ensembles. These results highlight the ability of a robust automatic machine learning model for, handling imbalanced power transformers faults datasets with high accuracy, using the minimum tuning effort by the electrical experts. By providing the most probable transformer fault condition will reduce the time required to find and solve the fault.

Safe, high-quality and economical electric energy transportation is the basic requirements of modern power system operation. Transformer, as one of the core equipment of power system and national grid system, has the characteristics of diverse types, variable models and wide deployment. It is the basic equipment for power system to realize voltage change and electric energy distribution. Because the power system transformer needs to operate with load for a long time, the probability of failure is usually higher than that of other power equipment in general. This paper starts from the transformer fault and fault diagnosis. Firstly, the transformer fault types, traditional transformer fault diagnosis techniques and the advantages of machine learning in transformer fault diagnosis are reviewed. Secondly, the application of information system theory and information entropy in transformer fault diagnosis is introduced. Then it introduces machine learning technology, feature selection and extraction technology in transformer fault diagnosis, and machine learning algorithms such as support vector machine and extreme learning machine for transformer fault diagnosis. Finally, the potential development trend of information system theory, information entropy and machine learning in transformer fault diagnosis is forecasted. The combination of transformer fault prediction and machine learning algorithm is helpful for power system maintenance personnel to accurately predict the running state of power equipment, and also provides a new method and technology for the safe and reliable operation and regular maintenance of power system.

Blind deconvolution (BD) is one of the effective methods that help pre-process vibration signals and assist in bearing fault diagnosis. Currently, most BD methods design an optimization criterion and use frequency or time domain information independently to optimize a deconvolution filter. It recovers weak periodic impulses related to incipient faults. However, the random noise interference may cause the optimizer to overfit. The time-domain-based BD methods tend to extract fault-unrelated single peak impulse, and the frequency-domain-based BD methods tend to retain the maximum energy frequency component, which will lose the fault-related harmonics frequency components. To solve the above issue, we propose a hybrid criterion that combines the kurtosis for time domain optimization and the $G-l_1/l_2$ norm for the frequency domain. These two criteria are monotonically increasing and decreasing, so they mutually constrain to avoid overfitting. After that, we design a multi-task one-dimensional convolutional neural network with time and frequency branches to achieve an optimal solution for this hybrid criterion. The multi-task neural network realizes the simultaneous optimization of two domains. Experimental results show that our proposed method outperforms other state-of-the-art methods.

Generalized stacking fault energies of palladium alloys were calculated using the density functional theory. The stacking fault energy of palladium alloys is correlated with the valence electron of the transition metal element. The twinning tendency is also modified by the presence of an alloying element in the plane of deformation. The obtained results suggest that Pd –transition metal alloys with elements such as Cr, Mo, W, Mn, Re are expected to exhibit high work hardening rate due to the tendency to emit of the partial dislocations and mechanical twins, which results in increased strength and ductility.