bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigation of Physiochemical Impact of Organic Molecule L-Lysine on Ammonium Dihydrogen Phosphate Single Crystal for Optoelectronics Applications
Authors: Patle Shruti , Rotake Dinesh , Rewatkar Kishor
Published: April 2023

Ammonium dihydrogen phosphate (ADP) single crystals along with the incorporated 0.5 and 1% L-lysine, an organic molecule which possesses a good nonlinear response, were grown with the vision to meet the requirements of the optoelectronic industry. The inclusion of the L-lysine molecule in the crystal was confirmed by the XRD and EDX. The experiment not only confirms the inclusion level of the impurity but also the capability of the amino acid molecule to bond hydrogen within the crystal facet. A minor decrease in lattice parameters was reported for all ADP: L-lysine crystals compared with pure ADP. The structures of the grown crystals were identified as tetragonal with the space group I42d by the single-crystal XRD analysis. Vibrational signatures and functional groups were confirmed using FTIR spectroscopy. The thermal stability and decomposition temperatures of 0.5 and 1% L-lysine-added crystals were measured by TG/DTA and found to be 203 °C and 207 °C, respectively. The UV–visible transmission spectra prove a higher transparency for doped crystals as compared to pure crystals; therefore, these doped crystals can be considered the best option for the frequency doubling process in a broad range of visible and near-IR spectra. The improved hardness of the doped crystals was confirmed by the Vickers hardness data. The nonlinear optical (NLO) behaviour investigated using a second-harmonic generation (SHG) technique, indicating an efficient quadratic nonlinear coefficient of ADP: Lysine crystals at a 1064 nm initial wavelength, shows about 1.5-fold higher efficiency compared with undoped ADP.
A Hardware-Efficient Perturbation Method to the Digital Tent Map
Authors: Nardo Lucas , Nepomuceno Erivelton , Muñoz Daniel , Butusov Denis , Arias-Garcia Janier
Published: April 2023

Digital chaotic systems used in various applications such as signal processing, artificial intelligence, and communications often suffer from the issue of dynamical degradation. This paper proposes a solution to address this problem in the digital tent map. Our proposed method includes a simple and optimized hardware architecture, along with a hardware-efficient perturbation method, to create a high-performance computing system that retains its chaotic properties. We implemented our proposed architecture using an FPGA (Field-Programmable Gate Array) and the 1’s complement fixed-point format. Our results demonstrate that the implemented digital circuit reduces logical resource consumption compared to state-of-the-art references and exhibits pseudo-random nature, as confirmed by various statistical tests. We validated our proposed pseudo-random number generator in a hardware architecture for particle swarm optimization, demonstrating its effectiveness.
A Survey on Parameters Affecting MANET Performance
Authors: Eltahlawy Ahmed M. , Aslan Heba K. , Abdallah Eslam G. , Elsayed Mahmoud Said , Jurcut Anca D. , Azer Marianne A.
Published: April 2023

A mobile ad hoc network (MANET) is an infrastructure-less network where mobile nodes can share information through wireless links without dedicated hardware that handles the network routing. MANETs’ nodes create on-the-fly connections with each other to share information, and they frequently join and leave MANET during run time. Therefore, flexibility in MANETs is needed to be able to handle variations in the number of existing network nodes. An effective routing protocol should be used to be able to route data packets within this dynamic network. Lacking centralized infrastructure in MANETs makes it harder to secure communication between network nodes, and this lack of infrastructure makes network nodes vulnerable to harmful attacks. Testbeds might be used to test MANETs under specific conditions, but researchers prefer to use simulators to obtain more flexibility and less cost during MANETs’ environment setup and testing. A MANET’s environment is dependent on the required scenario, and an appropriate choice of the used simulator that fulfills the researcher’s needs is very important. Furthermore, researchers need to define the simulation parameters and the other parameters required by the used routing protocol. In addition, if the MANET’s environment handles some conditions where malicious nodes perform network attacks, the parameters affecting the MANET from the attack perspective need to be understood. This paper collects environmental parameters that might be needed to be able to set up the required environment. To be able to evaluate the network’s performance under attack, different environmental parameters that evaluate the overall performance are also collected. A survey of the literature contribution is performed based on 50 recent papers. Comparison tables and statistical charts are created to show the literature contribution and the used parameters within the scope of the collected papers of our survey. Results show that the NS-2 simulator is the most popular simulator used in MANETs.
Differential Privacy High-Dimensional Data Publishing Based on Feature Selection and Clustering
Authors: Chu Zhiguang , He Jingsha , Zhang Xiaolei , Zhang Xing , Zhu Nafei
Published: April 2023

As a social information product, the privacy and usability of high-dimensional data are the core issues in the field of privacy protection. Feature selection is a commonly used dimensionality reduction processing technique for high-dimensional data. Some feature selection methods only process some of the features selected by the algorithm and do not take into account the information associated with the selected features, resulting in the usability of the final experimental results not being high. This paper proposes a hybrid method based on feature selection and a cluster analysis to solve the data utility and privacy problems of high-dimensional data in the actual publishing process. The proposed method is divided into three stages: (1) screening features; (2) analyzing the clustering of features; and (3) adaptive noise. This paper uses the Wisconsin Breast Cancer Diagnostic (WDBC) database from UCI’s Machine Learning Library. Using classification accuracy to evaluate the performance of the proposed method, the experiments show that the original data are processed by the algorithm in this paper while protecting the sensitive data information while retaining the contribution of the data to the diagnostic results.
Remanufacturing of Industrial Electronics: A Case Study from the GCC Region
Authors: Alkouh Ahmad , Keddar Kamel Abderrazak , Alatefi Saad
Published: April 2023

The Gulf Cooperation Council (GCC) region has witnessed significant growth in the global electrical and electronic equipment (EEE) market, especially in the industrial field due to the high demand from oil and gas and other related sectors. However, the lifespan of these end-of-life (EoL) products has become shorter, leading to electronic failure and generating electronic waste (e-waste). Disposing of such waste in recycling centers and landfills poses a challenge for policymakers and waste management officials due to its environmental impact. However, it is imperative to implement new management practices to overcome e-waste from landfills; thus, we propose the remanufacturing process as a viable and economic strategy for e-waste management. The process of industrial remanufacturing has the potential to decrease e-waste and promote the reuse of obsolete EEE and industrial devices., including those used in the Oil and Gas sector. This paper advocates for industrial remanufacturing as a solution to e-waste, aiming to increase the reusability of EoL EEE products. The authors provide a detailed analysis of the troubleshooting process and the tools employed, emphasizing the requirements for adopting this crucial remanufacturing solution. Moreover, the benefits of remanufacturing to industries and stakeholders are highlighted by offering a cost-effective alternative to replacing equipment, increasing the reusability of obsolete products, and reducing e-waste. By addressing the challenges of adopting remanufacturing, limitations and areas for future focus to enhance sustainability can be identified. Additionally, a comprehensive technical survey of the most common reasons for electronic failure at the board level demonstrates the feasibility and practicality of remanufacturing processes. These valuable insights reveal the possibility of realizing remanufacturing and guide technicians and stakeholders in implementing remanufacturing practices in various sectors, including oil and gas, petrochemicals, power generation, and factories. Lastly, by showcasing an example of a GCC region facility specializing in remanufacturing industrial electronic equipment, the potential contribution to a more sustainable future is emphasized,; this makes it easier to advocate for the adoption of remanufacturing as a more sustainable and economically viable approach in the industrial sector, particularly in oil and gas, for effective e-waste management.
A Fast VVC Intra Prediction Based on Gradient Analysis and Multi-Feature Fusion CNN
Authors: Jing Zhiyong , Zhu Wendi , Zhang Qiuwen
Published: April 2023

The Joint Video Exploration Team (JVET) has created the Versatile Video Coding Standard (VVC/H.266), the most up-to-date video coding standard, offering a broad selection of coding tools. The maturity of commercial VVC codecs can significantly reduce costs and improve coding efficiency. However, the latest video coding standards have introduced binomial and trinomial tree partitioning methods, which cause the coding units (CUs) to have various shapes, increasing the complexity of coding. This article proposes a technique to simplify VVC intra prediction through the use of gradient analysis and a multi-feature fusion CNN. The gradient of CUs is computed by employing the Sobel operator, the calculation results are used for predecision-making. Further decisions can be made by CNN for coding units that cannot be judged whether they should be segmented or not. We calculate the standard deviation (SD) and the initial depth as the input features of the CNN. To implement this method, the initial depth can be determined by constructing a segmented depth prediction dictionary. For the initial segmentation depth of the coding unit, regardless of its shape, it can also be determined by consulting the dictionary. The algorithm can determine whether to split CUs of varying sizes, decreasing the complexity of the CU division process and making VVC more practical. Experimental results demonstrate that the proposed algorithm can reduce encoding time by 36.56% with a minimal increase of 1.06% Bjøntegaard delta bit rate (BD-BR) compared to the original algorithm.
Content and Sentiment Analysis of The New York Times Coronavirus (2019-nCOV) Articles with Natural Language Processing (NLP) and Leximancer
Authors: Tunca Sezai , Sezen Bulent , Balcioglu Yavuz Selim
Published: April 2023

The purpose of this study was to prove the use of content and sentiment analysis to understand public discourse on Nytimes.com around the coronavirus (2019-nCOV) pandemic. We examined the pandemic discourses in the article contents, news, expert opinions, and statements of official institutions with natural language processing methods. We analyzed how the mainstream media (Nytimes.com) sets the community agenda. As a method, the textual data for the research were collected with the Orange3 software text-mining tool via the Nytimes.com API, and content analysis was conducted with Leximancer software. The research data were divided into three categories (first, mid, and last) based on the date ranges determined during the pandemic. Using Leximancer concept maps tools, we explained concepts and their relationships by visualizing them to show pandemic discourse. We used VADER sentiment analysis to analyze the pandemic discourse. The results gave us the distance and proximity positions of themes related to Nytimes.com pandemic discourse, revealed according to their conceptual definitions. Additionally, we compared the performance of six machine learning algorithms on the task of text classification. Considering the findings, it is possible to conclude that in Nytimes.com (2019-nCOV) discourse, some concepts have changed on a regular basis while others have remained constant. The pandemic discourse focused on specific concepts that were seen to guide human behavior and presented content that may cause anxiety to readers of Nytimes.com. The results of the sentiment analysis supported these findings. Another result was that the findings showed us that the contents of the coronavirus (2019-nCOV) articles supported official policies. It can be concluded that regarding the coronavirus (2019-nCOV), which has caused profound societal changes and has results such as death, restrictions, and mask use, the discourse did not go beyond a total of 15 main themes and about 100 concepts. The content analysis of Nytimes.com reveals that it has behavioral effects, such as causing fear and anxiety in people. Considering the media dependency of society, this result is important. It can be said that the agenda-setting of society does not go beyond the traditional discourse due to the tendency of individuals to use newspapers and news websites to obtain information.
Activity Recognition in Smart Homes via Feature-Rich Visual Extraction of Locomotion Traces
Authors: Zolfaghari Samaneh , Massa Silvia M. , Riboni Daniele
Published: April 2023

The proliferation of sensors in smart homes makes it possible to monitor human activities, routines, and complex behaviors in an unprecedented way. Hence, human activity recognition has gained increasing attention over the last few years as a tool to improve healthcare and well-being in several applications. However, most existing activity recognition systems rely on cameras or wearable sensors, which may be obtrusive and may invade the user’s privacy, especially at home. Moreover, extracting expressive features from a stream of data provided by heterogeneous smart-home sensors is still an open challenge. In this paper, we investigate a novel method to detect activities of daily living by exploiting unobtrusive smart-home sensors (i.e., passive infrared position sensors and sensors attached to everyday objects) and vision-based deep learning algorithms, without the use of cameras or wearable sensors. Our method relies on depicting the locomotion traces of the user and visual clues about their interaction with objects on a floor plan map of the home, and utilizes pre-trained deep convolutional neural networks to extract features for recognizing ongoing activity. One additional advantage of our method is its seamless extendibility with additional features based on the available sensor data. Extensive experiments with a real-world dataset and a comparison with state-of-the-art approaches demonstrate the effectiveness of our method.
Self-Supervised Learning for Online Anomaly Detection in High-Dimensional Data Streams
Authors: Mozaffari Mahsa , Doshi Keval , Yilmaz Yasin
Published: April 2023

In this paper, we address the problem of detecting and learning anomalies in high-dimensional data-streams in real-time. Following a data-driven approach, we propose an online and multivariate anomaly detection method that is suitable for the timely and accurate detection of anomalies. We propose our method for both semi-supervised and supervised settings. By combining the semi-supervised and supervised algorithms, we present a self-supervised online learning algorithm in which the semi-supervised algorithm trains the supervised algorithm to improve its detection performance over time. The methods are comprehensively analyzed in terms of computational complexity, asymptotic optimality, and false alarm rate. The performances of the proposed algorithms are also evaluated using real-world cybersecurity datasets, that show a significant improvement over the state-of-the-art results.
Clustered Federated Learning Based on Momentum Gradient Descent for Heterogeneous Data
Authors: Zhao Xiaoyi , Xie Ping , Xing Ling , Zhang Gaoyuan , Ma Huahong
Published: April 2023

Data heterogeneity may significantly deteriorate the performance of federated learning since the client’s data distribution is divergent. To mitigate this issue, an effective method is to partition these clients into suitable clusters. However, existing clustered federated learning is only based on the gradient descent method, which leads to poor convergence performance. To accelerate the convergence rate, this paper proposes clustered federated learning based on momentum gradient descent (CFL-MGD) by integrating momentum and cluster techniques. In CFL-MGD, scattered clients are partitioned into the same cluster when they have the same learning tasks. Meanwhile, each client in the same cluster utilizes their own private data to update local model parameters through the momentum gradient descent. Moreover, we present gradient averaging and model averaging for global aggregation, respectively. To understand the proposed algorithm, we also prove that CFL-MGD converges at an exponential rate for smooth and strongly convex loss functions. Finally, we validate the effectiveness of CFL-MGD on CIFAR-10 and MNIST datasets.
Smart Contracts-Based Demand Response Bidding Mechanism to Enhance the Load Aggregator Model in Thailand
Authors: Pinyo Anchisa , Bangviwat Athikom
Published: April 2023

In 2022, Thailand’s Demand Response (DR) business model was shifting from the Traditional Utility (TU) model to the Load Aggregator (LA) model in accordance with Thailand’s smart grid master plan. This research studied the current demand response model and mechanism to draw possible gaps in operations. This research deals with the data system owned by the individual load aggregator. The load aggregators collect meter data and evaluate demand adaptations before sending the results to claim compensation on behalf of their customers. This approach lacks transparency and facilitates distortion of the facts. Hence, this research introduces the data execution by smart contracts and data records on the blockchain that enhance transparent data sharing among multiple parties and maintain data integrity. Moreover, the proposed bidding algorithm allows customers to offer an expected price under the maximum incentive payment determined by the avoided costs of running the peaking power plants. Hence, the bidding helps reflect the DR operation costs on the customer side and control the budget for incentive payments. This study emphasized the smart contracts and decentralized application layer, so the public blockchain is a reasonable network for the test. However, implementation in real cases using the public blockchain requires careful considerations, such as network fees, transaction speeds, and the security of smart contract codes.
Diffusion Absorption Refrigeration Systems: An Overview of Thermal Mechanisms and Models
Authors: Mungyeko Bisulandu Baby-Jean Robert , Mansouri Rami , Ilinca Adrian
Published: April 2023

The energy transition, originating in the limitation of fossil resources and greenhouse gas (GHG) emission reduction, is the basis of many studies on renewable energies in different industrial applications. The diffusion absorption refrigeration machines are very promising insofar as they allow the use of renewable resources (solar, geothermal, waste gas, etc.). This technology is often considered an alternative to vapor compression systems in cooling and refrigeration applications. This paper aims to overview the thermal mechanisms related to modeling system energy sources and highlight the primary methodologies and techniques used. We study and analyze the technology’s current challenges and future directions and, finally, identify the gaps in the existing models to pave the way for future research. The paper also gives a classification of absorption refrigeration systems (ARS) to position and limit the scope of the study. The paper will help researchers who approach the various aspects to have a global synthetic analysis of the mechanisms characterizing the modeling of energy sources of absorption refrigeration machines.
Efficiency and Sustainability: The Role of Digitization in Re-Inhabiting the Existing Building Stock
Authors: Cinquepalmi Federico , Paris Spartaco , Pennacchia Elisa , Tiburcio Virginia Adele
Published: April 2023

Cities are complex and constantly evolving systems where changing social needs have always reshaped the built environment. Considering recent evolutionary trends in housing emergencies, amplified by the COVID-19 pandemic, and environmental sustainability goals, a rethinking of the building heritage is fundamental. This article aims to promote the conversion of buildings designed initially for nonresidential uses as a process and project strategy based on energy efficiency and a holistic and integrated vision of the circular economy. The methodological approach is based on two main phases: definition of evaluative parameters for the potential reuse of a building, and integration of the evaluation system in a BIM and GIS environment. The result is a tool for rapid automatic pre-evaluation of the potential conversion of a building into a residential space. Applying the developed methodology allows for a practical approach to the significant issue of sustainable construction, with particular attention to energy improvement and the reduction of environmental impact related to the construction of new buildings. The originality of the contribution lies in the systematization of various digital technologies to provide fundamental support for managing and transforming the varied and widespread unused real estate assets in a state of abandonment and degradation.
Wind Turbine Drivetrain Condition Monitoring through SCADA-Collected Temperature Data: Discussion of Selected Recent Papers
Authors: Astolfi Davide
Published: April 2023

Wind energy is going to be the leading renewable source of the next decades [...]
Greenhouse Gas Balance in the City of Reggio Calabria and Assessment of the Effects of Measures of Emission Reduction and Absorption
Authors: Marino Concettina , Nucara Antonino , Panzera Maria , Pietrafesa Matilde
Published: April 2023

Climate change and its ascertained attribution to anthropic activity need to be tackled with extreme urgency and must not be postponed. The need to reduce greenhouse gas emissions require a change in the energy paradigm, mainly affecting the construction and mobility sectors. Countries need to move towards climate neutrality with a widespread adoption of innovative models, systems, and technologies, reducing dependence on non-renewable sources and increasing energy efficiency. Measures to be adopted to achieve the prefixed decarbonization targets should involve, in addition to central governments, local communities. In this paper, in order to contribute to the definition of mitigation measures by the local administration, the balance of emissions and removal of greenhouse gases, assessed following the IPCC guidelines, for an Italian city (Reggio Calabria) on the date of 1 January 2020 is presented. Several hypotheses concerning reduction measures to be applied to the building and transport sectors, together with absorption interventions, have been developed and we have analyzed the effectiveness of each of them and their global effects on 1 January 2030. The proposed measures allow a reduction of 17% of total emissions, indicating that to reach carbon neutrality in 2050, more incisive and wide range interventions are required.
The Design and Fabrication of Multiple-Transmitter Coils and Single-Receiver Coils for a Wireless Power Transfer System to Charge a 3s LiPo Drone’s Battery
Authors: MohamedZain Ahmed O. , Hou Lee Wei , Chua Huangshen , Yap Kianmeng , Boon Lau Kim
Published: April 2023

Recent research has shown an increasing interest in wireless power transfer (WPT) technology for drone batteries. The inconvenience of wired charging, especially for drones, is a huge obstacle. In this research project, a WPT platform was proposed by applying four transmitting coils and a single receiving coil. To meet the industrial standards required for transmitter Tx and receiver Rx, a calculation of the parameters was implemented. An H-bridge MOSFET was used as a DC–AC inverter, a bridge diode was used as an AC–DC rectifier, and a Pi low pass filter was added to the receiver circuit design to filter the high-frequency noise. Experimental investigations were conducted to study the maximum power and power efficiency of the coil’s alignment. The focus of this article was to design and fabricate workable multiple-transmitter coils and a single-receiver coil for a wireless power transfer system, in order to charge a 3S LiPo drone’s battery. It not only covers an overview of wireless power transfer but also includes the method for charging a 3S LiPo drone’s battery, a misalignment study on the X and Y axes of the wireless charging system, and a stable charging of the battery that does not exceed the maximum current of 1.26 A for healthy charging. An efficiency of 58.29% was achieved at a power of 14.924 W and the minimum value was found to be 0.008 W. The efficiency of the typical coil design and the proposed coil design were 55.04% and 12.06%, respectively. The charging current obtained was 1.27 A, which gives an estimated charging time of 3.31 h based on the calculation. The actual charging time of the 3s 4200 mAh 11.1 V LiPo battery was 2 h.
3D Transient CFD Simulation of an In-Vessel Loss-of-Coolant Accident in the EU DEMO WCLL Breeding Blanket
Authors: Sprò Mauro , Froio Antonio , Zappatore Andrea
Published: April 2023

The in-vessel Loss-of-Coolant Accident (LOCA) is one of the design basis accidents in the design of the EU DEMO tokamak fusion reactor. System-level codes are typically employed to analyse the evolution of these transients. However, being based on a lumped approach, they are unable to quantify localised quantities of interest, such as local pressure peaks on the vacuum vessel walls, to which the failure criteria are linked. To calculate local quantities, the 3D nature of the phenomenon needs to be considered. In this work, a 3D transient model of the in-vessel LOCA from a water-cooled blanket is developed. The model is implemented in the commercial CFD software STAR-CCM+. It simulates the propagation of the water jet in the vessel from the beginning of the accident, thus accounting for the phase change of the water, i.e., from the pressurised liquid phase to the vapour phase inside the vessel, being the latter at a much lower pressure than in the blanket coolant pipes. Due to the large pressure ratio (>1000), shocks are expected; therefore, an Adaptive Mesh Refinement (AMR) algorithm is employed. The physical models (in particular, the multiphase model) are benchmarked to a 2D reference problem before being applied to the 3D EU DEMO-relevant problem. The simulation results show that the pressure peaks in front of the vessel walls are not dangerous as they are below the design limit. The entire evolution of the water jet is followed up to the opening of the burst disks, in order to compare the average pressure evolution with that computed with system-level codes. A comparison with the in-vessel LOCA from a helium-cooled blanket is also carried out, showing that the accident evolution in the water case is less violent than in the helium case.
A Study of a Generalized Photovoltaic System with MPPT Using Perturb and Observer Algorithms under Varying Conditions
Authors: Ali Zulfiqar , Abbas Syed , Mahmood Anzar , Ali Syed , Javed Syed , Su Chun-Lien
Published: April 2023

In recent years, renewable energy (RE) has shown promise as a sustainable solution to the rising energy demand worldwide. Photovoltaic (PV) technology has emerged as a highly viable RE alternative. The majority of PV schemes use specific PV models with specified parameters. This study proposes a PV model with generic specifications, a PV array, a DC/DC converter, a DC/AC inverter, maximum power point tracking (MPPT), and grid synchronization using a feedback control system under the MATLAB/Simulink environment. Various MPPT techniques have been adapted to track the PV’s maximum power point (MPP); however, there are various uncertainties. To address these challenges, this paper presented a perturb and observe (P&O) strategy to track the MPP of PV systems reliably. The MPP of a PV system varies according to meteorological order, such as solar radiation and cell temperature. The MPPT primarily gathers the maximum current and voltage of the PV array and provides them to the load using a boost converter. The MPPT performance and PV array attributes are analyzed during abrupt weather changes. Finally, a feedback controller is configured to perform synchronization of the inverter with the grid. The validity and reliability of the PV module using P&O methods provide a higher efficacy of MPPT under MATLAB/simulation. Finally, the presented results endorse the strength of the proposed technique.
Development of Surface Mining 4.0 in Terms of Technological Shock in Energy Transition: A Review
Authors: Zhironkin Sergey , Taran Ekaterina
Published: April 2023

The expansion of end-to-end Industry 4.0 technologies in various industries has caused a technological shock in the mineral resource sector, wherein itsdigital maturity is lower than in the manufacturing sector. As a result of the shock, the productivity and profitability of raw materials extraction has begun to lag behind the industries of its deep processing, which, in the conditions of volatile raw materials markets, can provoke sectoral crises. The diffusion of Industry 4.0 technologies in the mining sector (Mining 4.0) can prevent a technological shock if they are implemented in all segments, including quarrying (Surface Mining 4.0). The Surface Mining 4.0 technological platform would connect the advanced achievements of the Fourth Industrial Revolution (end-to-end digital artificial intelligence technologies, cyber-physical systems and unmanned production with traditional geotechnology) without canceling them, but instead bringing them to a new level of productivity, resource consumption, and environmental friendliness. In the future, the development of Surface Mining 4.0 will provide a response to the technological shock associated with the acceleration of the digital modernization of the mining sector and the increase in labor productivity, which are reducing the operating costs of raw materials extraction. In this regard, the given review is an attempt to analyze the surface mining digital transformation over the course of the diffusion of Industry 4.0 technologies covered in scientific publications. The authors tried to show the core and frontiers of Surface Mining 4.0 development to determine the production, economic, and social effect of replacing humans with digital and cyber-physical systems in the processes of mineral extraction. Particular attention was paid to the review of research on the role of Surface Mining 4.0 in achieving sustainable development goals.
Steady-State Load Flow Model of DFIG Wind Turbine Based on Generator Power Loss Calculation
Authors: Gianto Rudy , Purwoharjono  , Imansyah Fitri , Kurnianto Rudi , Danial
Published: April 2023

Penetration of wind power plants (WPPs) in the electric power system will complicate the system load flow analysis. Consequently, the traditional load flow algorithm can no longer be used to find the solution to the load flow problem of such a system. This paper proposes a doubly fed induction generator (DFIG)-based WPP model for a load flow analysis of the electric power system. The proposed model is derived based on the power formulations of the WPP—namely, DFIG power, DFIG power loss, and WPP power output formulas. The model can be applied to various DFIG power factor operating modes. In the present paper, applications of the proposed methods in two representative electric power systems (i.e., IEEE 14-bus and 30-bus systems) have been investigated. The investigation results verify the proposed method’s capability to solve the load flow problem of the system embedded with DFIG-based variable-speed WPPs.
Green Anisole Solvent-Based Synthesis and Deposition of Phthalocyanine Dopant-Free Hole-Transport Materials for Perovskite Solar Cells
Authors: Podapangi Suresh K. , Mancini Laura , Xu Jie , Reddy Sathy Harshavardhan , Di Carlo Aldo , Brown Thomas M. , Zanotti Gloria
Published: April 2023

Perovskite Solar Cells (PSCs) have attracted attention due to their low cost, easy solution processability, high efficiency, and scalability. However, the benchmark expensive hole transport material (HTM) 2,2′,7,7′-tetrakis[N, N-di(4-methoxyphenyl)amino]-9,9′-spirobifluorene (Spiro-MeOTAD), which is traditionally solution-processed with toxic solvents such as chlorobenzene (CB), dichlorobenzene (DCB), or toluene, is a bottleneck. To address this issue, this work investigates the implementation of Zn(II), Cu(II), or Co(II) tetra-tert-butylphthalocyanines (TBU4-Cu, TBU4-Zn, TBU4-Co), established macrocyclic derivatives whose synthesis and processing inside the devices have been redesigned to be more environmentally sustainable and cost-effective by substituting conventional solvents with greener alternatives such as anisole, propane-1,2-diol, and their mixture, as dopant-free HTMs in planar n-i-p PSCs. The anisole-processed HTMs provided power conversion efficiencies (PCE) up to 12.27% for TBU4-Cu and 11.73% for TBU4-Zn, with better photovoltaic parameters than the corresponding cells made with chlorobenzene for which the best results obtained were, respectively, 12.22% and 10.81%.
RETRACTED: Supervisory Control and Data Acquisition for Fault Diagnosis of Wind Turbines via Deep Transfer Learning
Authors: Simani Silvio , Farsoni Saverio , Castaldi Paolo
Published: April 2023

The installed wind power capacity is growing worldwide. Remote condition monitoring of wind turbines is employed to achieve higher up-times and lower maintenance costs. Machine learning approaches can be used for detecting developing faults in wind turbines in their earlier occurrence. However, training fault detection models may require large amounts of past and present data. These data are often not available or not representative of the current operation behaviour. These data can be acquired with supervisory control and data acquisition systems. Note also that newly commissioned wind farms lack data from previous operation, whilst older installations may also lack representative working condition data as a result of control software updates or component replacements. After such events, a turbine’s operation behaviour can change significantly so its data are no longer representative of its current behaviour. Therefore, this paper shows that cross–turbine transfer learning can improve the accuracy of fault detection models in turbines with scarce data from supervisory control and data acquisition systems. In particular, it highlights that combining the knowledge from turbines with scarce data and turbines with plentiful data enables earlier detection of faults than prior art methods. In this way, the reuse and the knowledge transfer across wind turbines allows us to overcome this lack of data, thus enabling accurate fault detection in wind turbines.
Neuromodel of an Eddy Current Brake for Load Emulation
Authors: Gulbahce Mehmet Onur
Published: April 2023

The eddy current brake (ECB) is an electromechanical energy conversion device that can be used as a load emulator to load a motor according to the intended load scenario. However, conducting an analysis in the time domain is difficult due to its complex behavior involving mechanical, electrical, and magnetic phenomena. The challenges with the time domain analysis of the ECB require new modeling approaches that provide reliability, robustness, and controllability over a wide speed interval. If the ECB can be modeled with high accuracy, it can be controlled like a load emulator that can simulate nonlinear industrial loads. This paper describes a neuromodeling approach taken to develop an ECB. The nonlinear characteristic of the brake system was modeled with a high performance by using an artificial neural network (ANN), which is a potent nonlinear system identification tool. Several characteristics of a designed and optimized brake system undergoing various excitation currents in whole speed regions are described and verified experimentally. Eventually, an electromechanical brake system is proposed that aims to provide the required linear or nonlinear load model dynamics throughout an emulation process in line with the obtained neuromodel. To identify the most suitable ANN architecture for the problem, various ANN configurations, ranging from 1 neuron to 20 neurons in the hidden layer, as well as a statistical approach that differs from the existing literature, are presented. Additionally, the suggested model’s scalability is discussed.
Numerical Simulation on the Safety and Quality of Cementing by Using Pad Fluid in Horizontal Wells
Authors: Qi Ben , Fu Jiawen , Sun Jinfei , Li Zaoyuan , Yang Xin , Yang Fujie , Wu Xuning
Published: April 2023

The failure of wellbore sealing will cause leakage of greenhouse gases, such as carbon dioxide and methane, which will harm oil and gas recovery and environmental safety. Cementing is an important part of wellbore sealing. Only good cementing can keep the wellbore seal for a long time and improve the well life. In this study, we considered the construction of a horizontal shale oil well in eastern China as the background and analysed the rheological properties of the annulus fluid. We developed a displacement motion model and a calculation model for the annulus dynamic equivalent circulation density, and numerical simulations were used to study the impact of the dosage and injection sequence of the pad fluid on the displacement efficiency and annulus dynamic equivalent circulation density. The results show that when the pad fluid is composed completely of flushing fluid, the displacement performance is better than that of the spacer. By increasing the dosage of the flushing fluid from 0.3 times the annular volume to 1.0 times, the displacement efficiency can be increased by 3.3%, and the retention of the drilling fluid is also reduced by 3.6%. However, it can lead to a significant reduction in the annulus dynamic equivalent circulation density and increase in the risk of leakage. After adding the spacer, the structure of the flushing fluid–spacer provides the optimal injection sequence. Considering the application status in the field example well, it was shown that it can not only ensure the safety of cementing operations, but also improve the displacement efficiency. The results of this study have important theoretical significance and application value and can provide guidance for the optimisation design of the engineering scheme.
Local Frequency Modulation Strategy Based on Controllable Load Characteristic Identification of Multi-Port Power Router
Authors: Lv Changhao , Jia Qingquan , Lin Lijuan , Cui Jinwei
Published: April 2023

The scarcity of inertial resources in the new AC–DC hybrid grids makes the grid frequency prone to fluctuation. In this paper, the relationship between the grid-side and load-side characteristics of the power router is constructed. By adjusting the port load parameters, the load power can respond quickly to the fluctuation of the grid frequency, thereby realizing rapid support of the grid frequency. Firstly, two kinds of mathematical models for sensitivity identification of load characteristics, variable voltage and variable frequency, are established to calculate the characteristic parameters of a multi-port load. The allocation rules of port power and allocation coefficients are designed according to the parameters. A frequency modulation control method that matches the load response capability of the multi-port router is proposed. Then, taking into consideration the uncertainty of load group characteristics and power, a variable coefficient frequency modulation control strategy for a multi-port power router that can adapt to the adjustable margin of loads is proposed. The proposed model is built based on a Simulink platform for validation. The simulation results show that the proposed frequency modulation strategy can be added, and the frequency modulation performance of the power grid is further improved compared to the situation without this method. The frequency is suppressed to 49.93 HZ. It is verified that this method can make the controllable load respond sensitively and effectively to grid disturbance.
Interpretable Predictive Modeling of Tight Gas Well Productivity with SHAP and LIME Techniques
Authors: Ma Xianlin , Hou Mengyao , Zhan Jie , Liu Zhenzhi
Published: April 2023

Accurately predicting well productivity is crucial for optimizing gas production and maximizing recovery from tight gas reservoirs. Machine learning (ML) techniques have been applied to build predictive models for the well productivity, but their high complexity and low interpretability can hinder their practical application. This study proposes using interpretable ML solutions, SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), to provide explicit explanations of the ML prediction model. The study uses data from the Eastern Sulige tight gas field in the Ordos Basin, China, containing various geological and engineering factors. The results show that the gradient boosting decision tree model exhibits superior predictive performance compared to other ML models. The global interpretation using SHAP provides insights into the overall impact of these factors, while the local interpretation using SHAP and LIME offers individualized explanations of well productivity predictions. These results can facilitate improvements in well operations and field development planning, providing a better understanding of the underlying physical processes and supporting more informed and effective decision-making. Ultimately, this study demonstrates the potential of interpretable ML solutions to address the challenges of forecasting well productivity in tight gas reservoirs and enable more efficient and sustainable gas production.
Negative Impacts of Trace Metal Contamination on the Macrobenthic Communities along the Santos Port Complex—Brazil
Authors: Delgado Jéssica de F. , Amorim Renan M. , Lima Leonardo da S. , Gaylarde Christine C. , Neto José Antônio Baptista , Pinto Samira C. de S. , Gonçalves Beatriz F. dos S. , Fonseca Estefan M. da
Published: April 2023

Port sites represent one of the most impacted coastal areas; this impact is due to intensive anthropogenic pressures. In addition to the port complex itself, associated activities, such as indiscriminate disposal of pollutants, including trace metals, affect the local ecosystem. Macroinvertebrate benthic communities are one of the most effective bioindicators of environmental health because of their importance as a primary food source for many fish, birds, and mammals, as well as their influence on sediment stability and geochemical composition. This article evaluates the benthic macrofauna in the Santos Estuarine System (SES), the location of the Santos Port Complex (SPC), linking trace metal levels to differences in microbenthic community structure and pollutant bioavailability. The distribution of Cd, Ni, and Pb was directly related to organic matter deposits, while Cu and Zn appeared to result from port activities. The SES contained a poor benthic macroinvertebrate community, resulting from the contaminated muddy sediments. A significant negative correlation was found between the macrobenthic diversity and concentrations of Cu in the soluble phase; this implied the pollution-induced degradation of the macrobenthos in SES.
Identifying Risk Factors and Evaluating Occupational Safety in South Korean Forestry Sector
Authors: Cho Min-Jae , Choi Yun-Sung , Lee Eunjai
Published: April 2023

The forestry industry poses a higher risk of occupational accidents than other sectors, and understanding the occurrence and characteristics of injuries and fatalities is crucial for improving the safety and health of workers and establishing future strategies. In South Korea, although occupational injuries have declined since 2010, the number of occupational accidents in forestry remains higher than that in other sectors. Occupational injuries and fatalities present considerable challenges, and thus, our objective was to analyze and compare forestry accidents between 2010 and 2020. The results indicate that the highest average number of injuries occurred among workers older than 51 years and those with less than 6 months of experience. Timber harvesting and silviculture are among the most dangerous operations in forestry. Based on the analyzed dataset, amputation and laceration injuries, as well as slip and trip injuries, were the most common, accounting for 29.2% and 22.3% of total annual occupational accidents, respectively. These results may be explained by the labor-intensive, steep terrain operations in forestry. Our findings represent an essential resource for improving worker safety and health.
Examining the Stand Level CO2 Fluxes of Spring Forest Geophytes
Authors: Czóbel Szilárd , Saláta Dénes , Baltazár Tivadar , Trenyik Petra , Szirmai Orsolya
Published: April 2023

Spring forest ephemerals often create homogeneous patches in the understory; however, our knowledge about their stand level characteristics is deficient. Our aims were to examine, parallel to their phenology, the stand level Net Ecosystem CO2 Exchange (NEE) and evapotranspiration (ET) fluxes as well as the dependence of NEE on leaf area (LA), air temperature (Tair) and light (PPFD) in three spring forest geophytes that are widespread in Europe. Furthermore, we compared the leaf and stand level net photosynthesis. The methods used included open chamber measurements with an infrared gas analyser in permanent plots on a weekly basis. The results showed that the stand levels of all three species proved to be carbon sinks from the beginning of the vegetation period until the end of it or until the last phase of fruit formation. The largest amount of carbon sink was observed at the peak of blooming. A positive linear correlation was measured between NEE and PPFD as well as between NEE and LA, while a negative linear regression was measured between NEE and Tair. The remarkable carbon uptake capacity indicates the non-negligible role of geophyte vegetation in the carbon flux of temperate forests. In addition, the research provided new proof about the role of stand level operation, stability and regulation.
Life History Traits of the Pentatomidae (Hemiptera) for the Development of Pest Management Tools
Authors: Pal Elisa , Allison Jeremy D. , Hurley Brett P. , Slippers Bernard , Fourie Gerda
Published: April 2023

Knowledge of the biology of a pest is essential for building sustainable management programmes. Pentatomidae have a hemimetabolous life cycle with egg, nymphal, and adult life stages, which differ in morphological, ecological, and behavioural traits. Some of these traits, such as mating behaviour, pheromones (alarm and aggregation pheromones) and the acquisition of gut symbionts can be targeted for pest management strategies. Here, we review the available literature on these life history traits of the Pentatomidae with potential for use in management programmes. Pheromone-mediated aggregation and the disruption of symbiont acquisition are two important targets for Pentatomidae control. Other traits such as the use of alarm pheromones for enhancing natural enemies and substrate-borne vibration for mating disruption deserve further consideration. Colour vision and flight ability are still poorly studied, despite their potential importance for stink bug management.