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Paper waste in the mockups design with regular, irregular, and amorphous patterns is a critical problem in digital printing presses. Paper waste reduction directly impacts production costs, generating business and environmental benefits. This problem can be mapped to the two-dimensional irregular bin-packing problem. In this paper, an iterated local search algorithm using a novel neighborhood structure to detect overlaps between amorphous shapes is introduced. This algorithm is used to solve the paper waste problem, modeled as one 2D irregular bin-packing problem. The experimental results show that this approach works efficiently and effectively to detect and correct the overlaps between regular, irregular, and amorphous figures.

Flying insect detection, identification, and counting are the key components of agricultural pest management. Insect identification is also one of the most challenging tasks in agricultural image processing. With the aid of machine vision and machine learning, traditional (manual) identification and counting can be automated. To achieve this goal, a particular data acquisition device and an accurate insect recognition algorithm (model) is necessary. In this work, we propose a new embedded system-based insect trap with an OpenMV Cam H7 microcontroller board, which can be used anywhere in the field without any restrictions (AC power supply, WIFI coverage, human interaction, etc.). In addition, we also propose a deep learning-based insect-counting method where we offer solutions for problems such as the “lack of data” and “false insect detection”. By means of the proposed trap and insect-counting method, spraying (pest swarming) could then be accurately scheduled.

Applied Sciences has achieved a major milestone this year: it is the 10th anniversary of the journal [...]

Over the last decades, microalgal biomass has gained a significant role in the development of different high-end (nutraceuticals, colorants, food supplements, and pharmaceuticals) and low-end products (biodiesel, bioethanol, and biogas) due to its rapid growth and high carbon-fixing efficiency. Therefore, microalgae are considered a useful and sustainable resource to attain energy security while reducing our current reliance on fossil fuels. From the technologies available for obtaining biofuels using microalgae biomass, thermochemical processes (pyrolysis, Hydrothermal Liquefaction (HTL), gasification) have proven to be processed with higher viability, because they use all biomass. However, due to the complex structure of the biomass (lipids, carbohydrates, and proteins), the obtained biofuels from direct thermochemical conversion have large amounts of heteroatoms (oxygen, nitrogen, and sulfur). As a solution, catalyst-based processes have emerged as a sustainable solution for the increase in biocrude production. This paper’s objective is to present a comprehensive review of recent developments on the catalyst-mediated conversion of algal biomass. Special attention will be given to operating conditions, strains evaluated, and challenges for the optimal yield of algal-based biofuels through pyrolysis and HTL.

This work addresses the joint management of residual microalgae and pine wood waste through pyrolysis to obtain a solid product for its use as soil amendment and two other by-products (liquid and gaseous) that can be used for energy purposes. Two management routes have been followed. The first route is through the co-pyrolysis of mixtures of both residual materials in several proportions and the later use of their solid fraction for soil amendment. The second route is the pyrolysis of pine wood waste and its direct combination with dried residual microalgae, also using it as soil amendment. The solid fraction assessment shows that from seven solid products (biochar) three stand out for their positive applicability in agriculture as soil amendment. In addition, they also present the benefit of serving as carbon sink, giving a negative balance of CO2 emissions. However, caution is suggested due to biochar applicability being subject to soil characteristics. To ensure the sustainability of the overall process, the energy available in liquid and gaseous fractions has been assessed for covering the drying needs of the residual microalgae in both cases. These results suggest that the pyrolysis process is a sustainable way to manage specific evaluated residues and their products.

Falls are unusual actions that cause a significant health risk among older people. The growing percentage of people of old age requires urgent development of fall detection and prevention systems. The emerging technology focuses on developing such systems to improve quality of life, especially for the elderly. A fall prevention system tries to predict and reduce the risk of falls. In contrast, a fall detection system observes the fall and generates a help notification to minimize the consequences of falls. A plethora of technical and review papers exist in the literature with a primary focus on fall detection. Similarly, several studies are relatively old, with a focus on wearables only, and use statistical and threshold-based approaches with a high false alarm rate. Therefore, this paper presents the latest research trends in fall detection and prevention systems using Machine Learning (ML) algorithms. It uses recent studies and analyzes datasets, age groups, ML algorithms, sensors, and location. Additionally, it provides a detailed discussion of the current trends of fall detection and prevention systems with possible future directions. This overview can help researchers understand the current systems and propose new methodologies by improving the highlighted issues. ispartof: SENSORS vol:21 issue:15 ispartof: location:Switzerland status: published

Microwave torrefaction of oat hull was conducted to enhance its physicochemical properties. A bench-top reactor with an internal stirrer was used for oat hull pretreatment at temperatures of 225 °C, 255 °C, and 285 °C, and residence times of 3, 6, and 9 min, respectively. Results showed that a high temperature level at 3 min residence time or severe torrefaction increased calorific values by up to 35% of its original value, while decreasing mass yield down to 60.77%. Severe torrefaction further decreased moisture absorption, moisture content, and grinding energy consumption but decreased energy yield and bulk density. Residence time had no significant effect on biomass physicochemical changes; however, production cost may be significantly increased by longer residence times. It was also concluded that increased microwave power levels from 400 to 650 W decreased energy consumption by shortening processing times, resulting in a positive economic impact of the process. Moderate and severe torrefaction significantly enhanced biomass fuel properties, and short residence times are recommended in order to decrease electricity consumption. In addition, microwave pretreatment enhances biomass in a similar way to conventional torrefaction, but at a faster processing time. Moreover, the liquid fraction as a by-product may represent a valuable product for the food industry.

Solid sago waste is a potential source of producing renewable energy in the form of biogas. This study investigated the effects of solid sago waste particle size, biological pretreatment using a microbial consortium of lignocelluloses, pretreatment with NaOH, and the ratio between solid sago waste and cow rumen based on the biogas production rate. Several variations of these conditions were used to achieve this. The anaerobic digestion process was conducted over two months at 30.42 °C ± 0.05 °C, and the biogas production rate was measured every two days. The 1:1 ratio showed better results compared to the 2:1, because it allows the bacteria to achieve metabolic balance. The highest cumulative biogas production (27.91 mL/g TS) was generated when the sago waste underwent milling (±1 mm), pretreatment with 4% NaOH g/g TS, and treatment with microbial consortium 5% v/v at a 1:1 ratio of solid sago waste to the rumen.

With increasing coffee production and consumption, the amount of coffee by-product is also increasing. Therefore, there is growing worldwide interest in using these by-products as a renewable energy source. In this study, hydrothermal carbonization was conducted with subcritical water to improve the fuel characteristics of spent coffee grounds. The water content was varied, with the mass ratio between the dry sample and water set to 1:1.5 and 1:4. The reaction temperature was increased by 10 °C from 180 to 250 °C. The fuel and thermal characteristics of the reaction products were investigated through mass and energy yields, elemental, proximate, and heating value analysis. In analysis results, as the reaction temperature increased, carbon and fixed carbon content increased, and oxygen and volatile matter content decreased, resulting in an increase in calorific value. Thermogravimetric analysis, derivative thermogravimetry, and Fourier transform infrared spectroscopy were also conducted on the reaction products. To investigate their storage characteristics, chemical oxygen demand analysis was conducted. The results showed that with increasing reaction temperature, the fixed carbon content and heating value increased, also, the fuel characteristics became similar to those of coal. In addition, the reaction products became more hydrophobic as the reaction temperature increased.

Global warming is predominantly caused by methane (CH4) and carbon dioxide (CO2) emissions. CH4 is estimated to have a global warming potential (GWP) of 28–36 over 100 years. Its impact on the greenhouse effect cannot be overstated. In this report, a dual-bed eight-step pressure swing adsorption (PSA) process was used to simulate the separation of high-purity CH4 as renewable energy from biogas (36% CO2, 64% CH4, and 100 ppm H2S) in order to meet Taiwan’s natural gas pipeline standards (&gt, 95% CH4 with H2S content &lt, 4 ppm). Three selectivity parameters were used to compare the performance of the adsorbents. In the simulation program, the extended Langmuir–Freundlich isotherm was used for calculating the equilibrium adsorption capacity, and the linear driving force model was used to describe the gas adsorption kinetics. After the basic case simulation and design of experiments (DOE) for the laboratory-scale PSA, we obtained a top product CH4 purity of 99.28% with 91.44% recovery and 0.015 ppm H2S purity, and the mechanical energy consumption was estimated to be 0.86 GJ/ton-CH4. Lastly, a full scale PSA process simulation was conducted for the commercial applications with 500 m3/h biogas feed, and the final CH4 product with a purity of 96.1%, a recovery of 91.39%, and a H2S content of 1.14 ppm could be obtained, which can meet the standards of natural gas pipelines in Taiwan.

Small-scale anaerobic digestion (AD) can be an effective organic waste management system that also provides energy for small businesses and rural communities. This study measured fuel production from digestions of single and mixed feedstocks using an unheated, 2 m3 digester operated continuously in a temperate climate for over three years. Using local food waste, brewery waste, grease waste, and agricultural residues, this study determined that small-scale AD co-digestions were almost always higher yielding than single feedstocks during psychrophilic operation and seasonal temperature transitions. Agricultural residues from Miscanthus&nbsp, x&nbsp, giganteus had the greatest impact on biomethane production during co-digestion (4.7-fold greater average biogas %CH4), while mesophilic digestion of brewery waste alone produced the most biogas (0.76 gCH4 gVS−1 d−1). Biogas production during the transition from mesophilic to psychrophilic was temporarily maintained at levels similar to mesophilic digestions, particularly during co-digestions, but biogas quality declined during these temperature shifts. Full-time operation of small-scale, unheated AD systems could be feasible in temperate climates if feedstock is intentionally amended to stabilize carbon content.

The properties, types, and physical and chemical aspects of pig slurry used in the fermentation process were presented. Characterization of the pig slurry microflora for a controlled biogas production process was performed. A pilot biogas treatment installation was presented on the example of a farm with 1100 Dan Bred fatteners kept in a grate system. The research was carried out to measure the biogas flow rate resulting from the reference pressure in the fermentor. An independent assessment of the amount of biogas and the pressure drop in the skeletal deposit was carried out. The basis for assessing the hydrodynamics of gas flow through the adhesive bed is the flow characteristic, which results from the pressure that forces this flow. In each case, the determination of this characteristic consists in determining the influence of the biogas stream on the value of this overpressure, equivalent to the pressure drop (it is tantamount to determining the total biogas flow resistance through the adhesive bed). The results of the measurements indicate the practical application of pig slurry-a substrate in a polydisperse system for the production of agricultural biogas in the context of renewable energies. The article indicates that the ferment was periodically mixed during the day, together with the fermentation of the ferment with fresh substrate. The tests were conducted for 49 days, thus demonstrating that it is more advantageous to mix the ferment hydrodynamically, obtaining a CH4 level of about 80%.

Iron-based industries are one of the main contributors to greenhouse gas (GHG) emissions. Partial substitution of fossil carbon with renewable biocarbon (biomass) into the blast furnace (BF) process can be a sustainable approach to mitigating GHG emissions from the ironmaking process. However, the main barriers of using biomass for this purpose are the inherent high alkaline and phosphorous contents in ash, resulting in fouling, slagging, and scaling on the BF surface. Furthermore, the carbon content of the biomass is considerably lower than coal. To address these barriers, this research proposed an innovative approach of combining two thermochemical conversion methods, namely hydrothermal carbonization (HTC) and slow pyrolysis, for converting biomass into suitable biocarbon for the ironmaking process. Miscanthus, which is one of the most abundant herbaceous biomass sources, was first treated by HTC to obtain the lowest possible ash content mainly due to reduction in alkali matter and phosphorous contents, and then subjected to slow pyrolysis to increase the carbon content. Design expert 11 was used to plan the number of the required experiments and to find the optimal condition for HTC and pyrolysis steps. It was found that the biocarbon obtained from HTC at 199 °C for 28 min and consecutively pyrolyzed at 400 °C for 30 min showed similar properties to pulverized coal injection (PCI) which is currently used in BFs due to its low ash content (0.19%) and high carbon content (79.67%).

Countries globally are focusing on alternative fuels to reduce the environmental pollution. An example is biodiesel fuel, which is leading the way to other technologies. In this research, the methyl esters of castor oil were prepared using a two-step transesterification process. The respective properties of the castor oil (Ricinus Communis) biodiesel were estimated using ASTM standards. The effect of performance and emission on diesel engines was noted for four various engine loads (25, 50, 75, and 100%), with two different blends (B5 and B20) and at two different engine speeds (1500 and 2000 rpm). The study determined that B5 and B20 samples at 1500 rpm engine speed obtained the same power, but diesel fuel generated greater control. The power increased at 2000 rpm for B5 samples, but B20 samples, as well as diesel, were almost the same values. In the 40–80% range, load and load values were entirely parallel for each load observed from the engine performance of the brake power in all samples.

The chemical composition of hop sediments from beer brewing and fermentation gives them the potential for further use. These wastes are not generated in large amounts, but the absence of proper characteristics may lead to processing errors. This study examines the possibility of using hop waste for aerobic biological transformation processes (composting). The study was carried out on two hop sediments from two different technological stages of beer production: hot trub and spent hops. Chemical, microbiological, and biochemical analyses were performed in the composted hop sediments, as was the assessment of phytotoxicity to Lepidium sativum L. The tested feedstocks were partially inhabited by microorganisms and thus safe from an epidemiological point of view, and they were not a source of microbial contamination. Inhibitory properties for plant development were found for hot trub, which most likely result from the organic compound content. If it is only a small portion of its biomass, the mineral composition of hot trub does not exclude the possibility of its composting. Spent hops were characterized by a significant total nitrogen content, which affected the composting process. Composting this sediment required the selection of substrates with a widely C:N ratio.

With the development of the Internet, network security has aroused people’s attention. It can be said that a secure network environment is a basis for the rapid and sound development of the Internet. Phishing is an essential class of cybercriminals which is a malicious act of tricking users into clicking on phishing links, stealing user information, and ultimately using user data to fake logging in with related accounts to steal funds. Network security is an iterative issue of attack and defense. The methods of phishing and the technology of phishing detection are constantly being updated. Traditional methods for identifying phishing links rely on blacklists and whitelists, but this cannot identify new phishing links. Therefore, we need to solve how to predict whether a newly emerging link is a phishing website and improve the accuracy of the prediction. With the maturity of machine learning technology, prediction has become a vital ability. This paper offers a state-of-the-art survey on methods for phishing website detection. It starts with the life cycle of phishing, introduces common anti-phishing methods, mainly focuses on the method of identifying phishing links, and has an in-depth understanding of machine learning-based solutions, including data collection, feature extraction, modeling, and evaluation performance. This paper provides a detailed comparison of various solutions for phishing website detection.

Recently, electric vehicles (EV) have gained much attention as a potential enabling technology to support CO2 emissions reduction targets. Relative to their internal combustion vehicle counterparts, EVs consume less energy per unit distance, and add the benefit of not emitting any carbon dioxide in operation and instead shift their emissions to the existing local fleet of power generation. However, the true success of EVs depends on their successful integration with the supporting infrastructure systems. Building upon the recently published methodology for the same purpose, this paper presents a “systems-of-systems” case study assessing the impacts of EVs on these three systems in the context of Abu Dhabi. For the physical transportation system, a microscopic discrete-time traffic operations simulator is used to predict the kinematic state of the EV fleet over the duration of one day. For the impact on the intelligent transportation system (ITS), the integration of EVs into Abu Dhabi is studied using a multi-domain matrix (MDM) of the Abu Dhabi Department of Transportation ITS. Finally, for the impact on the electric power system, the EV traffic flow patterns from the CMS are used to calculate the timing and magnitude of charging loads. The paper concludes with the need for an intelligent transportation-energy system (ITES) which would coordinate traffic and energy management functionality.

Maintaining hand hygiene has been an essential preventive measure for reducing disease transmission in public facilities, particularly during the COVID-19 pandemic. The large number of sanitizer stations deployed within public facilities, such as on university campuses, brings challenges for effective facility management. This paper proposes an IoT sensor network for tracking sanitizer usage in public facilities and supporting facility management using a data-driven approach. Specifically, the system integrates low-cost wireless sensors, LoRaWAN, and cloud-based computing techniques to realize data capture, communication, and analysis. The proposed approach was validated through field experiments in a large building on a university campus to assess the network signal coverage and effectiveness of sensor operation for facility monitoring. The results show that a LoRaWAN created from a single gateway can successfully connect to sensors distributed throughout the entire building, with the sensor nodes recording and transmitting events across the network for further analysis. Overall, this paper demonstrates the potential of leveraging the IoT-based Sanitizer Station Network to track public health mitigation methods in a large facility, which ultimately contributes to reducing the burden of maintaining public health during and post-pandemic.

The first part of a two-part series of papers provides a survey on recent advances in Deep Reinforcement Learning (DRL) applications for solving partially observable Markov decision processes (POMDP) problems. Reinforcement Learning (RL) is an approach to simulate the human’s natural learning process, whose key is to let the agent learn by interacting with the stochastic environment. The fact that the agent has limited access to the information of the environment enables AI to be applied efficiently in most fields that require self-learning. Although efficient algorithms are being widely used, it seems essential to have an organized investigation—we can make good comparisons and choose the best structures or algorithms when applying DRL in various applications. In this overview, we introduce Markov Decision Processes (MDP) problems and Reinforcement Learning and applications of DRL for solving POMDP problems in games, robotics, and natural language processing. A follow-up paper will cover applications in transportation, communications and networking, and industries.

The objective of this paper was to quantify the economic and environmental effects of changing a dairy farm’s milking start times. Changing morning and evening milking start times could reduce both electricity costs and farm electricity related CO2 emissions. However, this may also involve altering farmer routines which are based on practical considerations. Hence, these changes need to be quantified both in terms of profit/emissions and in terms of how far these milking start times deviate from normal operations. The method presented in this paper optimized the combination of dairy farm infrastructure setup and morning and evening milking start times, based on a weighting variable (α) which assigned relative importance to labor utilization, farm net profit and farm electricity related CO2 emissions. Multi-objective optimization was utilized to assess trade-offs between labor utilization and net profit, as well as labor utilization and electricity related CO2 emissions. For a case study involving a 195 cow Irish dairy farm, when the relative importance of maximizing farm net profit or minimizing farm electricity related CO2 emissions was high, the least common milking start times (06:00 and 20:00) were selected. When the relative importance of labor utilization was high, the most common milking start times (07:00 and 17:00) were selected. The 195 cow farm saved €137 per annum when milking start times were changed from the most common to the least common. Reductions in electricity related CO2 emissions were also seen when the milking start times were changed from most common to least common. However, this reduction in emissions was primarily due to the addition of efficient and renewable technology to the farm. It was deduced that the monetary and environmental benefits of altering farmer milking routines were unlikely to change normal farm operating procedures.

Global warming and climate change are becoming a global concern. In this regard, international agreements and initiatives have been launched to accelerate the use of renewable energy and to mitigate greenhouse gas (GHG) emissions. Yemen is one of the countries signed on these agreements. However, Yemen is facing the problem that the structure of the power grid is fragile and the power shortage is serious. Accordingly, this paper aims to study the potential for renewable energy in Yemen and assess the technical and economic feasibility of hybrid energy systems. Firstly, this paper introduces the status and challenges of Yemen&rsquo, s electricity sector, the status of renewable energy, and the status of GHG emission. Secondly, this study proposes the method of optimizing different configurations of off-grid hybrid (solar/wind/diesel engine) energy systems for electrifying various consumers in Taiz province, Yemen under three scenarios of energy strategies. The objective function is to seek the most optimal hybrid energy system that achieves the least cost and most advantageous technical performance, while instigating the best economic scenario of energy strategies. Finally, Homer pro software is used for simulation, optimization, and sensitivity analysis of the designed energy systems. The results found the best economically feasible scenario, the hybrid PV/wind/diesel energy system, among the other scenarios. A photovoltaic (PV)/wind energy system achieved the best technical performances of 100% CO2 reduction, with a 54.82% reduction in the net present cost (NPC) and cost of energy (COE), while the hybrid energy system (PV/wind/diesel engine) achieved the best economic cost of 61.95% reduction in NPC and COE, with a 97.44% reduction of CO2 emission.

International audience; The advent of telerobotic systems has revolutionized various aspects of the industry and human life. This technology is designed to augment human sensorimotor capabilities to extend them beyond natural competence. Classic examples are space and underwater applications when distance and access are the two major physical barriers to be combated with this technology. In modern examples, telerobotic systems have been used in several clinical applications, including teleoperated surgery and telerehabilitation. In this regard, there has been a significant amount of research and development due to the major benefits in terms of medical outcomes. Recently telerobotic systems are combined with advanced artificial intelligence modules to better share the agency with the operator and open new doors of medical automation. In this review paper, we have provided a comprehensive analysis of the literature considering various topologies of telerobotic systems in the medical domain while shedding light on different levels of autonomy for this technology, starting from direct control, going up to command-tracking autonomous telerobots. Existing challenges, including instrumentation, transparency, autonomy, stochastic communication delays, and stability, in addition to the current direction of research related to benefit in telemedicine and medical automation, and future vision of this technology, are discussed in this review paper

The use of renewable energy sources increases the energy self-sustainability of cities, enabling citizens to reduce energy costs, which results in an increase in their standard of living. However, solar energy penetration in Bosnia and Herzegovina, and its capital Sarajevo, is not in line with the possibilities. Furthermore, the Sarajevo Canton is extremely polluted during the winter months because of the use of unacceptable heating fuel. The aim of this paper is to introduce photovoltaic power systems use in heating electrification system. In this paper AQI is calculated based on historical data and the hybrid model EMD-SARIMA for air pollution and a solar production forecast is presented. The methodology was tested in the Sarajevo Canton, taking into account 35,000 households. In order to ensure clean air, renewable electric energy use for household heating should be implemented. The widespread use of inefficient individual heating systems characterized by inefficient and expensive use of firewood and the use of coal in individual furnaces in populated areas are the main problems of internal and urban air pollution in Sarajevo Canton. In order to reduce energy poverty in Sarajevo Canton, the use of a floating photovoltaic power plant located on Lake Jablanica with a capacity of 30 MW and the solar prosumers with capacity of 115 MW to provide the 196 GWh necessary for heating electrification of 35,000 households is implemented in this paper. Finally, based on correlation between AQI forecast and solar production it was calculated that the values of the AQI, considering the application of solar energy during 150 days (five months) in one heating season, have significantly decreased. Also renewable energy sources have a very important role in reducing carbon dioxide (CO2) emissions into the atmosphere and reducing urban pollution. With this approach, households would be heated by renewable electricity, which would make Sarajevo a cleaner, smarter city.

Energy poverty is high up on national and European Union policy agendas. A number of possible indicators to measure the issue have been identified in the literature, but comparable data with European coverage is scarce. The EU Commission thus proposes four independent indicators on the &ldquo, EU Energy Poverty Observatory&rdquo, based on self-reported items from the pan-European surveys on income and living conditions (SILC) and household budgets (HBS). It is of increasing public interest to analyse social impacts of energy policies, and quantify energy poverty indicators also from modelling. This paper first shortly outlines how the expenditure-based indicators using HBS micro data may be directly linked to existing macroeconomic models through their defining variables (energy expenditure and income). As endogenous modelling based on micro data is difficult, the link may be country-specific elasticities. The main contribution of the paper is a systematic in-depth sensitivity analysis of the two indicators to changes in income and energy expenditure following varying patterns in the underlying distributions of the micro data. The results may be used by future soft links to models. The results display sometimes counterintuitive effects. We find that whether these indicators increase/decrease after a change of income or energy expenditure largely depends on the specific country-wise income and energy expenditure distribution between households on a micro-level. Due to their definition, the examined indicators are especially sensitive, when income changes alter the indicator threshold values, which in these cases are the median values in underlying distributions. We discuss these findings and relate them to several indicator shortcomings and potential remedies through changes in indicator definition.

In this paper, we proposed a scheme that Injects artificial noise from the tag end (IANT) to enhance the physical layer security of the ambient backscatter communication (ABC) system. The difference between the ABC system and the traditional radio frequency identification system is whether it uses the radio frequency (RF) signals in the environment to supply energy and modulation information for passive tags. In the IANT scheme, we select the best tag to communicate with the reader according to the channel quality between tags and reader, and at the same time select another tag to generate artificial noise that affects the receiving effect of the eavesdropper. This paper uses the method of generating noise copies in the reader to reduce the interference of artificial noise on the signal received by the reader. The simulation results show that with the increase in channel quality between tags and reader and the increase in the number of tags, the proposed IANT scheme is significantly superior to the contrast scheme in terms of system achievable secrecy rate, effectively enhancing the physical layer security of the ABC system.

Wind energy conversion systems have become a key technology to harvest wind energy worldwide. In permanent magnet synchronous generator-based wind turbine systems, the rotor position is needed for variable speed control and it uses an encoder or a speed sensor. However, these sensors lead to some obstacles, such as additional weight and cost, increased noise, complexity and reliability issues. For these reasons, the development of new sensorless control methods has become critically important for wind turbine generators. This paper aims to develop a new sensorless and adaptive control method for a surface-mounted permanent magnet synchronous generator. The proposed method includes a new model reference adaptive system, which is used to estimate the rotor position and speed as an observer. Adaptive control is implemented in the pulse-width modulated current source converter. In the conventional model reference adaptive system, the proportional-integral controller is used in the adaptation mechanism. Moreover, the proportional-integral controller is generally tuned by the trial and error method, which is tedious and inaccurate. In contrast, the proposed method is based on model predictive control which eliminates the use of speed and position sensors and also improves the performance of model reference adaptive control systems. In this paper, the proposed predictive controller is modelled in MATLAB/SIMULINK and validated experimentally on a 6-kW wind turbine generator. Test results prove the effectiveness of the control strategy in terms of energy efficiency and dynamical adaptation to the wind turbine operational conditions. The experimental results also show that the control method has good dynamic response to parameter variations and external disturbances. Therefore, the developed technique will help increase the uptake of permanent magnet synchronous generators and model predictive control methods in the wind power industry.

In this paper, we provide a holistic survey of multimedia content protection applications in which blockchain technology is being used. A taxonomy is developed to classify these applications with reference to the technical aspects of blockchain technology, content protection techniques, namely, encryption, digital rights management, digital watermarking and fingerprinting (or transaction tracking), and performance criteria. The study of the literature reveals that there is currently no complete and systematic taxonomy dedicated to blockchain-based copyright protection applications. Moreover, the number of successfully developed blockchain-based content protection systems is very low. This points towards a research gap. To fill this gap, we propose a taxonomy that integrates technical aspects and application knowledge and can guide the researchers towards the development of blockchain-based multimedia copyright protection systems. Furthermore, the paper discusses some technical challenges and outlines future research directions.

Recent advancements in sensor, mobile computing, and wireless communication technologies is creating new opportunities to effectively exploit real-time traffic data. Onboard vehicles collect, process, simulate traffic states in a distributed fashion and a local transportation management center coordinates the overall simulation with an optimistic execution technique. Such a distributed approach can provide more up-to-date and robust estimates with decreased communication bandwidth requirements and increased computing capacity. This paper proposes an online ad hoc distributed simulation. The physical operating platform for the model including operating system, communicational middleware, and traffic simulation model are described. Also, this paper investigates the analytical background of rollback process of the proposed ad hoc distributed simulation model. Flow rate diagram and cumulative number of vehicle diagram show that the overall system simulation speed and estimate accuracy may differ significantly as a function of the selected threshold.

Remote farms in Africa are cultivated lands planned for 100% sustainable energy and organic agriculture in the future. This paper presents the load frequency control of a two-area power system feeding those farms. The power system is supplied by renewable technologies and storage facilities only which are photovoltaics, biogas, biodiesel, solar thermal, battery storage and flywheel storage systems. Each of those facilities has 150-kW capacity. This paper presents a model for each renewable energy technology and energy storage facility. The frequency is controlled by using a novel non-linear fractional order proportional integral derivative control scheme (NFOPID). The novel scheme is compared to a non-linear PID controller (NPID), fractional order PID controller (FOPID), and conventional PID. The effect of the different degradation factors related to the communication infrastructure, such as the time delay and packet loss, are modeled and simulated to assess the controlled system performance. A new cost function is presented in this research. The four controllers are tuned by novel poor and rich optimization (PRO) algorithm at different operating conditions. PRO controller design is compared to other state of the art techniques in this paper. The results show that the PRO design for a novel NFOPID controller has a promising future in load frequency control considering communication delays and packet loss. The simulation and optimization are applied on MATLAB/SIMULINK 2017a environment.

The field of human activity recognition (HAR) often utilizes wearable sensors and machine learning techniques in order to identify the actions of the subject. This paper considers the activity recognition of walking and running while using a support vector machine (SVM) that was trained on principal components derived from wearable sensor data. An ablation analysis is performed in order to select the subset of sensors that yield the highest classification accuracy. The paper also compares principal components across trials to inform the similarity of the trials. Five subjects were instructed to perform standing, walking, running, and sprinting on a self-paced treadmill, and the data were recorded while using surface electromyography sensors (sEMGs), inertial measurement units (IMUs), and force plates. When all of the sensors were included, the SVM had over 90% classification accuracy using only the first three principal components of the data with the classes of stand, walk, and run/sprint (combined run and sprint class). It was found that sensors that were placed only on the lower leg produce higher accuracies than sensors placed on the upper leg. There was a small decrease in accuracy when the force plates are ablated, but the difference may not be operationally relevant. Using only accelerometers without sEMGs was shown to decrease the accuracy of the SVM.

To accelerate the energy transition, the EU “Clean Energy for all Europeans” package aims to strengthen the involvement of end consumers in the energy market. To this end, together with so-called “active consumers” and provisions for individual and collective renewable energy self-consumption, two types of energy communities were introduced. The EU framework, however, leaves many details of the transposition process to the national level. The corresponding directives were supposed to be transposed by the end of December 2020 (recast Electricity Market Directive, defining active consumers and citizen energy communities) and by the end of June 2021 (Renewable Energy Directive, defining renewables self-consumption and renewable energy communities). In this paper, we critically discuss major developments of the transposition, including questions of the general distinction of the different concepts, governance and ownership, physical expansion, administrative barriers and the overall integration of energy communities into the energy system. The analysis builds on country case studies as well as on previous work by the authors on the status of the transposition process throughout the EU. The paper shows that the national approaches differ greatly and are at very different stages. While basic provisions are in place in most Member States to meet the fundamental EU requirements, the overall integration into the energy system and market is only partly addressed. This concerns, for instance, the analysis of system impacts of energy communities and measures that would allow and support energy system-friendly behaviour. In addition, several practical hurdles need to be overcome. These often relate to administrative requirements such as complex registration and licensing procedures, the need for the involvement of several institutions, or difficult procedures for access to relevant data. The paper concludes that discussed barriers will need to be carefully addressed if the high expectations for the role of energy communities are to be met.

International audience; This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution.This paper presents a detailed analysis of 1200 V Silicon Carbide (SiC) power MOSFETexhibiting different short-circuit failure mechanisms and improvement in reliability by VDS andVGS depolarization. The device robustness has undergone an incremental pulse under differentdensity decreasing; either drain-source voltage or gate-driver voltage. Unlike silicon device, the SiCMOSFET failure mechanism firstly displays specific gradual gate-cracks mechanism and progressivegate-damage accumulations greater than 4 µs/9 J·cm−2. Secondly, a classical drain-source thermalrunaway appears, as for silicon devices, in a time greater than 9 µs. Correlations with short-circuitenergy measurements and temperature simulations are investigated. It is shown that the firstmechanism is an incremental soft gate-failure-mode which can be easily used to detect and protectthe device by a direct feedback on the gate-driver. Furthermore, it is highlighted that this newmechanism can be sufficiently consolidated to avoid the second drain-source mechanism which is ahard-failure-mode. For this purpose, it is proposed to sufficiently depolarize the on-state gate-drivevoltage to reduce the chip heating-rate and thus to decouple the failure modes. The device is muchmore robust with a short-circuit withstand time higher than 10 µs, as in silicon, no risk of thermalrunaway and with an acceptable penalty on RDS-ON.

In this paper, we use an evolutionary swarm intelligence approach to build an automatic electric dispatch controller for an offshore wind power plant (WPP). The optimal power flow (OPF) problem for this WPP is solved by the Canonical Differential Evolutionary Particle Swarm Optimization algorithm (C-DEEPSO). In this paper, C-DEEPSO works as a control system for reactive sources in energy production. The control operation takes place in a daily energy dispatch, scheduled into 15 min intervals and resulting in 96 operating test scenarios. As the nature of the optimization problem is dynamic, a fine-tuning of the initialization parameters of the optimization algorithm is performed at each dispatch interval. Therefore, a version of the C-DEEPSO algorithm has been built to automatically learn the best set of initialization parameters for each scenario. For this, we have coupled C-DEEPSO with the irace tool (an extension of the iterated F-race (I/F-Race)) by using inferential statistic techniques. The experiments carried out showed that the methodology employed here is robust and able to tackle this OPF-like modeling. Moreover, the methodology works as an automatic control system for a dynamic schedule operation., European Commission, Agencia Estatal de Investigación, Comunidad de Madrid

With the automotive industry moving towards automated driving, sensing is increasingly important in enabling technology. The virtual sensors allow data fusion from various vehicle sensors and provide a prediction for measurement that is hard or too expensive to measure in another way or in the case of demand on continuous detection. In this paper, virtual sensing is discussed for the case of vehicle suspension control, where information about the relative velocity of the unsprung mass for each vehicle corner is required. The corresponding goal can be identified as a regression task with multi-input sequence input. The hypothesis is that the state-of-art method of Bidirectional Long–Short Term Memory (BiLSTM) can solve it. In this paper, a virtual sensor has been proposed and developed by training a neural network model. The simulations have been performed using an experimentally validated full vehicle model in IPG Carmaker. Simulations provided the reference data which were used for Neural Network (NN) training. The extensive dataset covering 26 scenarios has been used to obtain training, validation and testing data. The Bayesian Search was used to select the best neural network structure using root mean square error as a metric. The best network is made of 167 BiLSTM, 256 fully connected hidden units and 4 output units. Error histograms and spectral analysis of the predicted signal compared to the reference signal are presented. The results demonstrate the good applicability of neural network-based virtual sensors to estimate vehicle unsprung mass relative velocity.

International audience; Energy has become the backbone of humanities daily activities. Heating, ventilating, and air conditioning systems (HVAC), which consume around 39% of energy in the residential sector, have turned into an essential constituent for providing fresh air, especially after COVD-19, not only in hospitals but also in any simple construction. Thus, decreasing this percentage or recovering part of the energy lost is an essential issue in today’s energy management scenarios. In this context, the present manuscript suggests a comprehensive review, classifications, critical analysis, and potential recommendations for energy recovery in air conditioning systems. It classifies energy recovery into two main categories: using lost energy for external uses, such as heating domestic water, or with other devices; and using lost energy for internal uses, such as the hot airflow which can be reused again for increasing efficiency of HVAC. In addition, this paper presents a summary of previous research and undertakes a review of the devices used for recovering energy. Furthermore, this review identifies superior devices in terms of climate and weather conditions. These objectives are accomplished by investigating around 190 published papers to conclude that energy recovery devices show a considerable effect on energy consumption in HVAC, mainly the heat pipe, fixed plate, and rotary wheel devices.

In the context of public health emergency management, it is worth studying ways to mobilize the enthusiasm of government, community, and residents. This paper adopts the method of combining evolutionary game and system dynamics to conduct a theoretical modeling and simulation analysis on the interactions of the behavioral strategies of the three participants. In response to opportunistic behavior and inadequate supervision in the static reward and punishment mechanism, we introduced a dynamic reward and punishment mechanism that considers changes in the social environment and the situation of epidemic prevention and control. This paper proves that the dynamic reward and punishment mechanism can effectively suppress the fluctuation problem in the evolutionary game process under static scenarios and achieve better supervision results through scenario analysis and simulation experiments.

Recent advancements in information and communication technologies (ICT) have im‐proved the power grid, leading to what is known as the smart grid, which, as part of a critical economic and social infrastructure, is vulnerable to security threats from the use of ICT and new emerging vulner‐abilities and privacy issues. Access control is a fundamental element of a security infrastructure, and se‐curity is based on the principles of less privilege, zero‐trust, and segregation of duties. This work ad‐dresses how access control can be applied without disrupting the power grid’s functioning while also properly maintaining the security, scalability, and interoperability of the smart grid. The authentication in the platform presumes digital certificates using a web of trust. This paper presents the findings of the SealedGRID project, and the steps taken for implementing Attribute‐based access control policies specif‐ically customized to the smart grid. The outcome is to develop a novel, hierarchical architecture com‐posed of different licensing entities that manages access to resources within the network infrastructure. They are based on well‐drawn policy rules and the security side of these resources is placed through a context awareness module. Together with this technology, the IoT is used with Big Data (facilitating easy handling of large databases). Another goal of this paper is to present implementation and evaluations details of a secure and scalable security platform for the smart grid.

The exponential increase in bandwidth-sensitive multimedia traffic on the Internet has created new challenges and opportunities. With the shifting focus from service availability to service quality, there is a need to have quality management measures to serve the high needs of efficient transmission and delivery in time-constrained environments over IP networks. Quality of Experience (QoE) is now considered the most important measure to achieve the twin goals of application efficiency and user satisfaction from a user perspective. In this paper, we propose a framework that can be used to collect real-time QoE feedback through crowdsourcing and forward it to SD-WAN controllers to enhance streaming routes based on real-time user quality perceptions. We analyze how QoE can be affected by different network conditions, and how different streaming protocols compare against each other when the network parameters change dynamically. We compare the real-time user feedback to predefined network changes to measure if participants will be able to identify all degradation events, and to examine which combination of degradation events are noticeable to the participants. These QoE timestamped feedback is sent back to the SD-WAN controller continuously in order to locate problems and bottlenecks in the current service paths and to enable network controllers to take corrective action by rerouting the streamed traffic. Our aim is to demonstrate that real-time QoE feedback can enhance cloud-based services and can adjust services quality based on real-time, active participants’ interaction[1].[1] This paper extends the preliminary results presented by the authors at the PVE-SDN 2019 workshop in IEEE Conference on Network Softwarization (NetSoft 2019).

Concentrated solar power (CSP) is an important option as a competitive, secure, and sustainable energy system. At the moment, cost-effective solutions are required for a wider-scale deployment of the CSP technology: in particular, the industrial exploitation of CSP has been so far hindered by limitations in the materials used for the central receiver—a key component in the system. In this context, the H2020 NEXTOWER project is focused on next-generation CSP technologies, particularly on advanced materials for high temperatures (e.g., &gt, 900 °C) and extreme applications environments (e.g., corrosive). The research activity described in this paper is focused on two industrial solutions for new SiC ceramic receivers for high thermal gradient continued operations: porous SiC and silicon-infiltrated silicon carbide ceramics (SiSiC). The new receivers should be mechanically tough and highly thermally conductive. This paper presents the activity related to the manufacturing of these components, their joining, and characterization.

Emergency medical services (EMS) provide crucial emergency assistance and ambulatory services. One key measurement of EMS’s quality of service is their ambulances’ response time (ART), which generally refers to the period between EMS notification and the moment an ambulance arrives on the scene. Due to many victims requiring care within adequate time (e.g., cardiac arrest), improving ARTs is vital. This paper proposes to predict ARTs using machine-learning (ML) techniques, which could be used as a decision-support system by EMS to allow a dynamic selection of ambulance dispatch centers. However, one well-known predictor of ART is the location of the emergency (e.g., if it is urban or rural areas), which is sensitive data because it can reveal who received care and for which reason. Thus, we considered the ‘input perturbation’ setting in the privacy-preserving ML literature, which allows EMS to sanitize each location data independently and, hence, ML models are trained only with sanitized data. In this paper, geo-indistinguishability was applied to sanitize each emergency location data, which is a state-of-the-art formal notion based on differential privacy. To validate our proposals, we used retrospective data of an EMS in France, namely Departmental Fire and Rescue Service of Doubs, and publicly available data (e.g., weather and traffic data). As shown in the results, the sanitization of location data and the perturbation of its associated features (e.g., city, distance) had no considerable impact on predicting ARTs. With these findings, EMSs may prefer using and/or sharing sanitized datasets to avoid possible data leakages, membership inference attacks, or data reconstructions, for example.

It is the scientific way to promote the transformation and optimization of an industrial structure to promote the improvement of its green total factor productivity (GTFP) by formulating environmental regulation policies. Based on the GTFP panel data of 30 provinces in China from 2004 to 2017, this paper takes the “Air Pollution Control and Prevention Action Plan” (APCP Action Plan) as the proxy dummy variable of environmental regulation, and uses the difference-in-differences (DID) model to explore the impact of the implementation of the APCP Action Plan on GTFP. In addition, by constructing the industrial structure optimization index, this paper analyzes how the APCP Action Plan policy affects GTFP through the transformation and optimization of industrial structure. The following basic conclusions are obtained: First, environmental regulation policies like the APCP Action Plan can improve GTFP. Second, the APCP Action Plan has regional heterogeneity in promoting GTFP in different regions. The policy only significantly affects the GTFP in the Pearl River Delta region in southern China. Third, the “quantity” and “quality” of the optimization of industrial structure will weaken the promoting effect of the APCP Action Plan on GTFP. In contrast, the rationalization of industrial structure will aggravate this promoting effect.

Power grids are evolving with the aim to guarantee sustainability and higher levels of power quality for universal access to electricity. More specifically, over the last two decades, power grids have been targeted for significant changes, including migration from centralized to decentralized paradigms as a corollary of intensive integration of novel electrical technologies and the availability of derived equipment. This paper addresses a review of a disruptive vision of future power grids, mainly focusing on the use of hybrid AC/DC grids and solid-state transformers technologies. Regarding hybrid AC/DC grids in particular, they are analyzed in detail in the context of unipolar and bipolar DC grids (i.e., two-wire or three-wire DC grids), as well as the different structures concerning coupled and decoupled AC configurations with low-frequency or high-frequency isolation. The contextualization of the possible configurations of solid-state transformers and the different configurations of hybrid transformers (in the perspective of offering benefits for increasing power quality in terms of currents or voltages) is also analyzed within the perspective of the smart transformers. Additionally, the paper also presents unified multi-port systems used to interface various technologies with hybrid AC/DC grids, which are also foreseen to play an important role in future power grids (e.g., the unified interface of renewable energy sources and energy storage systems), including an analysis concerning unified multi-port systems for AC or DC grids. Throughout the paper, these topics are presented and discussed in the context of future power grids. An exhaustive description of these technologies is made, covering the most relevant and recent structures and features that can be developed, as well as the challenges for the future power grids. Several scenarios are presented, encompassing the mentioned technologies, and unveiling a progressive evolution that culminates in the cooperative scope of such technologies for a disruptive vision of future power grids., This work has been supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. This work has been supported by the FCT Project newERA4GRIDs PTDC/EEI-EEE/30283/2017, and by the FCT Project DAIPESEV PTDC/EEIEEE/30382/2017.

The development of electro-mobility is one of the centerpieces of European country attempts to reduce carbon emissions and increase the quality of life in cities. The goals of reducing emissions from the transport sector and phasing out fossil-fueled vehicles in (urban) transport by 2050 present unrivaled opportunities to foster electro-mobility. This paper provides a comprehensive review of the literature and provides a detailed analysis of the current development of electro-mobility in Europe, assessing social, economic, and environmental aspects under a circular economy (CE) context. It also examines the existing challenges and suggests ways of addressing them towards improving the environmental performance of electro-mobility and the urban quality of life. The paper argues that a narrow technology-only agenda in electro-mobility will be less successful without the imperative of the CE, including not just materials and resources but also energy, to unlock the medium-term co-benefits of de-carbonization of both the transport as well as the building and energy sectors. The paper critically reviews some of the anticipated future developments that may guide the growth of this rapidly growing field into a CE.

The paper presents a new method for modelling the warming up process of a water system with elements regulating the flow in a stochastic manner. The paper presents the basic equations describing the work of typical elements which the water installation is composed of. In the proposed method, a new computational algorithm was used in the form of an iterative procedure enabling the use of boundary conditions that can be stochastically modified during the warming-up process. A typical situation, when such a modification is processed, is the regulation of the medium flow through two-way or three-way valves or applying additional heat source. Moreover, the presented method does not require the transformation of the differential equations, describing the operation of individual elements, into a linear form, which significantly facilitates analytical work and makes it more flexible. The example of analysis of the operation of water installation used for controlling temperature of the process gases in a chemical installation shows the functionality and flexibility of the method. The adopted calculation schematics enable changing the direction of the heat flow while the heat exchanger is in operation. Additionally, the sequence of calculation processed in modules describing operation of installation elements is elective (there is no situation that output parameters from one element are used as input parameters for other element in the same calculation step).

In this paper, we propose a new type of visually induced reduction method for virtual reality (VR) sickness. This method induces a gaze based on a visual guide (VG). Although VGs are used in commercial VR game contents as crosshairs, no studies have been conducted related to VR sickness. VGs can have various properties determined by position, size, shape, color, etc., and it was necessary to investigate which properties affect VR sickness. In particular, size and position of VG are properties that directly affect the user’s gaze movement. Therefore, in this paper, five VR sickness reduction methods with different position and size of VG are proposed. Then, an experiment was conducted to investigate the effectiveness of the proposed VR sickness reduction method. To this end, a SSQ (including nausea, oculomotor discomfort, disorientation, and total score) and a questionnaire on fatigue and immersion were used. From the experimental results, a VG with a size 30% that of the aspect ratio, and a position synchronized to the user’s head movement direction, was most effective in terms of VR sickness reduction and immersion.

The usage of imbalanced databases is a recurrent problem in real-world data such as medical diagnostic, fraud detection, and pattern recognition. Nevertheless, in class imbalance problems, the classifiers are commonly biased by the class with more objects (majority class) and ignore the class with fewer objects (minority class). There are different ways to solve the class imbalance problem, and there has been a trend towards the usage of patterns and fuzzy approaches due to the favorable results. In this paper, we provide an in-depth review of popular methods for imbalanced databases related to patterns and fuzzy approaches. The reviewed papers include classifiers, data preprocessing, and evaluation metrics. We identify different application domains and describe how the methods are used. Finally, we suggest further research directions according to the analysis of the reviewed papers and the trend of the state of the art.

Summaries of scientific publications enable readers to gain an overview of a large number of studies, but users’ preferences have not yet been explored. In this paper, we conduct two user studies (i.e., short- and long-term studies) where Japanese university students read summaries of English research articles that were either manually written or automatically generated using text summarization and/or machine translation. In the short-term experiment, subjects compared and evaluated the two types of summaries of the same article. We analyze the characteristics in the generated summaries that readers regard as important, such as content richness and simplicity. The experimental results show that subjects are mainly judged based on four criteria, including content richness, simplicity, fluency, and format. In the long-term experiment, subjects read 50 summaries and answered whether they would like to read the original papers after reading the summaries. We discuss the characteristics in the summaries that readers tend to use to determine whether to read the papers, such as topic, methods, and results. The comments from subjects indicate that specific components of scientific publications, including research topics and methods, are important to judge whether to read or not. Our study provides insights to enhance the effectiveness of automatic summarization of scientific publications.

Social media rumor precise governance is conducive to better coping with the difficulties of rumor monitoring within massive information and improving rumor governance effectiveness. This paper proposes a conceptual framework of social media rumor precise governance system based on literature mining. Accordingly, insightful directions for achieving social media rumor precise governance are introduced, which includes (1) rational understanding of social media rumors, especially large-scale spreading false rumors and recurring false rumors, (2) clear classification of rumor spreaders/believers/refuters/unbelievers, (3) scientific evaluation of rumor governance effectiveness and capabilities. For the above three directions, advanced analysis technologies applications are then summarized. This paper is beneficial to clarify and promote the promising thought of social media rumor precise governance and create impacts on the technologies’ applications in this area.

The prediction accuracy of support vector regression (SVR) is highly influenced by a kernel function. However, its performance suffers on large datasets, and this could be attributed to the computational limitations of kernel learning. To tackle this problem, this paper combines SVR with the emerging Harris hawks optimization (HHO) and particle swarm optimization (PSO) algorithms to form two hybrid SVR algorithms, SVR-HHO and SVR-PSO. Both the two proposed algorithms and traditional SVR were applied to load forecasting in four different states of Nigeria. The correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used as indicators to evaluate the prediction accuracy of the algorithms. The results reveal that there is an increase in performance for both SVR-HHO and SVR-PSO over traditional SVR. SVR-HHO has the highest R2 values of 0.9951, 0.8963, 0.9951, and 0.9313, the lowest MSE values of 0.0002, 0.0070, 0.0002, and 0.0080, and the lowest MAPE values of 0.1311, 0.1452, 0.0599, and 0.1817, respectively, for Kano, Abuja, Niger, and Lagos State. The results of SVR-HHO also prove more advantageous over SVR-PSO in all the states concerning load forecasting skills. This paper also designed a hybrid renewable energy system (HRES) that consists of solar photovoltaic (PV) panels, wind turbines, and batteries. As inputs, the system used solar radiation, temperature, wind speed, and the predicted load demands by SVR-HHO in all the states. The system was optimized by using the PSO algorithm to obtain the optimal configuration of the HRES that will satisfy all constraints at the minimum cost.

Recent progress in deep learning has led to accurate and efficient generic object detection networks. Training of highly reliable models depends on large datasets with highly textured and rich images. However, in real-world scenarios, the performance of the generic object detection system decreases when (i) occlusions hide the objects, (ii) objects are present in low-light images, or (iii) they are merged with background information. In this paper, we refer to all these situations as challenging environments. With the recent rapid development in generic object detection algorithms, notable progress has been observed in the field of deep learning-based object detection in challenging environments. However, there is no consolidated reference to cover the state of the art in this domain. To the best of our knowledge, this paper presents the first comprehensive overview, covering recent approaches that have tackled the problem of object detection in challenging environments. Furthermore, we present a quantitative and qualitative performance analysis of these approaches and discuss the currently available challenging datasets. Moreover, this paper investigates the performance of current state-of-the-art generic object detection algorithms by benchmarking results on the three well-known challenging datasets. Finally, we highlight several current shortcomings and outline future directions.