bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel Deep Learning-Based Intrusion Detection System for IoT Networks
Authors: Awajan Albara
Published: February 2023

The impressive growth rate of the Internet of Things (IoT) has drawn the attention of cybercriminals more than ever. The growing number of cyber-attacks on IoT devices and intermediate communication media backs the claim. Attacks on IoT, if they remain undetected for an extended period, cause severe service interruption resulting in financial loss. It also imposes the threat of identity protection. Detecting intrusion on IoT devices in real-time is essential to make IoT-enabled services reliable, secure, and profitable. This paper presents a novel Deep Learning (DL)-based intrusion detection system for IoT devices. This intelligent system uses a four-layer deep Fully Connected (FC) network architecture to detect malicious traffic that may initiate attacks on connected IoT devices. The proposed system has been developed as a communication protocol-independent system to reduce deployment complexities. The proposed system demonstrates reliable performance for simulated and real intrusions during the experimental performance analysis. It detects the Blackhole, Distributed Denial of Service, Opportunistic Service, Sinkhole, and Workhole attacks with an average accuracy of 93.74%. The proposed intrusion detection system’s precision, recall, and F1-score are 93.71%, 93.82%, and 93.47%, respectively, on average. This innovative deep learning-based IDS maintains a 93.21% average detection rate which is satisfactory for improving the security of IoT networks.
Temozolomide Chronotherapy in Glioma: A Systematic Review
Authors: Jia Jason L. , Alshamsan Bader , Ng Terry L.
Published: February 2023

Outcomes for patients with high-grade glioma remain poor. Temozolomide (TMZ) is the only drug approved for first-line treatment of glioblastoma multiforme, the most aggressive form of glioma. Chronotherapy highlights the potential benefit of timed TMZ administration. This is based on pre-clinical studies of enhanced TMZ-induced glioma cytotoxicity dependent on circadian, oscillating expression of key genes involved in apoptosis, DNA damage repair, and cell-cycle mediated cell death. The current systematic review’s primary aim was to evaluate the efficacy and toxicity of TMZ chronotherapy. A systemic review of literature following PRISMA guidelines looking at clinical outcomes on TMZ chronotherapy on gliomas was performed. The search in the English language included three databases (PubMed, EMBASE, and Cochrane) and five conferences from 1946 to April 2022. Two independent reviewers undertook screening, data extraction, and risk-of-bias assessment. A descriptive analysis was conducted due to limited data. Of the 269 articles screened, two unique studies were eligible and underwent abstraction for survival and toxicity findings. Both studies—one a retrospective cohort study (n = 166) and the other a prospective randomized feasibility study (n = 35)—were conducted by the same academic group and suggested a trend for improved overall survival, but possibly increased toxicity when TMZ was administered in the morning (vs. evening). There was limited evidence suggesting possible therapeutic value from administering TMZ in the morning, which may be consistent with the pre-clinical observations of the importance of the timing of TMZ administration in vitro. Larger, pragmatic, prospective randomized controlled trials are needed to ascertain the value of TMZ chronotherapy to provide optimized and equitable care for this population.
Anti-Screenshot Watermarking Algorithm for Archival Image Based on Deep Learning Model
Authors: Gu Wei , Chang Ching-Chun , Bai Yu , Fan Yunyuan , Tao Liang , Li Li
Published: February 2023

Over recent years, there are an increasing number of incidents in which archival images have been ripped. Leak tracking is one of the key problems for anti-screenshot digital watermarking of archival images. Most of the existing algorithms suffer from low detection rate of watermark, because the archival images have a single texture. In this paper, we propose an anti-screenshot watermarking algorithm for archival images based on Deep Learning Model (DLM). At present, screenshot image watermarking algorithms based on DLM can resist screenshot attacks. However, if these algorithms are applied on archival images, the bit error rate (BER) of the image watermark will increase dramatically. Archival images are ubiquitous, so in order to improve the robustness of archival image anti-screenshot, we propose a screenshot DLM “ScreenNet”. It aims to enhance the background and enrich the texture with style transfer. Firstly, a preprocessing process based on style transfer is added before the insertion of an archival image into the encoder to reduce the influence of the screenshot process of the cover image. Secondly, the ripped images are usually moiréd, so we generate a database of ripped archival images with moiréd by means of moiréd networks. Finally, the watermark information is encoded/decoded through the improved ScreenNet model using the ripped archive database as the noise layer. The experiments prove that the proposed algorithm is able to resist anti-screenshot attacks and achieves the ability to detect watermark information to leak the trace of ripped images.
Learning Pathways and Students Performance: A Dynamic Complex System
Authors: Ortiz-Vilchis Pilar , Ramirez-Arellano Aldo
Published: February 2023

In this study, learning pathways are modelled by networks constructed from the log data of student–LMS interactions. These networks capture the sequence of reviewing the learning materials by the students enrolled in a given course. In previous research, the networks of successful students showed a fractal property; meanwhile, the networks of students who failed showed an exponential pattern. This research aims to provide empirical evidence that students’ learning pathways have the properties of emergence and non-additivity from a macro level; meanwhile, equifinality (same end of learning process but different learning pathways) is presented at a micro level. Furthermore, the learning pathways of 422 students enrolled in a blended course are classified according to learning performance. These individual learning pathways are modelled by networks from which the relevant learning activities (nodes) are extracted in a sequence by a fractal-based method. The fractal method reduces the number of nodes to be considered relevant. A deep learning network classifies these sequences of each student into passed or failed. The results show that the accuracy of the prediction of the learning performance was 94%, the area under the receiver operating characteristic curve was 97%, and the Matthews correlation was 88%, showing that deep learning networks can model equifinality in complex systems.
Random Walks on Networks with Centrality-Based Stochastic Resetting
Authors: Zelenkovski Kiril , Sandev Trifce , Metzler Ralf , Kocarev Ljupco , Basnarkov Lasko
Published: February 2023

We introduce a refined way to diffusely explore complex networks with stochastic resetting where the resetting site is derived from node centrality measures. This approach differs from previous ones, since it not only allows the random walker with a certain probability to jump from the current node to a deliberately chosen resetting node, rather it enables the walker to jump to the node that can reach all other nodes faster. Following this strategy, we consider the resetting site to be the geometric center, the node that minimizes the average travel time to all the other nodes. Using the established Markov chain theory, we calculate the Global Mean First Passage Time (GMFPT) to determine the search performance of the random walk with resetting for different resetting node candidates individually. Furthermore, we compare which nodes are better resetting node sites by comparing the GMFPT for each node. We study this approach for different topologies of generic and real-life networks. We show that, for directed networks extracted for real-life relationships, this centrality focused resetting can improve the search to a greater extent than for the generated undirected networks. This resetting to the center advocated here can minimize the average travel time to all other nodes in real networks as well. We also present a relationship between the longest shortest path (the diameter), the average node degree and the GMFPT when the starting node is the center. We show that, for undirected scale-free networks, stochastic resetting is effective only for networks that are extremely sparse with tree-like structures as they have larger diameters and smaller average node degrees. For directed networks, the resetting is beneficial even for networks that have loops. The numerical results are confirmed by analytic solutions. Our study demonstrates that the proposed random walk approach with resetting based on centrality measures reduces the memoryless search time for targets in the examined network topologies.
The Metastable State of Fermi–Pasta–Ulam–Tsingou Models
Authors: Reiss Kevin A. , Campbell David K.
Published: February 2023

Classical statistical mechanics has long relied on assumptions such as the equipartition theorem to understand the behavior of the complicated systems of many particles. The successes of this approach are well known, but there are also many well-known issues with classical theories. For some of these, the introduction of quantum mechanics is necessary, e.g., the ultraviolet catastrophe. However, more recently, the validity of assumptions such as the equipartition of energy in classical systems was called into question. For instance, a detailed analysis of a simplified model for blackbody radiation was apparently able to deduce the Stefan–Boltzmann law using purely classical statistical mechanics. This novel approach involved a careful analysis of a “metastable” state which greatly delays the approach to equilibrium. In this paper, we perform a broad analysis of such a metastable state in the classical Fermi–Pasta–Ulam–Tsingou (FPUT) models. We treat both the α-FPUT and β-FPUT models, exploring both quantitative and qualitative behavior. After introducing the models, we validate our methodology by reproducing the well-known FPUT recurrences in both models and confirming earlier results on how the strength of the recurrences depends on a single system parameter. We establish that the metastable state in the FPUT models can be defined by using a single degree-of-freedom measure—the spectral entropy (η)—and show that this measure has the power to quantify the distance from equipartition. For the α-FPUT model, a comparison to the integrable Toda lattice allows us to define rather clearly the lifetime of the metastable state for the standard initial conditions. We next devise a method to measure the lifetime of the metastable state tm in the α-FPUT model that reduces the sensitivity to the exact initial conditions. Our procedure involves averaging over random initial phases in the plane of initial conditions, the P1-Q1 plane. Applying this procedure gives us a power-law scaling for tm, with the important result that the power laws for different system sizes collapse down to the same exponent as Eα2→0. We examine the energy spectrum E(k) over time in the α-FPUT model and again compare the results to those of the Toda model. This analysis tentatively supports a method for an irreversible energy dissipation process suggested by Onorato et al.: four-wave and six-wave resonances as described by the “wave turbulence” theory. We next apply a similar approach to the β-FPUT model. Here, we explore in particular the different behavior for the two different signs of β. Finally, we describe a procedure for calculating tm in the β-FPUT model, a very different task than for the α-FPUT model, because the β-FPUT model is not a truncation of an integrable nonlinear model.
Exploring The Role of High Government Debt on Economic Growth: A Nonlinearity and Threshold Analysis for Africa’s Developing Countries
Authors: Mqolombeni Nokwanda , Tewari Devi Datt , Ilesanmi Kehinde Damilola
Published: February 2023

Purpose: This paper empirically investigated the impact of high government debt on economic growth, using a panel data of 12 developing countries in Africa for the period between 1991 and 2020. Furthermore, it sought to find the existence of nonlinearity between government debt and economic growth. The study used a cross-country panel data approach estimated through the panel smooth transition regression to find the threshold effect. The results reveal a threshold of 60.5% of government debt on economic growth, suggesting an inverted U-shaped relationship between government debt and economic growth for the whole sample. After this threshold, additional government debt starts impeding economic growth. The estimated slope parameter of 18.11 supports the smoothness of government debt from a low regime to a high regime of debt. In the lower debt regime, government debt boosts economic growth; however as the level of debt growth surpasses the peak point, economic growth decreases. By implication, policymakers should have strict debt management policies in place to keep the level of government debt low, and be able to respond robustly to an economic shock. While resorting to borrowing to finance public spending, especially during economic crises, may be an imperative, it should be done in a circumspect manner so that the borrowings are kept at tolerable levels and reduced and/or repaid when there is a recovery in the economy.
Microgrids Imitate Nature for Improved Performance—Use of Nature-Inspired Optimization Techniques in Future Power Systems
Authors: Ustun Taha Selim
Published: February 2023

There is a constant push towards increasing use of renewable energy-based distributed generators around the globe. While they provide a clean and sustainable source of energy, they employ technologies that are unknown to traditional power systems. These generators are interfaced via inverters that lack the inertia of large synchronous machines. This manifests itself as a more volatile frequency profile that is susceptible to disturbances. This phenomenon is more amplified in stand-alone microgrids which are utilized as a popular electrification alternative in isolated or underserved communities. One solution approach takes its inspiration from nature, e.g., behavior of bees, butterflies, or ants. When employed in a suitable way, animals’ natural behavior helps optimize interaction between different renewable-energy based generators and create a more stable microgrid. There are different approaches to stabilizing such systems with novel optimization approaches. Some of them optimize the ratio between generators that utilize rotating machines and inverters. Penetration of renewable energy generation is about increasing the share of inverter-interfaced generators in the system without causing stability issues. Since renewable energy resources are intermittent and not dispatchable, it is important to create a diverse portfolio where the overall system achieves some stability. For instance, if a local grid is fed by PV panels, wind generation and a small-scale hydroelectric power plant, the varying nature of these resources may complement each other. On a sunny day, PV output might be very high, and wind may not be so significant. On the other hand, on a rainy day, clouds may reduce PV output while precipitation may feed the local hydro power plant. Similarly, wind generation might complement others on a windy day. While the idea is easy to comprehend qualitatively, finding the correct ratio is not trivial. Furthermore, there are many factors at play that are independently changing and impacting the outcome. For different sites, the available renewable energy resources, their profiles as well as the local load conditions would be different. Therefore, a systematic approach is required to optimize these systems at planning, operation and control levels. Nature-inspired optimization algorithms seem to have an edge in doing just that.
Tool Chain for Deriving Consistent Storage Model Parameters for Optimization Models
Authors: Wode Kristin , Strube Tom , Schischke Eva , Hadam Markus , Pabst Sarah , Mittreiter Annedore
Published: February 2023

Since existing energy system models often represent storage behavior in a simplified way, in this work, a tool chain for deriving consistent storage model parameters for optimization models is developed. The aim of our research work is to identify what are non-negligible influences on the the technical characteristics and dynamic behavior of the storage, to quantify the effect of these influences, and represent these effects in the model. This paper describes the developed tool chain and presents its application using an example. The tool chain consists of the steps “parameter screening”, “dynamic simulation”, “regression analysis” and “refining optimization model”. It is investigated which parameters have an influence on the storage system (here pumped hydroelectric energy storage (PHES)), how the storage behavior is modeled, which influencing factors have a measurable effect on the system, and how these findings can be integrated into optimization models. The main finding is that in the case of PHES, the dependency of the charging and discharging efficiency on the power is significant, but no further influencing factor has to be considered for accurate modeling (0.946 ≤ R2 ≤ 0.988) of the efficiency. It is concluded that the presented toolchain is suitable for other storage technologies as well, including the analysis of aging behavior.
Temperature Field Analysis and Cooling Structure Optimization for Integrated Permanent Magnet In-Wheel Motor Based on Electromagnetic-Thermal Coupling
Authors: Wang Qiang , Li Rui , Zhao Ziliang , Liang Kui , Xu Wei , Zhao Pingping
Published: February 2023

Aiming at the impact of heat generation and temperature rise on the driving performance of a permanent magnet (PM) motor, taking the PM in-wheel motor (IWM) for electric vehicles as an object, research is conducted into the temperature distribution of the electromagnetic–thermal effect and cooling structure optimization. Firstly, the electromagnetic–thermal coupling model considering electromagnetic harmonics is established using the subdomain model and Bertotti’s iron loss separation theory. Combined with the finite element (FE) simulation model established by Ansoft Maxwell software platform, the winding copper loss, stator core loss and PM eddy current loss under the action of complex magnetic flux are analyzed, and the transient temperature distribution of each component is obtained through coupling. Secondarily, the influence of the waterway structure parameters on the heat dissipation effect of the PM-IWM is analyzed by the thermal-fluid coupled relationship. On the basis, the optimization design of waterway structure parameters is carried out to improve the heat dissipation effect of the cooling system based on the proposed chaotic mapping ant colony algorithm with metropolis criterion. The comparison before and after optimization shows that the temperature of key components is significantly improved, the average convection heat transfer coefficient (CHTC) is increased by 23.57%, the peak temperature of stator is reduced from 95.47 °C to 82.73 °C, and the peak temperature of PM is decreased by 14.26%, thus the demagnetization risk in the PM is improved comprehensively. The research results can provide some theoretical and technical support for the structural optimization of water-cooled dissipation in the PM motor.
An Analysis of Energy Consumption in Small- and Medium-Sized Buildings
Authors: Kampik Marian , Fice Marcin , Pilśniak Adam , Bodzek Krzysztof , Piaskowy Anna
Published: February 2023

Building energy efficiency has grown strong in a context of soaring energy prices, especially in Europe. The use of energy-saving devices strongly influences its improvement, but in many cases, it is far from sufficient., especially if the energy comes from renewable sources with forced production. In the case of buildings, these are usually photovoltaic (PV) sources. For this reason, energy management systems (EMS) are becoming increasingly popular as they allow the increase in self-consumption and reduce the size of energy storage. This article presents analyses of historical energy consumption profiles in selected small- and medium-sized buildings powered by renewable energy sources. The implementation limitations of this type of systems, depending on the profile of the building, were identified and guidelines were presented to assess low-cost solutions dedicated to small buildings and considering the actual conditions of existing systems. Statistical analyzes were conducted for the energy demand profiles of 15 different buildings. The analyzes consisted of the preparation of box plots for each hour of working days and the calculation of the relative standard deviation (RSD) index for annual profiles of 60 min periods. The analyzes showed that the RSD index has low values for commercial buildings (e.g., hospital 7% and bank 15%) and very high values for residential buildings—even over 100%. On this basis, it can be concluded about the usefulness of energy profiles for demand forecasting. The novelty of the proposed method is to examine the possibility of using measurement data as data to forecast energy consumption based on statistical analysis, dedicated to low-cost EMS system solutions.
Kinetics of Oxygen Reduction Reaction of Polymer-Coated MWCNT-Supported Pt-Based Electrocatalysts for High-Temperature PEM Fuel Cell
Authors: Haque Md Ahsanul , Rahman Md Mahbubur , Islam Faridul , Sulong Abu Bakar , Shyuan Loh Kee , Rosli Ros emilia , Chakraborty Ashok Kumar , Haider Julfikar
Published: February 2023

Sluggish oxygen reduction reaction (ORR) of electrodes is one of the main challenges in fuel cell systems. This study explored the kinetics of the ORR reaction mechanism, which enables us to understand clearly the electrochemical activity of the electrode. In this research, electrocatalysts were synthesized from platinum (Pt) catalyst with multi-walled carbon nanotubes (MWCNTs) coated by three polymers (polybenzimidazole (PBI), sulfonated tetrafluoroethylene (Nafion), and polytetrafluoroethylene (PTFE)) as the supporting materials by the polyol method while hexachloroplatinic acid (H2PtCl6) was used as a catalyst precursor. The oxygen reduction current of the synthesized electrocatalysts increased that endorsed by linear sweep voltammetry (LSV) curves while increasing the rotation rates of the disk electrode. Additionally, MWCNT-PBI-Pt was attributed to the maximum oxygen reduction current densities at −1.45 mA/cm2 while the minimum oxygen reduction current densities of MWCNT-Pt were obtained at −0.96 mAcm2. However, the ring current densities increased steadily from potential 0.6 V to 0.0 V due to their encounter with the hydrogen peroxide species generated by the oxygen reduction reactions. The kinetic limiting current densities (JK) increased gradually with the applied potential from 1.0 V to 0.0 V. It recommends that the ORR consists of a single step that refers to the first-order reaction. In addition, modified MWCNT-supported Pt electrocatalysts exhibited high electrochemically active surface areas (ECSA) at 24.31 m2/g of MWCNT-PBI-Pt, 22.48 m2/g of MWCNT-Nafion-Pt, and 20.85 m2/g of MWCNT-PTFE-Pt, compared to pristine MWCNT-Pt (17.66 m2/g). Therefore, it can be concluded that the additional ionomer phase conducting the ionic species to oxygen reduction in the catalyst layer could be favorable for the ORR reaction.
Improve Oil Recovery Mechanism of Multi-Layer Cyclic Alternate Injection and Production for Mature Oilfield at Extra-High Water Cut Stage Using Visual Physical Simulation Experiment
Authors: Zhao Lun , Wang Jincai , Fu Libing , Chen Li , Jia Zhihao
Published: February 2023

In order to achieve sustainable development of mature oilfield, a series of adjustment measures should be implemented to improve production performance at the extra-high water cut stage. South Kumkol reservoir is a typical multi-layer low viscosity oil reservoir, which has the characteristics of small sandstone body, high shale volume, and strong heterogeneity. At present, the water cut of the South Kumkol reservoir is about 90%, which is on the verge of being abandoned. Multi-layer cyclic alternate injection and production (MCA-IP) is an ideal adjustment measure for multi-layer oil reservoir to improve oil recovery (IOR) at the extra-high water cut stage. In this paper, we designed the double-plate visual physical device and the MCA-IP experimental program and then calculated the sweep coefficient using image recognition method. Furthermore, the sweep coefficient was quantitatively calculated by image recognition method. The results show that the sweep area extends to both sides of the main streamline and the sweep efficiency is gradually improved after the completion of MCA-IP. In addition, the IOR mechanism of MCA-IP mainly includes reperforation, well-pattern encryption, and asynchronous injection-production. The reperforation and well-pattern encryption increased the sweep coefficient by about 19.52%, while asynchronous injection-production increased the sweep coefficient by about 1.2%, and the overall sweep coefficient increased by about 20.7%. According to the experimental data statistics, the MCA-IP method can increase oil recovery by about 11% and reduce water cut by about 6%.
Prediction of Food Factory Energy Consumption Using MLP and SVR Algorithms
Authors: Lee Hyungah , Kim Dongju , Gu Jae-Hoi
Published: February 2023

The industrial sector accounts for a significant proportion of total energy consumption. Factory Energy Management Systems (FEMSs) can be a measure to reduce energy consumption in the industrial sector. Therefore, machine learning (ML)-based electricity and liquefied natural gas (LNG) consumption prediction models were developed using data from a food factory. By applying these models to FEMSs, energy consumption can be reduced in the industrial sector. In this study, the multilayer perceptron (MLP) algorithm was used for the artificial neural network (ANN), while linear, radial basis function networks and polynomial kernels were used for support vector regression (SVR). Variables were selected through correlation analysis with electricity and LNG consumption data. The coefficient of variation of root mean square error (CvRMSE) and coefficient of determination (R2) were examined to verify the prediction performance of the implemented models and validated using the criteria of the American Society of Heating, Refrigerating, and Air-Conditioning Engineers Guideline 14. The MLP model exhibited the highest prediction accuracy for electricity consumption (CvRMSE: 17.35% and R2: 0.84) and LNG consumption (CvRMSE: 12.52% and R2: 0.88). Our findings demonstrate it is possible to attain accurate predictions of electricity and LNG consumption in food factories using relatively simple data.
Phase-Homogeneous LiFePO4 Powders with Crystallites Protected by Ferric-Graphite-Graphene Composite
Authors: Agafonov Dmitry , Bobyl Aleksandr , Kamzin Aleksandr , Nashchekin Alexey , Ershenko Evgeniy , Ushakov Arseniy , Kasatkin Igor , Levitskii Vladimir , Trenikhin Mikhail , Terukov Evgeniy
Published: February 2023

Phase-homogeneous LiFePO4 powders have been synthesized. The content of impurity crystalline phases was less than 0.1%, according to synchrotron diffractometry (SXRD) data. Anisotropic crystallite sizes L¯Vhkl were determined by XRD. A low resistance covering layer of mechanically strong ferric-graphite-graphene composite with impregnated ferric (Fe3+) particles < 10 nm in size increases the cycleability compared to industrial cathodes. In accordance with the corrosion model, the destruction of the Fe3+-containing protective layer of crystallites predominates at the first stage, and at the second stage Fe escapes into the electrolyte and to the anode. The crystallite size decreases due to amorphization that starts from the surface. The rate capability, Q(t), has been studied as a function of L¯Vhkl, of the correlation coefficients rik between crystallite sizes, of the Li diffusion coefficient, D, and of the electrical relaxation time, τel. For the test cathode with a thickness of 8 μm, the values of D = 0.12 nm2/s, τel = 8 s were obtained. To predict the dependence Q(t), it is theoretically studied in ranges closest to experimental values: D = 0.5 ÷ 0.03 nm2/s, τel = 8/1 s, average sizes along [010] L¯1 = 90/30 nm, averaged r¯ = 0/1.
Study of the Influence of Dynamic and Static Capillary Forces on Production in Low-Permeability Reservoirs
Authors: Zhang Yuanzhang , Wang Youqi , Gao Jianwen , Cui Yuehua , Wang Shuoliang
Published: February 2023

Low-permeability reservoirs have strong heterogeneity, and the production prediction based on traditional seepage model is not accurate enough. The dynamic capillary-force seepage model can characterize the dynamic heterogeneity of seepage and more accurately describe the oil–water flow process. In this paper, the calculation formula of the dynamic capillary force is obtained through a real low-permeability core experiment, and the seepage model of dynamic capillary force is established. Based on the model, the authors quantitatively study the effects of formation pressure, heterogeneity and production speed on dynamic capillary force through numerical solutions. It is found that compared with the traditional static capillary-force seepage model, the dynamic capillary-force seepage model makes the predicted water cut increase and the recovery factor decrease. With the increase in development time, formation pressure and production rate will make the effect of dynamic capillary force more obvious. According to the comparison of heterogeneous reservoir models, results show that the horizontal heterogeneity will strengthen the dynamic capillary-force effect, while the vertical heterogeneity will weaken the dynamic capillary-force effect. In the range of research parameters, the recovery ratio predicted by the dynamic capillary-force seepage model can be reduced by 4.7%. A new oil–water seepage model is proposed, which can characterize the spatial difference and dynamic change of low-permeability reservoirs with time. It is of great significance for describing the remaining oil distribution of low-permeability reservoirs in detail and making decisions on efficient EOR measures.
A Novel Stochastic Optimizer Solving Optimal Reactive Power Dispatch Problem Considering Renewable Energy Resources
Authors: Ali Mohammed Hamouda , Soliman Ahmed Mohammed Attiya , Abdeen Mohamed , Kandil Tarek , Abdelaziz Almoataz Y. , El-Shahat Adel
Published: February 2023

Optimal Reactive Power Dispatch (ORPD is thought of as a noncontinuous, nonlinear global optimization problem. Within the system’s constraints, the ORPD manages to accomplish the reactive power flow. Due to its more intricate linkage of variables, the reactive power issue is more challenging to resolve than the optimum power flow issue. With the existence of renewable energy resources (RERs), solving the ORPD problem to attain the most stable and secure system condition has become a more challenging task. The goal of this article is to solve the objective function of ORPD combined with RERs using a metaheuristic novel optimizer named the African Vultures Optimization Algorithm abbreviated by (AVOA), where the formulation of the ORPD issue including minimization of three single objective functions as follows, voltage deviation, system operating cost, and real power loss, is introduced and also transmission power loss minimization is embraced with the simultaneous incorporation of the optimal renewable energy resources (RERs). Where the ORPD problem complexity grows exponentially with a mixture of continuous and discrete control variables, two distinct continuous and discrete types of optimization variables are considered, and the proposed single objective functions that meet different operating constraints are then transformed into a coefficient multi-objective ORPD problem and elucidated using the weighted sum approach. To validate the suggested algorithm’s effectiveness in addressing the ORPD issue, it is evaluated on three standard IEEE networks: the IEEE-30 bus small-scale network, the IEEE-57 bus medium-scale network, and the IEEE-118 bus large-scale network using different scenarios and the outcomes are compared to these other popular optimization techniques. The findings show that the suggested AVOA algorithm provides an efficient and sturdy high-quality solution for tackling ORPD situations and vastly enhances the overall system performance of power at all scales.
A Carbon Accounting and Trading Platform for the uk Construction Industry
Authors: Blumberg George , Sibilla Maurizio
Published: February 2023

Atmospheric carbon dioxide emanating from activities associated with the construction of buildings in the UK contributes approximately 16% of the uk’s total emissions and will need to be reduced significantly to meet international agreements. Against this scenario, this paper presents a novel perspective for carbon accounting and trading that proposes the use of a platform for the uk construction industry as a possible solution. This suggestion assumes that taxation should be synchronised with phases of the entire life cycle of the building and that tax credits (or deficits) should remain an asset of the building itself. In this regard, a strategy is in place in the uk, but with gaps in how it will be implemented. To resolve these gaps, firstly, this paper explores and integrates three socio-technical components (i.e., carbon accounting, trading, and certification) that form an essential set of tools required for the management of taxes directed at property developers and construction companies. Then, it points out the need for a suite of computer-based systems to facilitate the recording of emissions information, the purchase of carbon offsets, and a way to access specialist financial services. As a result, a trading platform is conceptualised that makes use of blockchain technology as a foundation for future research.
Analysis of the Influence of Nodal Reactive Powers on Voltages in a Power System
Authors: Okon Tomasz , Wilkosz Kazimierz
Published: February 2023

The paper deals with finding power system nodes where reactive powers have the greatest influence on system voltages. The problem to be solved is important in reactive power planning. Its proper solution indicates in which nodes new sources of reactive power should be installed in order to achieve the assumed goals in the aforementioned planning. So far, the problem formulated earlier has not been satisfactorily resolved. The paper presents an original method, which, based on the entire history of the system operation states, allows a solution to the problem mentioned above to be found. The proposed method assumes the use of measurement data of nodal-voltage magnitudes and nodal reactive power. Correlational relationships between the above-mentioned quantities are investigated. The paper shows that the considered correlational relationships are not linear. In this situation, Kendall’s rank correlation coefficient is used to evaluate the strength of these correlational relationships. Analysis of the strongest relationships allows us to identify those nodal reactive powers that have the greatest influence on the voltages in the power system. The results of the analysis are the basis for determining the location of additional reactive power sources in the power system, which is very essential in reactive power planning. The proposed method is relatively easy to implement and does not require complicated calculations. The paper additionally shows that failure to use the entire spectrum of representative system states when solving the problem under consideration can adversely affect the result.
Experimental Investigation of Graphene Nanoplatelets Enhanced Low Temperature Ternary Eutectic Salt Hydrate Phase Change Material
Authors: Kalidasan B. , Pandey A. K. , Rahman Saidur , Sharma Kamal , Tyagi V. V.
Published: February 2023

A sustainable approach to ensuring the thermal regulation of space is reliable with phase change materials (PCMs) operating at 15–25 °C. Henceforth, there is a need of a search of binary and ternary eutectic PCMs operating at desirable phase transition temperatures of 15–25 °C, high energy storage enthalpy (180–220 J/g), improved thermal conductivity and better absorptivity of solar energy. In this current research, we developed a ternary eutectic inorganic salt hydrate PCM intended for a low-temperature thermal regulation system. Based on the eutectic melting point theory, the phase transition temperature and proportion of sodium carbonate decahydrate (SCD), sodium phosphate dibasic dodecahydrate (SPDD) and sodium sulphate decahydrate (SSD) were determined. As per the calculated proportion, ternary eutectic PCM was experimentally prepared. Furthermore, to enhance the thermal property, graphene nanoplatelets (GNP) were dispersed at weight concentrations of 0.4%, 0.7% and 1.0%. The prepared nanoparticle-dispersed PCMs were characterized using an optical microscope, Fourier transform infrared (FT-IR) spectroscopy and a thermal conductivity meter, and a differential scanning calorimeter (DSC) was used to evaluate the morphology, chemical stability and thermal properties. The results showed increases in thermal conductivity and optical absorbance by 71.5% and 106.5%, respectively, with GNP at 1.0% weight concentration. Similarly, the degree of supercooling and transmissibility was reduced by 43.5% and 76.2% correspondingly. The prepared composite PCM is expected to contribute towards cooling, with an intention to contribute towards sustainable development.
Carbon Dioxide Storage Potential of Cenozoic Saline Aquifers in the South Yellow Sea Basin
Authors: Yuan Yong , Wang Jianqiang , Chen Jianwen , Cao Ke , Liang Jie , Lan Tianyu , Lu Dongyu , Guo Xudong
Published: February 2023

Carbon dioxide (CO2) storage in underwater reservoirs is a valuable method of reducing carbon emissions. Saline aquifers such as those in the South Yellow Sea Basin (SYSB), China, have great potential for geological CO2 storage. Thus, we use the recommended calculation method of USDOE and a formation volume model to determine the geological conditions for CO2 storage and estimate the CO2 storage capacity of the Cenozoic saline aquifers in the SYSB (depth: 800–3200 m). Overall, the SYSB exhibits weak fault activity and seismicity, medium and low geothermal fields, four types of source sandstone reservoir, and four sets of carbon reservoir–caprock assemblages developed from the Cenozoic strata, providing relatively good geological conditions for CO2 storage. The estimated capacity of the Cenozoic strata ranges from 39.59 Gt to 426.94 Gt (average: 155.25 Gt), indicating an extensive storage capacity that can meet the carbon sequestration needs of Shandong and Jiangsu Provinces for approximately 89 years. The Yantai Depression has a lower geothermal gradient and terrestrial heat, weaker seismic activity, and double the storage capacity of the Qingdao Depression, indicating that it is the most suitable area for Cenozoic CO2 storage in the SYSB, whereas the Laoshan Uplift is not suitable for storage. This study provides a scientific basis for the selection of offshore CO2 storage sites.
Electrochemical Impedance Spectroscopy: A New Chapter in the Fast and Accurate Estimation of the State of Health for Lithium-Ion Batteries
Authors: Zhang Ming , Liu Yanshuo , Li Dezhi , Cui Xiaoli , Wang Licheng , Li Liwei , Wang Kai
Published: February 2023

Lithium-ion batteries stand out from other clean energy sources because of their high energy density and small size. With the increasing application scope and scale of lithium-ion batteries, real-time and accurate monitoring of its state of health plays an important role in ensuring the healthy and stable operation of an energy storage system. Due to the interaction of various aging reactions in the aging process of lithium-ion batteries, the capacity attenuation shows no regularity. However, the traditional monitoring method is mainly based on voltage and current, which cannot reflect the internal mechanism, so the accuracy is greatly reduced. Recently, with the development of electrochemical impedance spectroscopy, it has been possible to estimate the state of health quickly and accurately online. Electrochemical impedance spectroscopy can measure battery impedance in a wide frequency range, so it can reflect the internal aging state of lithium-ion batteries. In this paper, the latest impedance spectroscopy measurement technology and electrochemical impedance spectroscopy based on lithium-ion battery health state estimation technology are summarized, along with the advantages and disadvantages of the summary and prospects. This fills the gap in this aspect and is conducive to the further development of this technology.
Evaluation of the Effects of Coal Jigging by Means of Kruskal–Wallis and Friedman Tests
Authors: Niedoba Tomasz , Surowiak Agnieszka , Hassanzadeh Ahmad , Khoshdast Hamid
Published: February 2023

Coal beneficiation processes are often based on gravitational enrichment techniques. Therefore, various types of jigs are often used in this area. A number of factors determine the quality of the final product. Therefore, proper and systematic monitoring of such a process is necessary. The present work examined the effect of the hutch water amount and the amount of processed coal (system capacity) on the yield and ash grade in individual density–size fractions. A statistical method in the form of the Kruskal–Wallis test and the Friedman test was used to evaluate the process. These tests were proposed as universal and reliable alternatives to classical analysis of variance (ANOVA) analysis. The performed analysis allowed for the determination of what process conditions should be selected in order to obtain certain effects. Therefore, it allowed us to apply certain combinations of hutch water amount, system capacity and particle characteristics to maximize the expected effects. The application of statistical analysis methods in the form of Kruskal–Wallis and Friedman tests to evaluate process efficiency creates new possibilities to monitor and optimize the jigging process in both context of amount and quality of final product and is very important not only for mining enterprise but also from an environmental point of view.
Modeling and Experimental Validation of a Voltage-Controlled Split-Pi Converter Interfacing a High-Voltage ESS with a DC Microgrid
Authors: Luna Massimiliano , Sferlazza Antonino , Accetta Angelo , Di Piazza Maria Carmela , La Tona Giuseppe , Pucci Marcello
Published: February 2023

The Split-pi converter can suitably interface an energy storage system (ESS) with a DC microgrid when galvanic isolation is not needed. Usually, the ESS voltage is lower than the grid-side voltage. However, limitations in terms of the ESS current make the use of a high-voltage ESS unavoidable when high power levels are required. In such cases, the ESS voltage can be higher than the microgrid voltage, especially with low microgrid voltages such as 48 V. Despite its bidirectionality and symmetry, the Split-pi exhibits a completely different dynamic behavior if its input and output ports are exchanged. Thus, the present work aims to model the Split-pi converter operating with an ESS voltage higher than the grid-side voltage in three typical microgrid scenarios where the controlled variable is the converter’s output voltage. The devised state-space model considers the parasitic elements and the correct load model for each scenario. Furthermore, it is shown that the presence of the input LC filter can make the design of the loop controllers more complicated than in the case of a lower ESS voltage than the grid-side voltage. Finally, the study is validated through simulations and experimental tests on a lab prototype, and a robustness analysis is performed.
Strategies of Energy Suppliers and Consumer Awareness in Green Energy Optics
Authors: Sus Aleksandra , Trzaska Rafał , Wilczyński Maciej , Hołub-Iwan Joanna
Published: February 2023

This research aims to identify (1) whether consumers have an impact on the energy sector, (2) to what extent consumers have an impact on the energy sector, and (3) whether there is so-called energy communism. We understand this phenomenon as the process of energy suppliers imposing energy sources on which the end consumer has zero or very little influence. The research, therefore, focused on a B2C analysis, in five selected countries: the USA, Canada, Australia, the UK, and Poland (N = 500). The research subjects are a homogeneous group in terms of the sources of green energy, and the volume of production of this type of energy and its increments. The investigation was conducted using the procedure appropriate for the triangulation of research methods. Three hypotheses were verified. The first one was rejected, which aimed to determine whether individual consumers are guided by green energy in their choices. The second hypothesis—that energy suppliers do not take into account customer needs/expectations and pursue their strategies—was partially confirmed but was also directed for further exploration. The third hypothesis was whether the consumer is free to make the decision to switch energy suppliers—if so, what is the hierarchy of the most and least decisive factors in the choice of supplier? The verification of this hypothesis indicates that there is no specific pattern that consumers follow when choosing an energy supplier.
Modelling Dominant Tree Heights of Fagus sylvatica L. Using Function-on-Scalar Regression Based on Forest Inventory Data
Authors: Engel Markus , Mette Tobias , Falk Wolfgang , Poschenrieder Werner , Fridman Jonas , Skudnik Mitja
Published: February 2023

European beech (Fagus sylvatica L.) is an important tree species throughout Europe but shifts in its suitable habitats are expected in the future due to climate change. Finding provenances that are still economically viable and ecologically resilient is an ongoing field of research. We modelled the dominant tree heights of European beech as a trait reflecting growth performance dependent on provenance, climate and soil conditions. We derived dominant tree heights from national forest inventory (NFI) data from six European countries spanning over large ecological gradients. We performed function-on-scalar regression using hierarchical generalized additive models (HGAM) to model both the global effects shared among all provenances and the effects specific to a particular provenance. By comparing predictions for a reference period of 1981–2010 and 2071–2100 in a RCP 8.5 scenario, we showed that changes in growth performance can be expected in the future. Dominant tree heights decreased in Southern and Central Europe but increased in Northern Europe by more than 10 m. Changes in growth performance were always accompanied by a change in beech provenances, assuming assisted migration without dispersal limitations. Our results support the concept of assisted migration for the building of resilient future forests and emphasize the use of genetic data for future growth predictions.